jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Veeramanikandan Ramachandiran | C. Sabarinathan | D. Tamilvendan | T. A. Sundaravadivel
Source: Materiali in tehnologije (article, 2025-06-11)
Field: Materials Science — General Materials Science

EXPERIMENTAL INVESTIGATIONS ON TRIBOLOGICAL AND MECHANICAL BEHAVIOR OF CNTs/Al2O3/GNP REINFORCED EPOXY AND POLYAMIDE HYBRID NANOCOMPOSITES In the present investigation, dry sliding tribological behavior and mechanical properties of pure epoxy, Polyamide 66, and their hybrid nanocomposites were systematically analyzed. The experimental investigation primarily focused on two critical aspects: firstly, the influence of contact pressure on the wear behavior under varying loading conditions, and secondly, the role of mechanical interlocking and adhesion between the polymer matrix and hybrid nanocomposites, enhancing the tensile properties. Nanocomposite formulations included multiwall carbon nanotubes (MWCNTs), graphene nanoplatelets (GNPs), and alumina (Al2O3) as the reinforcement materials, each added at a weight fraction of 0.2 %. The study explored significant contributions of these nanofillers in terms of their functional properties and impact on tribological performance and load transfer mechanisms within the composite materials. It was observed that epoxy and Polyamide 66 reinforced with 0.2 % CNT/Al2O3 demonstrated superior wear resistance, particularly under conditions of high contact pressure. Additionally, both polymer matrices exhibited enhanced tensile strength and nanoindentation when reinforced with a combination of 0.2 % CNT/Al2O3. This research highlights the potential of carefully selected nanofillers in optimizing the mechanical and tribological performance of polymer-based composites, providing valuable insights required for future material design and application in various engineering fields. Materials science ; Polyamide ; Tribology ; Nanocomposite ; Epoxy ; Composite material ; Carbon nanotube Material Properties and Applications
Authors: Zhaoyang Chen | Shanshan Wang | Yongjun Li | Yongsen Han
Source: Journal of Applied Polymer Science (article, 2025-07-29)
Field: Materials Science — Materials Chemistry

Study on the Electrical Properties of Low‐Density and High‐Density Polyethylene Blends for High‐Voltage Direct Current Cable ABSTRACT Blending low‐density polyethylene (LDPE) with high‐density polyethylene (HDPE) is an effective strategy to improve electrical properties for the high voltage direct current (HVDC) power cable insulation. However, the effects of HDPE on the space charge accumulation of the HDPE/LDPE blends are rarely reported. In this paper, the HDPE/LDPE blends are prepared with different HDPE contents, and their space charge distribution, conductivity, breakdown strength, surface potential decay, and morphology are measured, respectively. The results show that the optimal content range for the space charge inhibition is from 3 to 10 wt%. For the 10 wt% blend, it is 29.5% smaller in average space charge density than pure LDPE. Moreover, the 10 wt% blend is 85.5% lower in conductivity and is 33.7% higher in breakdown strength compared with pure LDPE. Thick lamellae structure is introduced into HDPE/LDPE by HDPE, and it leads to the formation of deep traps, contributing to the improvement of the electrical properties of the blends. This study provides a valuable foundation for the design of the new HVDC power cable insulating material. Polyethylene ; Materials science ; Current (fluid) ; Low-density polyethylene ; High-density polyethylene ; Composite material ; Current density ; Voltage ; High-voltage cable ; High voltage ; Electrical engineering ; Engineering ; Physics High voltage insulation and dielectric phenomena ; Electrical Fault Detection and Protection ; Lightning and Electromagnetic Phenomena
Authors: Wentao Niu | Chengpeng Yang | Ziyi Chen | Weiqiang Wang | Daizhou Zhang | Jin Fu | Zixiang Guo | Jie-Guang Song
Source: Journal of Physics Conference Series (article, 2025-08-01)
Field: Chemistry — Inorganic Chemistry

The Preparation and Properties of Semi-conductive Glaze for Electric Ceramics Abstract With the continuous development of the types and properties of electric porcelain glazes, electric porcelain insulators have been widely used in the power grid industry. Electric porcelain semiconductive glaze is a type of glaze with conductive properties formed by adding semiconductor materials to ceramic glaze and processing it through special techniques. The purpose of this topic is to study and explore the preparation process and properties of electric ceramic semiconducting glaze, providing new ideas for the development of the ceramic glaze field. We used experimental methods to transform ZnO and SnO 2 powders into electrically conductive ceramic glaze by adding them to the glaze slurry. Conduct performance tests on the glaze and sintered glaze surface to study the changes in glaze properties under different raw materials, contents, and temperature treatments, in order to determine the optimal formula for the glaze. Through research, it has been found that with the increase of metal oxides, the various properties of the glaze are enhanced. Zinc oxide glaze has the best Vickers hardness at 1300 °C, while tin oxide glaze has the best hardness at 1250 °C, the Vickers hardness is 539HV. Glaze ; Materials science ; Ceramic ; Electrical conductor ; Composite material Pigment Synthesis and Properties ; Recycling and utilization of industrial and municipal waste in materials production ; Aerogels and thermal insulation
Authors: Shashidhara Ks | Mahmed Juned Contractor | M. S. P. Kanavi | Prakash Koler | Prashanth Kumar H.P.
Source: Archives of Current Research International (review, 2025-08-08)
Field: Chemistry — Organic Chemistry

A Review on the Structure-function Relationship of Class II α-Mannsoidases Of fundamental importance to every function an enzyme or a protein performs is its three-dimensional structure. The structure plays a crucial, though often subtle, role in catalysis. The conformational changes in enzymes are often small, and even these small changes could be significant. Glycosylation modifications of proteins and glycan hydrolysis are critical for protein function in biological processes. Aberrations in glycosylation enzymes are linked to lysosomal storage disorders (LSD), immune interactions, congenital disorders, and tumour progression. Alpha-Mannosidases Class 2B is a lysosomal hydrolase. Dysfunction of this has been implicated as a causative factor in mannosidosis, a lysosomal storage disorder characterized by cognitive impairment, hearing loss, and immune system and skeletal anomalies. Despite decades of research, its role in pathogenic infections, autoimmune conditions, cancers, and neurodegenerative pathologies is highly ambiguous. Although many Class II α-mannosidases have been reported from various sources but not many of them have been characterized structurally. Through the combined efforts of various types of spectroscopies and protein crystallography, structure-function relationship studies in some Class II α-mannosidases from various sources have been carried out, and the same has been reported. The structure of Class II α-mannosidases from different sources has revealed the involvement of various types of folding of the protein, the presence of metal ions such as Zn+ in the active site, and their role in substrate specificity and catalytic mechanism is reviewed here. Glycosylation ; Enzyme ; Hydrolase ; Biology ; Immune system ; Glycan ; Protein structure ; Biochemistry ; Chemistry ; Cell biology ; Immunology ; Glycoprotein Carbohydrate Chemistry and Synthesis ; Lysosomal Storage Disorders Research ; Enzyme Production and Characterization
Authors: Serpil Gerni
Source: Iğdır Üniversitesi Fen Bilimleri Enstitüsü Dergisi (article, 2025-11-27)
Field: Chemistry — Organic Chemistry

Karboksilik Asit Bağlantılı Tiyazol Türevlerinin Laktoperoksidaz Enzimi Üzerindeki Etkilerinin Araştırılması Bu çalışmada, karboksilik asit fonksiyonel grubuna sahip tiyazol türevlerinin Laktoperoksidaz (LPO) enzimi üzerindeki inhibitör etkileri detaylı olarak incelenmiştir. Enzim saflaştırma işlemi, sığır sütünden elde edilen LPO’nun Sepharose 4B-L-tirozin-sülfanilamid afinite kolon kromatografisi ile başarılı bir şekilde izole edilmesiyle gerçekleştirilmiştir. İnhibisyon çalışmalarında substrat olarak 2,2'-azino-bis(3-etilbenzotiyazolin-6-sülfonik asit) (ABTS) kullanılmış ve elde edilen kinetik veriler doğrultusunda, karboksilik asit içeren tiyazol türevlerinin LPO enzim aktivitesini etkili biçimde inhibe ettiği belirlenmiştir. Test edilen beş farklı tiyazol türevinin inhibisyon parametreleri değerlendirilmiş; bileşiklerin Ki değerleri sırasıyla 1.734 ± 0.810 µM, 1.988 ± 0.934 µM, 1.226 ± 0.148 µM, 1.128 ± 0.053 µM ve 3.818 ± 0.264 µM olarak saptanmıştır. Yapılan inhibitör tip analizleri sonucunda yalnızca 2-bromo-tiyazol-5-karboksilik asidin (a) yarışmalı (kompetitif) inhibisyon mekanizması gösterdiği, diğer dört bileşiğin ise yarışmasız (nonkompetitif) inhibitör olarak davrandığı belirlenmiştir. Özellikle tiyazol-5-karboksilik asit türevi (d), 1.128 ± 0.053 µM’lik en düşük Ki değeri ve 2.840 µM’lik IC₅₀ değeri ile çalışmada en güçlü inhibitör etkiyi gösteren bileşik olmuştur. Bu sonuç, söz konusu bileşiğin LPO enzimi için potansiyel bir inhibitör aday olarak değerlendirilmesine olanak sağlamaktadır. Enzyme ; Sepharose ; Ion chromatography ; Chlorine Synthesis of Tetrazole Derivatives ; Enzyme function and inhibition ; Free Radicals and Antioxidants
Authors: Robert Joseph George | Carson Eisenach | Udaya Ghai | Dominique Perrault-Joncas | Anima Anandkumar | Dean C. Foster
Source: arXiv (Cornell University) (preprint, 2025-11-26)
Field: Materials Science — Materials Chemistry

BRIDGE: Building Representations In Domain Guided Program Synthesis Large language models (LLMs) are good at generating code, but remain brittle for formal verification in systems like Lean4. A core scalability challenge is that verified synthesis requires consistent outputs across multiple artifacts: executable code, precise specifications, theorem statements, and ultimately proofs. Existing approaches rarely treat these as a unified pipeline. We present BRIDGE, a structured prompting framework that decomposes verification into three interconnected domains: Code (implementations), Specifications (formal intent), and Theorem Statements (constructive correctness claims), and elicits domain-specific intermediate reasoning to connect them. In Lean4, BRIDGE often adopts a code-first workflow, using the generated implementation as a semantic anchor for downstream specification and theorem statement generation. Across 178 algorithmic problems and five LLMs, BRIDGE improves Lean executable correctness by nearly 1.5x (pass at 5) over direct baselines and can be 2x more sample-efficient at inference time, requiring fewer samples per verified solution at comparable generation lengths. We further find that specification-driven prompting improves Python pass rates by up to 17.5 percent. Beyond inference-time prompting, supervised fine-tuning on BRIDGE-style reasoning traces yields nearly 1.5x higher Lean pass success than code-only SFT, indicating that these intermediate representations are learnable. BRIDGE provides a practical foundation for scaling verified synthesis and motivates future work on expert iteration and full proof generation. Correctness ; Executable ; Scalability ; Python (programming language) ; Inference ; Automated theorem proving ; Bridge (graph theory) ; Formal specification ; Answer set programming Machine Learning in Materials Science ; Formal Methods in Verification ; Scientific Computing and Data Management
Authors: Mehrdad Jalali | Binh Vu | Sina Mehraeen | Swati Chandna | Farzad Jalali
Source: Repository KITopen (Karlsruhe Institute of Technology) (article, 2025-01-01)
Field: Chemistry — Inorganic Chemistry

MOF-LENS: Bio-inspired lotus effect optimization for accelerated discovery of metal–organic framework nanocarriers for doxorubicin delivery in cancer therapy Doxorubicin ; Nanocarriers ; Cancer therapy ; Paclitaxel ; Simvastatin ; Reuse Metal-Organic Frameworks: Synthesis and Applications ; Nanoplatforms for cancer theranostics ; Nanoparticle-Based Drug Delivery
Authors: Dongik Lee | Seunghun Lee
Source: Applied Sciences (article, 2025-12-18)
Field: Materials Science — Materials Chemistry

Measuring the Spin Polarization with a Superconducting Point Contact and Machine Learning Measuring spin polarization (P) of materials is essential for understanding their fundamental properties and for their application in spintronics. Point contact Andreev reflection (PCAR) spectroscopy is a straightforward yet powerful technique for measuring P. However, conventional analysis methods depend on iterative fitting procedures that are time-consuming, subjective, and often lead to non-unique solutions. This complexity arises from the interplay of multiple physical parameters with pressure, including temperature, superconducting gap, and interfacial barrier strength. Here, we present a machine learning (ML) approach that utilizes convolutional neural networks (CNNs) to facilitate the rapid and automated extraction of P from PCAR spectra. We validate the ML model by analyzing experimental PCAR spectra from various materials reported in the literature. The predicted parameters by the CNN model show excellent agreement with the literature values, demonstrating its robust performance across a wide range of materials and parameter sets. This approach significantly reduces analysis time while maintaining accuracy, providing a practical tool for material characterization, thus accelerating materials discovery for spintronics. Andreev reflection ; Convolutional neural network ; Superconductivity ; Polarization (electrochemistry) ; Point (geometry) ; Artificial neural network ; Spectroscopy Machine Learning in Materials Science ; Advanced Condensed Matter Physics ; Physics of Superconductivity and Magnetism
Authors: Przemysław Siekierka | E. Makarewicz | Sławomir Wilczewski | Katarzyna Skórczewska | Krzysztof Lewandowski | Jacek Mirowski | Magdalena Osial
Source: Materials (article, 2025-12-22)
Field: Materials Science — Polymers and Plastics

Poly(vinyl chloride) Plastisol Composites with Surface-Modified Wood Flour as Potential Coating and Insulating Materials for Modern Energy-Efficient Constructions This study investigates the development of sustainable PVC-based composites filled with surface-modified wood flour for potential use in modern, energy-efficient building systems. The aim was to enhance the mechanical performance, thermal stability, and interfacial compatibility of PVC plastisols by incorporating fine- and coarse-grained coniferous wood flour modified with silane and surfactants. Composites were formulated using emulsion PVC (Vinnolit E-2059), bis(2-ethylhexyl) adipate as a plasticizer, and MARK-17 MOK as a thermal stabilizer, and were gelled under pressure at 150 °C. Their physical, mechanical, structural, and thermal characteristics were evaluated using density and hardness measurements, SEM, thermomechanical analysis, DMA, and TGA. The results demonstrated that composites containing fine-grained, silane-treated wood flour (Lignocel C-120) exhibited the most advantageous balance of stiffness, elasticity, and thermal resistance, attributable to improved polymer-wood interfacial adhesion. The findings confirm the potential of modified wood flour as an effective bio-based filler enabling the design of durable, thermally stable coating and insulating materials with reduced environmental impact. The proposed composites may serve as protective, bonding, or insulating layers in sustainable construction, supporting the development of innovative, wood-based materials for low-carbon building applications. Wood flour ; Coating ; Thermal ; Emulsion ; Compatibility (geochemistry) ; Thermal conductivity Natural Fiber Reinforced Composites ; Polymer Science and PVC ; Flame retardant materials and properties
Authors: Aditi Nachnani | Kai K. Li-Caldwell | Saptarshi Biswas | Prince Sharma | Gaoyuan Ouyang | Prashant Singh
Source: ArXiv.org (preprint, 2025-04-28)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Interpretable machine learning-guided design of Fe-based soft magnetic alloys We present a machine-learning guided approach to predict saturation magnetization (MS) and coercivity (HC) in Fe-rich soft magnetic alloys, particularly Fe-Si-B systems. ML models trained on experimental data reveals that increasing Si and B content reduces MS from 1.81T (DFT~2.04 T) to ~1.54 T (DFT~1.56T) in Fe-Si-B, which is attributed to decreased magnetic density and structural modifications. Experimental validation of ML predicted magnetic saturation on Fe-1Si-1B (2.09T), Fe-5Si-5B (2.01T) and Fe-10Si-10B (1.54T) alloy compositions further support our findings. These trends are consistent with density functional theory (DFT) predictions, which link increased electronic disorder and band broadening to lower MS values. Experimental validation on selected alloys confirms the predictive accuracy of the ML model, with good agreement across compositions. Beyond predictive accuracy, detailed uncertainty quantification and model interpretability including through feature importance and partial dependence analysis reveals that MS is governed by a nonlinear interplay between Fe content, early transition metal ratios, and annealing temperature, while HC is more sensitive to processing conditions such as ribbon thickness and thermal treatment windows. The ML framework was further applied to Fe-Si-B/Cr/Cu/Zr/Nb alloys in a pseudo-quaternary compositional space, which shows comparable magnetic properties to NANOMET (Fe84.8Si0.5B9.4Cu0.8 P3.5C1), FINEMET (Fe73.5Si13.5B9 Cu1Nb3), NANOPERM (Fe88Zr7B4Cu1), and HITPERM (Fe44Co44Zr7B4Cu1. Our fundings demonstrate the potential of ML framework for accelerated search of high-performance, Co- and Ni-free, soft magnetic materials. Coercivity ; Ribbon ; Interpretability ; Saturation (graph theory) ; Annealing (glass) ; Alloy ; Density functional theory ; Nonlinear system Magnetic Properties and Applications ; Microstructure and Mechanical Properties of Steels ; Metallic Glasses and Amorphous Alloys
Authors: Chi Zhang | Elif Ertekin | Peter Orbanz | Ryan P. Adams
Source: ArXiv.org (preprint, 2025-12-16)
Field: Materials Science — Materials Chemistry

A Single Architecture for Representing Invariance Under Any Space Group Incorporating known symmetries in data into machine learning models has consistently improved predictive accuracy, robustness, and generalization. However, achieving exact invariance to specific symmetries typically requires designing bespoke architectures for each group, limiting scalability and preventing knowledge transfer across related symmetries. In the case of the space groups, symmetries critical to modeling crystalline solids in materials science and condensed matter physics, this challenge is particularly salient as there are 230 such groups in three dimensions. In this work we present a new approach to such crystallographic symmetries by developing a single machine learning architecture that is capable of adapting its weights automatically to enforce invariance to any input space group. Our approach is based on constructing symmetry-adapted Fourier bases through an explicit characterization of constraints that group operations impose on Fourier coefficients. Encoding these constraints into a neural network layer enables weight sharing across different space groups, allowing the model to leverage structural similarities between groups and overcome data sparsity when limited measurements are available for specific groups. We demonstrate the effectiveness of this approach in achieving competitive performance on material property prediction tasks and performing zero-shot learning to generalize to unseen groups. Homogeneous space ; Bespoke ; Leverage (statistics) ; Scalability ; Group (periodic table) ; Space (punctuation) ; Artificial neural network ; Inference ; Salient Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Quantum many-body systems
Authors: Bhuvaneshwar Rajesh Naik | N. Amarnatha | M. S. Kruthika | Pritish Chavan | Praveen Kumar | M P Rakshitha
Source: Archives of Current Research International (article, 2025-08-25)
Field: Materials Science — Biomaterials

A New Horizon in Chitosan Research: Silkworm-Derived Biopolymer and Its Applications Chitosan, a biodegradable biopolymer traditionally sourced from crustaceans, has growing significance in the food, biomedical and environmental sectors. However, concerns over allergenicity, heavy metal contamination and seasonal availability necessitate alternative sources. Silkworm pupae, an underutilized by-product of India's silk industry, offer a sustainable and efficient alternative. Chitosan extracted from silkworm pupae exhibits superior physicochemical properties, including high solubility (&gt;98%), low ash content (&lt;1%) and a high degree of deacetylation (&gt;85%), along with excellent antimicrobial, film-forming and biocompatible characteristics. Compared to shrimp shell-derived chitosan, it demonstrates a higher molecular weight and purity, with potential applications in food preservation, pharmaceuticals, textiles and sericulture. This review highlights the extraction techniques, structural attributes and multi-sectoral applications of pupal chitosan, advocating for its commercial development as a viable, eco-friendly substitute for conventional chitosan sources. Chitosan ; Biopolymer ; Horizon ; Polymer science ; Biotechnology ; Biochemical engineering ; Biology ; Chemistry ; Engineering ; Mathematics ; Materials science ; Chemical engineering ; Composite material ; Polymer Silk-based biomaterials and applications ; Silkworms and Sericulture Research ; Insect Utilization and Effects
Authors: Daye Zheng | Xing‐Liang Peng | Yike Huang | Yinan Wang | Duo Zhang | Zhengtao Huang | Cai, Zefeng | Linfeng Zhang | Mohan Chen | Ben Xu | Weiqing Zhou
Source: ArXiv.org (preprint, 2025-01-24)
Field: Materials Science — Electronic, Optical and Magnetic Materials

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 alpha-Fe close to experimental values. Deep learning ; Computer science ; Spin (aerodynamics) ; Artificial intelligence ; Engineering ; Mechanical engineering Magnetic Properties and Applications ; Magnetic Field Sensors Techniques ; Magnetic properties of thin films