jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Daning Zhang | Xinyuan Feng | Jiongting Jiang | Tianbo Zhang | Zhi Feng Yang | Guanjun Zhang
Source: High Voltage (article, 2025-05-22)
Field: Materials Science — Biomaterials

Preparation Method and Performance Study of Nanocellulose/Epoxy Resin Composite Materials Modified by Multiple Approaches ABSTRACT Nanocomposites are pivotal for enhancing epoxy resin performance, but traditional inorganic nanofillers' poor compatibility and nonrecyclability pose environmental concerns. This study utilises recyclable organic cellulose nanocrystals (CNC) to explore their modification impact on epoxy composite properties. Through infrared spectroscopy and X‐ray photoelectron spectroscopy (XPS), the degree of modification for CNC and its derivatives—KH560‐modified CNC (KH560‐CNC) and methyl methacrylate‐modified CNC (MMA‐CNC)—was assessed. Scanning electron microscope (SEM) performance characterisation of modified CNC/epoxy composites showed that higher CNC modification levels significantly improve the toughness of the composites. Regarding thermal conductivity, modified CNCs affected the epoxy composites differently; KH560‐CNC/EP exhibited the best thermal conductivity at low filler concentrations, whereas MMA‐CNC/EP showed higher thermal conductivity at high concentrations. Additionally, nanocellulose's varying degrees of modification differently impacted the composites' moisture absorption and dielectric properties. The higher the CNC modification level, the stronger its moisture absorption capability, with minimal effect on dielectric loss. This paper provides experimental evidence for CNC/epoxy composite applications, offering practical guidance for future design and manufacturing of epoxy resin composites. Nanocellulose ; Epoxy ; Composite number ; Materials science ; Composite material ; Composite epoxy material ; Chemistry ; Cellulose ; Organic chemistry Advanced Cellulose Research Studies ; Material Properties and Processing ; Digital Image Processing Techniques
Authors: Rui He | Liang Zhang | Mengjiao Lyu | Liangqing Lyu | Changbin Xue
Source: Aerospace (article, 2025-05-30)
Field: Materials Science — Materials Chemistry

Using Large Language Models for Aerospace Code Generation: Methods, Benchmarks, and Potential Values In recent years, Large Language Models (LLMs) have witnessed rapid advancements, revolutionizing various domains. Within the realm of software development, code generation technology powered by LLMs has emerged as a prominent research focus. Despite its potential, the application of this technology in the aerospace sector remains in its nascent, exploratory phase. This paper delves into the intricacies of LLM-based code generation methods and explores their potential applications in aerospace contexts. It introduces RepoSpace, the pioneering warehouse-level benchmark test for code generation of spaceborne equipment. Comprising 825 samples from five actual projects, this benchmark offers a more precise evaluation of LLMs’ capabilities in aerospace scenarios. Through extensive evaluations of seven state-of-the-art LLMs on RepoSpace, the study reveals that domain-specific differences significantly impact the code generation performance of LLMs. Existing LLMs exhibit subpar performance in specialized warehouse-level code generation tasks for aerospace, with their performance markedly lower than that of domain tasks. The research further demonstrates that Retrieval Augmented Generation (RAG) technology can effectively enhance LLMs’ code generation capabilities. Additionally, the use of appropriate prompt templates can guide the models to achieve superior results. Moreover, high-quality documentation strings are found to be crucial in improving LLMs’ performance in warehouse-level code generation tasks. This study provides a vital reference for leveraging LLMs for code generation in the aerospace field, thereby fostering technological innovation and progress in this critical domain. Aerospace ; Benchmark (surveying) ; Computer science ; Code (set theory) ; Data science ; Engineering ; Geography ; Programming language Machine Learning in Materials Science ; Topic Modeling ; Natural Language Processing Techniques
Authors: Abubakar Siddique | Muhammad Usama Shahid | Laraib Akram | Waseem Aslam | Kholod D. Alsufiani
Source: Processes (article, 2025-07-22)
Field: Materials Science — Materials Chemistry

A Holistic Integration of Machine Learning for Selecting Optimum Ratio of Nanoparticles in Epoxy-Based Nanocomposite Insulators Epoxy-based nanocomposites have drawn much interest in high-voltage insulation applications due to their improved dielectric properties. The determination of the optimal nanoparticle (NP) concentration required to achieve a significant enhancement in nanocomposite dielectric properties remains a subject of ongoing research. Previous work has employed iterative experimental methodologies, often characterized by the hit-and-trial method, in attempts to find the optimal nanoparticle concentration. However, these efforts have yielded suboptimal or inconsistent results. Moreover, experimental procedures for optimizing the nanoparticle concentration require significant time and cost. This research study proposed the predictive capabilities of machine learning (ML) for the selection of the nanoparticle concentration in epoxy-based nanocomposite insulators. The authors employed a novel systematic approach in this research work, comprising dataset preparation, ML model implementation, and experimental validation. A real-time dataset with varying concentrations of NPs (TiO2, SiO2, Al2O3) was developed in the High Voltage Lab, KFUEIT, Pakistan. Several advanced machine learning models are trained on this dataset. Support Vector Regression (SVR) exhibits the highest prediction accuracy, with an R2 score of 0.97. SVR predicted a breakdown voltage (BDV) of 46.26 kV, with a (w/w %) concentration of 5% TiO2, 1.17631% SiO2, and 3.95755% Al2O3. To validate the SVR prediction, a hardware prototype with predicted NP concentration is developed and tested. The experimentally measured BDV of the predicted nanocomposite sample, registering 44.72 kV, authenticates the predictive accuracy of machine learning. This work demonstrates the efficacy of machine learning as a viable and efficient alternative to traditional experimental methods for optimizing nanoparticle concentrations using a predictive approach in epoxy-based nanocomposites for high-voltage insulation applications. Epoxy ; Nanocomposite ; Nanoparticle ; Materials science ; Composite material ; Nanotechnology High voltage insulation and dielectric phenomena
Authors: A. Mustapha | Zainab Kasim Mohammed | Abdulraheem Giwa | Umar Ameuru Salami | Saifullahi Kabiru Sa’adu | Eli Danladi
Source: Discover Chemistry. (article, 2025-12-26)
Field: Chemistry — Spectroscopy

Naphthalimide dyes as fluorescent probes for emerging applications in medical diagnostics and therapeutics: a review Naphthalimide dyes have become an important class of fluorophores since they combine structural adaptability with favorable photophysical characteristics as well as good compatibility with biological systems. These qualities make them strong candidates for use in medical diagnostics and therapeutics. Their tunable emission, photobleaching resistance, and capability of undergoing different processes like intramolecular charge transfer, twisted charge transfer, and photoinduced electron transfer have contributed to their applications in biosensing and cancer imaging, photodynamic therapy and multifunctional drug delivery. Recent advances have led to the extension of this family into near infrared and two-photon designs for deep tissue imaging, activatable probes having high selectivity, nanocarrier formulations and targeted theranostics. Work is also well underway to integrate naphthalimide probes with artificial intelligence platforms and multiplexed sensing approaches as well and opens up the door to real-time monitoring and systems-level diagnostics. Despite these improvements, there are still critical hurdles to overcome, such as optimising compatibility with the body, enhancing light penetration through tissue, controlling pharmacokinetics and traversing the regulatory pathways. This review focuses on innovations in structural design, biomedical field and clinical translation of the naphthalimide derivatives, and further presents future research directions in smart probes, precision medicine, and economics in healthcare. Overall, naphthalimide scaffolds seem to be well poised to make a contribution in the future of molecular diagnostics and personalised therapeutics. Photobleaching ; Cancer therapy ; Nanocarriers ; Cancer treatment ; Biosensor ; Photodynamic therapy Molecular Sensors and Ion Detection ; Nanoplatforms for cancer theranostics ; Luminescence and Fluorescent Materials
Authors: Muhammad Zeeshan Khan | Farooq Aslam | Faisal Alsaif
Source: Scientific Reports (article, 2025-07-02)
Field: Materials Science — Materials Chemistry

Enhancing the dielectric characteristics and DC flashover of epoxy resin composites by surface modification of AgNbO3 nano particles The advancement of industrial technology has led to power modules facing challenges such as elevated temperatures, high voltages, and increased frequencies. The conventional epoxy resin (Ep) fails to satisfy the demands of high voltage and superior insulation. This study involved the synthesis of Ep resin/ AgNbO<sub>3</sub> nanocomposites aimed at addressing insulating flaws. AgNbO<sub>3</sub> was modified using the silane coupling agent KH550. The changes in the molecular chain after incorporating of unmodified and modified AgNbO<sub>3</sub> were evaluated through FTIR analysis. The SEM results indicate that following modification, AgNbO<sub>3</sub> particles are evenly distributed within the Ep resin matrix, demonstrating a lack of agglomeration. From thermal conductivity result it was found that modification of AgNbO<sub>3</sub> shows an enhancement of thermal conductivity, attributed to the reduction of interfacial phonon scattering and thermal resistance within the Ep resin. Additionally, the dielectric constant and dielectric loss in composites are greatly impacted by the addition of AgNbO<sub>3</sub>. According to the DC breakdown strength result, the addition of modified AgNbO<sub>3</sub> enhanced the process and caused deeper traps to form inside the bulk matrix. The distribution of trap energy levels is determined by Thermal Stimulated Depolarization Current (TSDC), which indicates that the incorporation of AgNbO<sub>3</sub> results in an increase in trap energy levels. Furthermore, the incorporation of 1 wt% AgNbO<sub>3</sub> particle into Ep resin has been observed to enhance the energy level of deep traps. After AgNbO<sub>3</sub> was added, DC Flashover in both air and vacuum significantly increased in comparison to pure Ep resin. Epoxy ; Composite material ; Materials science ; Dielectric ; Surface modification ; Nano- ; Arc flash ; Chemical engineering High voltage insulation and dielectric phenomena ; Dielectric materials and actuators ; Advanced Sensor and Energy Harvesting Materials
Authors: Sofía González-Núñez | Zeynep Sumer | Carlos Amador‐Bedolla | Prakash J. Madhav | Ajay Muralidharan | Claire S. Adjiman | Mariano Martı́n
Source: ACS Sustainable Chemistry & Engineering (article, 2025-08-20)
Field: Chemistry — Organic Chemistry

Design of Surfactant Molecules Under Performance Constraints The industrial applications of surfactant solutions are both numerous and extremely diverse, demonstrating the importance of these systems in everyday life and driving the need for a systematic approach to designing sustainable surfactant molecules adapted to the specific requirements of each application. Given the very large space of possible molecules, the identification of candidate surfactants that achieve a balance between the optimal physicochemical properties of the product and minimal environmental and health impacts is extremely challenging. In this work, a formulation and solution framework based on Computer-Aided Molecular Design is proposed for surfactant design. A novel multistage methodology is developed based on the initial generation of promising candidates for the two constituents of a surfactant, the hydrophilic head and the hydrophobic tail, followed by the multiobjective optimization of surfactant molecules. This decomposition results in an effective solution strategy. In addition to constraints that ensure the generation of feasible molecules, specific structural constraints can be incorporated in the formulation, accelerating the discovery and optimization process. Data-driven predictive models for the most relevant surfactant properties, such as critical micelle concentration, Krafft point, surface tension, toxicity, and biodegradability, are developed and implemented in the optimization formulation. Two case studies are tackled, successfully generating novel surfactant molecules. The proposed framework could be extended to more complex structures, such as two-headed or Gemini surfactants. Pulmonary surfactant ; Molecule ; Chemical engineering ; Chemistry ; Nanotechnology ; Process engineering ; Biochemical engineering ; Materials science ; Organic chemistry ; Engineering Surfactants and Colloidal Systems ; Analytical Chemistry and Chromatography ; Environmental Chemistry and Analysis
Authors: Elena V. Parfenyuk | Ekaterina S. Dolinina
Source: Reviews in Inorganic Chemistry (article, 2025-01-21)
Field: Materials Science — Materials Chemistry

Silicon-based particles as a platform for development of antiviral drugs Abstract The growing number of viral infections and viral strains from year to year requires the creation of new, more effective antiviral drugs. One of the cost-effective ways to increase drug efficiency is the development of delivery systems for already known and clinically used drugs in order to overcome the challenges currently limiting their efficiency. This review presents the current status of silicon-based particles in this area. Silicon-based materials consist mainly of silicon and its compounds and can contain other inorganic oxides, i.e. are inorganic in nature. Their inorganic nature provides a number of advantages over organic materials (e.g. polymers, lipids, micelles, etc.) which are widely proposed and already used for the indicated purpose. This review provides information about the structural features of the silicon-based materials, methods of their preparation. It contains studies showing why and how the particles themselves can serve as antiviral agents or, as carriers, can help overcome the disadvantages of active drugs and increase their antiviral efficacy. The review highlights the enormous potential of silicon-based inorganic particles (pristine or modified with various inorganic and organic species) in the fight against widespread viral infections. Chemistry ; Nanotechnology ; Combinatorial chemistry Silicon Nanostructures and Photoluminescence ; Bacteriophages and microbial interactions ; Advanced biosensing and bioanalysis techniques
Authors: Tensei Inukai | Aoi Yamato | Manato Akiyama | Yasubumi Sakakibara
Source: Communications Chemistry (article, 2025-08-05)
Field: Materials Science — Materials Chemistry

Leveraging tree-transformer VAE with fragment tokenization for high-performance large chemical model generation Molecular generation models, especially chemical language model (CLM) utilizing SMILES, a string representation of compounds, face limitations in handling large and complex compounds while maintaining structural accuracy. To address these challenges, we propose the Fragment Tree-Transformer based VAE (FRATTVAE), which treats molecules as tree structures with fragments as nodes. FRATTVAE incorporates several innovative techniques to enhance molecular generation. Molecules are decomposed into fragments and organized into tree structures, allowing for efficient handling of large and complex compounds. Tree positional encoding assigns unique positional information to each fragment, preserving hierarchical relationships. The Transformer's self-attention mechanism models complex dependencies among fragments. This architecture allows FRATTVAE to surpass existing methods, making it a robust solution that is scalable to unprecedented dataset sizes and molecular complexities. Distribution learning across various benchmark datasets, from small molecules to natural compounds, showed that FRATTVAE consistently achieved high accuracy in all metrics while balancing reconstruction accuracy and generation quality. In molecular optimization tasks, FRATTVAE generated high-quality, stable molecules with desired properties, avoiding structural alerts. These results highlight FRATTVAE as a robust and versatile solution for molecular generation and optimization, making it well-suited for a variety of applications in cheminformatics and drug discovery. Fragment (logic) ; Transformer ; Computer science ; Lexical analysis ; Natural language processing ; Programming language ; Engineering ; Electrical engineering ; Voltage Machine Learning in Materials Science ; Scientific Computing and Data Management ; Computational Drug Discovery Methods
Authors: Filip Ekström Kelvinius | Oskar B. Andersson | Abhijith S. Parackal | Dong Qian | Rickard Armiento | Fredrik Lindsten
Source: ArXiv.org (preprint, 2025-02-10)
Field: Materials Science — Materials Chemistry

WyckoffDiff -- A Generative Diffusion Model for Crystal Symmetry Crystalline materials often exhibit a high level of symmetry. However, most generative models do not account for symmetry, but rather model each atom without any constraints on its position or element. We propose a generative model, Wyckoff Diffusion (WyckoffDiff), which generates symmetry-based descriptions of crystals. This is enabled by considering a crystal structure representation that encodes all symmetry, and we design a novel neural network architecture which enables using this representation inside a discrete generative model framework. In addition to respecting symmetry by construction, the discrete nature of our model enables fast generation. We additionally present a new metric, Fréchet Wrenformer Distance, which captures the symmetry aspects of the materials generated, and we benchmark WyckoffDiff against recently proposed generative models for crystal generation. As a proof-of-concept study, we use WyckoffDiff to find new materials below the convex hull of thermodynamical stability. Generative grammar ; Symmetry (geometry) ; Diffusion ; Generative model ; Theoretical physics ; Crystal (programming language) ; Statistical physics ; Physics ; Mathematics ; Computer science ; Artificial intelligence ; Geometry ; Quantum mechanics ; Programming language Crystallization and Solubility Studies
Authors: Štefan Hardoň | Jozef Kúdelčík | Marián Janek | Anton Baran | Alena Kozáková | Tomáš Dérer
Source: Nanotechnology Reviews (article, 2025-01-01)
Field: Materials Science — Biomaterials

Halloysite nanotube enhanced polyurethane nanocomposites for advanced electroinsulating applications Abstract Halloysite nanotubes (HNT), as naturally occurring anisotropic nanofillers, were incorporated into a commercial two-component (2C) polyurethane (PU) system to develop multifunctional nanocomposites for electroinsulating applications. The nanocomposites were prepared via the method of direct dispersion. Dielectric, thermal, and mechanical properties were systematically analyzed at weight concentrations of 2 wt%, 5 wt%, and 10 wt% of HNT in the PU matrix. At 5 wt% HNT, an optimal balance was observed: volume resistivity increased nearly 17-fold, dielectric permittivity was enhanced through Maxwell–Wagner–Sillars interfacial polarization, and thermal conductivity rose modestly while maintaining high mechanical integrity. Broadband dielectric spectroscopy, modeled using the Havriliak–Negami function, showed broadened relaxation dynamics associated with filler–matrix interfaces. Incorporation of 10 wt% HNT caused agglomeration, reducing property uniformity. The results demonstrate that controlled integration of HNT into cold-curing 2C PU systems yields cost-effective, sustainable, and high-performance nanodielectrics, advancing their potential use in high-voltage insulation, aerospace encapsulation, and battery module safety. Halloysite ; Nanocomposite ; Dielectric ; Polyurethane ; Thermal conductivity ; Electrical resistivity and conductivity ; Nanotube ; Permittivity Clay minerals and soil interactions ; Electrospun Nanofibers in Biomedical Applications ; Polymer composites and self-healing
Authors: Yongwei Song | Yulin Wang | Jianlong Wang
Source: Sustainable polymer & energy (article, 2025-01-01)
Field: Chemistry — Organic Chemistry

Optimization of High Energy Radiation-Induced Degradation of Sulfamethoxazole Using Response Surface Methodology Ionizing irradiation is an emerging technology for the removal of toxic pollutants, such as antibiotics, in water and wastewater. In this study, gamma radiation-induced degradation of sulfamethoxazole (SMX) was optimized using response surface methodology (RSM) based on a Box-Behnken design. LC-MS analysis identified nine intermediate products (M1–M9), elucidating a dual oxidative-reductive degradation mechanism driven by hydroxyl radicals (•OH) and hydrated electrons (eaq⁻). These intermediates, characterized by hydroxylation, sulfonamide bond cleavage, and aromatic ring fragmentation, aligned with pathways distinct from conventional chlorination systems, underscoring the absence of toxic halogenated byproducts. According to experimental data, The study revealed that absorbed dose (0.2–2.0 kGy) and initial SMX concentration (5–40 mg/L) critically governed SMX degradation efficiency, achieving >99% removal under optimized conditions (≥1.2 kGy for 5–10 mg/L SMX). The robust RSM model (R2 = 0.9931) and experimental validation (±2% error) demonstrated the method’s reliability in reconciling nonlinear dose-concentration interactions as well as providing an effective approach to parameter optimization, offering practical insights for enhancing the treatment efficiency of antibiotic-containing wastewater. Response surface methodology ; Degradation (telecommunications) ; Radiation ; Sulfamethoxazole ; Materials science ; Computer science ; Mathematics ; Chemistry ; Statistics ; Physics ; Optics ; Telecommunications ; Antibiotics Synthesis and Biological Evaluation ; Metal complexes synthesis and properties ; Cancer therapeutics and mechanisms
Authors: Mohammed Jwher Saleh | Syed Sagir Ahmed | Jamil Nadhem Saleh
Source: Asian Journal of Chemical Sciences (article, 2025-04-09)
Field: Chemistry — Organic Chemistry

Synthesis, Characterization of Tetrazole Derivatives, and Evaluation of Bacterial and Fungal Activity The study involved the use of microwaves to react the prepared hydrazone with sodium azide to form various heterocyclic tetrazole-derived pentacyclics using THF as a solvent. In addition to measuring the prepared compounds' melting points, physical and spectroscopic techniques, including FT-IR spectroscopy, proton spectroscopy, and carbon nuclear magnetic resonance (1H-NMR) and (13C-NMR), were used to verify the synthesis' efficacy and accuracy. Two bacterial isolates were examined to see how the synthesized compounds affected their growth: Staphylococcus aureus, a Gram-positive (G+) bacterium, and E. coli, a Gram-negative (G-) bacterium. Control samples were antibiotics like amoxicillin, and several of the compounds that were made had strong inhibitory action against the bacterial species that were employed. It was also investigated how some of the produced chemicals affected the development of the fungus Candida albicans. Some of the synthesized compounds demonstrated good inhibitory efficacy against the utilized fungus when tested against control samples of medicines like fluconazole. The compound (SU7) showed the highest activity, reaching 30 mg/ml at its highest concentration against bacteria and fungi, reaching 30 mg/ml. Tetrazole ; Characterization (materials science) ; Chemistry ; Microbiology ; Combinatorial chemistry ; Stereochemistry ; Biology ; Materials science ; Nanotechnology Synthesis of Tetrazole Derivatives