jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Kewei Fang | Yan Liu | Kunjie Luo | Jian Shen | Jundong Lu | Erwei Liu
Source: Metals (article, 2026-02-14)
Field: Materials Science — Metals and Alloys

Corrosion Behavior of AISI 904L Austenitic Stainless Steel in High-Temperature and High-Pressure Water Environment AISI 904L stainless steel (904L SS) is a promising material for nuclear power plant primary circuits due to its superior corrosion resistance, but its corrosion behavior under simulated high-temperature and high-pressure water environments with different microstructures remains poorly understood. In order to systematically investigate and clarify the electrochemical behavior and corrosion behavior under stress of 904L SS with three different microstructures (as-received, sensitized, and solution-treated) in a simulated primary circuit water environment of a nuclear power plant, experiments are conducted using dynamic polarization, electrochemical impedance spectroscopy (EIS), and U-bend immersion methods. The results show that temperature has a significant effect on corrosion resistance. As the temperature increases, the impedance of all microstructures decreases significantly, the passivation zone narrows, and the corrosion current density increases. Under high-temperature and high-pressure conditions, the corrosion resistance of the sensitized samples is the worst, while the samples treated with solution have the best overall performance. That is, microstructural optimization through solution treatment can effectively enhance the high-temperature and high-stress corrosion resistance of 904LSS in the primary circuit water environment. Corrosion ; Dielectric spectroscopy ; Microstructure ; Passivation ; Austenitic stainless steel ; Intergranular corrosion ; Austenite Hydrogen embrittlement and corrosion behaviors in metals ; Erosion and Abrasive Machining ; Mechanical stress and fatigue analysis
Authors: Costanza Borghesi | Giacomo Giorgi | Daniele Varsano | Riccardo Rurali
Source: Advanced Electronic Materials (article, 2026-01-27)
Field: Materials Science — Materials Chemistry

Lattice Dynamics Across the High‐Pressure Phase Transition in CrTe ABSTRACT High‐pressure physics provides a powerful means of tuning interatomic interactions, enabling the discovery of novel structural and physical phenomena in materials. Chromium telluride, a transition metal chalcogenide, is particularly responsive to such external stimuli, exhibiting a broad spectrum of pressure‐, temperature‐, and stoichiometry‐dependent properties. One of its defining features is a pressure‐induced structural transition from a NiAs‐type to an MnP‐type phase at approximately 13 GPa, as experimentally reported by previous literature. In this work, we use density functional theory to investigate how the thermal properties — specifically, the lattice thermal conductivity — evolve across this structural transition. The complex interplay between structure and magnetism under pressure highlights the highly tunable and anisotropic nature of CrTe. Anisotropy ; Phase transition ; Magnetism ; Thermal conductivity ; Lattice (music) ; Density functional theory ; Thermal ; Chromium 2D Materials and Applications ; Heusler alloys: electronic and magnetic properties ; Iron-based superconductors research
Authors: Hana M. Abumelha Abumelha | Hayfa Almutairi | Abdulmajeed F. Alrefaei | Abdulrhman M. Alsharari | Khadra B. Alomari | Rami Adel Pashameah | Hanadi A. Katuah | Nashwa El-Metwaly
Source: Arabian Journal of Chemistry (article, 2026-02-14)
Field: Materials Science — Materials Chemistry

A multidisciplinary approach to green nanotechnology: Synthesis, characterization, molecular docking, and biological applications of CuO/TiO <sub>2</sub> and CuO/TiO <sub>2</sub> @Chitosan nanocomposites derived from <i>M. chamomilla</i> Green synthesis of antimicrobial nanocomposites (NCs) offers a promising strategy to combat drug-resistant bacteria, a growing global health threat. In this work, we report the first-time green synthesis of CuO/TiO 2 @Chitosan NCs using Matricaria chamomilla extract, uniquely combining phytochemicals and a natural biopolymer in a single-step biosynthesis. An anthrone assay confirms that CuO/TiO 2 NC stability is due to carbohydrates, while the interaction of NCs with chitosan causes increased carbohydrate content. The addition of chitosan to CuO/TiO 2 NC increased their antioxidant activity and antibacterial efficacy. CuO/TiO 2 @Cs NC showed excellent antibacterial activity against E. coli , S. typhimurium , B. cereus , S. aureus , and S. epidermidis bacteria, and the results were between 12.0 ± 1.27 mm and 19.0 ± 1.60 mm. The antibacterial findings, on the other hand, confirmed that M. chamomilla extract and CuO/TiO 2 NC were not effective in inhibiting the growth of the bacterial strains. In addition, a molecular docking study was conducted to assess the interaction among phytochemicals of M. chamomilla with bacterial targets. Compared to similar nanocomposites in the literature, the CuO/TiO 2 @Chitosan NCs developed here demonstrate enhanced dispersion stability, biofunctional surface chemistry, and selective antibacterial activity against Gram-positive and Gram-negative strains, positioning them as competitive candidates for biomedical applications. This paper reports for the first time green synthesis of CuO/TiO 2 @Chitosan NCs from M. chamomilla extract, wherein phytochemicals complement chitosan to enhance antibacterial efficacy against multidrug-resistant bacteria. Although M. chamomilla has been used in a few instances in research for metal nanoparticle synthesis and antioxidant studies, its use in a CuO/TiO 2 -chitosan hybrid is novel. This work offers a green protocol and provides a baseline for further optimization and in vivo studies towards biomedical applications. Biopolymer ; Chitosan ; Antibacterial activity ; Nanocomposite ; Antimicrobial ; Green chemistry ; Nanoparticle Nanoparticles: synthesis and applications ; Copper-based nanomaterials and applications ; Natural Products and Applications
Authors: Lylian Challier | Ambre Brachfeld | Giorgo Mattana | Agathe Laës | Vincent Noël
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Chemistry — Electrochemistry

Turning metals cations into catalytic nanoparticles on modified electrode: a general strategy for their electrochemical quantification Electrochemistry ; Metal ; Electrode ; Catalysis ; Faradaic current ; Detection limit ; Nanoparticle ; Faraday efficiency ; Graphite Electrochemical Analysis and Applications ; Electrodeposition and Electroless Coatings ; Electrochemical sensors and biosensors
Authors: Javed Abbas | Amber Jabeen | Asma Ajmal | Hajira Bibi | Zainab Naeem | Abdul Samad Mumtaz
Source: Discover Nano (article, 2026-02-13)
Field: Materials Science — Materials Chemistry

Biogenic synthesis and characterization of antimicrobial, anti-inflammatory, antioxidant, and biocompatible iron oxide nanoparticles (FeONPs) using Arthrospira sp. Extract Green nanotechnology offers a sustainable and eco-friendly pathway for large-scale nanoparticle synthesis, minimizing environmental hazards associated with conventional chemical methods. In this study, we report the phyco-synthesis of iron oxide nanoparticles (FeONPs) using Arthrospira sp., a cyanobacterium enriched with diverse bioactive compounds, as both a reducing and stabilizing agent. Despite extensive exploration of algal mediated nanoparticle synthesis, the biomedical potential of Arthrospira derived FeONPs remains largely underexplored. Here, FeONPs were synthesized via a two-step process involving the reaction of Arthrospira sp. aqueous extract with ferric chloride under optimized conditions, followed by calcination. The resultant FeONPs were comprehensively characterized through ultraviolet-visible spectroscopy (UV-Vis), Fourier transform infrared spectroscopy (FTIR), Scanning electron microscopy (SEM) coupled with energy dispersive X-ray spectroscopy (EDX), X-ray diffraction (XRD), thermo gravimetric analysis (TGA), dynamic surface analysis (DSA), and zeta potential measurements. Biomedical evaluation was performed through multiple in vitro assays, including assessments of antioxidant, antimicrobial, anti-inflammatory, anti-diabetic, and cytotoxic activities. The FeONPs exhibited pronounced antioxidant potential (IC<sub>50</sub>: 81.91-453.04 µg/mL), and notable antifungal activity against Aspergillus flavus (IC<sub>50</sub>: 22.51 µg/mL). Furthermore, they demonstrated dose-dependent α-amylase inhibition (IC<sub>50</sub>: 591 µg/mL), low cytotoxicity (IC<sub>50</sub>: 1324 µg/mL), and excellent biocompatibility. This study pioneers Arthrospira sp. as a scalable, cost-effective biofactory for the sustainable production of FeONPs, bridging the gap between green synthesis and biomedical applications. Future investigations will focus on in vivo validation, elucidation of antimicrobial mechanisms, and integration into drug delivery systems. With their multifunctional bioactivities, Arthrospira sp. mediated FeONPs hold significant promise for next-generation nanotherapeutics, leading towards a new paradigm in sustainable nanomedicine. Arthrospira ; Zeta potential ; Nanoparticle ; Fourier transform infrared spectroscopy ; Aqueous solution ; Iron oxide ; Iron oxide nanoparticles ; Drug delivery ; Gluconic acid Nanoparticles: synthesis and applications ; Nanoparticle-Based Drug Delivery ; Microbial Applications in Construction Materials
Authors: Aleksander E. P. Durumeric | Yaoyi Chen | Aldo S. Pasos-Trejo | Frank Noé | Cecilia Clementi
Source: Nature Communications (article, 2026-03-15)
Field: Materials Science — Materials Chemistry

Learning data-efficient coarse-grained molecular dynamics from forces and noise Molecular dynamics (MD) simulations are essential for elucidating biomolecular function, yet the computational cost of all-atom models often limits their reach. Machine-learned coarse-grained (MLCG) models offer a solution by simplifying the representation while maintaining near-atomistic accuracy. However, the training of MLCG models currently requires vast amounts of force-labeled sample conformations from reference atomistic MD. Here, we overcome this limitation by unifying the training of MLCG models with the principles of generative diffusion models. We demonstrate that accurate high-dimensional distributions of molecular ensembles can be recovered by integrating traditional force-matching with denoising objectives. This framework enables the construction of physically consistent and stable force fields while reducing atomistic data requirements by up to two orders of magnitude. Validated across diverse protein folds and scales, our work establishes a bridge between molecular dynamics simulation and modern generative learning, substantially lowering the computational cost of constructing accurate MLCG models and broadening their applicability to large biomolecular systems. Molecular dynamics ; Representation (politics) ; Noise (video) ; Generative grammar ; Work (physics) ; Computational model ; Generative model ; Force field (fiction) Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Protein Structure and Dynamics
Authors: Alice E. A. Allen | Emily Shinkle | Roxana Bujack | Nicholas Lubbers
Source: npj Computational Materials (article, 2026-01-17)
Field: Materials Science — Materials Chemistry

Optimal invariant sets for atomistic machine learning The representation of atomic configurations for machine learning models has led to numerous sets of descriptors. However, many descriptor sets are incomplete and/or functionally dependent. Incomplete sets cannot faithfully represent atomic environments. Yet complete constructions often suffer from a high degree of functional dependence, where some descriptors are functions of others. These redundant descriptors do not improve discrimination between atomic environments. We employ pattern recognition techniques to remove dependent descriptors to produce the smallest possible set that satisfies completeness. We apply this in two ways: First, we refine an existing description, the atomic cluster expansion. Second, we augment an incomplete construction, yielding a new message-passing neural network architecture that can recognize up to 5-body patterns. This architecture shows strong accuracy on state-of-the-art benchmarks while retaining low computational cost. Our results demonstrate the utility of this strategy to optimize descriptor sets across a range of descriptors and application datasets. Invariant (physics) ; Representation (politics) ; Set (abstract data type) ; Artificial neural network ; Range (aeronautics) ; Pattern recognition (psychology) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Nanocluster Synthesis and Applications
Authors: Vinh Van Tran
Source: Biosensors (article, 2026-04-09)
Field: Materials Science — Polymers and Plastics

Polymers-Based Biosensors and Bioelectronics: Designs and Applications Biosensors and bioelectronics (B&amp;B) are typical devices working at the interfaces of biology and electronics [...] Bioelectronics ; Biosensor ; Electronics ; Design elements and principles ; Nanoelectronics Conducting polymers and applications ; Electrochemical sensors and biosensors ; Advanced Sensor and Energy Harvesting Materials
Authors: Zhijian Zhong | Hu Xiong | Liangshan Ming | Yongmei Guan | Ailing Wen | Pengdi Cui | Caiyun Sun | Weifeng Zhu | Zhe Li
Source: Pharmaceuticals (article, 2026-04-01)
Field: Materials Science — Materials Chemistry

Edible Ultralong Organic Phosphorescent Maltodextrin with Different Dextrose Equivalents Values for Afterglow Visualizing the Quality of Tablets Background: This study deeply explores the influence of different dextrose equivalents (DE) values on room-temperature phosphorescence (RTP) properties of maltodextrin (MD) and its luminescence mechanism. The potential applications of MD tablets in non-destructive detection for afterglow visualizing are also explored. Methods: MD tablets with different DE values were prepared to investigate their RTP properties and afterglow effects. MD tablets were validated for afterglow signals and phosphorescence lifetimes under varying environmental conditions. Additionally, the unique afterglow effect of MD was used to detect the uniformity of tablets. Theoretical calculations of MD monomers and dimers were performed using time-dependent density functional theory. Results: The results demonstrated that MD with different DE values exhibited RTP properties, with phosphorescence lifetimes from 186.91 to 618.85 ms. The afterglow signals and phosphorescence lifetimes of MD tablets were influenced by multiple environmental conditions, i.e., relative humidity, temperature, oxygen, ultraviolet light, etc. Based on the afterglow effect of the MD, it is possible to non-destructively detect the uniform tablet. MD is an RTP material regulated by its DE value. Its phosphorescence mechanism is governed by a clustering-triggered emission mechanism, which is dominated by the rich hydrogen bond network. The material’s stimuli-responsive properties and pronounced afterglow effect make it a potential application for non-destructive detection. Conclusions: This study not only investigates the stimulus-responsive behavior of MD but also discovers a common, safe, and edible stimulus-responsive RTP material. These findings provide a new method for non-destructive detection of drugs and reducing the potential pharmacological risks during production, storage, and transportation. Afterglow ; Phosphorescence ; Luminescence ; Maltodextrin ; Phosphorescent organic light-emitting diode ; Ultraviolet Luminescence and Fluorescent Materials ; Carbon and Quantum Dots Applications ; Microencapsulation and Drying Processes
Authors: Domenic Kratzer | Martina Plank | Meike Koenig | Tahereh Mohammadi Hafshejani | Joerg Lahann
Source: Chemistry - A European Journal (article, 2026-01-19)
Field: Materials Science — Biomaterials

A Tandem Chemical Vapor Deposition Platform for the Solvent‐Free Synthesis of Polypeptide Architectures The precise engineering of surfaces decorated with polypeptides is critical for advanced diagnostics, biomedical coatings, and cellular interfaces. However, conventional methods are plagued by the need for solvents, multistep procedures, substrate limitations, and the abundance of side reactions. Here, we report that two-step chemical vapor polymerization can result in the fast, efficient, and substrate-independent synthesis of polypeptide films without the use of solvents or excipients. The first step involves deposition of an initiator layer, i.e., poly(4-amino-p-xylylene), via chemical vapor deposition (CVD) polymerization of 4,16-diamino[2.2]paracyclophane. The second step involves evaporation and ring-opening polymerization of N-carboxy anhydrides. This fully integrated CVD approach ensures substrate-independent, conformal growth of poly(propargyl-(S)-glycine) and poly(O-propargyl-(S)-tyrosine) films of up to 198 nm thickness. The use of CVD processes eliminates the concern of side reactions, such as transfer and termination reactions, and is a prerequisite for the successful peptide micropatterning, demonstrated in this study. Successful peptide growth and post-polymerization modifications via click chemistry were confirmed by time-of-flight secondary mass spectrometry, x-ray photoelectron spectroscopy, and infrared spectroscopy. The application of entirely solvent-free workflows to develop biomacromolecular coatings, such as the polypeptide films demonstrated in this study, addresses a critical gap in the pursuit of advanced and scalable biologization methods. Chemical vapor deposition ; Polymerization ; Evaporation ; Substrate (aquarium) ; Polymer ; Deposition (geology) ; Tandem ; Click chemistry Diatoms and Algae Research ; Nanofabrication and Lithography Techniques ; Supramolecular Self-Assembly in Materials
Authors: Selma Bal
Source: Turkish Journal of Science and Technology (article, 2026-03-29)
Field: Chemistry — Organic Chemistry

Novel Catalysts Derived From Carbazole; Characterization, Spectral, Catalytic Features, Examination of Thermal Behaviour and DNA Binding Studies By using N-ethylcarbazole and an aromatic primer amine, a schiff base ligand and its three metal complexes have been synthesized through imine condensation reaction. The structures of all the synthesized compounds have been proposed on the basis of various spectral analyses like nuclear magnetic resonance, infrared and mass spectroscopy techniques. All the synthesized compounds have been searched for their catalytic activities on the oxidation reactions of cyclohexene and styrene. The results of these experiments revealed quite good activities of the complexes as catalysts. DNA binding studies were also carried out on all the synthesized compounds by using UV-Vis spectroscopic measurements and all the coordiation compounds revealed much better binding konstants than the ligand itself. Finally the thermal features of all the compounds were reported with this paper Catalysis ; Ligand (biochemistry) ; Schiff base ; Infrared spectroscopy ; Cyclohexene ; Metal ; Condensation ; DNA Inorganic and Organometallic Chemistry ; Metal complexes synthesis and properties ; Synthesis of Tetrazole Derivatives
Authors: Shujaat Ali Khan | Waleed Iqbal Waseer
Source: arXiv (Cornell University) (article, 2026-02-09)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Optimizing Spectral Prediction in MXene-Based Metasurfaces Through Multi-Channel Spectral Refinement and Savitzky-Golay Smoothing The prediction of electromagnetic spectra for MXene-based solar absorbers is a computationally intensive task, traditionally addressed using full-wave solvers. This study introduces an efficient deep learning framework incorporating transfer learning, multi-channel spectral refinement (MCSR), and Savitzky-Golay smoothing to accelerate and enhance spectral prediction accuracy. The proposed architecture leverages a pretrained MobileNetV2 model, fine-tuned to predict 102-point absorption spectra from $64\times64$ metasurface designs. Additionally, the MCSR module processes the feature map through multi-channel convolutions, enhancing feature extraction, while Savitzky-Golay smoothing mitigates high-frequency noise. Experimental evaluations demonstrate that the proposed model significantly outperforms baseline Convolutional Neural Network (CNN) and deformable CNN models, achieving an average root mean squared error (RMSE) of 0.0245, coefficient of determination \( R^2 \) of 0.9578, and peak signal-to-noise ratio (PSNR) of 32.98 dB. The proposed framework presents a scalable and computationally efficient alternative to conventional solvers, positioning it as a viable candidate for rapid spectral prediction in nanophotonic design workflows. Smoothing ; Feature (linguistics) ; Convolutional neural network ; Mean squared error ; Scalability ; Artificial neural network ; Pattern recognition (psychology) Metamaterials and Metasurfaces Applications ; Advanced Wireless Communication Technologies ; Electromagnetic wave absorption materials
Authors: Roman Shantsila | Chantal Michelle Ilse Baer Baer | Albert Bartók-Pártay | Bora Karasulu
Source: ChemRxiv (article, 2026-04-02)
Field: Materials Science — Materials Chemistry

STING, guided active learning for Machine Learned Interatomic Potentials examined on Lithium Thiophosphate solid state electrolytes All solid state batteries, based on lithium thiophosphate (LiPS) solid state electrolytes, offer a promising route to safer, higher energy density storage. Computational characterisation of these materials, and their interfaces with electrode materials, demands accurate and efficient machine learned interatomic potentials (MLIPs). Foundation MLIPs provide attractive performance out of the box, however reliability across novel materials is poorly characterised. In this work, we systematically benchmark seven foundation MLIPs across six LiPS compositions: two established electrolytes, Li3PS4 and Li7P3S11 along with four novel materials from the Li2S-Li3P tie line, Li11P3S, Li5PS, Li7PS2, Li8P2S. We evaluate performance across geometry optimisation, phonon calculations, and molecular dynamics simulations. Foundation MLIPs show impressive stability and qualitative accuracy, but exhibit inconsistent performance across materials and their predicted properties. Phonon second-order force constants predictions are the most discriminating test with MACE-OMAT being the only model that predicts realistic phonons on novel materials. This demonstrates a lack of quantitative accuracy while using foundation MLIPs, necessitating custom MLIPs with system specific data. To develop reliable custom MLIPs, we introduce Strategic Training via Iterative Network Guidance (STING), a light-weight active learning framework that uses committee based uncertainty estimation to iteratively build system specific training datasets. STING incorporates stable foundation MLIP performance to minimise the required volume of DFT calculations. We also introduce the coefficient of standard deviation in the force components as a metric for structure selection, allowing for scaled comparison across temperature ranges. STING trained MLIPs consistently outperform foundation MLIPs across all benchmarks, showing sub 0.1 meV/atom energy RMSEs, and a reduction in force RMSEs by factors of 2-10 meV/Å. We also observe stable second-order force constants for all materials and a reduction in second-order force constants RMSEs by factors of 2-10. We further demonstrate that the STING coefficient of the standard deviation uncertainty metric produces well balanced training data across all training temperatures. These results establish a practical two stage strategy for development in solid state electrolyte research, and lay the groundwork for future interfacial simulations of LiPS electrolytes against Sulphur cathode material. Thiophosphate ; Foundation (evidence) ; Phonon ; Benchmark (surveying) ; Stability (learning theory) ; Metric (unit) ; Interatomic potential Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Inorganic Chemistry and Materials