jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Veronique Van Speybroeck | Massimo Bocus | Wei Chen | Pieter Cnudde
Source: Faraday Discussions (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

A consistent dynamics view on nanoporous catalysts in action across length and time scales This paper demonstrates the need for a consistent dynamic view of all processes from the nano- to mesoscale with quantum accuracy to design industrial catalysts. This stems from the observation... Nanoporous ; Mesoscale meteorology ; Action (physics) ; Dynamics (music) ; Catalysis ; Molecular dynamics Chemical and Physical Properties of Materials ; Advanced Physical and Chemical Molecular Interactions ; Nanopore and Nanochannel Transport Studies
Authors: Hao Wu | Huawei Lv | Xingnuo Li
Source: Tumor Discovery (article, 2026-02-06)
Field: Materials Science — Biomaterials

Self-assembled peptide delivery systems: Applications in tumor therapy Self-assembled peptides are emerging as versatile nanocarriers for cancer therapy, benefiting from programmable sequences, biocompatibility, and dynamic supramolecular assembly. This review summarizes recent advances in the molecular design and assembly mechanisms of peptide-based nanostructures (nanospheres, nanofibers, and nanogels) formed through non-covalent interactions, such as hydrogen bonding, electrostatics, van der Waals forces, and &amp;pi;-&amp;pi; stacking. We discuss how peptide composition, secondary structure, and environmental cues (pH, enzymes, redox state, temperature, and ionic strength) govern assembly, stability, and biological performance, enabling efficient loading and targeted delivery of small-molecule drugs, nucleic acids (DNA/small interfering RNA), and peptide or protein therapeutics. Particular emphasis is placed on stimuli-responsive strategies and in situ assembly approaches that enable controlled drug release and, where applicable, provide imaging readouts (fluorescence, photoacoustic, and nuclear imaging) for tumor visualization, image-guided delivery, and treatment-response monitoring. Key translational challenges are also addressed, including serum stability and pharmacokinetics, immunogenicity and off-target effects, endosomal escape and delivery efficiency, scalable manufacturing and cost, controllability of release, and standardized characterization and safety evaluation. Finally, we outline future directions, including multifunctional and smart responsive peptide architectures, hybrid systems combining peptides with liposomes, polymers, or viral vectors, and rational design rules linking assembly state to in vivo fate. Collectively, these advances are expected to accelerate the clinical translation of self-assembled peptide delivery systems for precision cancer therapy. Nanocarriers ; Peptide ; Rational design ; Drug delivery ; Aptamer ; Cancer therapy ; Translation (biology) ; In vivo ; Immunogenicity Supramolecular Self-Assembly in Materials ; RNA Interference and Gene Delivery ; Polydiacetylene-based materials and applications
Authors: Sathishkumar Mani
Source: Journal on Materials and its Characterization (article, 2026-03-28)
Field: Materials Science — Polymers and Plastics

Design and Development of Fire-Resistant Composites with Fire-Retardant Fillers for Electrical Applications The integration of fire-retardant fillers and enhanced electrical conductivity plays a vital role in improving safety, efficiency, and durability across multiple industries. Fire-retardant materials are crucial for reducing fire risks, while advancements in dielectric and conductive materials significantly enhance electronic applications. This overview highlights recent progress in both areas and their impact on modern safety standards and technological development. Fire-retardant fillers such as alumina trihydrate (ATH), metal hydroxides, and specialized additives are widely used to improve the fire resistance of polymer-based composites like epoxies, polyesters, and polyurethanes. These fillers help delay ignition, limit flame propagation, and reduce smoke generation. Their effectiveness depends on factors such as chemical composition, particle size, and uniform dispersion within the polymer matrix, which is essential for consistent performance. Studies also show that combining mineral fillers like calcium carbonate and talc with additives such as antimony trioxide enhances flame-retardant efficiency. Intumescent coatings further improve protection by forming a char layer that insulates materials during combustion. Simultaneously, conductive fillers like graphene nanoplatelets and carbon nanotubes are increasingly used to improve electrical and thermal properties in polymer composites. These materials are beneficial in electronics packaging, electromagnetic shielding, and heating systems. Improved dispersion techniques, including surface modification of fillers, have led to better compatibility and performance. Additionally, environmental regulations such as RoHS and WEEE encourage the use of safer, recyclable materials. Filler (materials) ; Carbon black ; Carbon nanotube ; Durability ; Nanocomposite ; Intumescent ; Electrical conductor ; Electronics ; Graphite ; Thermal conductivity Flame retardant materials and properties ; Fire dynamics and safety research ; Fiber-reinforced polymer composites
Authors: QUDUS KOLAWOLE | Veera LD Badisa | Musiliyu A. Musa | Lekan M. Latinwo
Source: Anticancer Research (article, 2026-01-30)
Field: Chemistry — Organic Chemistry

Coumarin-based Oximes Exert Monoamine Oxidase Inhibitory Activity The 7-diethylamino-substituted 3-acylcoumarin-oxime derivative (compound <b>7f</b>, bearing a diethylamino group at the C-7 position of the coumarin ring) can be considered a promising scaffold for developing new and more potent MAOI drugs for treating neurodegenerative disorders. Monoamine oxidase ; Coumarin ; Inhibitory postsynaptic potential ; Monoamine oxidase B ; Derivative (finance) ; Central nervous system ; In vitro ; Monoamine oxidase A Oxidative Organic Chemistry Reactions ; Synthesis and biological activity ; Parkinson's Disease Mechanisms and Treatments
Authors: Aditya Sharan | Sriram Hebbale | Dhruv Kumar
Source: ArXiv.org (article, 2026-03-15)
Field: Materials Science — Materials Chemistry

Infinite Problem Generator: Verifiably Scaling Physics Reasoning Data with Agentic Workflows Training large language models for complex reasoning is bottlenecked by the scarcity of verifiable, high-quality data. In domains like physics, standard text augmentation often introduces hallucinations, while static benchmarks lack the reasoning traces required for fine-tuning. We introduce the Infinite Problem Generator (IPG), an agentic framework that synthesizes physics problems with guaranteed solvability through a Formula-as-Code paradigm. Unlike probabilistic text generation, IPG constructs solutions as executable Python programs, enforcing strict mathematical consistency. As a proof-of-concept, we release ClassicalMechanicsV1, a high-fidelity corpus of 1,335 classical mechanics problems expanded from 165 expert seeds. The corpus demonstrates high structural diversity, spanning 102 unique physical formulas with an average complexity of 3.05 formulas per problem. Furthermore, we identify a Complexity Blueprint, demonstrating a strong linear correlation ($R^2 \approx 0.95$) between formula count and verification code length. This relationship establishes code complexity as a precise, proxy-free metric for problem difficulty, enabling controllable curriculum generation. We release the full IPG pipeline, the ClassicalMechanicsV1 dataset, and our evaluation report to support reproducible research in reasoning-intensive domains. Python (programming language) ; Executable ; Probabilistic logic ; Decidability ; Metric (unit) ; Workflow ; Scaling ; Soundness ; Automated reasoning Machine Learning in Materials Science ; Topic Modeling ; Text Readability and Simplification
Authors: Jiayi Wang | Yuan Fang | Shaoqing Chen | Zongxi ZHANG | Dianbo Zhou | Yuhang He | Jing Zhang
Source: International Journal of Environment and Pollution (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

State diagnosis technology of metal enclosed gas insulation equipment based on Apriori algorithm in cloud computing environment In recent years, with the increasing failure rate of gas-insulated switchgear (GIS), there has been a growing need for people to understand the most common insulator issues.Therefore, real-time monitoring of its operating status is crucial to ensure the safe and reliable operation of power lines.This study proposes the use of Apriori algorithm (CFSA-AA) for cloud based fault state analysis to predict discharge faults, mechanical faults, and abnormal mechanical vibrations in metal enclosed GIS.This data is sourced from the Kaggle repository used for VSB power line fault detection.This study summarises GIS abnormal heating faults, including circuit breakers, isolating switches, shell grounding, and disc insulator bolts.The experimental results show that compared with other Switchgear ; Cloud computing ; Insulator (electricity) ; A priori and a posteriori ; Fault (geology) ; Vibration ; State (computer science) High voltage insulation and dielectric phenomena ; Thermal Analysis in Power Transmission ; Power Transformer Diagnostics and Insulation
Authors: Nurefşan Kuvvet | Elif Ulutaş | Münir Taşdemir
Source: Mühendislik Bilimleri ve Tasarım Dergisi (article, 2026-03-20)
Field: Materials Science — Materials Chemistry

GRAFEN NANOPLAKALARIN CAM ELYAF TAKVİYELİ PA6 KOMPOZİTLERİN YAPISAL VE MEKANİK ÖZELLİKLERİNE ETKİSİ Bu çalışmada, poliamid 6 (PA6) ve cam elyaf (CE) esaslı hazır kompozitlere, farklı oranlarda (ağırlıkça %1, %2 ve %3) grafen nanoplakalar (GnP) eklenerek elde edilen nanokompozitlerin mekanik, termal ve mikroyapısal özellikleri incelenmiştir. Kompozitler, eriyik ekstrüzyon yöntemiyle hazırlanmış ve ardından enjeksiyon kalıplama ile test numuneleri üretilmiştir. Üretilen numuneler; çekme, darbe, sertlik, yoğunluk, nem oranı ve eriyik akış indeksi (MFI) gibi testlerle karakterize edilmiş, mikroyapı ise taramalı elektron mikroskobu (SEM) ile değerlendirilmiştir. Elde edilen bulgular, GnP ilavesinin elastisite modülünü artırarak rijitliği geliştirdiğini, ancak artan GnP oranının darbe dayanımı ve kopma uzamasını sınırladığını göstermektedir. %3 GnP içeriğinde elastisite modülü %14 artarken, kopma uzaması %14 ve darbe dayanımı %19 azalmıştır. Ayrıca %1 GnP katkısıyla MFI değerinde %145’lik artış elde edilmiş ve işlenebilirlikte belirgin bir iyileşme sağlanmıştır. Bu sonuçlar, GnP’nin cam elyaf takviyeli PA6 kompozitlerde mekanik davranış, mikroyapı ve işlenebilirlik arasındaki ilişkiyi inceleyen kapsamlı bir değerlendirme sunmakta; çok düşük GnP oranlarının dahi performansı önemli ölçüde etkileyebileceğini göstermektedir. Çalışmanın özgün katkısı, GnP’nin cam elyaf takviyeli PA6 matrisindeki çok düşük katkı oranlarında dahi oluşturduğu yapı–özellik ilişkisini nicel olarak ortaya koyması ve özellikle işlenebilirlik ile mekanik performans arasındaki çelişkili etkiyi detaylı şekilde açıklamasıdır. Crunch ; Work (physics) ; Context (archaeology) Graphene research and applications ; Polymer crystallization and properties ; Polymer Nanocomposites and Properties
Authors: P. von Maltitz | N. van Hilten | T. Weil | T. Thummaraj | J. Methorst | D. Aschmann | A. Kros | C. Read | J. Gačanin | H. Risselada | J. Münch
Source: MPG.PuRe (Max Planck Society) (article, 2026-03-18)
Field: Materials Science — Biomaterials

Identification and Characterization of anIn Silico Designed Membrane‐Active Peptide with Antiviral Properties In silico ; Peptide ; Identification (biology) ; Peptide sequence ; Characterization (materials science) Supramolecular Self-Assembly in Materials ; Lipid Membrane Structure and Behavior ; Chemical Synthesis and Analysis
Authors: H. K. Abdelsalam | Asmaa A. H. El-Bassuony
Source: The European Physical Journal Special Topics (article, 2026-03-03)
Field: Materials Science — Materials Chemistry

Study of spinel-based nanocomposites Abstract Novel multifunctional nanocomposites 0.3CoFe 2 O 4 /0.7Ag 0.5 Cr 2.5 O 4 (Co) and 0.3CuFe 2 O 4 /0.7Ag 0.5 Cr 2.5 O 4 (Cu) were prepared using the flash auto-combustion technique. The crystallite size was calculated from an X-ray diffraction pattern (XRD), and the morphology was studied using atomic force microscopy (AFM). The structural properties were analyzed to ensure the nanoscale nature of the samples. The Co nanocomposite exhibited a 5.7-fold larger coercivity ( H c ) and a 2.6-fold larger saturation magnetization ( M s ) than the Cu nanocomposite. Moreover, the high-frequency applications of Co nanocomposite suggest its suitability in the super high frequency range in the microwave range at the S band, whereas the Cu nanocomposite is more suitable for ultra-high frequency in the microwave region at the L band. The materials showed a strong synergistic anti-bacterial activity, with the Co nanocomposite achieving a minimum inhibitory concentration of 31.22 μg/ml against Salmonella typhimurium and Bacillus Subtilis . Furthermore, cytotoxicity tests of the optimal nanocomposite revealed low toxicity against normal Vero cells (CC 50 = 280.4 ± 19.3 μg/ml) and selective anti-cancer activity against HepG-2 cells (IC 50 = 26.7 ± 2.31 μg/ml). Thus, the investigated samples are strongly recommended to be anti-bacterial nanomaterials. Nanocomposite ; Coercivity ; Crystallite ; Microwave ; Morphology (biology) ; Nanoscopic scale ; Saturation (graph theory) Magnetic Properties and Synthesis of Ferrites ; Electromagnetic wave absorption materials ; Pigment Synthesis and Properties
Authors: Joonyoung F. Joung
Source: Bulletin of the Korean Chemical Society (article, 2026-01-25)
Field: Materials Science — Materials Chemistry

Defining chemical problems for artificial intelligence models Abstract Artificial intelligence has become an increasingly important tool in chemistry, yet its impact is often constrained by how chemical problems are defined for machine learning. In this Personal Account, I argue that dataset construction plays a central role in AI‐driven chemistry, not merely as a preparatory step but as a means of formalizing new chemical learning problems. I summarize recent efforts to build machine‐learning‐ready datasets in three domains: molecular property prediction, property‐conditioned molecular design, and reaction mechanism prediction. Experimentally measured optical property datasets were curated to explicitly incorporate environmental context, enabling models to learn structure–environment–property relationships that reflect real experimental conditions and to support inverse design of functional chromophores. For chemical reactivity, large‐scale mechanistic datasets were constructed through rule‐based imputation of elementary steps, transforming overall reactions into supervised learning problems for mechanism prediction. Limitations of sequence‐based reaction models motivated the development of FlowER, which represents reactions as electron redistribution processes using bond–electron matrices and enforces mass conservation by construction, allowing direct integration with quantum chemical validation. Together, these studies illustrate a unifying perspective in which datasets, representations, models, and downstream workflows are designed jointly, guided by chemical intuition. By framing dataset construction as an act of problem definition, this Account highlights how chemists can shape the scope and direction of artificial intelligence as a tool for chemical discovery. Workflow ; Mechanism (biology) ; Property (philosophy) ; Scope (computer science) ; Framing (construction) ; Quantum chemical Machine Learning in Materials Science ; Asymmetric Hydrogenation and Catalysis ; Scientific Computing and Data Management
Authors: Rui M. R. Pinto | A. Retolaza | Joåo Cunha | Pedro Matos | Júlio Silva | Diana Silva | Rosana Alves Dias | K. B. Vinayakumar
Source: Advanced Optical Materials (article, 2026-01-30)
Field: Materials Science — Materials Chemistry

Temperature‐Dependent Zinc Oxide Cathodoluminescence: Toward Compact Wavelength‐Tunable Solid‐State Photoionization Sources Abstract When exciting cathodoluminescence from a material using an electron beam, the light emission is due to electronic transitions within the material. The fundamental transition is related to the bandgap, while other emissions can be related to material defects and/or impurities. Since the bandgap of materials is temperature‐ and strain‐dependent, cathodoluminescence emission can, in principle, be dynamically tuned. Herein, a proof‐of‐concept of a thermally tunable cathodoluminescence light source is demonstrated using a triode electron gun and a wide‐bandgap z ‐cut ZnO crystal as anode. At room temperature (295 K), an ultraviolet (UV) emission peak corresponding to the ZnO bandgap is identified (398 nm), and a wide emission in the visible range (centered at ≈510 nm) is also detected (recombination of electron‐hole pairs in singly occupied oxygen vacancies). Additionally, the cathodoluminescence emission spectrum is measured as a function of the ZnO anode temperature (295–373 K), resulting in a shift of the UV emission peak from 398 to ≈409 nm (3.115–3.032 eV), which translates to a temperature dependency of −1.06 ± 0.06 meV K −1 . The results establish the feasibility of a compact, wavelength‐tunable cathodoluminescence‐based UV source, with future potential for integration into field‐deployable systems for gas sensing and lab‐on‐chip spectroscopy applications. Cathodoluminescence ; Emission spectrum ; Photoionization ; Band gap ; Spectroscopy ; Ultraviolet ; Triode ; Wide-bandgap semiconductor ; Light emission ZnO doping and properties ; Luminescence Properties of Advanced Materials ; Silicon Nanostructures and Photoluminescence
Authors: Ebru Kuyumcu Savan | Süleyman Köytepe | İmren Özcan | Burhan Ateş | Sevgi Balcıoğlu
Source: Anatolian Journal of Pharmaceutical Sciences (article, 2026-02-05)
Field: Materials Science — Biomaterials

Synthesis and Characterization of the Chitosan-Coated Methotrexate-Conjugated Hybrid Magnetic Nanoparticles In this study, a rapid and alternative magnetic nanoparticle structure was developed for the hyperthermia treatment of cancer, a growing and significant problem. Magnetic nanoparticles hold significant potential in cancer treatment. The most important feature of magnetic nanoparticles is their ability to be targeted to tumor sites by applying an external magnetic field. Furthermore, their small size, high surface area, and the ease of control of their surface structure through surface modification are other important properties. The ability of magnetic nanoparticles to acquire drug-carrying capacity through surface modification provides a significant application advantage. Another feature is that the magnetic field applied to the cancerous area induces hyperthermia, thereby destroying cancer cells. Therefore, the prepared magnetic-core nano cancer drugs exhibit a dual-agent therapeutic effect. In this study, we first synthesized nanosized, magnetically steerable CoFe2O4 nanoparticles. Subsequently, these nanoparticles were surface-modified, and the anti-carcinogenic chemotherapeutic drug methorotaxane was conjugated. In the final phase of the study, chitosan was coated on the surface of the methotrexate-conjugated magnetic nanoparticle to increase biocompatibility. All structures obtained were initially characterized using FTIR and EDX spectroscopy. Furthermore, all CoFe2O4-based structures were morphologically characterized using scanning electron microscopy. This resulted in the creation of a dual-action drug structure that offers both diagnostic, monitoring, and therapeutic potential. Magnetic nanoparticles ; Nanoparticle ; Magnetic hyperthermia ; Characterization (materials science) ; Surface modification ; Targeted drug delivery ; Cancer therapy ; Hyperthermia Nanoparticle-Based Drug Delivery ; Nanoplatforms for cancer theranostics ; Cancer Research and Treatment
Authors: Daye Zheng | Xulong Peng | Yike Huang | Yinan Wang | Duo Zhang | Zhengtao Huang | Zefeng Cai | Linfeng Zhang | Mohan Chen | Ben Xu | Weiqing Zhou
Source: npj Computational Materials (article, 2026-01-07)
Field: Materials Science — Materials Chemistry

Integrating deep-learning-based magnetic model and non-collinear spin-constrained method: methodology, implementation and application We propose a non-collinear spin-constrained method that generates training data for deep-learning-based magnetic model, which provides a powerful tool for studying complex magnetic phenomena that requires large-scale simulations at the atomic level. First, we propose a basis-independent projection method for calculating atomic magnetic moments by applying a radial truncation to numerical atomic orbitals. A double-loop Lagrange multiplier method is utilized to ensure the satisfaction of constraint conditions while achieving accurate magnetic torque. The method is implemented in ABACUS with both plane wave basis and numerical atomic orbital basis. We benchmark the iron (Fe) systems and analyze differences from calculations with the plane wave basis and numerical atomic orbitals basis in describing magnetic energy barriers. Based on an automated workflow composed of first-principles calculations, magnetic model, active learning, and dynamics simulation, more than 30,000 first-principles data with the information of magnetic torque are generated to train a deep-learning-based magnetic model DeePSPIN for the Fe system. By utilizing the model in large-scale molecular dynamics simulations, we successfully predict Curie temperatures of α-Fe close to experimental values. Basis (linear algebra) ; Lagrange multiplier ; Magnetostatics ; Plane wave ; Magnetic moment ; Magnetic energy ; Atomic orbital ; Magnetic field Machine Learning in Materials Science ; Magnetic properties of thin films ; Model Reduction and Neural Networks
Authors: Mahmoud Rihan | Abdelhameid Ghazzaly | A. H. Fahmy | A. A. Ebnalwaled | L. S. Nasrat
Source: Electrical Engineering & Electromechanics (article, 2026-03-02)
Field: Materials Science — Materials Chemistry

Influence of zinc oxide nanoparticles on flashover voltage of unsaturated polyester resin-based composites for electrical insulators Introduction. Polyester-based composites are increasingly used in electrical applications for their insulation, mechanical, and thermal properties. Nanofillers have shown promise in enhancing the properties of polymer-based composites. Goal. This study aims to improve the flashover voltage of unsaturated polyester resin (UPR)-based composites by incorporating zinc oxide (ZnO) nanoparticles. Methodology. UPR/ZnO nanocomposites were prepared with various ZnO nanofiller ratios (0 %, 1 %, 3 %, 5 %, and 7 % by weight) with different sample lengths (0.5, 1.5, 2, and 2.5 cm). The flashover voltage was measured for the pure UPR sample and each composition of the studied filler ratios at various sample lengths. X-ray diffraction analysis was performed. A curve-fitting method was applied to estimate the flashover voltage of UPR/ZnO nanocomposites containing intermediate filler ratios between those experimentally tested. Results. Incorporation of ZnO nanofillers significantly enhanced the flashover voltage of polyester-based nanocomposites. The pure UPR sample exhibited the lowest flashover voltage, whereas the composite with 7 wt.% ZnO nanofiller demonstrated the highest. Notably, increasing the sample length further improved flashover voltage. Scientific novelty. This study examines the influence of ZnO nanoparticles on the flashover voltage of UPR-based composites. Practical value. The obtained findings can contribute to the development of polyester-based nanocomposite insulators with enhanced flashover voltage. References 26, tables 3, figures 6. Arc flash ; Nanocomposite ; Composite number ; Zinc ; Voltage ; Nanoparticle ; Polyester High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Polymer Science and PVC