jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Giulio Cordaro | Juande Sirvent | Cristian Mocuta | Fjorelo Buzi | Thierry Martin | Federico Baiutti | Alex Morata | Albert Tarancón | Dominique Thiaudière | Guilhem Dezanneau
Source: Open MIND (preprint, 2026-02-19)
Field: Materials Science — Materials Chemistry

Wide-Surface Furnace for In Situ X-Ray Diffraction of Combinatorial Samples using a High-Throughput Approach The combinatorial approach applied to functional oxides has enabled the production of material libraries that formally contain infinite compositions. A complete ternary diagram can be obtained by pulsed laser deposition (PLD) on 100 mm silicon wafers. However, interest in such materials libraries is only meaningful if high-throughput characterization enables the information extraction from the as-deposited library in a reasonable time. While much commercial equipment allows for XY-resolved characterization at room temperature, very few sample holders have been made available to investigate structural, chemical, and functional properties at high temperatures in controlled atmospheres. In the present work, we present a furnace that enables the study of 100 mm wafers as a function of temperature. This furnace has a dome to control the atmosphere, typically varying from nitrogen gas to pure oxygen atmosphere with external control. We present the design of such a furnace and an example of X-ray diffraction (XRD) and fluorescence (XRF) measurements performed at the DiffAbs beamline of the SOLEIL synchrotron. We apply this high-throughput approach to a combinatorial library up to 735 {\textdegree}C in nitrogen and calculate the thermal expansion coefficients (TEC) of the ternary system using custom-made MATLAB codes. The TEC analysis revealed the potential limitations of Vegard's law in predicting lattice variations for high-entropy materials. Diffraction ; Characterization (materials science) ; TEC ; Beamline ; In situ ; Ternary operation ; Analytical Chemistry (journal) ; Lattice (music) ; Silicon Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; X-ray Diffraction in Crystallography
Authors: M. Busch | M. Tacke | Sviatlana V. Lamaka | Mikhail L. Zheludkevich | Christian J. Cyron | Christian Feiler | Roland C. Aydin
Source: arXiv (Cornell University) (preprint, 2026-03-26)
Field: Materials Science — Materials Chemistry

In-Context Molecular Property Prediction with LLMs: A Blinding Study on Memorization and Knowledge Conflicts The capabilities of large language models (LLMs) have expanded beyond natural language processing to scientific prediction tasks, including molecular property prediction. However, their effectiveness in in-context learning remains ambiguous, particularly given the potential for training data contamination in widely used benchmarks. This paper investigates whether LLMs perform genuine in-context regression on molecular properties or rely primarily on memorized values. Furthermore, we analyze the interplay between pre-trained knowledge and in-context information through a series of progressively blinded experiments. We evaluate nine LLM variants across three families (GPT-4.1, GPT-5, Gemini 2.5) on three MoleculeNet datasets (Delaney solubility, Lipophilicity, QM7 atomization energy) using a systematic blinding approach that iteratively reduces available information. Complementing this, we utilize varying in-context sample sizes (0-, 60-, and 1000-shot) as an additional control for information access. This work provides a principled framework for evaluating molecular property prediction under controlled information access, addressing concerns regarding memorization and exposing conflicts between pre-trained knowledge and in-context information. Blinding ; Memorization ; Property (philosophy) ; Causal chain ; Control (management) ; Sample (material) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Arjun Sundaram | Mohit Chakraborty | Sai Mani Kumar Devathi | B. Pabitramohan Prusty | Rohit Batra
Source: Open MIND (preprint, 2026-02-04)
Field: Materials Science — Materials Chemistry

Automated Extraction of Multicomponent Alloy Data Using Large Language Models for Sustainable Design The design of sustainable materials requires access to materials performance and sustainability data from literature corpus in an organized, structured and automated manner. Natural language processing approaches, particularly large language models (LLMs), have been explored for materials data extraction from the literature, yet often suffer from limited accuracy or narrow scope. In this work, an LLM-based pipeline is developed to accurately extract alloy-related information from both textual descriptions and tabular data across the literature on high-entropy (or multicomponent) alloys (HEA). Specifically two databases with 37,711 and 148,069 entries respectively are retrieved; one from the literature text, consisting of alloy composition, processing conditions, characterization methods, and reported properties, and other from the literature tables, consisting of property names, values, and units. The pipeline enhances materials-domain sensitivity through prompt engineering and retrieval-augmented generation and achieves F1-scores of 0.83 for textual extraction and 0.88 for tabular extraction, surpassing or matching existing approaches. Application of the pipeline to over 10,000 articles yields the largest publicly available multicomponent alloy database and reveals compositional and processing-property trends. The database is further employed for sustainability-aware materials selection in three application domains, i.e., lightweighting, soft magnetic, and corrosion-resistant, identifying multicomponent alloy candidates with more sustainable production while maintaining or exceeding benchmark performance. The pipeline developed can be easily generalized to other class of materials, and assist in development of comprehensive, accurate and usable databases for sustainable materials design. Pipeline (software) ; USable ; Data extraction ; Benchmark (surveying) ; Matching (statistics) ; Information extraction ; Pipeline transport ; Class (philosophy) Machine Learning in Materials Science ; High Entropy Alloys Studies ; Material Selection and Properties
Authors: Sepideh Baghaee Ravari | Abril Azócar Guzmán | Sarath Menon | Stefan Sandfeld | Tilmann Hickel | Markus Stricker
Source: arXiv (Cornell University) (preprint, 2026-01-18)
Field: Materials Science — Materials Chemistry

Ontology-aligned structuring and reuse of multimodal materials data and workflows towards automatic reproduction Reproducibility of computational results remains a challenge in materials science, as simulation workflows and parameters are often reported only in unstructured text and tables. While literature data are valuable for validation and reuse, the lack of machine-readable workflow descriptions prevents large-scale curation and systematic comparison. Existing text-mining approaches are insufficient to extract complete computational workflows with their associated parameters. An ontology-driven, large language model (LLM)-assisted framework is introduced for the automated extraction and structuring of computational workflows from the literature. The approach focuses on density functional theory-based stacking fault energy (SFE) calculations in hexagonal close-packed magnesium and its binary alloys, and uses a multi-stage filtering strategy together with prompt-engineered LLM extraction applied to method sections and tables. Extracted information is unified into a canonical schema and aligned with established materials ontologies (CMSO, ASMO, and PLDO), enabling the construction of a knowledge graph using atomRDF. The resulting knowledge graph enables systematic comparison of reported SFE values and supports the structured reuse of computational protocols. While full computational reproducibility is still constrained by missing or implicit metadata, the framework provides a foundation for organizing and contextualizing published results in a semantically interoperable form, thereby improving transparency and reusability of computational materials data. Workflow ; Workflow ; Reuse ; Reuse ; Interoperability ; Interoperability ; Reusability ; Reusability ; Structuring ; Structuring Machine Learning in Materials Science ; Magnesium Alloys: Properties and Applications ; Inorganic Chemistry and Materials
Authors: Sepideh Baghaee Ravari | Abril Azócar Guzmán | Sarath Menon | Stefan Sandfeld | Tilmann Hickel | Markus Stricker
Source: ArXiv.org (article, 2026-01-18)
Field: Materials Science — Materials Chemistry

Ontology-aligned structuring and reuse of multimodal materials data and workflows towards automatic reproduction Reproducibility of computational results remains a challenge in materials science, as simulation workflows and parameters are often reported only in unstructured text and tables. While literature data are valuable for validation and reuse, the lack of machine-readable workflow descriptions prevents large-scale curation and systematic comparison. Existing text-mining approaches are insufficient to extract complete computational workflows with their associated parameters. An ontology-driven, large language model (LLM)-assisted framework is introduced for the automated extraction and structuring of computational workflows from the literature. The approach focuses on density functional theory-based stacking fault energy (SFE) calculations in hexagonal close-packed magnesium and its binary alloys, and uses a multi-stage filtering strategy together with prompt-engineered LLM extraction applied to method sections and tables. Extracted information is unified into a canonical schema and aligned with established materials ontologies (CMSO, ASMO, and PLDO), enabling the construction of a knowledge graph using atomRDF. The resulting knowledge graph enables systematic comparison of reported SFE values and supports the structured reuse of computational protocols. While full computational reproducibility is still constrained by missing or implicit metadata, the framework provides a foundation for organizing and contextualizing published results in a semantically interoperable form, thereby improving transparency and reusability of computational materials data. Workflow ; Reuse ; Interoperability ; Reusability ; Structuring ; Graph ; Schema (genetic algorithms) ; Normalization (sociology) Machine Learning in Materials Science ; Magnesium Alloys: Properties and Applications ; Inorganic Chemistry and Materials
Authors: Z. Ma | Qiang Fu | Jianye Zhang | Mingbo Yue | Xiaoyu Xu
Source: Carbon (article, 2026-03-17)
Field: Materials Science — Materials Chemistry

Dynamic phosphorescent “palettes” on carbon dots: Photoinduced oxygen vacancy and B–N linkage for anti-counterfeiting and encryption Phosphorescence ; Vacancy defect ; Carbon fibers ; Linkage (software) ; Oxygen Carbon and Quantum Dots Applications ; Luminescence and Fluorescent Materials ; Luminescence Properties of Advanced Materials
Authors: Rahadian Zainul | Alwi Nofriandi | Norhayati Hashim | Mohamad Idris Saidin | Mohamad Syahrizal Ahmad | Siti Nur Akmar Mohd Yazid | Sharifah Norain Mohd Sharif | Wan Rusmawati | Illyas Md Isa
Source: Journal of Electrochemical Science and Engineering (article, 2026-01-05)
Field: Chemistry — Electrochemistry

High performance ferrocenium tetrachloromagnesate modified MWCNTs electrode for trace level detection of bismuth(III) in aqueous pollution This study presents the fabrication of a ferricenium tetrachloromagnesate(FTM) modified multi-walled carbon nanotubes(MWCNTs) electrode for the voltammetric determination of bismuth ions (Bi(III)) in aquatic environments. The FTM was synthesized from the reaction of magnesium chloride with ferrocene. The incorporation of FTM , a redox-active species, was shown to markedly improve electron transfer kinetics and overall electrochemical conductivity. Structural and morphological characterizations via Fourier transform infrared spectroscopy(FTIR), transmission electron microscopy(TEM), energy dispersive x-ray(EDX), field emission scanning electron microscopy(FESEM), and x-ray diffraction(XRD) confirmed the successful embedding of FTM within the electrode matrix. Electrochemical assessment using cyclic voltammetry, electrochemical impedance spectroscopy, and chronocoulumetry revealed that a 10% (w/w) FTM loading provided the most optimal charge transfer and interfacial behavior. Differential pulse stripping voltammetry further demonstrated the high sensitivity of the developed electrode, achieving an ultralow detection limit of 0.543 nM across a broad linear range of 1.0 nM to 0.1 mM of Bi(III). The sensor also exhibited remarkable reproducibility(RSD 8.67%) and stability(RSD 7.77%). Furthermore, excellent selectivity towards Bi(III) was maintained in the presence of potentially interference ions such as Mn(II), La(III), Ni(II), Li, Zn(II), Fe(II), Cd(II), Cu(II), Er(II), and Ca(II). Real water sample analyses produced recoveries between 89% and 104%, confirming the practical feasibility of the electrode for the environmental monitoring. Electrode ; Detection limit ; Electrochemistry ; Cyclic voltammetry ; Analytical Chemistry (journal) ; Electrochemical gas sensor ; Electron transfer ; Aqueous solution ; Differential pulse voltammetry Electrochemical Analysis and Applications ; Analytical Chemistry and Sensors ; Electrochemical sensors and biosensors
Authors: Andrzej Piotrowicz | Janusz Piotr Kolczyński | M. Kostrzewa | Wojciech Kaczmarek | Bogdan Samojeden
Source: Clean Technologies (article, 2026-03-19)
Field: Chemistry — Organic Chemistry

Microwave Depolymerization of Various Plastic Wastes—Quarter-Scale Testing Microwave-assisted depolymerization (MD) of heterogeneous postconsumer plastics was carried out in a quarter-scale reactor to evaluate product composition and the influence of feedstock type on oil quantity and quality. Various waste streams, including: PS, PP, ABS materials, keyboard housings, textile plastics, PCBs, and mixed electronic components, were processed in 3–6 kg batches using magnetron powers up to 2 × 1.55 kW. All experiments yielded a condensed liquid fraction, with color intensity correlating with aromatic content. FTIR spectroscopy showed that all oils consisted of hydrocarbon matrices dominated by aliphatic C-H stretching bands (2956–2850 cm−1). Aromatic contributions varied significantly: PS produced oils rich in aromatic OOP C-H bands (900–650 cm−1), PP yielded predominantly aliphatic oils with minor aromatic features, and ABS or electronics materials produced mixed aliphatic–aromatic profiles. Textile oils additionally exhibited carbonyl and O-H bands, indicating oxygenated decomposition products. Fractional distillation separated the oils into low-boiling aliphatic (&lt;250 °C) and heavier aromatic (250–350 °C) fractions. These results suggest that MD reliably converts diverse plastic wastes into hydrocarbon oils whose spectroscopic characteristics reflect both feedstock composition and thermal pathways intrinsic to microwave heating. Depolymerization ; Raw material ; Aromatic hydrocarbon ; Hydrocarbon ; Distillation ; Pyrolysis ; Decomposition ; Microwave Microwave-Assisted Synthesis and Applications ; Thermochemical Biomass Conversion Processes ; Recycling and Waste Management Techniques
Authors: Ilona van Zandvoort | Antje Ota | Esther Cobussen‐Pool | Ronald Beyer | Karla Dussan | A. van Zomeren | Frank Hermanutz | J.W. van Hal
Source: Biofuels Bioproducts and Biorefining (article, 2026-02-17)
Field: Materials Science — Biomaterials

Organosolv fractionation and pulp upgrading of wheat straw for production of continuous cellulosic fibers by wet‐spinning Abstract Textile production is an important industrial sector but also one of the most polluting industries and highly dependent on fossil resources and cotton for the production of fibers. The use of cellulosic fibers from local agricultural residues can offer a sustainable alternative for the current textile industry. In this work, an environmentally friendly, ionic liquid‐based fiber spinning process is applied. Cellulose was produced via a biorefinery concept comprising pre‐extraction, mild acetone organosolv fractionation and pulp upgrading by alkaline treatment. Pre‐extraction of the wheat straw increased the cellulose content of the pulp obtained from organosolv fractionation and improved downstream processing. The Si‐rich ash components were successfully removed by alkaline treatment of the organosolv pulp under optimized conditions. Based on the final cellulose and ash content, upgraded wheat straw pulp was found to be suitable for wet spinning into filament fibers in an ionic liquid‐based process. The obtained fibers had comparable properties (fineness, elongation and crystallinity) as reference filaments; however, further optimization of the spinning process of this new material would be required to improve tenacity as well as produce textile‐grade fibers. Organosolv ; Pulp (tooth) ; Cellulose ; Fractionation ; Straw ; Biorefinery ; Cellulosic ethanol ; Dissolving pulp ; Steam explosion Advanced Cellulose Research Studies ; Natural Fiber Reinforced Composites ; Lignin and Wood Chemistry
Authors: Deepti Rane | Pranjita Zantye | Savita Kerkar | Meenal Kowshik | Sutapa Roy Ramanan
Source: Apmis (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Impact of Ag– <scp>SiO</scp> <sub>2</sub> Nanocomposite on Metal Corrosive <i>Desulfovibrio</i> Biofilm Microbiologically influenced corrosion (MIC) is a significant issue causing material damage of metals in pipelines, storage tanks, and marine infrastructure, leading to significant industrial damage and maintenance costs. Anaerobic sulfate-reducing bacteria (SRB) are the main causative agent for MIC. Their capacity to form biofilms facilitates SRB to cause high corrosion rates. In this study, Ag-SiO<sub>2</sub> nanocomposite with silver nanoparticle (Ag NP) dispersed in the silicon dioxide (SiO<sub>2</sub>) matrix was synthesized using a one-pot sol-gel method. Ag-SiO<sub>2</sub> NC was evaluated for its capacity to deter biofilm formation and impact the transcript gene expression of key markers HydA and DVU1817 Cytochrome c553 genes for the SRB biofilm-formation and metal corrosion in Desulphovibrio gigas strain. The effect of Ag-SiO<sub>2</sub> NC on the SRB mediated Carbon steel corrosion was also examined using potentiodynamic polarization electrochemical corrosion testing. The findings from our study validate the antibacterial and corrosion inhibitory characteristics of nanocomposite Ag-SiO<sub>2</sub> NC against D. gigas. A simple and reproducible Ag-SiO<sub>2</sub> NC synthesis will significantly boost the industrial production of silver-doped silica products for controlling SRB population, enabling development of advanced high-performance protective metal coatings with enhanced antimicrobial properties. Biofilm ; Corrosion ; Nanocomposite ; Metal ; Carbon steel ; Electrochemistry ; Bioremediation Corrosion Behavior and Inhibition ; Marine Biology and Environmental Chemistry ; Concrete Corrosion and Durability
Authors: Sana Iqbal | Afraz Numan
Source: Cancer Research and Medicine (article, 2026-03-25)
Field: Materials Science — Biomaterials

In vitro analysis of silkworm sericin induced modifications in migration related genes in colorectal cancer cells Background: Colorectal cancer is a leading cause of cancer-related deaths worldwide. The search for natural compounds with anticancer activity has gained increasing interest. Sericin, a silk-derived protein, has demonstrated various biological properties, including potential anticancer effects. This study aimed to evaluate anticancer effects of sericin and influence on the expression of genes associated with cancer progression in colorectal cancer cells. Methods: Colorectal cancer cells (SW620, SW480, HCT116) were treated with different concentrations of sericin. Effects on cell viability were monitored and confirmed by MTT assay. The expression levels of focal adhesion kinase (FAK) and Ras-related C3 botulinum toxin substrate (RAC1) genes were analyzed using real-time quantitative polymerase chain reaction. Online data tool (STRING software) was used to assess the protein-protein interactions, biological processes and molecular functions associated with the two genes. Results: Sericin treatment resulted in reduced cellular viability and downregulation of FAK and RAC1 gene expression compared to untreated control cells. The inhibition of these genes indicates suppression of signaling pathways involved in migration and invasion of the colorectal cancer cells. Bioinformatics showed important protein interactors of the genes with the involvement of key biological and molecular functions including growth signaling pathways, secondary messenger networks and kinase activities. Conclusion: The findings suggest that sericin exhibits anticancer activity against colorectal cancer and deregulate the expression of FAK and RAC1 genes. Further in vitro studies are required to investigate the fine cellular tunings to confirm the efficacy. Colorectal cancer ; RAC1 ; Cancer ; Downregulation and upregulation ; Sericin ; PAK1 ; Kinase ; Gene ; Viability assay ; In vitro Silk-based biomaterials and applications ; Silkworms and Sericulture Research ; Bioactive Compounds in Plants
Authors: Roghayeh Tayebi | Esmaiel Soleimani
Source: Research Square (preprint, 2026-01-19)
Field: Materials Science — Polymers and Plastics

Unique thermal properties of polyvinyl alcohol embedded with reduced graphene oxide-copper nanoparticles composite Nanocomposite ; Graphene ; Thermal stability ; Polyvinyl alcohol ; Glass transition ; Polymer ; Oxide ; Polymer nanocomposite Polymer Nanocomposite Synthesis and Irradiation ; Graphene and Nanomaterials Applications ; Polymer Science and PVC
Authors: Chang Liu | Jessica Nguyen | R David Britt | Jonathan Rittle
Source: ChemRxiv (article, 2026-03-24)
Field: Chemistry — Inorganic Chemistry

The Factors Governing Metal Dependence of an Emergent Superfamily of Bimetallic Oxygenases Metalloenzyme superfamilies are typically defined by their protein scaffolds and active sites.Owing to the high tunability of protein structures, members of a single superfamily can catalyze diverse reactions with the same metallocofactor.Some superfamilies, such as Amidohydrolase-related dinuclear oxygenases (ARO), display further versatility by utilizing multiple metallocofactors.We have shown that certain AROs catalyze monooxygenation reactions with diiron, dimanganese and/or mixed manganese-iron cofactors, but the molecular factors governing the selection of a particular cofactor remain unknown and the extent of this superfamily in biology is unclear.Here, we report bioinformatic analyses that expand the ARO superfamily to approximately 17,000 unique UniProt sequences, far exceeding the number of previously characterized enzymes.Through the integration of structural, spectroscopic and thermodynamic analyses of representative proteins with a bioinformatic pipeline that identifies key secondary-and tertiary-sphere residues, we can predict in silico the metal preference for the majority of reported ARO sequences.These annotations were validated via the characterization of multiple new AROs, including ones implicated in key oxidative steps of natural product biosyntheses.This study establishes the key structure-function relationships governing metal preferences in AROs and highlights their vastly underappreciated role in myriad biological processes. Bimetallic strip ; Oxygenase ; SUPERFAMILY ; Metal ; Dioxygenase Metal-Catalyzed Oxygenation Mechanisms ; Chromium effects and bioremediation ; Environmental Toxicology and Ecotoxicology