jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Еvgeny V. Nazarchuk | Oleg I. Siidra | Yana G. Tagirova | Dmitri Charkin | Dmitri N. Dmitriev | Anatoly V. Kasatkin
Source: Zeitschrift für Kristallographie - Crystalline Materials (article, 2025-01-01)
Field: Chemistry — Inorganic Chemistry

Thermal evolution of soddyite, (UO<sub>2</sub>)<sub>2</sub>SiO<sub>4</sub>(H<sub>2</sub>O)<sub>2</sub> and structurally related Na<sub>2</sub>(UO<sub>2</sub>)<sub>2</sub>SiO<sub>4</sub>F<sub>2</sub> Abstract Thermal expansion of the mineral soddyite, (UO 2 ) 2 SiO 4 (H 2 O) 2 , and structurally related synthetic compound Na 2 (UO 2 ) 2 SiO 4 F 2 ( NAUSIF ) has been studied by means of high-temperature single-crystal and powder X-ray diffraction. The mineral is orthorhombic, Fddd , while NAUSIF is tetragonal, I 4 1 / amd . The framework structures of both compounds are comprised of either neutral [(UO 2 ) 2 (SiO 4 )(H 2 O) 2 ] or negatively charged [(UO 2 ) 2 (SiO 4 )F 2 ] 2- chains of similar topology. In the structure of soddyite, the chains cross at the angle of 72°, while in NAUSIF of 90°. Upon increasing temperature, the acute inter-chain angles in soddyite increase due to hinge deformations, the overall symmetry approaching tetragonal. The mineral is stable below 325 ± 25 °С; between 325 and 640 °С, the decomposition products cannot be identified unambiguously and contain significant amount of amorphous phases; at higher temperatures, a mixture of U 3 O 8 polymorphs is formed. NAUSIF is stable until its melting point of 625 ± 25 °С. The thermal expansion of both compounds is strongly anisotropic; for NAUSIF , it is due to difference in bond strength in the uranium and sodium polyhedra. Anisotropic thermal expansion of soddyite is controlled by shear deformations of the structure upon the temperature rise. Materials science ; Crystallography ; Analytical Chemistry (journal) ; Chemistry Radioactive element chemistry and processing ; Nuclear materials and radiation effects ; Nuclear Materials and Properties
Authors: Suman Tiwari | Akash Kumar Jha | S. L. Arora | Debanjana Das | Sri Rama Koti Ainavarapu
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-11-10)
Field: Materials Science — Materials Chemistry

Electrostatics Govern Protein Orientation on Citrate-Capped Gold Nanoparticles Abstract Conjugation of proteins with gold nanoparticles (GNPs) is crucial in developing nanoparticle-based drugs, which are used in making targeted delivery systems for therapeutic and diagnostic use. This conjugation is often mediated by covalent gold–thiol (Au-S) bonds involving cysteine residues. However, the influence of other interactions, like electrostatics, on protein-GNP interactions remains poorly understood. While the independent roles of cysteine (thiol interactions) and lysine (electrostatics) are studied, how these two distinct mechanisms couple and mutually influence one another remains unknown. It is particularly unclear how the position and the electrostatics around cysteine residues within the protein structure modulate their interaction with GNPs. Hence, to isolate the electrostatic contribution from Au-S interaction, we systematically investigated the role of positively charged lysine in protein interaction with citrate-capped GNPs using protein L, a cysteine-free antibody-binding protein. Protein L has seven lysine residues, positively charged at pH 7.4. Each lysine was individually mutated to cysteine to create single-cysteine variants. The capping strength of these mutants on citrate-capped GNPs was assessed using various biophysical techniques. Surprisingly, not all cysteine mutants formed stable covalent conjugates. The K7C mutant, in particular, showed strong and irreversible GNP capping, not solely due to the introduced cysteine, but also because of nearby positively charged residues that promoted electrostatic attraction to the negatively charged citrate-capped GNP surface. This critical role of electrostatics was further confirmed using lysine acrylation, which effectively capped lysines and prevented their interaction. Our experimental plan was thus able to successfully decouple the contributions of Au-S bonding from lysine-driven electrostatics. These sequential and cooperative studies underscore that electrostatic and covalent factors are not independent but part of an interconnected process, thereby underscoring the need to consider local charge environments in protein engineering for nanoparticle conjugation. Electrostatics ; Lysine ; Cysteine ; Static electricity ; Covalent bond ; Colloidal gold ; Electrostatic interaction ; Protein–protein interaction Nanocluster Synthesis and Applications ; Advanced Biosensing Techniques and Applications ; Gold and Silver Nanoparticles Synthesis and Applications
Authors: Sanvidha A. Mane | Ravindra L. Bakal | Pooja R. Hatwar
Source: International Journal of Pharmaceutical Sciences Review and Research (article, 2025-06-01)
Field: Materials Science — Biomaterials

Advancements in Targeted Drug Delivery Systems Targeted drug delivery systems (TDDS) have revolutionized the field of pharmaceuticals by providing a precise and efficient way to deliver medications to specific sites of action.This approach reduces adverse effects, enhances therapeutic efficacy, and improves patient compliance.TDDS utilizes carriers such as liposomes, nanoparticles, and microspheres to deliver drugs to targeted tissues or cells.Active and passive targeting strategies enable the delivery of therapeutic agents to specific sites, increasing treatment outcomes.This review highlights the types, advantages, and applications of TDDS in various diseases, including cancer, breast cancer, heart disease, infectious diseases, and chronic diseases.Recent advancements in nanotechnology, precision medicine, and innovative delivery mechanisms are also discussed.The benefits of TDDS include increased efficacy, decreased toxicity, and improved patient outcomes.However, challenges such as resistance, limited availability, regulatory barriers, and high costs need to be addressed.Future perspectives include integrating TDDS with emerging technologies like artificial intelligence and machine learning to create more effective and personalized drug delivery systems.Overall, TDDS holds great promise for improving treatment outcomes and patient quality of life. Drug delivery ; Drug ; Targeted drug delivery ; Medicine ; Computer science ; Pharmacology ; Nanotechnology ; Materials science Nanoparticle-Based Drug Delivery ; Advanced Drug Delivery Systems ; Graphene and Nanomaterials Applications
Authors: Haorong Wu | Yong Lin | Wenbin Zhang | Zikang Cao | Chunlin Zhao | Zhaoyi Yin | Yue Lu | Liju Liu | Hu Ding
Source: Micromachines (article, 2025-11-29)
Field: Chemistry — Analytical Chemistry

Design of a Portable Nondestructive Instrument for Apple Watercore Grade Classification Based on 1DQCNN and Vis/NIR Spectroscopy To address the challenge of nondestructively identifying watercore disease in apples during growth and maturation, a portable device was developed for real-time grading of apple watercore using visible/near-infrared (Vis/NIR) spectroscopy combined with a one-dimensional quadratic convolutional neural network (1DQCNN). The instrument enables rapid, nondestructive, and accurate detection of apple watercore grades. The AI-OX2000-13 micro-spectrometer is used as the core data acquisition unit, and an ARM processing system is built with the STM32F103VET6 as the main control chip. A 4G wireless communication module enables efficient and stable data transmission between the processor and computer, meeting the real-time detection needs of apple watercore content in orchard environments. To improve the scientific and accurate classification of watercore grades, this paper combines the BiSeNet and RIFE algorithms to construct a 3D model of apple watercore, allowing quantification of the degree of watercore and classification into four levels. Based on this, quadratic convolution operations are incorporated into a one-dimensional convolutional neural network (1DCNN), leading to the development of the 1D quadratic convolutional neural network (1DQCNN) model for watercore grade classification. Experimental results indicate that the model achieves a classification accuracy of 98.05%, outperforming traditional methods and conventional CNN models. The designed portable instrument demonstrates excellent accuracy and practicality in real-world applications. Convolutional neural network ; Artificial neural network ; Orchard ; Convolution (computer science) ; Pattern recognition (psychology) ; Quadratic classifier Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Biosensors and Analytical Detection
Authors: Martin Girard
Source: Biophysical Journal (article, 2025-02-01)
Field: Materials Science — Polymers and Plastics

BPS2025 - Low complexity polymers—relations between microphase, polymer collapse transition, and sequence Sequence (biology) ; Polymer ; Polymer science ; Transition (genetics) ; Materials science ; Chemistry ; Composite material ; Biochemistry Polymer crystallization and properties ; Machine Learning in Materials Science
Authors: Yaqiong Su | Boyang Li
Source: Research Square (preprint, 2025-09-04)
Field: Materials Science — Materials Chemistry

Long-Range Oxygen Spillover at the Cu/CeO2 Interface Spillover effect ; Range (aeronautics) ; Oxygen ; Interface (matter) ; Materials science ; Environmental science ; Chemistry ; Economics ; Composite material ; Macroeconomics Catalytic Processes in Materials Science ; Electronic and Structural Properties of Oxides ; Semiconductor materials and devices
Authors: Zhongbin Liu | Fengkui Xiong | Guangzhong Hu | Hong‐Wei Xiao | Jia Zheng
Source: Foods (article, 2025-11-24)
Field: Materials Science — Materials Chemistry

Research on High-Efficiency and No-Additive Physical Aging Equipment and Process of Baijiu Production Based on High-Speed Jet Catalysis Newly brewed Baijiu often contains harmful substances such as mercaptan and methanol, which are spicy and harmful to health. At present, this is mainly solved by long-term cellaring, but this is faced with some problems such as a long cycle, high cost, high fire hazards and so on. Therefore, based on the principle of liquid jet cavitation explosion catalyzing the heterogeneous association of Baijiu molecules, this paper first developed the physical aging process and equipment without radiation and additives. Then, based on the traditional computational fluid dynamics (CFD) model of high-speed jet simulation, an N-CFD model which can accurately simulate the cavitation explosion catalytic process of high-speed jet of Baijiu was established by optimizing the three sub models of conservation, turbulence and VOF. Finally, the N-CFD model was used to optimize the distance between the nozzle and the reaction chamber wall of the new aging equipment. Through the 15 min aging experiment on 100 L Baijiu, the methanol concentration of Baijiu decreased by 68.14 ± 2.25%, and the concentration of ethyl acetate, ethyl lactate and ethyl palmitate increased from 6.105 ± 0.014, 3.498 ± 0.015 mg/L and 0.621 ± 0.010 mg/L to 6.332 ± 0.016, 4.868 ± 0.012 mg/L and 0.681 ± 0.008 mg/L. The results show that the aging technology equipment can adjust the self-coupling characteristics and dynamic characteristics of various molecules in Baijiu through high-speed jet, and catalyze the alternating phase transition and association of various molecules. Finally, the goal of high-efficiency and healthy aging Baijiu without additives was achieved, which helps the rapid and healthy development of the Baijiu brewing industry. Nozzle ; Catalysis ; Jet (fluid) ; Methanol ; Process (computing) ; Turbulence ; Cavitation ; Accelerated aging Ultrasound and Cavitation Phenomena ; Combustion and flame dynamics ; Cavitation Phenomena in Pumps
Authors: Ambikadevi | Avinash Singh | Dhirendra Singh Kshatri
Source: International Journal of Scientific Research in Science and Technology (article, 2025-12-31)
Field: Materials Science — Materials Chemistry

Machine Learning Approaches for Predicting Electronic Properties of Oxide Thin Films Electronic properties of oxide thin films including band gap, carrier concentration, electrical conductivity, mobility, resistivity, and dielectric constant govern their functionality in electronics, spintronics, optoelectronics, and energy devices. However, extracting these properties experimentally or through first-principles simulations is often time-consuming, resource-intensive, and constrained by limited parameter exploration. This study presents a robust machine learning (ML) framework for accurate prediction of electronic properties of oxide thin films using a multi-modal dataset comprising experimental measurements, computational descriptors, and physics-informed engineered features. Multiple ML models were developed, including Random Forests (RF), Gradient Boosting, Artificial Neural Networks (ANN), Support Vector Regression (SVR), Gaussian Process Regression (GPR), and Physics-Informed Neural Networks (PINNs). PINNs achieved superior band-gap prediction (MAE = 0.06 eV, R² = 0.97), while ANN demonstrated the best performance for mobility prediction (RMSE = 1.12 cm²/V·s). Carrier concentration and conductivity predictions showed strong accuracy using ensemble models (R² &gt; 0.93). SHAP-based interpretability revealed that electronic correlations were strongly influenced by strain-induced structural distortions, oxygen partial pressure, cation valence states, surface/interface defect density, and film thickness. Validation against independent DFT calculations and experimental datasets confirmed high generalizability across perovskite, spinel, and binary oxide thin films. The proposed ML framework offers a powerful tool for accelerating electronic property prediction, enabling dynamic optimization of deposition conditions and supporting inverse design of thin films with targeted electronic performance. Thin film ; Kriging ; Artificial neural network ; Dielectric ; Support vector machine ; Gaussian process ; Interpretability ; Random forest ; Oxide Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Heusler alloys: electronic and magnetic properties
Authors: Feng Mei | Sun Tingting | Wang Yan
Source: Journal of International Education and Development (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Curriculum Construction of &amp;quot;Soft Matter and Molecular Simulation&amp;quot; Integrating Computational Physics and Artificial Intelligence in Graduate Education Molecular simulation plays an increasingly important role in the study of soft matter and biomolecular systems, driven in part by the growing use of computational physics and artificial intelligence (AI). In response to the need for interdisciplinary training at the graduate level, a new course entitled Soft Matter and Molecular Simulation was developed and offered to physics graduate students at our university. The course is organized around statistical physics and soft matter theory, and systematically introduces molecular dynamics simulations, free energy perturbation methods, and selected AI-assisted approaches for molecular modeling and protein-related studies. A progressive teaching framework is employed to link theoretical instruction with hands-on computational practice and research-oriented examples. Based on classroom implementation and student learning activities, the course has been found to support the development of fundamental modeling skills and to help students gain a clearer understanding of interdisciplinary research workflows. The curriculum design and teaching practices described in this paper also reflect a broader attempt to align graduate physics education with emerging computational and intelligent research paradigms. Curriculum ; Graduate students ; Applications of artificial intelligence ; Graduate education ; Physics education Machine Learning in Materials Science ; Genetics, Bioinformatics, and Biomedical Research ; Nanotechnology research and applications
Authors: Dongqing Lin | Yulong Duan | Suraya Kazi | Magnus P. Jonsson
Source: Advanced Materials (article, 2025-12-22)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Dynamic Optical Lattices Through Conducting Polymer‐Gated Confinement Organic metasurfaces based on conducting polymers are emerging as new opportunities for active nano-optic devices, but their tunable plasmonic resonances are limited to infrared wavelengths (≥960 nm) and mainly with low quality-factors (Q < 2). Here we propose the concept of a "conducting polymer gate" to endow organic metasurfaces with switchable high-Q nonlocal resonances within the visible/NIR-I regime (640-950 nm). Poly(3,4-ethylenedioxythiophene) (PEDOT) is integrated into organic dielectric nanocylinder lattices and serves as a gate that controls optical leakage from the nanocylinders to the substrate. The quasi-metallic PEDOT blocks such leakage channel and induces light confinement within the dielectric nanocylinders despite their low refractive index (n<sub>die</sub> < 1.7). This facilitates the generation of Mie collective lattice resonances (CLRs) at the air superstrate with Q-factor reaching 25, which is ∼16 times higher than previously reported organic metasurfaces near this spectral regime. Converting the PEDOT into its dielectric state opens the leakage channel and eliminates the CLRs, which can be reversibly switched by redox reactions. The concept of conducting polymer-gated optical confinement in low-index nanostructures provides new routes for intelligent nano-optics including dynamic nonlocal metasurfaces. Plasmon ; Dielectric ; PEDOT:PSS ; Leakage (economics) ; Nanostructure ; Wavelength ; Polymer Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Synthesis and Properties of Aromatic Compounds
Authors: Eszter Virágh | Bálint Kiss | Dániel András Drexler
Source: Acta Polytechnica Hungarica (article, 2025-01-01)
Field: Chemistry — Physical and Theoretical Chemistry

Motion Planning for Chemical Systems: Consecutive Reactions Motion planning is widely used for analyzing and controlling nonlinear systems, such as mobile robot navigation and robotic automation.When a mathematical model of the system is available, motion planning allows for system analysis and control without the need for sampling or physical intervention, thereby avoiding any disturbance to the behavior of the system.This study explores the application of motion planning methods to chemical reaction systems.Specifically, it demonstrates how to define desired trajectories for consecutive reactions using the parametrized function class method.The planning assumes isothermal conditions, which are modeled by setting the temperature as a constant time function.Under this assumption, the system describing consecutive reactions becomes linear.Starting from the kinetic model, we derive the time-dependent behavior of the state variables.Based on these results, a controller is designed for a two-step consecutive reaction.The state of the system can be determined at any time from the computed time functions, and the future behavior of the system can be accurately predicted.This approach helps prevent accidents and undesired outcomes by ensuring the reaction proceeds along the planned trajectory. Computer science ; Motion (physics) ; Artificial intelligence Various Chemistry Research Topics
Authors: Yun Xiang | Liang Tian | Yuanpeng Bu | Shangwen Cai | Jingjie Guo | Zhongjing Su | Jinxuan Hu | Chang Cai | Bin Wang | Zhijuan Feng | Guwen Zhang | Na Liu‎ | Yaming Gong
Source: Agronomy (article, 2025-07-12)
Field: Chemistry — Analytical Chemistry

Pre-Symptomatic Detection of Nicosulfuron Phytotoxicity in Vegetable Soybeans via Hyperspectral Imaging and ResNet-18 Herbicide phytotoxicity represented a critical constraint on crop safety in soybean–corn intercropping systems, where early detection of herbicide stress is essential for implementing timely mitigation strategies to preserve yield potential. Current methodologies lack rapid, non-invasive approaches for early-stage prediction of herbicide-induced stress. To develop and validate a spectral-feature-based prediction model for herbicide concentration classification, we conducted a controlled experiment exposing three-leaf-stage vegetable soybean (Glycine max L.) seedlings to aqueous solutions containing three concentrations of nicosulfuron herbicide (0.5, 1, and 2 mL/L) alongside a water control. Hyperspectral imaging of randomly selected seedling leaves was systematically performed at 1, 3, 5, and 7 days post-treatment. We developed predictive models for herbicide phytotoxicity through advanced machine learning and deep learning frameworks. Key findings revealed that the ResNet-18 deep learning model achieved exceptional classification performance when analyzing the 386–1004 nm spectral range at day 7 post-treatment: 100% accuracy in binary classification (herbicide-treated vs. water control), 93.02% accuracy in three-class differentiation (water control, low/high concentration), and 86.53% accuracy in four-class discrimination across specific concentration gradients (0, 0.5, 1, 2 mL/L). Spectral analysis identified significant reflectance alterations between 518 and 690 nm through normalized reflectance and first-derivative transformations. Subsequent model optimization using this diagnostic spectral subrange maintained 100% binary classification accuracy while achieving 94.12% and 82.11% accuracy for three- and four-class recognition tasks, respectively. This investigation demonstrated the synergistic potential of hyperspectral imaging and deep learning for early herbicide stress detection in vegetable soybeans. Our findings established a novel methodological framework for pre-symptomatic stress diagnostics while demonstrating the technical feasibility of employing targeted spectral regions (518–690 nm) in field-ready real-time crop surveillance systems. Furthermore, these innovations offer significant potential for advancing precision agriculture in intercropping systems, specifically through refined herbicide application protocols and yield preservation via early-stage phytotoxicity mitigation. Hyperspectral imaging ; Phytotoxicity ; Environmental science ; Remote sensing ; Agronomy ; Biology ; Geology Spectroscopy and Chemometric Analyses