Publications Repository - Helmholtz-Zentrum Dresden-Rossendorf

One of the critical aspects for the accurate determination of neutron capture cross sections when combining time-of-flight and total energy detector techniques is the characterization and control of systematic uncertainties associated to the measuring devices. In this work we explore the most conspicuous effects associated to harsh count rate conditions: dead-time and pile-up effects. Both effects, when not properly treated, can lead to large systematic uncertainties and bias in the determination of neutron cross sections. In the majority of neutron capture measurements carried out at the CERN n_TOF facility, the detectors of choice are the C6D6 liquid-based either in form of large-volume cells or recently commissioned sTED detector array, consisting of much smaller-volume modules. To account for the aforementioned effects, we introduce a Monte Carlo model for these detectors mimicking harsh count rate conditions similar to those happening at the CERN n_TOF 20 m flight path vertical measuring station. The model parameters are extracted by comparison with the experimental data taken at the same facility during 2022 experimental campaign. We propose a novel methodology to consider both, dead-time and pile-up effects simultaneously for these fast detectors and check the applicability to experimental data from 197Au(n,gamma), including the saturated 4.9 eV resonance which is an important component of normalization for neutron cross section measurements.

140Ce⁢(n,gamma) is a key reaction for slow neutron-capture (s-process) nucleosynthesis due to being a bottleneck in the reaction flow. For this reason, it was measured with high accuracy (uncertainty ≈5%) at the n_TOF facility, with an unprecedented combination of a high purity sample and low neutron-sensitivity detectors. The measured Maxwellian averaged cross section is up to 40% higher than previously accepted values. Stellar model calculations indicate a reduction around 20% of the s-process contribution to the Galactic cerium abundance and smaller sizeable differences for most of the heavier elements. No variations are found in the nucleosynthesis from massive stars.

The understanding of the formation of the elements has been an intriguing topic within the last decades. It is now proven that the heaviest naturally occurring elements, the actinides, are produced in the astrophysical r-process. However, the exact site of this process is still under debate. Recently, the amount of interstellar 244Pu (T1/2 = 81.3 Myr) in various geological archives like deep-sea ferromanganese crusts and sediments has been investigated by applying highly sensitive accelerator mass spectrometry (AMS) measurements.[1,2] Correlation of the influx of 244Pu with 60Fe (T1/2 = 2.6 Myr), which is produced by the s-process in massive stars and ejected into the interstellar medium by supernovae, could point to supernovae as the origin of the r-process in the universe. To further prove this hypothesis, recent investigations focus on the determination of other long-lived radionuclides which are also produced in the r-process, e.g. 247Cm (T1/2 = 15.6 Myr) and 182Hf (T1/2 = 8.9 Myr). However, the separation of the expected ultra-trace amounts of these nuclides (a few 100 atoms per gram) from huge amounts of matrix and interfering elements represents a major analytical challenge. Thus, this contribution aims to probe existing chemical treatment strategies for the determination of minute amounts of actinides and Hf from various geological archives. The separation method is based on anion exchange for Pu separation and extraction chromatography for Cm and Hf, respectively.[3,4] The yield of the different elements is monitored by a combination of AMS, γ-counting and ICP-MS measurements. The effective separation strategy of different actinides and Hf from major matrix elements allows for processing multi-gram amounts of different geological samples. This is a prerequisite for the detection of live interstellar 244Pu, 247Cm and 182Hf in terrestrial and lunar geological archives. Furthermore, this method can be adapted for the analysis of other environmental samples regarding their content and isotopic ratio of anthropogenically produced Pu, Am and Cm which holds potential for nuclear safeguards and nuclear forensics studies.

Degradation of plastics in the environment and processing of plastics in industry are main
examples for processes responsible for the release of tonnes of microplastics (MP) every year.
Current research suggests that MPs are distributed throughout the environment with risks
and consequences not yet fully understood. To decrease the release the of MPs the European
Union (EU) started to ban intentional microplastics and decreases landfilling space, finally
generating awareness to waste streams as a resource. However, the fine nature as well as the
heterogeneity of MP samples pose difficulties for recycling processes.
This work aims to provide a low-cost and sustainable detection system for MPs allowing
fast identification of polymers in environmental and industrial samples. The method utilizes
polymer-specific peptides covalently linked to fluorescent probes that ultimately label parti-
cles of different polymer types in solution.
Polymer specific phages were determined using the phage surface technology. In additional
biopanning rounds binding properties of promising phages were characterized. Synthetic
peptides of best binding phages were ordered and evaluation of binding constants will be
performed using suitable ligand binding assays. Using Fourier Transform Infrared and Ra-
man Spectroscopy the amino acids involved in binding will be determined and experimentally
proven by alanine scanning mutagenesis. Optimized peptides will be expressed heterologous
in varying lengths and combinations to optimize their binding properties. Finally, peptides
will be fused to fluorescent probes and analysis of peptide bound plastic particles will occur
using fluorescence microscopy and flow cytometry.
Here we present the selection of polyethylene terephthalate and nylon 6 binding phages
identified by phage surface display. By application of adapted biopanning procedures, phage-
particle interaction was visualized and higher binding affinities compared to the control phage
were detected. These results form the basis for further developments.

Awarness about microplastic (MP) pollution as well as about the risks and consequences these particles present to the environment and human health is nowadays widely spread throughout research, politics and society. The European Union (EU) initiated to ban intentional MPs, however there are innummerable sources of unintentional MP production, especially in industrial processing of macroscopic plastic. Finest waste streams from industry that contain beside MPs numerous valuables are landfilled and resources are lost. However, due to new policies in the EU and reduced landfilling space is reduced, resource efficiency is forced to increase, finally making recycling economically attractive. Nevertheless, recycling of fine waste material, such as MPs, is challenging primarily due to sample heterogeneity.
This study aims to provide low-cost and environmentally friendly peptides binding specifically and with high affinity to polymer particles of different type. These peptides will be fused to fluorescent molecules to rapidly detect and discriminate plastic particles in mixed waste samples to provide insights for futher treatment. Moreover, the peptides should be considered as a platform for any sort of modification and enable applications in the field of MP studies.
Polymer-binding peptides were identified using the phage surface display technology (PSD) on micrometer-sized polymer particles. Phage-particle interactions were proven by pull-down assays and fluorescence microscopy. In future, pull-down assays and fourier-transformed infrared spectroscopy (FTIR) will be used to determine the binding constants of the peptide-polymer binding. Chemical groups involved in peptide-polymer interaction will be determined using FTIR alongside with alanine scanning mutagenesis. Upon optimization, the polymer-binding peptides will be coupled to fluorescent labels to probe polymer particles in mixed sample analysis using flow cytometry.
For polyethylene terephthalat and nylon 6, the PSD delivered eight polymer-binding phages, each. Further, first studies indicate interactions between polymer particles and PSD derived peptides.

The unique optical features of gold nanoparticles (AuNPs), such as localized surface plasmon resonance (LSPR), have attracted substantial attention in biosensing applications. In closely spaced nanoparticle assemblies, the electromagnetic fields of neighboring particles interact strongly, leading to significant near-field coupling which influence the LSPR signature. Therefore the collective plasmonic characteristics in ordered arrangements of AuNPs are more sensitive to variations in the local refractive index (RI) than in individual particles. Such local RI modifications also take place when biomolecules bind to the AuNP surfaces. Therefore, by increasing RI sensitivity, AuNP assemblies have the potential to detect biomolecules of interest with much higher accuracy.
In this study, a comprehensive investigation is presented comparing the plasmonic spectra of linear periodic assemblies of AuNPs against individual particles (50 nm diameter) for biosensing applications. Simulations using the Finite-Difference Time-Domain (FTDT) method suggested that longitudinal coupling along the AuNP lines were more sensitive to RI changes than transversal coupling. Practical experiments supported the simulation results through an exemplary attachment of the biomolecules to AuNP assemblies. A pro-inflammatory cytokine- Tumor Necrosis Factor Alpha (TNF-α), which is an important marker in cancer research influencing various aspects of tumorigenesis, tumor progression, and therapeutic response was chosen for the bio-functionalization process. Polydimethylsiloxane (PDMS) templates were used to confine large arrays (cm²) of amine-functionalized AuNPs into linear assemblies on glass substrates. RI sensitivity of the AuNP assemblies during various steps of the functionalization process were investigated using UV-Vis spectrometry. Promising experimental results exhibited enhanced RI sensitivity of the linear assemblies as compared to individual or randomly ordered AuNPs, offering a favorable approach towards plasmonic biosensing applications.

Existing diagnostic methods for cancer, for example- imaging and tissue biopsies, are expensive, invasive and impractical for repeated examination. While soluble biomarkers can be quantified quickly and non-invasively, their detection in ultra-low concentrations remains a challenge. Sensors based on Field Effect Transistors (bioFETs) with silicon channels are highly sensitive [1] and offer an ideal platform. Plasmonic gold nanoparticles (AuNPs) have tunable optical properties and their free electron clouds undergo collective oscillation upon interaction with light of specific wavelengths, exhibiting Localized Surface Plasmon Resonance (LSPR) [2]. Furthermore, ordered arrays of NPs exhibit coupled plasmonic properties which are highly susceptible to variations in the local refractive index- a change which also occurs when biomolecules attach to NP surface.
Our presented work investigates template-assisted assembly of AuNP chains and biofunctionalization with exemplary cytokine: Tumor Necrosis Factor Alpha (TNF-α). The research aims towards utilization of the coupled plasmonic properties of the AuNP chains to contribute to the change in interfacial charges at the bioFET channel and thereby improve the performance of bioFETs through optical gating.

[1] Trang Anh Nguyen-Le et al., Biosensors & Bioelectronics (2022); 206:114124
[2] Christoph Hanske et al., Nano Letters 2014 14 (12), 6863-6871

Gold nanoparticles (AuNPs) attract substantial interest in biosensing applications due to their unique optical properties, such as localized surface plasmon resonance (LSPR). The near field interactions between individual NPs, e.g. in assemblies or arrays, have a significant impact on LSPR signature. Compared to individual particles, ordered arrangements of AuNPs offer collective plasmonic properties which are more susceptible to variations in the local refractive index (RI). Such local changes in RI also occur when biomolecules attach to the surface of AuNPs. Hence, AuNP assemblies could potentially enable the detection of biomolecules of interest with unprecedented accuracy due an enhancement of RI sensitivity. This study presents a comprehensive investigation of the plasmonic spectra of linear periodic assemblies of AuNPs (590 nm periodicity) against individual particles (50 nm diameter) for biosensing applications. Simulations using Finite-Difference Time-Domain (FTDT) method showed that longitudinal coupling along the AuNP lines is more sensitive to RI changes than transversal coupling. This was further investigated experimentally through an exemplary attachment of the biomolecules to the nanoparticle lines. Tumor Necrosis Factor Alpha (TNF-α), a pro-inflammatory cytokine which plays a multifaceted role in cancer research, influencing various aspects of tumorigenesis, tumor progression, and therapeutic response was chosen for the bio-functionalization experiments. Wrinkled polydimethylsiloxane (PDMS) templates were used to confine the cm² large arrays of amine-functionalized AuNPs into lines on glass substrates. RI sensitivity of the AuNP assemblies during various steps of the functionalization were investigated using UV-Vis spectrometry. Promising experimental results demonstrate enhanced RI sensitivity of the linear assemblies, compared to single NPs, offering a new approach towards plasmonic biosensing applications.

Understanding charge transport properties of large-area single-layer 2D materials is crucial for the future development of novel optoelectronic devices. In this work, the synthesis and electrical characterization of large-area single-layers of Cu3BHT 2D conjugated coordination polymers are reported. The Cu3BHT are synthesized on the water surface by the Langmuir-Blodgett method and then transferred to SiO2/Si substrates with pre-patterned electrical contacts. Electrical measurements revealed ohmic responses across areas up to ≈1 cm2, with a mean resistance of approximately 53 ± 3 kΩ at a probe separation of 50 µm. Cooling and heating cycles show hysteresis in the electrical response, suggesting different current pathways are formed as the samples underwent structural-chemical changes during temperature sweeps. This hysteresis vanished after several cycles and the conductivity shows a stable exponential behavior as a function of temperature, suggesting that a temperature-dependent tunneling process is governing the conduction mechanism in the analyzed polycrystalline single-layer Cu3BHT samples. These results, together with density functional theory calculations and valence band X-ray photoelectron spectroscopy data suggest that the single-layer samples exhibit a semiconducting rather than a metallic behavior.

In this study, we unveil a conceptual technology for fabricating artificial metalloenzymes (ArMs) by deeply integrating hemin into protein scaffolds via a protein refolding process, a method that transcends the conventional scope of surface-level modifications. Our approach involves denaturing proteins, such as benzaldehyde lyase, green fluorescent protein, and Candida antarctica lipase B, to expose extensive reactive amino acid residues, which are then intricately linked with hemin using orthogonal click reactions, followed by protein refolding. This process not only retains the proteins’ structural integrity but expands proteins’ functionality. The most notable outcome of this methodology is the hemin@BAL variant, which demonstrated a remarkable 83.7% conversion rate in cyclopropanation reactions, far surpassing the capabilities of traditional hemin-based catalysis in water. This success highlights the significant role of protein structure in the ArMs’ activity and marks a substantial leap forward in chemical modification of proteins. Our findings suggest vast potentials of protein refolding approaches for ArMs across various catalytic applications, paving the way for future advancements in synthetic biology and synthetic chemistry.

Das Verständnis der hydrogeochemischen Prozesse und deren numerische Modellierung sind für die Beurteilung der Schadstoffmigration in Grundwassersystemen, einschließlich der Anwendungen bei der Entsorgung radioaktiver Abfälle, von entscheidender Bedeutung. Das Projekt SANGUR (Systematische Sensitivitätsanalyse für mechanistische geochemische Modelle unter Verwendung von Felddaten aus kristallinem Gestein) befasst sich mit der Frage, welche Methoden und Parameter für den reaktiven Transport unter besonderer Berücksichtigung von Heterogenitäten in kristallinem Gestein relevant sind. Es wurde ein Workflow entwickelt, der die Datenerfassung mit geostatistischen Ansätzen und einer Modellreduktion basierend auf einer Sensitivitätsanalysen kombiniert. Ziel ist die Verbesserung der Vorhersage der Radionuklidrückhaltung im Fernfeld eines Endlagers.

Die Gesteinssimulation wird in unserem Ansatz mit Hilfe von Multinary Random Fields realisiert.[1] Als Trainingsdatensatz dienen Kristallinproben aus der Lausitz, deren mineralogische Zusammensetzung mittels MLA (Mineral Liberation Analysis) bestimmt wurde. Der gewählte Simulationsansatz erlaubt es, eine Vielzahl von Realisierungen zu berechnen und damit die mineralogische Zusammensetzung und deren Variabilität entlang der Migrationspfade unter Berücksichtigung von Unsicherheiten zu beschreiben. Dies ist eine Voraussetzung für die Auswahl realistischer Oberflächenkomplexierungsmodelle und –parameter, die wiederum die Berechnung smarter Kd-Matrizen zur Beschreibung der Radionuklidmigrationsmuster ermöglichen.[2] Sowohl die Eingabeparameter als auch die Smart-Kd-Matrix werden in die anschließende Sensitivitätsanalyse einbezogen, um die Relevanz einzelner Parameter, aber auch deren Abhängigkeiten für die Simulation der Radionuklidrückhaltung zu ermitteln. Dadurch kann einerseits mehr Aufwand in die Bestimmung der wichtigsten Parameter und ihrer Unsicherheiten investiert werden, andererseits können Parameter mit geringerem Einfluss als Konstanten gesetzt werden, was zu weniger komplexen und rechenzeitintensiven Modellen führt.

Wir stellen die Arbeitsschritte sowie die Ergebnisse des gesamten Workflows vor und können so erste Aussagen zur Frage der Relevanz von Modellparametern wie Mineralzusammensetzung der Festphase, Zusammensetzung der Fluidphase, pH-Wert und Simulationsskala präsentieren.

Referenzen:

[1] Menzel, P. et al. (2020) Math. Geosci. 52, 731 – 757. [2] Stockmann, M. et al. (2017) Chemosphere 187, 277 – 285.

The FLUKA Monte Carlo Radiation Transport and Interaction code package is widely used to simulate the interaction of particles with matter in a variety of fields, including high energy physics, space radiation, medical applications, radiation protection and shielding assessments, accelerator studies, astrophysical studies and well logging. This paper gives a brief overview of the FLUKA program and describes recent developments, in particular, improvements in the modelling of particle interactions and transport are described in detail. In addition, an overview of selected applications is given.

Electrostatically driven carrier magnetic separation of the fluorescent powder Y₂O₃:Eu and process upscaling with high-gradient magnetic separation.

"Peptide-based Separation System for the Recovery of Palladium from the Chemical-Pharmaceutical Industry"
Presented by Dr. Nora Schönberger at BioKreativ - Forum, Berlin, 8 November 2024.

Palladium is a critical raw material essential for pharmaceutical synthesis, yet its limited availability and high cost present significant challenges for the industry. Current methods for palladium recovery are inefficient, resource-intensive, and environmentally harmful, exacerbating the dependency on politically sensitive supply regions and increasing production expenses. This presentation introduces a novel bio-based separation system leveraging palladium-binding peptides to efficiently recycle this valuable metal.

The innovative approach combines biotechnological peptide development with functionalized membranes, enabling selective palladium recovery from catalytic processes. Through rational design, phage surface display, and AI-assisted optimization, peptides are tailored for high binding affinity and stability. An interdisciplinary roadmap ensures scalability and integration into industrial systems, incorporating life cycle assessments and eco-efficiency analyses to align with sustainable chemistry principles.

This cutting-edge technology not only enhances resource security and lowers production costs but also minimizes toxic waste and environmental impact, advancing the circular economy and promoting sustainable industrial practices.

The use of actinium-based radiopharmaceuticals is on the rise, but the coordination chemistry of trivalent actinium remains poorly understood. The most stable isotope of Ac (227Ac) has a short half-life of 21.77 years, making experiments with this element quite ambi-tious. Computational chemistry is the way forward for exploring actinium chemistry. There have been several attempts to apply combined experimental and theoretical approaches for designing suitable chelators for Ac3+-radiopharmaceuticals, including 1,4,7,10-tetraazacyclododecane-1,4,7,10-tetramethylene phosphonic acid (DOTP),[1] and 3,4,3,3-(LI-1,2-HOPO).[2]
Here, DFT calculations were first performed to compare the binding affinity of Ac3+ with different chelators. It was revealed that diethylenetriaminepentaacetic acid (DTPA) and “HOPO-O8” are slightly more effective chelators than the widely-used DOTA. DOTP demonstrated the most optimal performance. It is important to pursue the specific high affinity of the chelator to-wards Ac3+ to deliver the radionuclide to the target cells. However, it is also imperative to have molecular interactions with the receptor for the recognition of the radiopharmaceuticals. In the next step, molecular dynamics (MD) simulations of somatostatin receptor 2 (SSTR2) overex-pressed in neuroendocrine tumors in complex with several different radiopharmaceutical com-pounds have been performed. Bonding and non-bonding parameters involving actinium have been developed using Metal Center Parameter Builder implemented in Amber 20 as well as using DFT calculations with B3LYP functionals using Gaussian16. MD simulations performed using GROMACS program package as depicted in Figure 1.
DOTP, which has been suggested in previous study as an excellent alternative to DOTA,[1] was found to perform better than DOTA not only because of better affinity to Ac3+ but also in terms of the overall higher affinity of Ac3+-DOTP-TATE to the receptor compared to the corre-sponding DOTA complex. Detailed energetic analysis revealed that this is primarily due to elec-trostatic interactions stemming from high negative charge of DOTP. Further analysis of the sec-ondary structure of the receptor revealed that Ac3+-DOTP-TATE perform excellently also in terms of ligand recognition and affect the “toggle switch” for the activation of somatostatin recep-tor. Furthermore, effect of adding “linker” between the chelator and the peptide part of the radio-pharmaceutical have been investigated and it has been revealed that the addition of linker indeed increases the ligand affinity to the receptor.

Derzeit sind etwa 5 Milliarden Leuchtstofflampen in der Europäischen Union in Gebrauch. Diese Lampen funktionieren, nach dem Prinzip, dass ein gemischtes Pulver aus rotem, grünem und blauem Leuchtstoffpulver durch UV-Licht angeregt wird, um sichtbares Licht mit einem gewünschten Spektrum zu erzeugen (Abbildung 1). Diese Leuchtstoffpulver enthalten hohe Mengen an fluoreszierenden Seltenen Erden, kostbaren Metallen mit hohem Versorgungsrisiko, die in den meisten technologischen Anwendungen verwendet werden und eine sehr wichtige Rolle in der Energiewende erfüllen.
Momentan werden Leuchtstoffpulver überall in Europa deponiert (Abbildung 2). Die Aufreinigung dieser Abfallpulver würde es ermöglichen, sie in neuen Produkten zu recyceln. Allerdings ist es mit herkömmlichen Verfahren sehr aufwendig, die einzelnen Pulver voneinander zu trennen. Wir haben eine Lösung gefunden, um dieses Material mithilfe von Biotechnologie effizient und kostengünstig aufzureinigen und zu recyceln. Dabei verwenden wir Biomoleküle, um die Oberfläche von magnetischen Partikeln so zu verändern, dass sie selektiv an eines der Leuchtstoffpulver binden. Durch das Hinzufügen eines Magneten werden die magnetischen Partikel angezogen, und das gebundene Leuchtstoffpulver wird mitgezogen und gereinigt (Abbildung 3).
Unsere ersten Experimente mit diesem neuen Trennverfahren, die wir mit dem roten Leuchtstoffpulver durchgeführt haben, zeigen, dass wir eine Rückgewinnung von über 80 % und eine Reinheit von über 90 % erreichen können. Wir haben ein Patent angemeldet und eine Finanzierung für ein Validierungsprojekt namens Magnetische Aufbereitung zur Gewinnung Seltener Erden aus Leuchtstoffpulvern (MAGSEL) erhalten. Wir hoffen, dass die Biotechnologie mit MAGSEL dazu beitragen kann, eine Kreislaufwirtschaft für die wichtigen Metalle zu schaffen, die wir für die Energiewende benötigen.

Abbildung 1 Funktionsprinzip von Leuchtstofflampen: Wenn die Lampe unter Strom steht, fliegen Elektronen von der Kathode zur Anode. Sie regen gasförmige Quecksilberatome an, die daraufhin UV-Licht emittieren. Dieses UV-Licht regt eine Mischung aus roten (typischerweise Y₂O₃: Eu³⁺), grünen (typischerweise LaPO₄: Ce³⁺, Tb³⁺) und blauen (typischerweise BaMgAl₁₀O₁₇: Eu²⁺) Leuchtstoffpulvern an.

Abbildung 2 Deponie von Leuchtstoffpulvermischungen, die aufgrund eines Mangels an geeigneten Trennverfahren derzeit nicht recycelt werden. Quelle: https://indaver.com/expertise/materials-recovery-from-waste/lamps

Abbildung 3 Darstellung des Verfahrens zur Reinigung von Leuchtstoffpulvern. Biomoleküle werden verwendet, um die Oberfläche von magnetischen Partikeln so zu modifizieren, dass sie selektiv an eines der Leuchtstoffpulver binden. Nachdem jedes Abfallpulver in einem Magnetfeld gereinigt wurde, werden die Leuchtstoffpulver in neuen elektronischen Geräten recycelt.

Presentation of the MAGSEL validation project for Rare Earth Element Recovery from Fluorescent Powder to representatives of LAREC.

Presentation of the MAGSEL validation project for Rare Earth Element Recovery from Fluorescent Powder to representatives of INDAVER NV.

Modern electronic devices play a crucial role for evolving the technological landscape within the European Union and to facilitate the transition into a future energy system based on renewables. However, such devices typically incorporate 20-60 different raw materials, including many that face significant supply risks and that have been categorized either as “critical” or even “strategic”. Moreover, the extraction of these much needed raw materials from geogenic ore deposits is typically energy intensive and results in significant environmental impacts. Although current flows of waste electric and electronic equipment (WEEE) contain greatly elevated concentrations of many of these raw materials – often exceeding concentrations in geogenic ore deposits - only a very small number of them are typically recovered as secondary raw materials. The development of concepts and technologies required for a more comprehensive recycling typically faces practical challenges, mainly due to the complex composition of WEEE and the minute scale of its components.

To overcome the challenges of recovering multiple metals from WEEEs, this study proposes a workflow to evaluate the recyclability of state-of-the-art electronic devices by detailed characterization of components and a particle-based evaluation of the separation efficiency of target components with several separation technologies. A case study is used to illustrate the intended workflow. The investigated flow sheet comprises comminution of WEEE to obtain particle sizes in the scale of individual electronic components, followed by subsequent physical separation processes, including size, density, magnetic and eddy current separation. The particles present in the various streams of each processing step are characterized by X-ray computed tomography (CT) to obtain their 3D geometrical properties and composition in metallic and polymeric phases. These particle datasets are then used for particle-based separation modelling, to quantify the influence of particle size, shape, liberation, and association in their recovery. In future work, this approach will be used to evaluate recyclability already during the design of electronic devices, also considering exergy and life-cycle assessment perspectives.

Electronic waste contains high amounts of valuable metals in the form of ultrafine (<10 μm) inorganic powders. Currently, only a minor fraction of these metals is economically recycled, whereas the vast majority ends up in landfill. Separation of the inorganic powders would significantly enhance the recyclability of these secondary resources. However, the most prominent particle separation (froth flotation, gravity, magnetic and electric separation) processes were developed by the mining industry for primary resources. These processes are only partially suitable for electronic waste recycling because they face challenges related to the ultrafine particle sizes and the complex waste composition (typically >60 elements in electronic waste).
In a novel approach, we propose the use of magnetic nanoparticles (MNPs) as carriers for the magnetic separation of critical raw materials from electronic waste. MNPs can be synthesized costeffectively with a broad variety of surface functionalization possibilities and exhibit unique superparamagnetic properties. We present a case study for the recycling of rare-earth elements from ultrafine fluorescent lamp powders by separation based on the selective attachment of MNPs.
First, we obtained a Massart ferrofluid with monodisperse maghemite nanoparticles, electrostatically stabilized with a negatively charged citric acid coating. These MNPs form an electrostatically driven selective monolayer on the surface of the red fluorescent powder Y2O3:Eu (YOX). Subsequently, a gradient magnetic field is used to selectively purify YOX from other fluorescent powders. After magnetic separation, the pH is increased beyond the isoelectric point of YOX, the MNPs detach from the surface, the two types of particles are then separated based on their size difference and the MNPs are successfully reused in new rounds of magnetic carrier separation. The presented study represents a significant advancement in the utilization of MNPs for the recycling of ultrafine inorganic powders from electronic waste and has been submitted for a European patent application. In coming work, we will collaborate with a lamp recycling company to scale up this process by means of high-gradient magnetic separation.
[1] Acknowledgements: The MAGSEL project is co-financed by tax revenue on the basis of the budget adopted by the Saxon state parliament and the European Union.

Electronic waste contains high amounts of valuable metals in the form of ultrafine (<10 µm) inorganic powders [1]. Currently,
only a minor fraction of these metals is recycled economically. Separation of the inorganic powders would strongly enhance
the recyclability of these secondary resources. However, the most prominent particle separation (froth flotation, gravity,
magnetic and electric separation) processes were developed by the mining industry for primary particles [2,3]. These
processes are only partially suitable for secondary resources and face challenges with regards to the ultrafine particle sizes
and the high complexity (typically, >60 elements are present in electronic waste).
In a novel approach, we propose the use of magnetic carriers derived from various life science applications (such as magnetic
drug delivery, purification, hyperthermia, imaging, etc. [4]) for the magnetic separation of critical raw materials from
electronic waste. Magnetic nanoparticles (MNPs) exhibit excellent properties and can be synthesized cost-effectively. The
small size and high specific surface area of ultrafine powders provide benefits for the attachment of MNPs, as opposed to
their hindrance of conventional separation processes. Achieving attachment selectivity of MNPs to the desired target
powders is crucial for the selectivity of the separation process. This draws inspiration from the common practice of MNP
functionalization with biomolecules in the aforementioned fields of life science[5].
In this presentation, we discuss a case study involving biotechnologically functionalized MNPs for the carrier magnetic
separation of rare-earth element-containing phosphors from fluorescent lamps Figure 1 [6,7]. We provide a comprehensive
overview of MNP synthesis and functionalization, determination of their interaction affinity with various phosphors,
application in magnetic separation, as well as post-separation detachment and MNP reuse. Special emphasis is placed on
MNP colloidal stability and magnetic field gradient.
Our work presents a novel approach to recycling rare-earth elements from fluorescent lamps. More broadly, it represents a
significant advancement in the utilization of biotechnologically functionalized MNPs for the recycling of ultrafine inorganic
powders from electronic waste.
Figure 1 Overview of a case study involving biotechnologically functionalized MNPs for the carrier magnetic separation of rare-earth element-containing
phosphors from fluorescent lamps. [A] The blue (BaMgAl10O17: Eu2+), green (LaPO4: Ce3+, Tb3+ or CeMgAl11O19: Tb3+) and red (Y2O3:Eu3+) phosphors coated as ultrafine particles on the inner surface of a glass tube. [B] Sequential separation of the phosphors after grinding of the lamps by utilizing selective magnetic
carriers. [C] Low carbon-footprint reuse of the critical raw materials in new electronic devices.
1. Rudolph, M. A Aufbereitungs-Technik/Mineral Processing 2018, 59, 65-73.
2. Eckert, K.; Schach, E.; Gerbeth, G.; Rudolph, M. Materials Science Forum 2019, 959, 125-133
3. Luo, L.; Nguyen, A.V. Separation and Purification Technology 2017, 172, 85-99
4. Schwaminger, S.P.; Bauer, D.; Fraga-García, P.; Wagner, F.E.; Berensmeier, S. CrystEngComm 2017, 19, 246-255.
5. Le Jeune, M.; Secret, E.; Trichet, M.; Michel, A.; Ravault, D.; Illien, F.; Siaugue, J.-M.; Sagan, S.; Burlina, F.; Ménager, C. ACS Applied Materials &
Interfaces 2022, 14, 15021-15034
6. Boelens, P.; Lei, Z.; Drobot, B.; Rudolph, M.; Li, Z.; Franzreb, M.; Eckert, K.; Lederer, F. Minerals 2021, 11
7. Boelens, P.; Bobeth, C.; Hinman, N.; Weiss, S.; Zhou, S.; Vogel, M.; Drobot, B.; Azzam, S.S.A.; Pollmann, K.; Lederer, F. Journal of Magnetism and
Magnetic Materials 2022, 563, 169956

Long-term safety assessments for nuclear waste disposal face considerable challenges due to uncertainties resulting from the complex geological, geochemical and environmental processes. This work focuses on enhancing the predictive capability of reactive transport models (RTM) for radionuclide migration in fluids within repositories in crystalline host rock. In particular, the work is focused on investigating the influence of uncertain parameters on radionuclide sorption behavior in crystalline rocks. This is achieved by means of systematic Global Sensitivity analysis (GSA) techniques. The distribution coefficient (Kd) is a key parameter quantifying sorption behavior, obtained by means of geochemistry databases. A Quasi Monte Carlo sampling of input parameters, including mineral composition, pH/Eh, and Uranyl concentrations, was employed to study their effects on Kd values. GSA identifies the important variables affecting the uncertainty in the assessment results. Two GSA methodologies where utilized in this work, namely CUSUNORO and High Dimensional Model Representation (HDMR). By performing CUSUNORO and HDMR together, we capture first-order non-linear and second-order effects, respectively, revealing interaction effects between input parameters on the distribution coefficient. Moreover, the compositional data sampling poses a challenge due to the interdependencies which can alter the results of sensitivity analysis. To address this, we implemented transformation techniques to mitigate the interdependency problem. Our findings contribute to a deeper understanding of these processes, providing valuable insights for enhancing the reliability and robustness of long-term safety assessments for nuclear waste disposal sites.

Predictive models are increasingly used for managerial and operational decision-making. The use of complex machine learning algorithms, the growth in computing power, and the increase in data acquisitions have amplified the black-box effects in data science. Consequently, a growing body of literature is investigating methods for interpretability and explainability. We focus on methods based on Shapley values, which are gaining attention as measures of feature importance for explaining black-box predictions. Our analysis follows a hierarchy of value functions, and proves several theoretical properties that connect the indices at the alternative levels. We bridge the notions of totally monotone games and Shapley values, and introduce new interaction indices based on the Shapley-Owen values. The hierarchy evidences synergies that emerge when combining Shapley effects computed at different levels. We then propose a novel sensitivity analysis setting that combines the benefits of both local and global Shapley explanations, which we refer to as the “glocal” approach. We illustrate our integrated approach and discuss the managerial insights it provides in the context of a data-science problem related to health insurance policy-making.

We examine the construction of variable importance measures for multivariate responses using the theory of optimal transport. We start with the classical optimal transport formulation. We show that the resulting sensitivity indices are well-defined under input dependence, are equal to zero under statistical independence, and are maximal under fully functional dependence. Also, they satisfy a continuity property for information refinements. We show that the new indices encompass Wagner’s variance-based sensitivity measures. Moreover, they provide deeper insights into the effect of an input’s uncertainty, quantifying its impact on the output mean, variance, and higher-order moments. We then consider the entropic formulation of the optimal transport problem and show that the resulting global sensitivity measures satisfy the same properties, with the exception that, under statistical independence, they are minimal, but not necessarily equal to zero. We prove the consistency of a given-data estimation strategy and test the feasibility of algorithmic implementations based on alternative optimal transport solvers. Application to the assemble-to-order simulator reveals a significant difference in the key drivers of uncertainty between the case in which the quantity of interest is profit (univariate) or inventory (multivariate). The new importance measures contribute to meeting the increasing demand for methods that make black-box models more transparent to analysts and decision makers.

Cubic zirconia (c-ZrO2) is considered a highly radiation-tolerant material. It is also capable of incorporating a variety of large cations within its crystal structure, making it a promising material as a waste matrix for actinide immobilization. In this study, various syntheses of cerium(IV)-doped zirconia co-doped with Gd(III)/Y(III) were conducted to identify compositions exhibiting a pure cubic structure, with cerium serving as a plutonium analogue. Four compositions were chosen for the production of dense ceramics. The ceramic production of ZrO2 was conducted with a constant Ce(IV) concentration of 18 mol% and varying Gd/Y concentrations. Purely cubic solid solutions phases were obtained for compositions where the trivalent dopant concentrations exceeded 15 mol% (Fig. 1). The full width at half maximum (FWHM) of the XRD peaks in the dense ceramics increased by a factor of 2 in relation to the starting powder material. Their radiation tolerance was assessed through external ion irradiation experiments. In preparation for these experiments, the ceramic surfaces was polished, and half of the pellet was masked using Al-foil. The non-masked part of the pellet was irradiated with 14 MeV Au4+ ions to simulate the recoil of daughter products from alpha decay. Samples were irradiated at two different fluences, 1014 ions/cm2 (A1) and 1015 ions/cm2 (A2). Subsequent to irradiation, analyses were conducted with scanning electron microscopy (SEM) and synchrotron X-ray diffraction in grazing incidence mode (GI-XRD).
The cubic ceramic phases demonstrated excellent radiation tolerance, displaying no significant radiation damage of the structure and maintaining their cubic crystal structure even after irradiation at the highest fluence, A2 (Fig.2). However, diffraction peak broadening following irradiation is visible, suggesting that irradiation has induced microstructural changes to the samples (Fig. 2, right). A non-systematic shift of the Bragg peaks towards lower angles is observed in the irradiated part, particularly pronounced for fluence A2, indicating an expansion of the lattice. No amorphous contributions could be observed in the diffractograms. These observations demonstrate the high radiation tolerance of the ZrO2 crystal structure, and corroborate their use as waste forms for high-level actinide-bearing waste.

Zirconia (ZrO2), exhibits several advantageous properties, including high thermal
stability, chemical inertness, and the capacity to incorporate substantial quantities of
actinides and lanthanides into its host crystal structure [1]. These characteristics make
zirconia a promising candidate for the immobilization of radionuclides from spent nuclear
fuel [2][3]. In the present study, the chemical durability and radiation resistance of doped
zirconia materials has been investigated. Cerium (Ce) has been used as a plutonium
(Pu) analogue. To stabilize the cubic ZrO2 phase at low tetravalent Ce doping
concentrations, trivalent yttrium (Y) was incorporated as a co-dopant during synthesis.
Both powder samples and dense ceramic pellets were produced for solubility and
irradiation investigations, respectively. For the irradiation investigations, half of the pellet
surface was masked with aluminum foil to protect the pristine side, and the other half
was irradiated with 14 Mev Au4+ ions applying two fluences: 1x1014 ions/cm2 (A1), and
1x1015 ions/cm2 (A2). The pellets were then analyzed using scanning electron
microscopy (SEM), vertical scanning interferometry (VSI), and synchrotron powder x-ray
diffraction (SPXRD) in gracing incidence mode. The results (Fig.1) showed no significant
difference between the pristine and the irradiated side, indicating a high radiation
tolerance of these pellets. Solubility studies of powder samples with identical composition
to the irradiated pellets were conducted in a low-pH environment (0 ≤ pHm ≤ 0.8).
Additional solubility investigations for selected U-doped zirconia samples, under both
oxidizing and reducing conditions were performed in parallel. After 6 months, the yttriumstabilized
samples with cubic structure exhibited slightly lower solubility compared to
those without yttrium (monoclinic or tetragonal structure). These findings speak for an
enhanced chemical stability in addition to the exceptional radiation tolerance of especially
the cubic zirconia modifications.

We present the Materials Learning Algorithms (MALA) package, a scalable machine learning framework designed to accelerate density functional theory (DFT) calculations suitable for large-scale atomistic simulations. Using local descriptors of the atomic environment, MALA models efficiently predict key electronic observables, including local density of states, electronic density, density of states, and total energy. The package integrates data sampling, model training and scalable inference into a unified library, while ensuring compatibility with standard DFT and molecular dynamics codes. We demonstrate MALA's capabilities with examples including boron clusters, aluminum across its solid-liquid phase boundary, and predicting the electronic structure of a stacking fault in a large beryllium slab. Scaling analyses reveal MALA's computational efficiency and identify bottlenecks for future optimization. With its ability to model electronic structures at scales far beyond standard DFT, MALA is well suited for modeling complex material systems, making it a versatile tool for advanced materials research.

Zirconia (ZrO2), exhibits several advantageous properties, including high thermal stability,
chemical inertness, and the capacity to incorporate substantial quantities of actinides and
lanthanides into its host crystal structure [1]. These characteristics make zirconia a promising
candidate for the immobilization of radionuclides from spent nuclear fuel [2][3]. In the present
study, the chemical durability and radiation resistance of doped zirconia materials have been
investigated. Cerium (Ce) has been used as a plutonium (Pu) analog. To stabilize the cubic ZrO2
phase at low tetravalent Ce doping concentrations, trivalent yttrium (Y) was incorporated as a codopant
during synthesis. Both powder samples and dense ceramic pellets were produced for
solubility and irradiation investigations, respectively. For the irradiation investigations, half of
the pellet surface was masked with aluminum foil to protect the pristine side, and the other half
was irradiated with 14 Mev Au4+ ions applying two fluences: 1x1014 ions/cm2 (A1), and 1x1015
ions/cm2 (A2). The pellets were then analyzed using scanning electron microscopy (SEM),
vertical scanning interferometry (VSI), the powder diffraction in Bragg Brentano mode and
grazing incidence diffraction. The results (Fig.1) showed no significant difference between the
pristine and the irradiated side, indicating a high radiation tolerance of these pellets. Solubility
studies of powder samples with identical composition to the irradiated pellets were conducted in
a low-pH environment (0 ≤ pHm ≤ 0.8). Additional solubility investigations for selected U-doped
zirconia samples, under both oxidizing and reducing conditions were performed in parallel. After
6 months, the yttrium-stabilized samples with cubic structure exhibited slightly lower solubility
compared to those without yttrium (monoclinic or tetragonal structure). These findings speak for
enhanced chemical stability in addition to the exceptional radiation tolerance, especially the cubic
zirconia modifications.

The Fibroblast Activation Protein (FAP) plays a pivotal role, particularly in cancer, being overexpressed in the microenvironment of solid tumors, rendering it an attractive target. Based on the UniCAR platform technology, UniCAR target modules (TMs) have been engineered to specifically address this antigen. These TMs, comprising either a single-chain variable fragment (ScFv) or immunoglobulin G (IgG) format, coupled with the UniCAR peptide epitope E5B9, act as a bridge between universal CAR-T cells and target cells, enhancing safety, and efficiency [1]. This study explores gold nanoparticles (AuNPs), both spherical and branched, as nanocarriers for anti-FAP TMs. Branched AuNPs with NIR absorbance extend beyond conventional targeting, holding potential as photothermal agents for localized therapy. This multifaceted approach aims for enhanced cell labeling, photothermal effects, and cytokine activation, advancing the therapeutic capabilities of anti-FAP-targeted immunotherapy. Surface biofunctionalization of particles was achieved through site-directed immobilization of biomolecule-peptide epitope conjugates, utilizing the cysteine terminus at the peptide epitope, to facilitate the formation of a protein monolayer, allowing precise and stable functionalization. Incubation of the FAP-expressing cell line (HT1080 hFAP) with anti-FAP TM coated NPs, monitored via surface plasmon resonance Scattering (SPRS) imaging, indicated successful cell labeling without inducing toxicity at an optical density of 0.1 OD (~272 pM). Viability assessments conducted on all treated cells demonstrated no toxicity concerns. Specificity testing conducted on PC3 cells, employed as a negative control, revealed no discernible increase in scattering intensity. Ongoing investigations are dedicated to optimizing parameters, including concentration and incubation time, to maximize therapeutic potential, aiming to optimize FAP-targeted nanoparticles for advanced therapeutic and diagnostic applications.

The interplay of electronic charge, spin, and orbital currents, coherently driven by picosecond long oscillations of light fields in spin-orbit coupled systems, is the foundation of emerging terahertz lightwave spintronics and orbitronics. The essential rules for how terahertz fields interact with these systems in a nonlinear way are still not understood. In this work, we demonstrate a universally applicable electronic nonlinearity originating from spin-orbit interactions in conducting materials, wherein the interplay of light-induced spin and orbital textures manifests. We utilized terahertz harmonic generation spectroscopy to investigate the nonlinear dynamics over picosecond timescales in various transition metal films. We found that the terahertz harmonic generation efficiency scales with the spin Hall conductivity in the studied films, while the phase takes two possible values (shifted by π), depending on the d-shell filling. These findings elucidate the fundamental mechanisms governing nonequilibrium spin and orbital polarization dynamics at terahertz frequencies, which is relevant for potential applications of terahertz spin- and orbital-based devices.

Ecologists have long investigated how demographic and movement parameters determine the spatial distribution and critical habitat size of a population. However, most models oversimplify movement behaviour, neglecting how landscape heterogeneity influences individual movement. We relax this assumption and introduce a reaction–advection–diffusion equation that describes population dynamics when individuals exhibit space-dependent movement bias toward preferred regions. Our model incorporates two types of these preferred regions: a high-quality habitat patch, termed ‘habitat’, which is included to model avoidance of degraded habitats like deforested regions; and a preferred location, such as a chemoattractant source or a watering hole, that we allow to be asymmetrically located with respect to habitat edges. In this scenario, the critical habitat size depends on both the relative position of the preferred location and the movement bias intensities. When preferred locations are near habitat edges, the critical habitat size can decrease when diffusion increases, a phenomenon called the drift paradox. Also, ecological traps arise when the habitat overcrowds due to excessive attractiveness or the preferred location is near a low-quality region. Our results highlight the importance of species-specific movement behaviour and habitat preference as drivers of population dynamics in fragmented landscapes and, therefore, in the design of protected areas.

Direct encounters, in which two or more individuals are physically close to one another, are a topic of increasing interest as more and better movement data become available. Recent progress, including the development of statistical tools for estimating robust measures of changes in animals’ space use over time, facilitates opportunities to link direct encounters between individuals with the long-term consequences of those encounters. Working with movement data for coyotes (Canis latrans) and grizzly bears (Ursus arctos horribilis), we investigate whether close intraspecific encounters were associated with spatial shifts in the animals’ range distributions, as might be expected if one or both of the individuals involved in an encounter were seeking to reduce or avoid conflict over space. We analyze the movement data of a pair of coyotes in detail, identifying how a change in home range overlap resulting from altered movement behavior was apparently a consequence of a close intraspecific encounter. With grizzly bear movement data, we approach the problem as population-level hypothesis tests of the spatial consequences of encounters. We find support for the hypotheses that (1) close intraspecific encounters between bears are, under certain circumstances, associated with subsequent changes in overlap between range distributions and (2) encounters defined at finer spatial scales are followed by greater changes in space use. Our results suggest that animals can undertake long-term, large-scale spatial changes in response to close intraspecific encounters that have the potential for conflict. Overall, we find that analyses of movement data in a pairwise context can (1) identify distances at which individuals’ proximity to one another may alter behavior and (2) facilitate testing of population-level hypotheses concerning the potential for direct encounters to alter individuals’ space use.

High-content image-based screens are a well-established technique relying on the perturbation of biological or biochemical systems and the subsequent phenotypic readout with the aim of identifying e.g. genes or chemical compounds modulating a process of interest. Noteworthy examples are RNAi screens, which leverage the gene silencing mechanism of RNA interference to interrogate the role of individual genes in specific processes, or small molecule screens employed by pharmaceutical companies as a pivotal step of the drug discovery process. Regardless of the precise type of biological perturbation method employed, off-target effects are an inevitable nuisance requiring special processing to filter them out. Furthermore, high-content screens harbour invaluable information on biological interactions potentially benefiting other research endeavours in systems biology. In a bid to both reliably identify off-target effects and unearth buried phenotypes, we conducted an image-based human genome-wide RNAi screen involving infection with vaccinia virus and subject the results to computational methodologies. In detail, we perform an enrichment via databases and apply XGBoost to the obtained tabular data. In order to harness recent advances in the realm of natural language processing and its applications to biological sequences, we also incorporate the sequence information of proteins identified as hits. The resulting model thus represents an instance of multimodal AI. The proposed method is also applicable to other screening techniques, such as CRISPR-based screening.

ML and DL are revolutionising our abilities to analyse biomedical images. Among other host-pathogen interactions may be readily deciphered from microscopy data using convolutional neural networks (CNN). We demonstrate in several studies how the definition of novel ML/DL tasks may aid in studying infection and disease phenotypes. Specifically, ML/DL algorithms may allow unambiguous scoring of virus-infected and uninfected cells in the absence of specific labelling. Accompanied by interpretability approaches, the ability of CNN to learn representations, without explicit feature engineering, may allow for uncovering yet unknown phenotypes in microscopy. Furthermore, we demonstrate novel ML/DL approaches to simplified 3D microscopy acquisition using conventional 2D hardware. Finally, we exemplify how generative AI can be applied to tasks like image denoising, reconstruction and resolution enhancement in fluorescence and brightfield microscopy. Taken together, we show novel approaches to established algorithms in Computer Vision and Data Science. Applied to microscopy data, these approaches allow for the extraction of observations from datasets large enough to not be suitable for manual analysis. We argue that this shows that reformulating conventional ML/DL tasks to answer biological questions may facilitate novel discoveries in Infection and Disease Biology.

Detecting virus-infected cells in light microscopy requires a reporter signal commonly achieved by immunohistochemistry or genetic engineering. While classification-based machine learning approaches to the detection of virus-infected cells have been proposed, their results lack the nuance of a continuous signal. Such a signal can be achieved by virtual staining. Yet, while this technique has been rapidly growing in importance, the virtual staining of virus-infected cells remains largely uncharted. In this work, we propose a benchmark and datasets to address this. We collate microscopy datasets, containing a panel of viruses of diverse biology and reporters obtained with a variety of magnifications and imaging modalities. Next, we explore the virus infection reporter virtual staining (VIRVS) task employing U-Net and pix2pix architectures as prototypical regressive and generative models. Together our work provides a comprehensive benchmark for VIRVS, as well as defines a new challenge at the interface of Data Science and Virology.

Computational image-quality enhancement for microscopy (deblurring, denoising, and optical deconvolution) provides researchers with detailed information on samples. Recent general-purpose deep learning solutions advanced in this task. Yet, without consideration of the underlying physics, they may yield unrealistic and non-existent details and distortions during image restoration, requiring domain expertise to discern true features from artifacts. Furthermore, the large expressive capacity of general-purpose deep learning models requires more resources to train and use in applications. We introduce DeBCR, a physics-informed deep learning model based on wavelet theory to enhance microscopy images. DeBCR is a light model with a fast runtime and without hallucinations. We evaluated the image restoration performance of DeBCR and 12 current state-of-the-art models over 6 datasets spanning crucial modalities in advanced light microscopy and cryo-electron tomography. Leveraging optic models, DeBCR demonstrates superior performance in denoising, optical deconvolution, and deblurring tasks across both LM and cryo-ET modalities.

Phase imaging is gaining importance due to its applications in fields like biomedical imaging and material characterization. In biomedical applications, it can provide quantitative information missing in label-free microscopy modalities. One of the most prominent methods in phase quantification is the Transport-of-Intensity Equation (TIE). TIE often requires multiple acquisitions at different defocus distances, which is not always feasible in a clinical setting. To address this issue, we propose to use chromatic aberrations to induce the required through-focus images with a single exposure, effectively generating a through-focus stack. Since the defocus distance induced by the aberrations is small, conventional TIE solvers are insufficient to address the resulting artifacts. We propose Zero-Mean Diffusion, a modified version of diffusion models designed for quantitative image prediction, and train it with synthetic data to ensure robust phase retrieval. Our contributions offer an alternative TIE approach that leverages chromatic aberrations, achieving accurate single-exposure phase measurement with white light and thus improving the efficiency of phase imaging. Moreover, we present a new class of diffusion models that are well-suited for quantitative data and have a sound theoretical basis. To validate our approach, we employ a widespread brightfield microscope equipped with a commercially available color camera. We apply our model to clinical microscopy of patients' urine, obtaining accurate phase measurements.

The exploration of the coordination chemistry of actinides significantly lags behind that of transition metals as well as their lanthanide homologues. As such, a fundamental understanding of the binding properties in actinide compounds is still leaving many open questions. Therefore, systematic investigation of various coordination motives around an actinide center can be used as benchmark to evaluate what analytic techniques can reveal about novel actinide-ligand bonding. In this study, we focus on a square antiprism coordination of only oxygen donor atoms in an actinide series ranging from thorium to plutonium. Installing either one or two transition metals in close proximity to the actinide, leads to an 8+2 coordination at the actinide center. These heterobi- and trimetallic complexes have been investigated using single-crystal X-ray diffraction, NMR, HERFD-XANES, and SQUID magnetometry. The experimental findings were further analyzed with quantum chemical calculations. A comparison with their monometallic counterparts gives new insight into actinide-transition metal bonding.

One of the aims of the Work Package 2 “Exposure” of the European project RadoNorm (2020-2025) is to develop methodologies and tools applicable at European level to identify and quantify exposure of population and environment due to Naturally Occurring Radioactive Material (NORM). In particular, focus is on adapting and optimizing current approaches for identification and evaluation of exposure in light of international and national requirements for handling NORM more compelling than in the past and based on new available scientific evidence. The project is in an advanced stage of progress and several results have been obtained. However, these also let emerge research questions, which may be worth addressing in the future. For example, a methodology to establish a NORM inventory has been developed and applied to gather systematic information on NORM from European countries. Information gained, abundant for naturally occurring radionuclides (NOR), indicated that additional and more systematic information on amounts and handling approaches of other contaminants is needed. This as a basis for the establishment of a more efficient optimized and integrated approach for evaluating NORM involving situations.Impact of most recent ICRP dose coefficients for intake of radionuclides by workers and for external radiation has been analyzed for several generic NORM scenarios. Moreover, with the aim to help stakeholders for a practicable implementation of the radiation protection requirements, screening values (defined in terms of activity concentration corresponding to annual effective dose of 1 mSv/year) have been derived for NORM residues disposable in conventional landfills, and for the reuse of NORM sludge as fertilizer in agriculture. For obtaining these screening values, however, generic consideration of groundwater pathway proved difficult and a systematic analysis of types of landfills and typical hydrological characteristics of these landfills at EU level (i.e. a kind of mapping) would be necessary to use water flow and solute transport models with optimized and less conservative parameters.
In addition, for the groundwater pathway, in order to better define the sorption of NOR to soil, e.g. via the Kd parameter, sorption/desorption properties of uranium, radium and polonium have been investigated experimentally or via geochemical models (e.g. smart-Kd approach). To improve the predictive capability of these models, gaps for thermodynamic NOR data have to be filled, especially for radium and polonium. In addition, NOR interactions with organic matter and quantification of the microbial influence on NOR migration in soil are needed to better predict the radionuclide mobility over space and time, which is needed for realistic dose calculations and evaluation of remediation activities.

The Helmholtz-Zentrum Dresden-Rossendorf (HZDR) is advancing towards an integrated data management strategy that spans the entire data lifecycle, embracing the needs of diverse stakeholders such as Helmholtz, the National Research Data Infrastructure (NFDI), and the European Open Science Cloud (EOSC). This talk will address how the HELPMI initiative, a metadata framework designed for laser and plasma research (HELPMI), aligns with and supports HZDR’s comprehensive data management objectives. We will explore HELPMI’s contributions to data standardization, sharing, and FAIR principles (Findable, Accessible, Interoperable, Reusable) within HZDR, discussing its synergy with the broader data management lifecycle and strategic goals. Attendees will gain insights into HELPMI’s role in enhancing data transparency and collaboration, laying a foundation for sustainable research data practices that resonate with national and European frameworks. Finally, we will highlight the roadmap for deeper integration, challenges, and potential areas of development that will help HZDR achieve a unified, robust and sustainable data strategy.

Gallium is classified as a technology metal as it is important for technological
innovations. It is also referred to as a strategic metal, which emphasizes its economic
relevance. In addition, gallium is a critical raw material that is strategically important but
only available in limited quantities. However, recycling dissolved gallium from lowconcentration
wastewater is often not done due to the lack of suitable technologies.
This research presents a membrane-based approach using the siderophore
Desferrioxamine B for the recycling of gallium. Nanofiltration membranes were used to
separate gallium from other metal impurities (such as arsenic). The membranes
recovered about 70% of gallium from low-concentrated synthetic wastewater.
Afterward, the membranes were tested using industrial wastewater, and a similar
recovery rate was observed. A model was developed to predict operation parameters
that would lead to the highest recovery rate of gallium with the minimum impurities. The
model showed that recycling more than 90% of gallium from wastewater is possible
using this approach. Therefore, the siderophore-assisted nanofiltration approach
demonstrated in this research showed great potential for the sustainable recycling of
gallium from industrial wastewater.

Background and purpose: In recent years, ultra-high dose rate (UHDR) irradiation has emerged as a promising innovative approach to cancer treatment. Characteristic feature of this regimen, commonly referred to as FLASH effect, demonstrated primarily for electrons, photons or protons, is the improved
normal tissue sparing, while the tumor control is similar to the one of the conventional dose-rate (CDR) treatments. The FLASH mechanism is, however, unknown. One major question is whether this effect is maintained when using densely ionizing (high-LET) heavy nuclei.

Materials and Methods: Here we report the effects of 20 Gy UHDR heavy ion irradiation in clinically relevant conditions, i.e., at high-LET in the spread-out Bragg peak (SOBP) of a 12C beam using an osteosarcoma mouse model.

Results: We show that UHDR irradiation was less toxic in the normal tissue compared to CDR while maintaining tumor control. The immune activation was also comparable in UHDR and CDR groups. Both UHDR and CDR exposures steered the metagenome toward a balanced state.

Conclusions: These results suggest that the UHDR irradiations can improve the safety and effectiveness of heavy ion therapy, and provide a crucial benchmark for current mechanistic FLASH models. However, additional experiments are needed to validate these findings across other animal and tumor models.

Traumatic brain injury represents a significant global health burden and has the highest prevalence among neurological disorders. Even mild traumatic brain injury can induce subtle, long-lasting changes that increase the risk of future neurodegeneration. Importantly, this can be challenging to detect through conventional neurological assessment. This underscores the need for more sensitive diagnostic tools, such as electroencephalography, to uncover opportunities for therapeutic intervention. Progress in the field has been hindered by a lack of studies linking mechanistic insights at the microscopic level from animal models to the macroscale phenotypes observed in clinical imaging. Our study addresses this gap by investigating a rat model of mild blast traumatic brain injury using both immunohistochemical staining of inhibitory interneurons and translationally relevant electroencephalography recordings. Although we observed no pronounced effects immediately post-injury, chronic time points revealed broadband hyperexcitability and increased connectivity, accompanied by decreased density of inhibitory interneurons. This pattern suggests a disruption in the balance between excitation and inhibition, providing a crucial link between cellular mechanisms and clinical hallmarks of injury. Our findings have significant implications for the diagnosis, monitoring, and treatment of traumatic brain injury. The emergence of electroencephalography abnormalities at chronic time points, despite the absence of immediate effects, highlights the importance of long-term monitoring in traumatic brain injury patients. The observed decrease in inhibitory interneuron density offers a potential cellular mechanism underlying the electroencephalography changes and may represent a target for therapeutic intervention. This study demonstrates the value of combining cellular-level analysis with macroscale neurophysiological recordings in animal models to elucidate the pathophysiology of traumatic brain injury. Future research should focus on translating these findings to human studies and exploring potential therapeutic strategies targeting the excitation-inhibition imbalance in traumatic brain injury.

In the modern technological landscape, magnetic field sensors play a crucial role and are indispensable across a range of high-tech applications. In conjunction with magnets, magnetic field sensors can accurately detect any form of relative movement of objects without physical contact. For instance, in the precise control of robotic arms or machine tools, a permanent magnet is used as a reference. The magnetic sensor detects the relative movement of magnet by sensing changes in the magnetic field strength. These changes are converted into electrical signals, which are fed back to the control system, enabling accurate positioning and control of the device. This advanced detection technology not only greatly enhances measurement precision but also significantly extends the lifespan of equipment. Among various types of magnetic field sensors, magnetoresistive (MR) sensors stand out for their exceptional performance. The high sensitivity allows them to detect minimal changes of magnetic fields in high-precision measurements. Today, MR sensors are widely used across numerous fields, including automobile industries, information processing and storage, navigation systems, biomedical applications, etc. With their outstanding performance and wide-ranging applications, MR sensors are at the forefront of sensor technology.
Over the past decades, the rapid advancement of emerging technologies such as the internet of things (IoT), wearable electronics, digital healthcare and disposable electronics has significantly broadened the application fields for MR sensors. In turn, such innovative applications have introduced unprecedented demands. Beyond traditional metrics, these new applications require sensors to possess unconventional attributes such as flexibility/stretchability, self-healing capabilities, recyclability, transparency, and lightweight, which have presented new challenges in the design and manufacturing of MR sensors. Although there has been significant progress in developing thin-film MR sensors with these unconventional features, meeting all the practical demands remains challenging. Researchers and engineers are actively exploring new materials and manufacturing methods to address these challenges. Printing techniques stand out for their numerous inherent advantages, such as cost effectiveness, scalability, rapid prototyping, versatility, customization, and environmental friendliness. Printable MR sensors have leaped forward benefiting from the rapid development of printing manufacturing techniques, such as screen printing, inkjet printing, roll to roll printing, and 3D printing. The core advantage of these printable sensors lies in their exceptional design flexibility and customization, which enable the production of MR sensors with unconventional properties. These inks/pastes combine functional MR fillers with polymer binders. The incorporation of polymeric binders offers a range of unique attributes, including excellent flexibility and stretchability, as well as self-healing, recyclability, transparency, and lightweight properties [7,8], thus making printed MR sensors adaptable to a wide variety of applications. On the other hand, the functional fillers come in various forms (e.g., particles, wires, flakes, cubes, and complex structures) and their distribution within the binder matrix can be precisely controlled using external magnetic fields. Those together enable the creation of MR sensors with unique and tailored performance characteristics , paving the way to a wide range of applications that are otherwise unavailable.

We have developed an innovative recyclable printed magnetoresistive sensor using GMR microflakes and
AMR microparticles as functional fillers, with PECH as the elastomer binder. Under saturation magnetic
fields of 100 mT and 30 mT, these sensors respectively exhibit magnetoresistance values of 4.7% and
0.45%. The excellent mechanical properties and thermal stability of the PECH elastomer binder endow
these sensors with outstanding flexibility and temperature stability. This flexibility, low cost, and scalability
make these sensors highly suitable for integration into flexible electronic devices, such as smart security
systems and home automation. Moreover, these sensors are fully recyclable and reusable, allowing the
materials to be separated, reused, and remanufactured without loss of performance. The low energy
consumption of the production process and the recyclability of the materials significantly reduce the
environmental impact of these magnetic field sensors.

The growing interest in ferroelectric materials has witnessed the thriving prospect of bio-inspired artificial neuromorphic system, where multi-level polarization states play a crucial role. In this work, with typical BaTiO3 ferroelectric thin film as the model system, we explore the physical effects of inhomogeneity on polarization switching dynamics and neuromorphic performance. Inhomogeneous films exhibited pinched polarization–electric field hysteresis loops, leading to a high recognition accuracy of 96.03% for hand-written digits, compared to about 10.31% for homogeneous films. The inhomogeneity in switching dynamics was analyzed by inhomogeneous field mechanism. Diffusive distributions of switching time and local electric fields were observed, aligning with experimental results and the expected inhomogeneity. The prolonged domain wall depinning time and lowered energy consumption suggest the potential for multi-level polarization states, a possibility further confirmed by phase-field simulations that demonstrated their presence during long-term potentiation/depression. Our work highlights the positive influence of inhomogeneity in enhancing the performance of ferroelectric-based neuromorphic systems.

Performance assessments of geological repositories for the underground disposal of high-level radioactive waste require a deep understanding of the phenomena influencing the mobility of radionuclides, e.g. sorption, redox immobilization, surface precipitation, incorporation, etc. Reliable thermodynamic databases (TDB) are required in order to generate speciation calculations, surface complexation and reactive transport models to predict the aforementioned mechanisms. In this work, the focus was set on europium (Eu), a lanthanide used for decades as a chemical analogue of trivalent actinides (Pu, Am). This study aims at providing a reliable, robust, and internally consistent TDB for europium. Recently, results of our critical evaluation for the chloride, sulphate, phosphate, and hydroxide ligands were published and will be discussed. An example of data acquisition related to the complexation of Eu with aqueous phosphate will also be shown.

High-content image-based screening is widely used in Drug Discovery and Systems Biology. However, sample preparation artefacts may significantly deteriorate the quality of image-based screening assays. While detection and circumvention of such artefacts could be addressed using modern-day machine learning and deep learning algorithms, this is widely impeded by the lack of suitable datasets. To address this, here we present a purpose-created open dataset of high-content microscopy sample preparation artefact. It consists of high-content microscopy of laboratory dust titrated on fixed cell culture specimens imaged with fluorescence filters covering the complete spectral range. To ensure this dataset is suitable for supervised machine learning tasks like image classification or segmentation we propose rule-based annotation strategies on categorical and pixel levels. We demonstrate the applicability of our dataset for deep learning by training a convolutional-neural-network-based classifier.

Poster for Goldschmidt 2024

Optic deconvolution in light microscopy (LM) refers to recovering the object details from images, revealing the ground truth of samples. Traditional explicit methods in LM rely on the point spread function (PSF) during image acquisition. Yet, these approaches often fall short due to inaccurate PSF models and noise artifacts, hampering the overall restoration quality. In this paper, we approached the optic deconvolution as an inverse problem. Motivated by the nonstandard-form compression scheme introduced by Beylkin, Coifman, and Rokhlin (BCR), we proposed an innovative physics-informed neural network Multi-Stage Residual-BCR Net (m-rBCR) to approximate the optic deconvolution. We validated the m-rBCR model on four microscopy datasets-two simulated microscopy datasets from ImageNet and BioSR, real dSTORM microscopy images, and real widefield microscopy images. In contrast to the explicit deconvolution methods (eg Richardson-Lucy) and other state-of-the-art NN models (U-Net, DDPM, CARE, DnCNN, ESRGAN, RCAN, Noise2Noise, MPRNet, and MIMO-U-Net), the m-rBCR model demonstrates superior performance to other candidates by PSNR and SSIM in two real microscopy datasets and the simulated BioSR dataset. In the simulated ImageNet dataset, m-rBCR ranks in the second-best place (right after MIMO-U-Net). With the backbone from the optical physics, m-rBCR exploits the trainable parameters with better performances (from 30 times fewer than the benchmark MIMO-U-Net to 210 times than ESRGAN). This enables m-rBCR to achieve a shorter runtime (from 3 times faster than MIMO-U-Net to 300 times faster than DDPM). To summarize, by leveraging physics constraints our model reduced potentially redundant parameters significantly in expertise-oriented NN candidates and achieved high efficiency with superior performance.

Understanding processes regulating thioarsenate (HxAsSnO4−n3−x; n = 1 – 3; x = 1 – 3) mobility is essential to predicting the fate of arsenic (As) in aquatic environments under anoxic conditions. Under such conditions, natural organic matter (NOM) is known to effectively sorb arsenite and arsenate due to metal cation-bridged ternary complexation with the NOM. However, the extent and mechanism of thioarsenate sorption onto NOM via similar complexation has not been investigated. By equilibrating monothioarsenate (representative of thioarsenate) with a peat (model NOM) with different Fe(III) loadings, this study shows that NOM can sorb monothioarsenate considerably via Fe(III)-bridging. Iron and As K-edge XAS analysis of the monothioarsenate-treated Fe-loaded peats revealed that monothioarsenate forms bidentate mononuclear edge-shared (1E) (RAs···Fe: 2.89 ± 0.02 Å) and bidentate binuclear corner-shared (2C) (RAs···Fe: 3.32 Å) complexes with organically bound Fe(O,OH)6 octahedra, in addition to direct covalent bonds with oxygen-containing functional groups (e.g., –COOH and –OH) (RAs···C: 2.74 ± 0.02 Å), upon equilibration with the Fe(III)-loaded peat. However, the extent of monothioarsenate sorption was considerably less than that of its precursor As species, arsenite, due to higher electrostatic repulsion between the negatively charged monothioarsenate and peat. This study implies that thioarsenate formation under anoxic conditions would increase As mobility by decreasing its sorption onto the NOM.

Monitoring cell behaviour in hydrogel-based 3D models is critical for assessing their growth and response to cytotoxic treatment. Resazurin-based PrestoBlue and AlamarBlue reagents are frequently used metabolic activity assays when determining cell responses. However, both assays are largely applied to cell monolayer cultures but yet to have a defined protocol for use in hydrogel-based 3D models. The assays' performance depends on the cell type, culture condition and measurement sensitivity. To better understand how both assays perform, we grew pancreatic cancer cells in gelatin methacryloyl and collagen hydrogels and evaluated their metabolic activity using different concentrations and incubation times of the PrestoBlue and AlamarBlue reagents. We tested reagent concentrations of 4 % and 10 % and incubation times of 45 min, 2 h and 4 h. In addition, we co-cultured cancer cells together with cancer-associated fibroblasts and peripheral blood mononuclear cells in gelatin methacryloyl hydrogels and subjected them to gemcitabine and nab-paclitaxel to evaluate how both assays perform when characterising cell responses upon drug treatment. CyQuant assays were conducted on the same samples and compared to data from the metabolic activity assays. In cancer monocultures, higher reagent concentration and incubation time increased fluorescent intensity. We found a reagent concentration of 10 % and an incubation time of 2 h suitable for all cell lines and both hydrogels. In multicellular 3D cultures, PrestoBlue and AlamarBlue assays detected similar cell responses upon drug treatment but overestimated cell growth. We recommend to assess cell viability and growth in conjunction with CyQuant assays that directly measure cell functions.

Prostate-specific membrane antigen (PSMA) and gastrin-releasing peptide receptor (GRPR) have been used for diagnostic molecular imaging/therapy of prostate cancer (PCa). To address tumor heterogeneity, we synthesized and evaluated a bispecific PSMA/GRPR ligand (3) combining PSMA-617 (1) and the GRPR antagonist RM2 (2) with the radiometal chelator DOTA. 3 was radiolabeled with 68Ga ([68Ga]Ga-3) and 177Lu ([177Lu]Lu-3). [68Ga]Ga-3 was tested in the following PCa cell lines for receptor affinity, time kinetic cell-binding/specificity, and cell-internalization: PC-3 and LNCaP. Compared to the monomers (1 and 2), ligand 3 showed specific cell binding, similar receptor affinities, and higher lipophilicity, while its internalization rates and cell-binding were superior. Docking calculations showed that 3 can have binding interactions of PSMA-617 (1) inside the PSMA receptor funnel and RM2 (2) inside the GRPR. In vivo biodistribution studies for [68Ga]Ga-3 showed dual targeting for PSMA(+) and GRPR(+) tumors and higher tumor uptake, faster pharmacokinetic, and lower kidney uptake compared to 1 and 2.

Aim/Introduction:

Patients with locally advanced non-small-cell lung cancer (NSCLC) have a high risk of developing distant metastases. It has been shown that applying immunotherapy after radiochemotherapy can significantly improve the prognosis for affected patients. In this context, biomarkers for individualized therapy escalation are urgently needed. One such biomarker could be the total metabolic volume of primary tumor and lymph node (LN) metastases (total tumor burden, TTB). However, delineation of tumor lesions with conventional methods is time consuming and error-prone, especially for the LN metastases. The goal of this study was to investigate feasibility of such delineation with deep learning methods.

Materials and Methods:

Automated delineation was performed with a 3D U-Net convolutional neural network (CNN) developed with the nnU-Net software package [1]. The default nnU-Net training parameters were modified to provide better training stability with small lesion targets as well as to better balance sensitivity vs. positive predictive value (PPV) of lesion detection. A dataset consisting of 517 [18F]FDG-PET/CT scans of NSCLC patients was used for the network training and testing following 5-fold cross-validation scheme. In these data, the ground truth labels were defined via manual delineation and labeling of primary tumor and metastases by an experienced physician.

Results:

The derived CNN models were capable of accurate delineation, achieving a mean (median) Dice similarity coefficient of 0.831 (0.891). The sensitivity and PPV of lesion detection was 0.974/0.829/0.887 and 0.963/0.741/0.824 for primary tumor/LN metastases/union of both, respectively. Accuracy of lesion classification as primary tumor or LN metastases was 92.1%. Manually and automatically derived TTBs were highly correlated with R2=0.96 and a mean absolute difference of 5.4 ml (after rejecting 1% of the outliers).

Conclusion:

In this work, we present CNN models able to perform delineation of and discrimination between primary tumor and lymph node metastases in NSCLC in [18F]FDG-PET/CT with only sporadic manual corrections required. This provides the ability to accelerate large-scale study data evaluation in quantitative PET and has potential for clinical application.

References:

[1] Isensee, F., Jaeger, P.F., Kohl, S.A.A. et al. nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat Methods 18, 203-211 (2021)

Ziel/Aim: Patients with locally advanced non-small-cell lung cancer (NSCLC) have a high risk of developing distant metastases. It has been shown that immunotherapy after radiochemotherapy can significantly improve the prognosis. Therefore, biomarkers for individualized therapy escalation are urgently needed. One such biomarker could be the total metabolic volume of primary tumor and lymph node (LN) metastases. However, delineation of LN metastases with currently available methods is time consuming and error-prone. The goal of this study was to investigate to which extend this delineation can be performed with deep learning methods.

Methodik/Methods: Automated delineation was performed with a pretrained 3D U-Net convolutional neural network (CNN) previously derived for a different head and neck cancer delineation task. 517 [18F]FDG PET/CT scans of NSCLC patients were used for further network training and testing using a 5-fold cross-validation scheme. In these data, manual delineation and labeling of primary tumor and metastases was performed by an experienced physician serving as the ground truth for network training and testing.

Ergebnisse/Results: The derived CNN models are capable of accurate delineation, achieving a Dice similarity coefficient of 0.854. Sensitivity of lesion detection was 0.841 and positive predictive value was 0.847. Accuracy of lesion classification as primary tumor or LN metastases was 82.2%.

Schlussfolgerungen/Conclusions: In this work, we present a CNN able to perform delineation of and discrimination between primary tumor and lymph node metastases in NSCLC with only minimal manual corrections possibly required. It thus is able to accelerate study data evaluation in quantitative PET and does also have potential for clinical application.

The chemistry of actinides (An) is an ongoing subject of current research, particularly with regard to their environmental behaviour and nuclear waste disposal. From a fundamental point of view, the properties of these 5f elements differ significantly from their lanthanide homologues, where the 4f electrons are strongly shielded. Especially for the early actinides Th – Pu, a variety of oxidation states is accessible, ranging from +I to +VII. Furthermore, the 5f electrons are involved in chemical bonding. This results in characteristic magnetic and spectroscopic properties.
Yet, exploration of the coordination chemistry of the actinides significantly lags behind that of transition metals and lanthanides. As such, a fundamental understanding of the binding properties in actinide compounds is still leaving many open questions, as can be illustrated by the limited number of structurally characterized An compounds. Furthermore, previous studies have mainly focused on Th and U and hard donor ligands according to the HSAB principle, such as alkoxides or amines. In order to expand knowledge of the electronic and magnetic properties of the 5f elements, systematic studies of An complexes with different donor atoms on a fundamental level are necessary.
Previous studies have shown that with sulphur as a soft donor, higher covalent contributions can be found in the U-ligand bonds. For the systematic investigation of the An–S binding properties, we synthesized a series of AnIV complexes with the bidentate ligand pyridine-2-thiolate (PyS‾).
Using the complex [U(PyS)₄(THF)] presented by Neu et al. as a blueprint, we established two synthetic routes for the complexation of tetravalent An with PyS‾: a salt metathesis reaction with KPyS and a reaction with PyS–SiMe₃. The obtained compounds of the types [An(PyS)₄(THF)] (An: Th, U, Np, Pu) and K[An(PyS)₅] (An: Th, U) were comprehensively characterized in solution and in solid phase, by single-crystal X-ray diffraction, NMR spectroscopy, HERFD-XANES, and SQUID measurements. Supported by quantum chemical calculations, electronic and magnetic properties of the metal centres as well as bonding trends along the An series (Th – Pu) were investigated.

The 2-pyridyloxy ligand (PyO⁻) has proven to be a useful ligand to isolate heterobimetallic complexes and thus supporting bonds between transition metals (TM) and/or main-group elements. Although interesting coordination motifs can be expected especially with actinides, metal-metal interactions remain a scarce phenomenon in actinide chemistry. Together with the high coordination numbers and various oxidation states, actinide 2-hydroxypyridinolate complexes would have the necessary flexibility to form a variety actinide complexes also containing a transition metal.
Initially, we have synthesised and characterised a series of heteroleptic actinide 2-pyridone complexes [AnCl(HPyO)₇]Cl₃ starting from [AnCl₄(THF)₃] (An = Th, U, Np, Pu). [AnCl(HPyO)₇]Cl₃ complexes were found to be a good candidate to synthesise heterobimetallic complexes by the addition of [TMCl₂(THT)₂] (TM = Pd, Pt; THT = tetrahydrothiophene) and NEt₃ as a supporting base. By this approach, we were able to isolate a series of 8 complexes of the motif [TM(µ-PyO)₄An(µ-PyO)₄TM] (An = Th, U, Np, Pu; TM = Pd, Pt), that allows us to draw comparisons along the tetravalent actinide series and between palladium and platinum. These complexes have been investigated by single-crystal XRD, NMR, HERFD-XANES, and SQUID magnetometry. The experimental findings were supported by quantum chemical calculations, whereby an unexpected trend in An-TM bonding has been observed.

Yb 5Rh6 Sn18 crystallizes with a unique structural arrangement [space group P42 /nmc, a = 9.6997(4) Å, c = 13.7710(7) Å], which is related with primitive cubic Yb 3Rh4 Sn13 and body-centered tetragonal (Sn 1−xTbx) Tb4 Rh6 Sn18 types. X-ray absorption spectroscopy showed that Yb atoms exhibit temperature-dependent valence fluctuations (VF) (i.e., intermediate valence state). Its complex mechanism is corroborated by the fact that the well-pronounced maximum in magnetic susceptibility can only be fairly described by the Bickers–Cox–Wilkins model developed for a J = 3/2 multiplet, atypical for Yb ions. Both Hall and Seebeck coefficients revealed a switch of the sign, indicating the change of charge carrier type from electrons to holes between 120 and 220 K. Both these effects together with electrical resistivity and theoretical DFT calculations confirm Yb5 Rh6Sn18 to be a metal, which disobeys the free electron gas theory. ‘Rattling’ motion of Sn1 atoms within the enlarged 16-vertices distorted Frank–Kasper polyhedra, concluded from the specific heat measurements, is argued to be the main reason for the appearance of a phonon resonance behavior, resulting in an ultra-low thermal conductivity in the studied stannide

Mostly produced as a by-product of zinc (Zn) mining, cadmium (Cd) is used in solar cells, battery storage, alloys, pigments, plating, and in nuclear reactors. However, it is also a regulated toxic substance with a long history of environmental and health impacts. As the mining of both Zn and Cd will increase to support the global energy transition, the status of Cd as either a resource or a pollutant has major implications for global supply chains and environmental management. Here, we present a new global, site-specific database and analysis of Cd resources in Zn-bearing mineral deposits and mines. Our database, which exceeds past Cd studies in scope, transparency and replicability is made available in full to support future assessments of Cd and Zn resources, mine production and associated risks. It includes 927 sites subject to detailed geological data compilation and analysis. Collectively, these sites suggest a new global resource estimate of 3.3 Mt Cd (95% confidence interval: 2.7 – 6.1 Mt).

A preliminary geospatial analysis of sites in our database and mine toxicity indicators was also conducted. It shows that:

• A human population of approximately 3.27 million live within 10 km of sites containing Cd resources,

• ~31% of the world’s Cd resources sit within 20 km of International Union for the Conservation of Nature (IUCN) protected areas, and

• Some 28% of Cd mobilised annually by mining originates from areas hosting seasonal or permanent surface water cover.

As ~27% of Cd resources are in countries that do not refine it, our study highlights the need for further research exploring global Cd trade flows and associated emissions. Heavy metal pollution in mining and metal production regions is an ongoing challenge, and our global dataset refines our understanding of its magnitude and distribution.

The Plaka porphyry Mo-Cu system occurs in the world-class Lavrion Ag-Pb-Zn district in Attica, southern Greece. It is spatially associated with a granodiorite porphyry that intruded the Attic-Cycladic Crystalline Complex in the late Miocene, along the footwall of the Western Cycladic detachment fault. A Re-Os age of 9.51 ± 0.04 Ma indicates that molybdenite formed during the early stage of the granodiorite porphyry intrusion and that subsequent cooling was very rapid. Brittle deformation and hydrothermal fluid flow created a network of A-, B-, diopside-actinolite and D- veins, associated with potassic-, sodic-calcic- and sericitic alterations. Potassic alteration is characterized by secondary biotite + K-feldspar + quartz + magnetite ± apatite, contains disseminated molybdenite, pyrite, and chalcopyrite, and formed at 420–500 °C, at pressures up to 530 bars (< 5.3 km depth) from hydrothermal fluids that underwent phase separation. Sodic-calcic alteration is devoid of Cu-Mo mineralization and, consists of diopside + actinolite + oligoclase/andesine + titanite + magnetite ± epidote-allanite ± chlorite ± quartz, which corresponded to a temperature range of between 350 and < 500 °C. Primary magnetite, titanite and biotite crystallized between the nickel‑nickel oxide (NNO) and hematite-magnetite (HM) buffers, indicating fairly oxidizing conditions for the granodioritic magma. Hydrothermal biotite plots closer to the HM buffer suggesting increasing oxygen fugacity during exsolution of the hydrothermal fluids associated with potassic alteration. The system evolved toward more reducing conditions during sericitic alteration and associated pyrite-molybdenite mineralization. A combination of evaporated seawater and magmatic fluids likely caused formation of the sodic-calcic alteration through the decarbonation of the host marble

Curvilinear magnetism emerged as a new route to tailor properties of magnetic solitons by the choice of geometry and topology of a magnetic architecture. Here, we develop an anodized aluminum oxide template-based approach to realize hierarchical 3D magnetic nanoarchitectures of nanoflower shape. The technique provides defect-free regular arrays of magnetic nanoflowers of tunable shape with a period of 400\,nm over cm$^2$ areas. We combined advanced magnetic imaging methods with micromagnetic simulations to study complex magnetic states in nanoflowers originated due to magnetostatics-driven symmetry break in curvilinear nanomembranes. An interaction between surface and volume magnetostatic charges in 3D curved nanoflowers leads to the stabilization of asymmetric and shifted vortices as well as states with two Bloch lines. Ordered large area arrays of complex-shaped magnetic nanoarchitectures developed in this work are relevant for prospective research on 3D magnonics and spintronics.

Germanium (Ge) is a critical raw material for emerging high-tech and green industries, resulting in considerable recent interest in understanding its distribution and geochemical behavior in ore deposits. In this contribution, the distribution of Ge and related trace elements in the Fule Pb-Zn(-Ge) deposit, South China, is investigated to reveal the distribution of Ge in the hydrothermal ores and during sulfide weathering, using multiple microanalytical techniques, including scanning electron microscopy, electron probe microanalysis and laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS). In the Fule MVT deposit, sphalerite (ZnS) is the most significant Ge-carrier relative to other sulfides, though the five recognized textural types of sphalerite display progressive depletion in Ge from the first sphalerite generation to the late one. In the early stage, sphalerite with fine-grained chalcopyrite inclusions has the highest Ge concentrations, probably accounting for a significant proportion of the total Ge. We interpret that high Ge concentrations in the early sphalerite may be attributable to high Cu activity in the mineralizing fluids. During oxidative weathering, Ge was redistributed from its original host, sphalerite, to the weathering product willemite (Zn2SiO4) rather than smithsonite (ZnCO3), with high levels of Ge (up to 448 μg/g) present in the willemite. The formation of abundant willemite largely prevents the dispersion of Ge during weathering. In principle, willemite-hosted Ge should be fully recoverable, and the Zn-silicate ores may, therefore, be a potential target to meet future demand.
This study provides new information on how Ge behaves from sulfide- to weathering-stage in MVT systems, which directly impacts Ge mobility and deportment changes and the development of metallurgical strategies for Ge recovery.

The Irish Orefield is characterised by the presence of both Zn-Pb- and Cu-Ni-As-rich deposits, prospects, and orebodies in similar structural and stratigraphic positions. However, the genetic relationships between these mineralisation types are still debated. In this article, we present new mineralogical, paragenetic, and mineral-chemical observations from the Cu-Ni-As-rich ores at the classic Lisheen deposit, County Tipperary. These observations indicate the intimate association and cogenetic nature of these ores with the more abundant Zn-Pb-rich mineralisation. Specifically, both mineralisation types appear to have formed at the same time, under similar physicochemical conditions, and from the same ore fluids. In addition, both types of mineralisation contain elevated Ge contents. The cogenetic nature of the two mineralisation types, the relative absence of Cu-Ni-As-rich ores from most of the larger Irish-type Zn-Pb deposits compared to expectations derived from probable ore fluid compositions, and finally, the known geological characteristics of larger Cu-Ni-As-rich ore bodies, like Gortdrum, indicate that significant Cu-Ni-As-rich mineralisation could be present at lower stratigraphic levels across the Irish Orefield. Areas with extensive known Zn-Pb mineralisation are expected to be particularly prospective for such ores, which may occur at stratigraphic levels as deep as the Old Red Sandstone. This may have additional implications beyond Ireland, and could point to the potential for undiscovered Cu-rich ores in low-temperature carbonate-hosted Zn-Pb districts elsewhere.

KI-Modelle haben die Softwareindustrie, Wissenschaft und Gesellschaft in den letzten Jahren im Sturm erobert. Ihre Vorhersagen sind teilweise besser als menschliche Fähigkeiten und teilweise unverholens realitätsfern oder falsch. Aber wie können wir die Qualität der Vorhersagen einschätzen ohne gleich Use-Cases abzuschreiben oder blind KI-Methoden verbannen? In diesem Impuls möchte ich mich genau dieser Frage stellen. Meine Antwort heißt "Uncertainty Quantification". Ich werde diesen Zweig der KI-Methodik motivieren und aktuelle Ergebnisse aus der Forschung geben.

For research data please contact the corrsponding author. Data publications from research of Freiberg University of Mining and Technology can be found here: http://opara.zih.tu-dresden.de/handle/123456789/21.

One mode of biomolecular self-organization in aqueous environments is compartmentalization through liquid-liquid phase separation into biomolecular condensates. These condensates are created through transient, multivalent interactions between intrinsically disordered proteins. The formation of such condensates involves extensive rearrangement of the water network, including stripping away of the protein solvation shell. This work aims to find out how the dynamics of the hydrogen bond network within biomolecular condensates are modulated by co-solutes and amino acid level chemical modifications of the protein. The effects of post-translational (added in the cell after the polypeptide chain is synthesized) modifications and salts on the water network inside condensates formed by the Fused in Sarcoma (FUS) protein have been studied using Terahertz (THz) spectroscopy. This spectroscopic method probes the intermolecular hydrogen bonding network of water and reports on the protein hydration water. A comparison of spectra of FUS with added monovalent and divalent chloride salts was performed. The phase behavior of FUS shows a non-monotonic trend with respect to salt concentration, where the protein undergoes a salt-dependent reentrant phase transition. Comparison of THz spectra between the droplets formed in the low ionic strength and high ionic strength regime show a significant change in the amount of hydrophobic hydration water. Post-translational modifications were found to introduce new sub-environments of hydration water in the condensates, which disappeared in the high salt regime. These findings show, that the hydration network within biomolecular condensates is more rigid and structured in the presence of biologically introduced chemical modifications of the protein, and that a high salt concentration abolishes this effect, possibly through weakening the hydrogen bonding between water and protein.

ROCK-IT is a collaboration which aims to demonstrate the ability to perform complex operando catalysis experiments in a highly automated way, enabling remote operation. The project finds common solutions between different facilities which have various control systems and infrastructure. Ophyd provides a common abstraction layer to Tango, EPICS and SECoP. In the demonstrators of this project, Bluesky is used to orchestrate experiments which involve simultaneous control of a sample environment and various measurement techniques. In this presentation the challenges associated with controlling such an experiment will be presented and the proposed solutions explored.

Nonlinear THz transmission is utilized to investigate the solvation dynamics of proteins. Protein structure and function are inherently dependent on their solvation shell, and an understanding of the underlying solvation dynamics provides valuable insight into how solute-solvent interactions impact biophysical processes. Here, the results of concentration dependent z-scan experiments are reported.

High-power coherent terahertz (THz) radiation from accelerator facilities such as free-electron lasers (FELs) is frequently used in pump-probe experiments where the pump or probe (or both) signals are intense THz pulses. Detectors for these applications have unique requirements that differ from those of low-power table-top systems. In this study, we demonstrate GaAs antenna-coupled field effect transistors (FETs) as a direct THz detector operating across a broad frequency spectrum ranging from 0.2 THz to 29.8 THz. At approximately 0.5 THz, the maximum current responsivity (ℜ I) of 0.59 mA/W is observed, signifying a noise equivalent power (NEP) of 2.27 nW/√H z. We report an empirical roll-off of f −3 for an antenna-coupled GaAs TeraFET detector. Still, NEP of 0.94 μW/√H z and a current responsivity ℜ I = 1.7μA/W is observed at 29.8 THz, indicating that with sufficient power the FET can be used from sub-mm wave to beyond far-infrared frequency range. Current and voltage noise floor of the characterized TeraFET is 2.09 pA and 6.84 μV, respectively. This characteristic makes GaAs FETs more suitable for applications requiring higher frequencies, ultra-broadband capabilities and robustness in the THz domain, such as beam diagnostics and alignment at particle accelerators.

RE-doped β-Ga₂O₃ seems attractive for future high-power LEDs operating in high irradiation environments. In this work, we pay special attention to the issue of radiation-induced defect anisotropy in β-Ga₂O₃, which is crucial for device manufacturing. Using the RBS/c technique, we have carefully studied the structural changes caused by implantation and post-implantation annealing in two of the most commonly used crystallographic orientations of β-Ga₂O₃, namely the (-201) and (010). The analysis was supported by advanced computer simulations using the McChasy code. Our studies reveal a strong dependence of the structural damage induced by Yb-ion implantation on the crystal orientation, with a significantly higher level of extended defects observed in the (-201) direction than for the (010). In contrast, the concentration and behavior of simple defects seem similar for both oriented crystals, although their evolution suggests the co-existence of two different types of defects in the implanted zone with their different sensitivity to both, radiation and annealing. It has also been found that Yb ions mostly occupy the interstitial positions in β-Ga₂O₃ crystals that remain unchanged after annealing. The location is independent of the crystal orientations. We believe that these studies noticeably extend the knowledge of the radiation-induced defect structure, because they dispel doubts about the differences in the damage level depending on crystal orientation, and are important for further practical applications.

The project “Recycling of mineral fractions from tailings” aims to separate pollutants and valuable materials tailings from tailings. The use of remaining mineral fractions for building materials as a secondary raw material is made possible. Bioleaching is used to extract metal, especially lead, mineral fraction, which have been recovered by flotation from tailings. The metals can be released from the mineral through microbial catalyzed redox reactions or complex formation. For this purpose, the mineral fractions were incubated with sulfur-oxidizing bacteria, microorganisms that form low-molecular organic acids and with low-molecular organic acids. After acidophilic bioleaching, manganese (approx. 2 g/l), iron (approx. 8.5 g/l) and zinc (approx. 1 g/l) were detected in the leaching solution. In a second campaign, microorganisms that produce low molecular weight organic acids were used and the growth of these microorganisms was inhibited. Easily mobilizable amounts of zinc and copper were identified as the reasons for this. In a third series of experiments culture supernatants with desired low molecular weight organic acid were used for leaching. This procedure allowed between 30 and 40% of the lead contained in the mineral fraction were leached.

Conventional mining and extraction methods for REEs are energy-intensive and environmentally harmful. Bioleaching processes offer a promising and eco-friendly approach to enhance the sustainability of REE extraction. This study evaluates the potential of bioleaching REEs from unprocessed carbonatitic and alkaline bulk rocks. Bioleaching experiments were conducted on a Carbonatite sample from the Fen-Complex (Norway) and two nepheline syenites (a Grennaite and a pegmatitic Grennaite from Norra Kärr, Sweden), utilizing the heterotrophic organisms Yarrowia lipolytica DSM3286 and Tea fungus Kombucha.
The results demonstrate varying recovery rates based on mineralogy and leaching methods, with preferential leaching of light or heavy REEs depending on the selected organisms. Notably, the highest leaching efficiency of 54% REE recovery was achieved with Y. lipolytica DSM3286 supernatant leaching on pegmatitic Grennaite during a 19-day experiment. Carbonatite and Grennaite samples exhibited lower maximum leaching rates of 5% and 8%, respectively. The findings demonstrate the proof-of-concept feasibility of bioleaching REEs from unprocessed bulk rock materials and highlight its strong potential, especially in providing a sustainable solution for utilizing low-grade ores and mine waste.

On the basis of an exact power history and accurate geometric modelling, plant-specific neutron fluences were calculated for in each case a pre- and convoy unit of German nuclear power plant for reactor components and for concrete and structural elements close to the reactor. These neutron fluences are the basis for determining the generated activation of the construction materials during the power operation of the plant. The calculations were supported by an extensive measurement program in the last cycles of two plants, where neutron fluence values were determined ex-perimentally with the help of activation foils (monitors). A spectral analysis was possible by using different monitor materials. The monitors were measured by gam-ma spectrometry after sampling using a high-purity germanium (HP-Ge) detector. The comparison of the calculated and measured activities shows, with a few excep-tions, good to very good agreement between the values. This means that the real ratios of neutron radiation in the elements were calculated very well and the method and model can be used to determine the activity distribution.
Due to the possibility of the accurate simulation of the resulting activities on the ba-sis of these "best estimate" calculations, detailed planning of the decommissioning can already begin during the operation of the plant. It is not necessary to wait until extensive sampling after the shutdown.
In addition, the accurate mathematical determination of the activity distribution in the components enables improved cut planning and thus minimization of the waste volume for the final storage. A further advantage would be that the necessary exper-imental activity determinations could be reduced to a few samples thanks to the supporting experiments and thereby validated neutron fluence calculations.

Motivated by the exceptional optoelectronic properties of 2D Janus layers (JLs), we explore the properties of group Va antimony-based JLs SbXY (X=Se/Te, Y=I/Br). From the Bader charges, the electric dipole moment in the out-of-plane direction of all the JLs is studied and the largest dipole moment is found to be in the SbSeI JL. Our results on the formation energy, phonon spectra, elastic constants, and ab initio molecular dynamic (AIMD) simulation, predict the energetic, vibrational, mechanical, and thermal stability of JLs. After confirming the stability, the three-dimensional phase diagram is investigated to propose the experimental conditions required to fabricate the predicted JLs. Then, the electronic band structure is calculated using different levels of theory namely generalized gradient approximation (GGA), GGA+ spin-orbit coupling (GGA+SOC), hybrid Heyd-Scuseria-Ernzerhof (HSE), and many-body perturbation theory-based Green’s function method (GW). According to the HSE results, JLs show band gaps between 1.653 and 1.852 eV. The GGA+SOC calculations reveal Rashba spin splitting in these JLs. The calculated carrier mobility using deformation potential theory shows that the electrons have exceptionally high mobility compared to holes, which assists the spatial separation of both charge carriers. The optical spectra are determined using GGA, HSE, and GW methods. With respect to GGA results, HSE and GW optical spectra show a blue shift. More accurate calculations by the GW-Bethe Salpeter equation (BSE) yield optical absorption spectra which are dominated by strong excitonic effects with excitonic binding energy (BEex) in the range of 550-800 meV. Compared to GW-BSE, the Mott-Wannier (MW) model predicts lower BEex. The strong e-h coupling is observed for dispersions along K −M in the Brillouin zone from the fat band analysis. From our study, SbSeI JL is a potential candidate for photocatalytic and photovoltaic applications due to its largest dipole moment and low excitonic binding energy.

We present a comprehensive theoretical analysis of the structural and electronic properties of a van der Waals heterostructure composed of CdS and α-Te single layers (SLs). The investigation includes an in-depth study of fundamental structural, electronic, and optical properties with a focus on their implications for photocatalytic applications. The findings reveal that the α-Te SL significantly influences the electronic properties of the heterostructure. Specifically, the optical property of the heterostructure is notably dominated by the contribution of α-Te. The layer-resolved density of states analyses indicate that the valence and conduction bands near the Fermi level are mainly determined by the α-Te SL. Band edge analyses demonstrate a type-I band alignment in the heterostructure, causing charge carriers (electrons and holes) to localize within α-Te. The electronic properties can be further modulated by external strain and electric fields. Remarkably, the CdS-α-Te heterostructure undergoes a transition from type-I to type-II band alignment when subjected to biaxial strain and an external electric field. This may be interesting for the application of the heterostructure for photocatalysis.

Layered framework materials, a rapidly advancing class of porous materials, are composed of molecular components stitched together via covalent bonds and are usually synthesized through wet-chemical methods. Computational infrared (IR) and Raman spectra are among the most important characterization tools for this material class. Besides the a priori known spectra of the molecular building blocks and the solvent, they allow for in situ monitoring of the framework formation during synthesis. Therefore, they need to capture the additional peaks from host–guest interactions and the bands from emerging bonds between the molecular building blocks, verifying the successful synthesis of the desired material. In this work, we propose a robust computational framework based on ab initio molecular dynamics (AIMD), where we compute IR and Raman spectra from the time-correlation functions of dipole moments and polarizability tensors, respectively. As a case study, we apply our methodology to a covalent organic framework (COF) material, COF-1, and present its AIMD-computed IR and Raman spectra with and without 1,4-dioxane solvent molecules in its pores. To determine robust settings, we meticulously validate our model and explore how stacking disorder and different methods for computing dipole moments and polarizabilities affect IR and Raman intensities. Using our robust computational protocol, we achieve excellent agreement with experimental data. Furthermore, we illustrate how the computed spectra can be dissected into individual contributions from the solvent molecules, the molecular building blocks of COF-1, and the bonds connecting them.

The emerging laser writing represents an efficient and promising strategy for covalent two dimensional (2D)-patterning of graphene yet remains a challenging task due to the lack of applicable reagents. Here, we report a versatile approach for covalent laser patterning of graphene using a family of trivalent organic iodine compounds as effective reagents, allowing for the engraving of a library of functionalities onto the graphene surface. The relatively weak iodine-centered bonds within these compounds can readily undergo laser-induced cleavage to in situ generate radicals localized to the irradiated regions for graphene binding, thus completing the covalent 2D-structuring of this 2D-film. The tailor-made attachment of distinct functional moieties with varying electrical properties as well as their thermally reversible binding manner enables programming the surface properties of graphene. With this delicate strategy the bottleneck of a limited scope of functional groups patterned onto the graphene surface upon laser writing is tackled.

Ferromagnetism and antiferromagnetism require robust long-range magnetic ordering, which typically involves strongly interacting spins localized at transition metal atoms. However, in metal-free systems, the spin orbitals are largely delocalized, and weak coupling between the spins in the lattice hampers long-range ordering. Metal-free magnetism is of fundamental interest to physical sciences, unlocking unprecedented dimensions for strongly correlated materials and biocompatible magnets. Here, we present a strategy to achieve strong coupling between spin centers of planar radical monomers in π-conjugated two-dimensional (2D) polymers and rationally control the orderings. If the π-states in these triangulene-based 2D polymers are half-occupied, then we predict that they are antiferromagnetic Mott-Hubbard insulators. Incorporating a boron or nitrogen heteroatom per monomer results in Stoner ferromagnetism and half-metallicity, with the Fermi level located at spin-polarized Dirac points. An unprecedented antiferromagnetic half-semiconductor is observed in a binary boron-nitrogen–centered 2D polymer. Our findings pioneer Stoner and Mott-Hubbard magnetism emerging in the electronic π-system of crystalline-conjugated 2D polymers.

The diabolical problem of pentagonal symmetry was already highlighted by the German polymath and writer Johann Wolfgang von Goethe around 1800: “The Pentagramma is causing you pain?” Faust asked Mephistopheles in the famous tragic play Faust. Indeed, among the many ways to tile the Euclidean plane, there is none incorporating regular pentagons1. The Cairo tessellation (Fig. 1a) is a possibility to completely fill two-dimensional (2D) space using congruent, albeit irregular, pentagons. In this 2D lattice, four pentagons can be combined to form a stretched hexagon of D2h symmetry (Fig. 1b), and this symmetry, again, can be mapped to a Truchet lattice2 made of squares decorated by a pattern of D2h symmetry, which is rotated by 90° in each neighbour (Fig. 1c). This arrangement is easily mapped to a regular square periodic cell (Fig. 1d). This turns the pentagon problem, at least in part, back into square or hexagon problems. Materials with the peculiar structure of the Cairo pentagonal lattice may have highly in-plane anisotropic properties with strong dependence on strain3,4, high carrier mobilities and slanted Dirac cones, which open a playground for condensed-matter physicists. However, materials with pentagonal lattices are rare, as most of them are predicted to be unstable or metastable. This is caused by the odd number of vertices in the pentagonal ring, and by the irregularity of the congruent pentagons. Notable exceptions predicted include the noble metal dichalcogenides PdS2 and PdSe2 (ref. 3). The PdSe2 monolayer with Cairo tessellation has been realized by micromechanical exfoliation from its orthorhombic bulk5. Most pentagonal noble metal dichalcogenides are, however, unstable or metastable3, and none of these forms has been realized experimentally so far. Now, writing in Nature Materials, Lina Liu and colleagues report a way to realize metastable 2D PdTe2 with the pentagonal Cairo lattice6.

While moiré structures in twisted bilayer transition metal dichalcogenides (TMDCs) have been studied for over a decade, the importance of lattice relaxation effects was pointed out only in 2021 by DiAngelo and MacDonald1, who reported the emergence of a Dirac cone upon relaxation. TMDCs of group 6 transition metals MX2 (M = Mo, W, X = S, Se) share layered structures with pronounced interlayer interactions, exhibiting a direct band gap when exfoliated to a two-dimensional (2D) monolayer. As their heterolayers are incommensurable, moiré structures are present in the bilayers even if stacked without a twist angle. This study addresses the challenge of accurately modeling and understanding the structural relaxation in twisted TMDC heterobilayers. We show that the typical experimental situation of finite-size flakes stacked upon larger flakes can reliably be modeled by fully periodic commensurate models. Our findings reveal significant lattice reconstruction in TMDC heterobilayers, which strongly depend on the twist angle. We can categorize the results in two principal cases: at or near the untwisted configurations of 0° and 60°, domains with matching lattice constants form and the two constituting layers exhibit significant in-phase corrugation—their out-of-plane displacements are oriented towards the same direction in all local stackings—while at large twist angles—deviating from the 0° and 60°—the two layers show an out-of-phase corrugation. In particular, we reveal that the lattice reconstruction results from the competition between the strain energy cost and the van der Waals energy gain. Additionally, our systematical study highlights structural disparities between heterostructures composed of different or identical chalcogen atoms. Our research not only confirms the reliability of using periodic commensurate models to predict heterostructure behavior but also enriches the understanding of TMDC bilayer heterostructures.

Exotic band features, such as Dirac cones and flat bands, arise directly from the lattice symmetry of materials. The Lieb lattice is one of the most intriguing topologies, because it possesses both Dirac cones and flat bands which intersect at the Fermi level. However, the synthesis of Lieb lattice materials remains a challenging task. Here, we explore two-dimensional polymers (2DPs) derived from zinc-phthalocyanine (ZnPc) building blocks with a square lattice (sql) as potential electronic Lieb lattice materials. By systematically varying the linker length (ZnPc-xP), we found that some ZnPc-xP exhibit a characteristic Lieb lattice band structure. Interestingly though, fes bands are also observed in ZnPc-xP. The coexistence of fes and Lieb in sql 2DPs challenges the conventional perception of the structure–electronic structure relationship. In addition, we show that manipulation of the Fermi level, achieved by electron removal or atom substitution, effectively preserves the unique characteristics of Lieb bands. The Lieb Dirac bands of ZnPc-4P shows a non-zero Chern number. Our discoveries provide a fresh perspective on 2DPs and redefine the search for Lieb lattice materials into a well-defined chemical synthesis task

Porphyrins are excellent light-harvesting complexes. Presently they are unsuitable for photovoltaic applications, as their excellent light absorbance is compensated to a large extent by their poor transport properties, where most excitons are lost by recombination. Arranging porphyrins in regular, strongly bound, lattices of surface-anchored metal-organic frameworks (PP-SURMOFs) may facilitate charge carrier dissociation, but does not significantly enhance the conductive properties. In most cases, photogenerated excitons traverse undirected, Brownian motion through a hopping process, resulting in a substantial diffusion length to reach electrodes, leading to significant exciton loss through recombination. Here, we propose to guide exciton diffusion indirectly by an external electric field. We show that electric fields, even as strong as 1 V nm−1, do not affect the HOMO-LUMO gap of the porphyrins. However, fields of 0.1 V nm−1 and even less demonstrate a notable Stark effect, with slight band gap reductions, for some PP-SURMOFs. When applied as an electric field gradient, for instance, via the substrate, it creates a unidirectional hopping pathway for the excitons. Consequently, we expect a significant reduction of exciton diffusion length leading to increased utilization of photogenerated excitons as they reach the electrodes. This strategy holds promise for integrating photoactive molecules in photovoltaic and photocatalytic applications.

2D conjugated metal–organic frameworks (2D c-MOFs) are emerging as unique electroactive materials for electronics and spintronics. The structural design and discovery of Kagome-type 2D c-MOFs exhibiting a metallic state are of paramount significance, yet remain rarely explored. Here, the solution synthesis of benzenehexol-based 2D c-MOFs based is presented on the tetrahydroxy-1,4-quinone (THQ) ligand. This study shows that controlling the pH of the reaction system to ≈7.5 yields an energetically favorable nonporous Cu3(C6O6) with a Kagome lattice, while at a pH of ≈10, the known porous Cu3(C6O6)2 with a honeycomb lattice is obtained. The crystal structures of both Cu3(C6O6)2 and Cu3(C6O6) are resolved with near-atomic precision (resolution, 1.8 Å) using an imaging technique. Unlike the p-type semiconducting behavior of Cu3(C6O6)2, theoretical studies identify Cu3(C6O6) as a metal due to its unique structural topology. The metallic state of Cu3(C6O6) is experimentally validated by terahertz time-domain spectroscopy (THz-TDS), which shows an increase in conductivity upon cooling. Scattering-type scanning near-field optical microscopy (s-SNOM) measurements further support these findings by revealing an increase in normalized reflectivity with decreasing temperature. This work provides a new avenue for tailoring the structural topology of 2D c-MOFs to attain the Kagome lattice and metallic state.

Two-dimensional group 6 transition metal dichalcogenide (2D TMDC) bilayers show various high-symmetry stacking configurations, which have also been observed in extended domains formed in their twisted homo- and heterobilayers. The interlayer energy varies for these stacking configurations, and the energy differences determine the relative size of the stacking domains. Therefore, the precise prediction of the composition- and stacking-dependent interlayer energy is crucial to model the domain structure of 2D TMDCs in their twisted bilayer homo- and heterostructures. For the validation of approximate methods that are necessary to tackle these systems encompassing thousands of atoms precise reference data is still lacking. Here, we employ the random phase approximation (RPA) on previously validated SCAN-rVV10 geometries to obtain interaction energies of state-of-the-art accuracy on the six high-symmetry stacking configurations of MX2 (M = Mo, W; X = S, Se) bilayers and compare them with the dispersion-corrected density-functional theory (DFT) functionals Perdew–Burke–Ernzerhof (PBE)+D3(BJ), PBE-rVV10L, and SCAN-rVV10. We identify SCAN-rVV10 as most reliable DFT variant with an average deviation of 1.2 meV/atom in relative energies from the RPA reference, and a root mean squared error of less than 2 meV/atom for interlayer interaction energies. We find interlayer distances obtained by PBE+D3(BJ) as being too short, with an impact on the electronic structure, resulting in the incorrect prediction of the band gap character in some cases. A further result of this work is the significant lowering of the interlayer energy and increasing of the interlayer distance in the high-energy stacking configurations. These stackings can be accessible via shear strain and promote exfoliation.

Taking into account the electron-rich and visible light response of thiophene, first-principles calculations have been carried out to explore the photocatalytic activity of donor–acceptor polymers incorporating thiophene and boron. Honeycomb-kagome boron–thiophene (BTP) polymers with varying numbers of thiophene units and fixed B center atoms are direct bandgap semiconductors with tunable bandgaps ranging from 2.41 to 1.88 eV and show high absorption coefficients under the ultraviolet and visible regions of the solar spectrum. Fine-tuning the band edges of the BTP polymer is efficiently achieved by adjusting the pore size through the manipulation of thiophene units between the B centers. This manipulation, achieved without excessive chemical functionalization, facilitates the generation of an appropriate quantity of photoexcited electrons and/or holes to straddle the redox potential of the water. Our study demonstrates that two units between B centers of thiophene in BTP polymers enable overall photocatalytic water splitting, whereas BTP polymers with larger pores solely promote photocatalytic hydrogen reduction. Moreover, the thermodynamics of hydrogen and oxygen reduction reactions either proceed spontaneously or need small additional external biases. Our findings provide the rationale for designing metal-free and single-material polymer photocatalysts based on thiophene, specifically for achieving efficient overall water splitting.

The GaAs based diluted magnetic semiconductor, (Ga, Mn)As, with the unique advantage of manipulating the spin and charge was widely investigated in the scientific community and considered as a potential material for the spintronic devices. However, its Curie temperature (Tc), which is limited to around 200 K, hinders the research progress of diluted magnetic semiconductors for potential device applications. Herein, we propose an approach to prepare the MnGa nanoparticles embedded in (Ga, Mn)As matrix using the magnetron sputtering deposition of Mn on GaAs surface, followed by the nano-second pulsed laser annealing (PLA), which gives a Tc above 400 K. We demonstrate that the MnGa nanoparticles are only formed in (Ga, Mn) As thin film during the nano-second PLA under a critical range of energy density (0.4–0.5 J cm−2). The highest achieved coercivity, saturation magnetization and remanent magnetization are 760 Oe, 11.3 emu cm−3 and 9.6 emu cm−3, respectively. This method for preparing the hybrid system of ferromagnetic metal/dilute magnetic semiconductor builds a platform for exploring the interesting spin transport phenomenon and is promising for the application of spintronic devices.

Technological advances and societal changes are transforming industrial resource demand, both in terms of volume and complexity1, pushing an overhaul in traditional practices. The shift towards bio-based "green" technologies and circular economies is pivotal for sustainable resource management and pollution reduction, minimizing waste from mining and landfilling. The development of biosorption processes, particularly through customized peptide-based biosorbents is a promising approach for metal recovery from wastewater. These biocomposites offer an eco-friendly solution, contrasting with conventional biosorbents, which often fall short in stability, selectivity, and cost-effectiveness. To close this gap, our research is developing customized peptide-based biosorbents. By applying phage surface display technology, highly specific peptides are being developed that can selectively bind metal ions such as cobalt, nickel, gallium, arsenic, indium, germanium, copper, palladium and europium2. This innovative approach combines high-throughput screening with next-generation sequencing and identifies peptide ligands that selectively bind metals, characterizing their interactions by isothermal titration microcalorimetry to determine key thermodynamic parameters3. Our peptides, which are produced sustainably and cost-effectively by recombinant expression, are used in biocomposites for use in environmentally friendly, biotechnology-based separation processes. These biocomposites, including magnetic nanoparticles and ceramic or polymeric filters, represent a significant advance in engineered metal recovery applications. Looking ahead, Pep2Rec, a new junior research group, is set to broaden directed evolution methods to include metals in non-aqueous solutions, addressing a significant gap in metal recovery from the chemical-pharmaceutical industry's organic solvents. Merging experimental techniques with rational design, and developing specialized circular peptide libraries, Pep2Rec aims to recover palladium catalysts from organic solvents. This research aims to use these innovative peptides in membrane filter technologies to establish a circular economy in an industrial sector where efficient use of resources is currently poorly practiced.

Acknowledgments: The authors thank to the German Federal Ministry of Education and Research (BMBF) for
supporting the project PepMetal 2.2 (grant number 031B1348A, 2023-2026).

References
1) Graedel, T. E. Clean Energy Technologies, Critical Materials, and the Potential for Remanufacture. Technology
Innovation for the Circular Economy: Recycling, Remanufacturing, Design, Systems Analysis and Logistics,
2024, pp. 95-100.
2) Braun, R., et al. Peptides as biosorbents–Promising tools for resource recovery. Research in Microbiology, 2018,
169, 10, pp. 649-658.
3) Schönberger, N., et al. Gallium-binding peptides as a tool for the sustainable treatment of industrial waste
streams. Journal of Hazardous Materials, 2021, 414, pp. 125366

Gliomas are a type of malignant brain tumor where the most common mutation is a gain-of-function alteration IDH1R132H, which correlates to prognosis. Accurate assessment of this mutation is crucial for effective patient management and currently the gold standard of its detection is through invasive biopsies.The development of a PET-radiotracer that specifically binds to the IDH1R132H introduces a promising strategy for non-invasive tumor diagnosis and treatment, potentially making a significant impact on overall patient therapy. The lead compound selected for this work is LY-3410738, currently in phase I clinical trials. By modifying the molecular moieties, a library of fluorinated derivatives is synthesized. The synthesis pathway was successful but lacks enantioselectivity. Compounds were tested as enantioenriched mixture (approx. 75% ee), showing that fluorine does not interfere with inhibition potency, while differences are observed between the (S,S) and (S,R) non-fluorinated diastereomers.
Work is ongoing with alternative synthetic strategies aimed at removing the chiral center, while new parameters are being tested for HPLC-semipreparative separation.

Clay rock is considered a suitable host rock for the long-term storage of high-level radioactive waste, with bentonite used as backfill material. In the event of a worst-case scenario where water enters the repository, naturally occurring microorganisms might interact with the radionuclides, potentially altering their chemical speciation or inducing redox reactions.
Among various sulfate-reducing bacteria, Desulfosporosinus species are significant members of the microbial communities in both clay rock and bentonite. Desulfosporosinus hippei DSM 8344T, closely related to a bacterium isolated from bentonite, was selected for a detailed investigation into uranium(VI) interactions with naturally occurring microorganisms from deep geological layers.
Time-dependent experiments in artificial Opalinus Clay pore water (100 µM uranium(VI), pH 5.5) demonstrated a substantial removal of uranium from the supernatants within a short period. UV/Vis studies of the dissolved cell pellets provided clear evidence of partial reduction of uranium(VI) to uranium(IV). These findings suggest a combined association-reduction process as the mechanism of interaction.
TEM images showed uranium aggregates forming on the cell surface. Moreover, cells released membrane vesicles, possibly as a defense mechanism against cell encrustation. Additionally, HERFD-XANES measurements confirmed the reduction of uranium(VI) and revealed the presence of uranium(V) in the cell pellets, marking the first evidence of the involvement of uranium(V) in uranium(VI) reduction by sulfate-reducing microorganisms. Subsequent EXAFS measurements identified different cell-related uranium species.
This study enhances our understanding of the complexity of redox processes in the environment and contributes to the safety concept for nuclear repositories in clay rock. Moreover, it presents new insights into the uranium(VI) reduction mechanisms of sulfate-reducing bacteria.