jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yongjie Nie | Yihang Jiang | Pengju Wang | D. Chen | Yongsen Han | Jialiang Song | Yuanwei Zhu | Shengtao Li
Source: Applied Sciences (article, 2025-07-17)
Field: Materials Science — Materials Chemistry

Electroluminescent Sensing Coating for On-Line Detection of Zero-Value Insulators in High-Voltage Systems In high-voltage transmission lines, insulators subjected to prolonged electromechanical stress are prone to zero-value defects, leading to insulation failure and posing significant risks to power grid reliability. The conventional detection method of spark gap is vulnerable to environmental interference, while the emerging electric field distribution-based techniques require complex instrumentation, limiting its applications in scenes of complex structures and atop tower climbing. To address these challenges, this study proposes an electroluminescent sensing strategy for zero-value insulator identification based on the electroluminescence of ZnS:Cu. Based on the stimulation of electrical stress, real-time monitoring of the health status of insulators was achieved by applying the composite of epoxy and ZnS:Cu onto the connection area between the insulator steel cap and the shed. Experimental results demonstrate that healthy insulators exhibit characteristic luminescence, whereas zero-value insulators show no luminescence due to a reduced drop in electrical potential. Compared with conventional detection methods requiring access of electric signals, such non-contact optical detection method offers high fault-recognition accuracy and real-time response capability within milliseconds. This work establishes a novel intelligent sensing paradigm for visualized condition monitoring of electrical equipment, demonstrating significant potential for fault diagnosis in advanced power systems. Insulator (electricity) ; Materials science ; Electroluminescence ; Optoelectronics ; Electrical engineering ; Reliability (semiconductor) ; Coating ; Voltage ; Fault detection and isolation ; Electronic engineering ; Engineering ; Power (physics) ; Nanotechnology ; Actuator High voltage insulation and dielectric phenomena ; Analytical Chemistry and Sensors ; Electrical Fault Detection and Protection
Authors: Jing Qian | Taoyu Han | Pengfei Xia | Zhongliang Sun | Stavros Avramidis | Zhengbin He | Jiejie Sun
Source: Holzforschung (article, 2025-06-17)
Field: Materials Science — Biomaterials

Improving dimensional stability of <i>Ailanthus altissima</i> wood by ultrasonic alkali-assisted DMDHEU treatment Abstract The effectiveness of DMDHEU (1,3-dimethylol-4,5-dihydroxy ethylene urea) modification is influenced by factors such as pore size distribution. In this study, ultrasonic-assisted-alkali pretreatment combined with DMDHEU impregnation was used to enhance the hygroscopic dimensional stability of Ailanthus altissima wood. The weight percentage gain (WPG), absolute dry density, tangential swelling, resin distribution, and chemical functional groups were analyzed. The results showed a marked reduction in the tangential swelling of DMDHEU-ultrasonic-4 % NaOH-pretreated samples, decreasing from about 2.0 % and 4.9 % in untreated samples (without any pretreatment or DMDHEU impregnation) to 0.6 % and 3.2 % at approximately 11.3 % and 75.3 % RH, Respectively. Compared to untreated samples, DMDHEU-ultrasonic-pretreated samples exhibited increased WPG, Absolute dry density, and dimensional stability. Ultrasound-assisted pretreatment also influenced resin distribution within vessels, with raised-membranous resin formations observed in the DMDHEU-ultrasonic-4 % NaOH-pretreated samples, in contrast to the sporadic deposition in untreated samples. Furthermore, XPS and ATR-FTIR results indicated ultrasonic pretreatment facilitated deeper DMDHEU resin penetration into the wood cell wall, converting hydrophilic hydroxyl groups into hydrophobic ester and ether bonds. These findings underscore the significance of ultrasonic alkali pretreatment in enhancing the efficacy of DMDHEU modification for A. altissima , contributing to improved durability and expanding potential applications for this fast-growing timber species. Ailanthus altissima ; Alkali metal ; Ultrasonic sensor ; Materials science ; Chemistry ; Botany ; Composite material ; Pulp and paper industry ; Chemical engineering ; Organic chemistry ; Biology ; Acoustics ; Physics ; Engineering Advanced Cellulose Research Studies ; Lignin and Wood Chemistry ; Wood Treatment and Properties
Authors: Rima Heider Al Omari | Shelesh krishna saraswat | Munthar Kadhim Abosaoda | M. M. Rekha | Subhashree Ray | Kattela Chennakesavulu | Renu Sharma | Aditya Kashyap
Source: Scientific Reports (article, 2025-11-23)
Field: Chemistry — Organic Chemistry

Unveiling the gas-sensing potential of methylene-bridged [6]cycloparaphenylene nanobelt: a DFT perspective The rapid expansion of industrialization has led to the release of numerous toxic gases into the atmosphere, posing significant environmental and health risks even at low concentrations. This has created an urgent need for novel carbon-based nanostructures capable of detecting gases selectively and efficiently. In this study, we systematically investigate the adsorption behavior and sensing performance of the methylene-bridged [6]cycloparaphenylene ([6]MCPP) nanobelt for detecting hazardous gases, including hydrogen sulfide (H₂S), carbon monoxide (CO), ammonia (NH₃), ozone (O₃), sulfur dioxide (SO₂), and nitrogen dioxide (NO₂), using density functional theory (DFT)-based electronic structure analysis. The [6]MCPP nanobelt demonstrated excellent structural robustness, with a cohesive energy of − 301.22 kcal/mol, confirming its intrinsic stability for gas-sensing applications. Among the gases examined, NO₂ exhibited the most pronounced interaction-induced electronic modulation, reflected by a 67.17% reduction in the HOMO–LUMO energy gap (from 2.62 to 0.84 eV) and a sharp rise in electrical conductivity (up to 239 S/m). Its rapid recovery time of 978 µs (9.78 × 10⁻⁴ s) at 298 K suggests both high sensitivity and excellent reusability. Although SO₂ showed the strongest adsorption energy (-20.8 kcal/mol), the moderate binding strength of -12.33 kcal/mol for NO₂, combined with its large band gap variation and short recovery time, highlights an optimal balance between selectivity and reversibility. Density of states (DOS) analysis revealed significant redistribution of electronic states near the Fermi level, confirming enhanced charge transfer upon gas adsorption. Additionally, quantum theory of atoms in molecules (QTAIM), electron localization function (ELF), and non-covalent interaction (NCI) analyses identified van der Waals forces as the dominant stabilizing interactions, with notably strong attractive regions for [6]MCPP@O₃, [6]MCPP@SO₂, and [6]MCPP@NO₂ complexes. These findings not only establish [6]MCPP as a stable and reusable nanobelt sensor but also reveal its superior selectivity and sensitivity toward NO₂, positioning it as a next-generation candidate for efficient environmental monitoring applications. Density functional theory ; van der Waals force ; Adsorption ; Hydrogen sulfide ; Carbon monoxide ; Electronic structure ; Nitrogen dioxide ; Density of states ; Molecule ; Sulfur dioxide Synthesis and Properties of Aromatic Compounds ; Covalent Organic Framework Applications ; Polydiacetylene-based materials and applications
Authors: Natesh Singh | Philippe Vayer | Bruno O. Villoutreix
Source: Briefings in Bioinformatics (article, 2025-11-01)
Field: Chemistry — Organic Chemistry

The covalent docking software landscape: features and applications in drug design Covalent small-molecule ligands have re-emerged as powerful tools in drug discovery, offering prolonged target engagement, enhanced potency, and the ability to modulate proteins once considered undruggable. However, the rational design and virtual screening (VS) of covalent ligands remain challenging. Many docking tools cannot accurately model the energetics of covalent bond formation, often requiring more rigorous quantum mechanical (QM) or semi-empirical QM calculations for reliable predictions. Despite these limitations, the computational landscape is rapidly evolving. An increasing number of open-source, commercial, and web-based platforms now support binding mode exploration, lead optimization, and structure-based VS of covalent ligands. Alongside traditional approaches, new artificial intelligence (AI) and machine learning (ML) tools are assisting in prioritizing candidate molecules. This review introduces the fundamental principles and mechanisms of covalent inhibition, then provides a comprehensive overview of computational tools including covalent docking, warhead placement algorithms, and pharmacophore modeling, supporting early-stage drug discovery and chemical biology. Case studies highlight practical applications. We also cover curated databases of covalent binders and experimental 3D protein-ligand complexes, plus tools for assessing nucleophilic residue reactivity, all essential for robust covalent modeling. Finally, we briefly address risks associated with covalent chemistry. While progress is notable, further advances are needed. Nonetheless, today's covalent docking and AI-driven tools already make a meaningful impact by enabling rational design, generating new ideas, refining hypotheses, and expanding the boundaries of druggability. Covalent bond ; Pharmacophore ; Virtual screening ; Dynamic covalent chemistry ; Drug discovery ; Covalent binding ; Docking (animal) Click Chemistry and Applications ; Protein Degradation and Inhibitors ; Computational Drug Discovery Methods
Authors: Lyudmyla Antypenko | Kostiantyn Shabelnyk | Oleksii Antypenko | Mieko Arisawa | Valentyn Oksenych | Oleksandr Kamyshnyi | С. І. Коваленко
Source: Preprints.org (preprint, 2025-04-18)
Field: Chemistry — Organic Chemistry

In Silico Identification and Characterization of Spiro [1,2,4]triazolo[1,5-&lt;i&gt;c&lt;/i&gt;]quinazolines as Diacylglycerol Kinase α Modulators This study unveils a new class of spiro[1,2,4]triazolo[1,5-c]quinazoline derivatives as promising diacylglycerol kinase α (DGKα) modulators, a target implicated in cancer, neurological disorders, and immune dysfunction. Through structure-based computational design using the CB-Dock2 platform with human DGKα EF-hand domains (PDB ID: 6IIE), 40 novel compounds were systematically evaluated along established inhibitors (ritanserin, R59022, R59949, BMS502, and (5Z,2E)-CU-3) across five distinct binding pockets. Several compounds demonstrated remarkable binding profiles at the level or surpassing reference compounds. The physicochemical analysis revealed balanced drug-like properties with favorable molecular weights (252-412 g/mol) and appropriate three-dimensionality. Toxicological assessment indicated reassuring safety profiles with predicted LD50 values of 1000-2000 mg/kg and minimal hepatotoxicity, carcinogenicity, and mutagenicity potential. Notably, compound 33 (adamantyl-substituted) emerged as exceptionally promising, exhibiting strong binding affinity, moderate solubility, and selective CYP inhibition patterns that minimize drug-drug interaction risks. Detailed molecular interaction mapping identified critical binding determinants, including strategic hydrogen bonding with TRP151, GLU166, and ARG126. The multidimensional evaluation identified compounds 13, 18, 33, and 40 as particularly promising candidates that balance potent target engagement with favorable pharmaceutical profiles, establishing this scaffold as a valuable platform for developing next-generation therapeutics targeting DGKα-mediated signaling pathways. In silico ; Diacylglycerol kinase ; Chemistry ; Identification (biology) ; Kinase ; Combinatorial chemistry ; Protein kinase C ; Biochemistry ; Biology Quinazolinone synthesis and applications ; Synthesis and Characterization of Heterocyclic Compounds ; Biochemical and Molecular Research
Authors: Victor H. R. Nogueira | Rishabh Sharma | Rafael V. C. Guido | Michael J. Keiser
Source: Journal of Chemical Information and Modeling (article, 2025-02-05)
Field: Materials Science — Materials Chemistry

Fuzz Testing Molecular Representation Using Deep Variational Anomaly Generation Researchers are developing increasingly robust molecular representations, motivating the need for thorough methods to stress-test and validate them. Here, we use a variational auto-encoder (VAE), an unsupervised deep learning model, to generate anomalous examples of SELF-referencIng Embedded Strings (SELFIES), a popular molecular string format. These anomalies defy the assertion that all SELFIES convert into valid SMILES strings. Interestingly, we find specific regions within the VAE's internal landscape (latent space), whose decoding frequently generates inconvertible SELFIES anomalies. The model's internal landscape self-organization helps with exploring factors affecting molecular representation reliability. We show how VAEs and similar anomaly generation methods can empirically stress-test molecular representation robustness. Additionally, we investigate reasons for the invalidity of some discovered SELFIES strings (version 2.1.1) and suggest changes to improve them, aiming to spark ongoing molecular representation improvement. Anomaly (physics) ; Representation (politics) ; Mathematics ; Artificial intelligence ; Computer science ; Pattern recognition (psychology) ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Anomaly Detection Techniques and Applications ; Machine Learning and Data Classification
Authors: Veerendra Yadav | Bhumika Bhumika | S. Deepak | Ramachandran Thulasiram
Source: Natural and Engineering Sciences (article, 2025-04-01)
Field: Chemistry — Organic Chemistry

Examining the Liver Metabolic Alterations Induced by Olive Leaf Compounds in Aquatic Species Olea europaea L., frequently recognized for its traditional medicinal uses, contains bioactive compounds with antioxidant, anti-inflammatory, and metabolic regulatory properties. While these compounds have been widely investigated for their properties on human well-being, their consequences on aquatic species remain less discovered. The aim is to assess the effects of seven olive leaf metabolites caffeic acid, oleuropein, corosolic acid, moronic acid, lupeol, cycloartenol, and betulinic acid on liver metabolism in Nile tilapia (Oreochromis niloticus). The metabolites were incorporated into the fish food at three absorptions: 1g/100g, 3g/100g, and 5g/100g feed, and the fish were fed twice daily for 96 hours. Biochemical examination of fish serum revealed significantly improved Alkaline Phosphatase (ALP), Aspartate Aminotransferase (AST), Alanine Aminotransferase (ALT), and glucose levels associated with the control group. Particularly, corosolic acid and moronic acid encouraged the greatest noticeable development in liver enzyme activities, signifying potential disruptions in liver metabolism and function. These consequences indicate that bioactive compounds in olive leaves can influence liver physiology in aquatic species, emphasizing their potential effect on fish health. The research delivers valuable insights into the metabolic effects of these complexes, with implications for aquaculture nutrition approaches. Further investigation are essential to assess their long-term security and effectiveness. Biology ; Botany ; Zoology Edible Oils Quality and Analysis ; Metabolomics and Mass Spectrometry Studies ; Aquaculture Nutrition and Growth
Authors: Binrong Zhu | Qian-Cheng Wang | Yang Wei | J. Pan | Huzi Ye
Source: Materials (article, 2025-10-02)
Field: Materials Science — Polymers and Plastics

Sustainable Bio-Gelatin Fiber-Reinforced Composites with Ionic Coordination: Mechanical and Thermal Properties A novel bio-gelatin fiber-reinforced composite (BFRC) was first developed by incorporating industrial bone glue/gelatin as the matrix, magnesium oxide (MgO) as an additive, and natural or synthetic fibers as reinforcement. Systematic tests evaluated mechanical, impact, and thermal performance, alongside microstructural mechanisms. Results showed that polyethylene (PE) fiber-reinforced composites achieved a tensile strength of 3.40 MPa and tensile strain of 10.77%, with notable improvements in compressive and flexural strength. PE-based composites also showed excellent impact energy absorption, while bamboo fiber-reinforced composites exhibited higher thermal conductivity. Microstructural analysis revealed that coordination between Mg<sup>2+</sup> ions and amino acids in gelatin formed a stable cross-linked network, densifying the matrix and improving structural integrity. A multi-criteria evaluation using the TOPSIS model identified the BC-PE formulation as the most balanced system, combining strength, toughness, and thermal regulation. These findings demonstrate that ionic coordination and fiber reinforcement can overcome inherent weaknesses of gelatin matrices, offering a sustainable pathway for building insulation and cushioning packaging applications. Ultimate tensile strength ; Flexural strength ; Composite number ; Compressive strength ; Biocomposite ; Fiber ; Gelatin ; Young's modulus ; Thermal insulation Natural Fiber Reinforced Composites ; Additive Manufacturing and 3D Printing Technologies ; Advanced Cellulose Research Studies
Authors: Elżbieta Gumieniczek–Chłopek | Joanna Odrobińska | Adriana Gilarska | Gabriela Opiła | M. R. Ibarra | Cz. Kapusta | Szczepan Zapotoczny
Source: Nanotechnology Science and Applications (article, 2025-06-01)
Field: Materials Science — Biomaterials

Intracellular Uptake of Magnetic Nanocapsules with Ionic Chitosan Shells and Magnetically Triggered Cargo Release This approach is very versatile as various lipophilic compounds could be encapsulated, then transported to desired tissues without active or passive targeting and kept there using static magnetic field, limiting undesired side effects of a therapy to the whole organism. The proposed capsules with MNs respond efficiently to magnetic field stimulation - they can be magnetically navigated into the cells and release their cargo after application of alternating magnetic field. This approach opens opportunities for controlled intracellular delivery of hydrophobic actives using easily applicable magnetic stimuli for both delivery and release. Nanocapsules ; Chitosan ; Intracellular ; Intracellular transport ; Biophysics ; Ionic bonding ; Chemistry ; Materials science ; Nanotechnology ; Chemical engineering ; Ion ; Biochemistry ; Nanoparticle ; Biology ; Engineering Nanoparticle-Based Drug Delivery ; Polymer Surface Interaction Studies ; Characterization and Applications of Magnetic Nanoparticles
Authors: Najam Aziz | Norfadzlia Mohd Yusof | Azah Kamilah Muda | Farhad Soleimanian Gharehchopogh
Source: Journal of Advanced Research Design (article, 2025-06-26)
Field: Chemistry — Analytical Chemistry

Chaotic Binary Manta-Ray Foraging Optimization Algorithm Based Descriptor Selection for Amphetamine-Type Stimulants Drug Classification This study proposes a novel approach to optimize descriptor selection for classifying Amphetamine-Type Stimulants (ATS) and non-ATS drugs. The approach applies a Manta Ray Foraging Optimization (MRFO) framework enhanced with time-varying transfer function and a chaotic map. These enhancements aim to achieve a balance between exploration (finding new possibilities) and exploitation (refining promising solutions) within the improved version of binary MRFO (BMRFO) algorithm to solve descriptor selection problem. Two types of time-varying transfer functions recommended by earlier research are used to convert continuous MRFO to binary MRFO (BMRFO) for binary optimization problem. In addition, the pseudorandom number of the MRFO's probability operator is replaced with eleven different chaotic maps to prevent convergence problem. To evaluate the effectiveness of the suggested approach, a particular high-dimensional chemical dataset containing molecular descriptors of ATS and non-ATS drugs was utilized. The experimental results and statistical analysis show that the proposed BMRFOTV2-C9 employing the non-linear time-varying Sigmoid transfer function and Sinusoidal chaotic map is superior with lowest average fitness indicating its ability to convergence to optimal solution by selecting relevant descriptor subset and achieved better classification accuracy. Additionally, this study evaluated the performance of BMRFOTV2-C9 to other known BMRFO variations from the literature and the findings show that it is more efficient than others. Foraging ; Chaotic ; Amphetamine ; Binary number ; Selection (genetic algorithm) ; Computer science ; Algorithm ; Type (biology) ; Artificial intelligence ; Pattern recognition (psychology) ; Mathematics ; Biology ; Neuroscience ; Arithmetic Spectroscopy and Chemometric Analyses
Authors: Caree A. García-Maro | Carmen S. Rochín-Wong | L.G. Ceballos-Mendivil | J. L. Jiménez-Pérez | R. Gutiérrez-Fuentes | Carlos Pérez-Rábago | J. Tánori
Source: Fluids (article, 2025-11-30)
Field: Materials Science — Materials Chemistry

Study of Stability, Viscosity, and Thermal Diffusivity of SiC-HfC Hybrid Nanofluids in 50EG-50H2O Mixture The growing global population has resulted in a higher demand for energy, leading researchers to prioritize the development of alternative energy sources and the improvement of current technologies. Nanofluids (NFs) are a promising method for enhancing heat transfer and efficiently utilizing solar thermal energy. This study describes the preparation of four NFs: two mono NFs of SiC and HfC containing nanoparticle concentrations ranging from 0.10–1.0 wt.%. Moreover, two hybrid NFs were synthesized within the same concentration range (0.10–1.0 wt.%) of SiC-HfC nanocomposites in proportions of 60 wt.% SiC-40 wt.% HfC and 40 wt.% SiC-60 wt.% HfC, all dispersed in a mixture of ethylene glycol (EG) and distilled water (50EG-50H2O). The materials were synthesized by carbothermal reduction, and the NFs were prepared using the two-step method. The NFs showed stable dispersion, with HfC and 40SiC-60HfC systems exhibiting the higher zeta potential (ζ) values. Viscosity remained largely unaffected by particle addition. The thermal diffusivity of the NFs was measured by the thermal lens spectroscopy (TLS) technique using 1:20 diluted samples. The hybrid nanofluid 40SiC-60HfC improved diffusivity by 66.93%, presenting a synergistic effect in its performance, highlighting its potential in clean energy systems. Nanofluid ; Thermal diffusivity ; Ethylene glycol ; Viscosity ; Zeta potential ; Distilled water ; Nanoparticle ; Particle size ; Range (aeronautics) Thermal properties of materials ; Nanofluid Flow and Heat Transfer ; Solar Thermal and Photovoltaic Systems