jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Bayan Kurbanova | Yuriy Sizyuk | Ansar Aryngazin | Zhanna Alsar | A. Hassanein | З. Инсепов
Source: Journal of Nuclear Engineering (article, 2025-11-30)
Field: Materials Science — Materials Chemistry

Thermal Hydraulics and Solid Mechanics Multiphysics Safety Analysis of a Heavy Water Reactor with Thorium-Based Fuel Growing environmental awareness has renewed interest in thorium as a nuclear fuel, underscoring the need for further studies to evaluate how reactors perform when conventional fuels are replaced with thorium-based alternatives. In this study, thermal hydraulics and solid mechanics computations were simulated using COMSOL multiphysics to investigate the safe operating conditions of a heavy water reactor with thorium-based fuel. The thermo-mechanical analysis of the fuel rod under transient heating conditions provides critical insights into strain, displacement, stress, and coolant flow behavior at elevated volumetric heat sources. After 3 s of heating, the strain distribution in the fuel exhibits a high-strain core surrounded by a low-strain rim, with peak volumetric strain increasing nearly linearly from 0.006 to 0.014 as heat generation rises. Displacement profiles confirm that radial deformation is concentrated at the outer surface, while axial elongation remains uniform and scales systematically with power. The resulting von Mises stress fields show maxima at the outer surface, increasing from ~0.06 to 0.15 GPa at the centerline with higher heat input but remaining within structural safety margins. Cladding simulations demonstrate nearly uniform axial expansion, with displacements increasing from ~0.012 mm to 0.03 mm across the investigated power range, and average strain remains negligible (≈10−4), while mean stresses increase moderately yet stay well below the yield strength of zirconium alloys, confirming safe elastic behavior. Hydrodynamic analysis shows that coolant velocity decreases smoothly along the axial direction but maintains stability, with only minor reductions under increased heat sources. Overall, the coupled thermo-mechanical and fluid-dynamic results confirm that both the fuel and cladding remain structurally stable under the studied conditions. By using COMSOL’s multiphysics capabilities, and unlike most legacy codes optimized for uranium-based fuel, this work is designed to easily incorporate non-traditional fuels such as thorium-based systems, including user-defined material properties, temperature-dependent thermal polynomial formulas, and mechanical response. Multiphysics ; Thermal hydraulics ; Coolant ; Cladding (metalworking) ; Deformation (meteorology) ; Pressurized water reactor ; Heat transfer ; Nuclear reactor core ; Light-water reactor ; Stress (linguistics) Nuclear Materials and Properties ; Nuclear reactor physics and engineering ; Nuclear and radioactivity studies
Authors: Evangelia K. Tripoliti | Andrew R. Thomson | D. P. Dobson | P. F. Schofield | I. G. Wood
Source: Physics and Chemistry of Minerals (article, 2025-04-02)
Field: Materials Science — Electronic, Optical and Magnetic Materials

The thermal expansion of the clinopyroxene and garnet polymorphs of Na2MgSi5O12 determined by X-ray powder diffraction Abstract The temperature dependence of the unit-cell parameters of synthetic Na(Mg 0.5 Si 0.5 )Si 2 O 6 clinopyroxene and Na 2 MgSi 5 O 12 garnet were measured, at atmospheric pressure, using X-ray powder diffraction, from 40 K up to their decomposition temperatures (673 K and 793 K respectively). At 300 K the cell parameters were found to be: (i) a = 9.4073(3) Å, b = 8.6487(3) Å, c = 5.2685(2) Å, β = 108.113(2)°, V = 407.41(3) Å 3 and ρ = 3.286 g/cm 3 , for Na(Mg 0.5 Si 0.5 )Si 2 O 6 pyroxene in space-group P2/n with Z = 4 and (ii) a = 11.4155(7) Å, V = 1487.61(5) Å 3 and ρ = 3.561 g/cm 3 for Na 2 MgSi 5 O 12 garnet in space-group Ia $$\overline{3}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mover> <mml:mn>3</mml:mn> <mml:mo>¯</mml:mo> </mml:mover> </mml:math> d with Z = 8. The thermal expansion tensors have been calculated and analysis of the lattice parameters in terms of a Grüneisen-Debye model is used to estimate the Debye temperature and the incompressibility of the two materials at ambient pressure. Chemistry ; Powder diffraction ; Thermal expansion ; Crystallography ; X-ray ; Diffraction ; X-ray crystallography ; Thermodynamics ; Optics ; Physics Crystal Structures and Properties ; X-ray Diffraction in Crystallography ; Glass properties and applications
Authors: Ufuk Kortağ
Source: Gazi Journal of Engineering Sciences (article, 2025-12-31)
Field: Materials Science — Materials Chemistry

PREDICTING STRUCTURAL DYNAMICS CHARACTERISTICS OF A TURBINE BLADE USING ARTIFICIAL INTELLIGENCE In high-performance engineering systems such as turbomachinery and aerospace structures, accurately predicting structural dynamic properties like natural frequency is critical for avoiding resonance, ensuring fatigue life, and maintaining rotodynamic stability.While finite element analysis (FEA) offers high accuracy, it becomes computationally expensive when repeated across a broad design space defined by geometric variations.To address this, a deep learning-based surrogate model was developed to predict natural frequencies from basic geometric parameters such as length, thickness, and angle of attack.A dataset of 21 geometrically distinct configurations was created using coarse-mesh FEA simulations, each taking approximately 30 seconds.Twenty samples were used for training a deep neural network (DNN), with one sample reserved for validation.The trained model achieved prediction times around 1 millisecond and relative errors of 1.1-3.3%within the training range and 0.1-16.5% outside it.This represents a drastic reduction in computational cost while maintaining acceptable accuracy.The proposed method enables rapid design iterations, sensitivity analysis, and early-stage decision-making in structural dynamics.It offers a practical alternative to traditional FEA for scenarios requiring fast and repeated evaluations across varying geometries. Turbine blade ; Turbine ; Dynamics (music) ; Blade (archaeology) ; Artificial neural network Material Properties and Failure Mechanisms ; Mechanical Failure Analysis and Simulation ; Mechanical Engineering Research and Applications
Authors: Yeliz Durgun | Mahmut Durgun
Source: Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi (article, 2025-01-15)
Field: Chemistry — Analytical Chemistry

Hot-Air Drying Using Near UV Visible NIR Radiation Spectroscopy and Edge Computing Control System : A Study On Apple Slices Near-UV, Visible Light, and Near-Infrared Radiation are some of the light wavelength regions that are of great interest for energy, agriculture, medical, and food industry research. The use of light spectrum for non-destructive, real-time sensing for imaging, food quality, and safety evaluation is becoming increasingly important in all these fields. Edge computing, which enables real-time monitoring of devices and control using machine learning methods, is necessary to improve system stability, minimize errors and develop tools that facilitate robotic intervention. In this study, the effect and performance of a drying system using Near-UV-vis-NIR radiation measurement in food drying using edge computing is presented. The system comprises three multi-spectral sensors that allow 18 different measurements. Sensors are also placed to measure the weight of the objects, temperature, and humidity inside the cabin. The data acquired is processed in real-time using a microcontroller (Arduino Nano 33 BLE) that can perform machine learning algorithms and control the cabin. Edge computing enables data processing and analytic operations to be performed on the device, thus providing real-time results and control operations. In this study, the change in radiation levels and the effect on drying quality during the drying process of apple slices are investigated. The results show that measurements performed using edge computing technology can effectively be performed during the drying process of apple slices. Spectroscopy ; Ultraviolet visible spectroscopy ; Radiation ; Materials science ; Enhanced Data Rates for GSM Evolution ; Optics ; Computer science ; Chemistry ; Physics ; Artificial intelligence Spectroscopy and Chemometric Analyses ; Food Drying and Modeling ; Food Supply Chain Traceability
Authors: Matthew Thelen | Sebastian A. Flores | James A. Kelley | Brianna R. Swank | Jill K. Winkler‐Moser | Matthew Fhaner
Source: Journal of the American Oil Chemists Society (article, 2025-06-24)
Field: Chemistry — Organic Chemistry

Toward Rapid Analysis of Unsaturated Fatty Acid Oxidation in Edible Oils via Square‐Wave Voltammetry ABSTRACT Square‐wave voltammetry (SWV) was investigated as a potential analytical tool for quantitation of unsaturated fatty acid oxidation in edible oils exposed to high heat. A traditional linear regression calibration curve and a novel single‐point calibration method were used to quantitate linoleic acid in oxidized oil samples. These methods were compared with a standard method for fatty acid analysis via gas chromatography flame ionization detection (GC‐FID). Both GC‐FID and SWV methods were able to detect a decrease in linoleic acid concentration for heated oil samples. SWV methods routinely calculated higher linoleic acid concentrations compared with GC‐FID analysis. A novel finding is that SWV oxidation peak intensities, which are associated with electroactive compounds, decrease in intensity over time for peaks below 1.0 V and increase for peaks above this voltage. These findings represent a step forward in electrochemical characterization of heated oils in addition to further advancing opportunities for electrochemical oil quality screening. Edible oil ; Chemistry ; Fatty acid ; Voltammetry ; Organic chemistry ; Food science ; Square wave ; Electrochemistry ; Electrode Edible Oils Quality and Analysis ; Spectroscopy and Chemometric Analyses ; Electrochemical sensors and biosensors
Authors: Shuo-Lin Weng | Priyesh Mohanty | Jeetain Mittal
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-05-01)
Field: Materials Science — Materials Chemistry

Elucidation of the molecular interaction network underlying full-length FUS conformational transitions and its phase separation using atomistic simulations Fused in Sarcoma (FUS), a multi-domain RNA-binding protein, orchestrates cellular functions through liquid-liquid phase separation (LLPS), which promotes the formation of biomolecular condensates <i>in vivo</i> . While crucial to understanding cellular processes, an atomic-level view of the interdomain interactions associated with full-length (FL) FUS LLPS remains challenging due to its low solubility <i>in vitro</i> . Here, using all-atom (AA) molecular dynamics (MD) simulations, we examined the conformational dynamics and interdomain interactions of FL FUS in both dilute and condensed phases. Comparing two modern force fields (FFs) - Amber ff03ws and ff99SBws-STQ, we found that monomer simulation ensembles generated by both FFs exhibited qualitatively similar intramolecular interaction profiles dominated by intrinsically disordered regions (IDRs). While the two folded domains minimally participated in interdomain interactions, their stabilities significantly influenced the chain dimension and led to discrepancies compared to experimental data for both FFs. We observed that the Amber ff99SBws-STQ coupled with bond parameters adopted from the Zinc Amber force field (ZAFF) maintained stable folded domains and improved estimates of the chain dimensions. Finally, a microsecond-timescale simulation of FL FUS condensate revealed an extensive network of electrostatic interactions which are strongly correlated with those that modulate the dilute phase chain dimensions. Overall, insights from our all-atom simulations illuminate the interplay between folded domain stability and IDR interactions in modulating protein conformation and phase separation, advancing our understanding of FUS-related pathologies at the molecular level and aiding in the development of new therapeutics. Molecular dynamics ; Chemical physics ; Statistical physics ; Phase (matter) ; Chemistry ; Materials science ; Computational chemistry ; Physics Machine Learning in Materials Science ; RNA Research and Splicing ; Protein Structure and Dynamics
Authors: Tirupathi Mannala
Source: Journal of Drug Delivery and Therapeutics (article, 2025-10-15)
Field: Materials Science — Materials Chemistry

Hot Melt Extrusion: A Viable Option for Implementing Continuous Manufacturing in the Pharmaceutical Industry In recent years, hot melt extrusion (HME) has been most widely investigated for developing pharmaceutical medications. HME is a single-step manufacturing process and is suitable for the extrusion of drug-loaded filaments, films, or patches, and granules. The filaments can be processed as pellets or can be processed into tablets or capsules. The HME can be coupled with various downstream processing equipments and process analytical technology (PAT) tools for transforming into a continuous manufacturing line. Establishing a continuous manufacturing line will ensure product quality and will benefit both the industries and the patient population. In fact, the HME can also be paired with additive manufacturing platforms such as fused deposition modeling (FDM) for the fabrication of on-demand and patient-centric medications. Even complex medications can be easily manufactured using an additive manufacturing approach. Despite various advantages of HME, a few limitations, such as the availability of suitable materials and a more in-depth understanding of the process, is still warranted. However, compared with other manufacturing approaches, the HME-based continuous manufacturing is a viable option for the pharmaceutical industry. Keywords: hot melt extrusion, solubility enhancement, continuous manufacturing, additive manufacturing, 3D printing Pharmaceutical manufacturing ; Extrusion ; Manufacturing process ; Fused deposition modeling ; Advanced manufacturing ; 3D printing ; Process (computing) ; Quality (philosophy) Crystallization and Solubility Studies ; Drug Solubulity and Delivery Systems ; Protein purification and stability
Authors: Zehong Zhang | Mei Wu | Max Ruwolt | Ying Zhu | Pin‐Lian Jiang | Diogo Borges Lima | Fan Liu
Source: Analytical Chemistry (article, 2025-08-27)
Field: Chemistry — Spectroscopy

AIRPred: A Deep Learning Model Predictor for Peptide Intensity Ratios in Cross-Linking Mass Spectrometry Improves Cross-Link Spectrum Matching Cross-linking mass spectrometry (XL-MS) is a powerful tool in structural proteomics, offering insights into protein conformations, interactions and dynamics by linking spatially proximal residues. However, current cross-linked spectrum match (CSM) scoring methods rely heavily on mass-to-charge ratio (<i>m</i>/<i>z</i>) comparisons, often neglecting fragment ion intensity information, which limits their ability to accurately distinguish true CSMs from false positives. To overcome this limitation, we present AIRPred, a deep learning model that predicts intensity ratios between cross-linked peptide pairs to improve CSM identification. AIRPred employs convolutional neural network (CNN) blocks to capture peptide fragmentation patterns and an attention layer to model peptide interactions. Our results show that intensity ratios remain consistent across experiments and can reliably differentiate true CSMs from random mismatches. In external validation, AIRPred outperformed traditional methods, demonstrating high accuracy in predicting intensity ratios. This model significantly enhances XL-MS analysis by leveraging intensity data for more accurate peptide identification. Chemistry ; Mass spectrometry ; Matching (statistics) ; Cross-link ; Link (geometry) ; Peptide ; Chromatography ; Analytical Chemistry (journal) ; Statistics ; Biochemistry ; Computer network ; Organic chemistry Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Goswami, Rohit
Source: ArXiv.org (article, 2025-12-08)
Field: Materials Science — Materials Chemistry

Two-dimensional RMSD projections for reaction path visualization and validation Transition state or minimum energy path finding methods constitute a routine component of the computational chemistry toolkit. Standard analysis involves trajectories conventionally plotted in terms of the relative energy to the initial state against a cumulative displacement variable, or the image number. These dimensional reductions obscure structural rearrangements in high dimensions and are often history dependent. This precludes the ability to compare optimization histories of different methods beyond the number of calculations, time taken, and final saddle geometry. We present a method mapping trajectories onto a two-dimensional projection defined by a permutation corrected root mean square deviation from the reactant and product configurations. Energy is represented as an interpolated color-mapped surface constructed from all optimization steps using a gradient-enhanced Gaussian Process with the inverse multiquadric kernel, whose posterior variance contours delineate data-supported regions from extrapolated ones. A rotated coordinate frame decomposes the RMSD plane into reaction progress and orthogonal distance. We show the utility of the framework on a cycloaddition reaction, where a machine-learned potential saddle and density functional theory reference lie on comparable energy contours despite geometric displacements, along with the ratification of the visualization for more complex reactions, a Grignard rearrangement, and a conrotatory bicyclobutane ring opening. Visualization ; Projection (relational algebra) ; Gaussian ; Energy minimization ; Path (computing) ; Permutation (music) ; Intersection (aeronautics) ; Saddle point ; Piecewise Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Marta Tornago
Source: EPJ Web of Conferences (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Operating a PbWO<sub>4</sub> EM calorimeter in a harsh radiation environment The CMS Electromagnetic Calorimeter (ECAL) is the largest homogeneous calorimeter operating in a high energy physics experiment. During the course of the LHC Run 1, Run 2, and Run 3, ECAL has made essential contributions to the CMS physics program by precisely measuring the energy, position, and time of arrival of photons and electrons, and of hadronic jets. Among the masterpieces of physics results achieved with its excellent energy resolution is the observation of the Higgs boson in its two photon decay in 2012, and the precise measurement of its properties. Operating a lead-tungstate scintillating calorimeter to such high precision and in a harsh radiation environment requires full control of the environmental conditions, such as temperature and bias voltages of the photodetectors, and a continuous correction of the crystal response changes. This paper focuses on the challenges faced over the recent Run 3 years – experiencing the largest instantaneous luminosity up to now – and describes the calibration techniques developed and the achieved results, also including the evolution of the monitoring system in preparation of the High Luminosity phase of the LHC. Calorimeter (particle physics) ; Radiation ; Nuclear engineering ; Environmental science ; Nuclear physics ; Physics ; Engineering physics ; Engineering ; Optics ; Detector Luminescence Properties of Advanced Materials ; Solid-state spectroscopy and crystallography ; Radiation Detection and Scintillator Technologies
Authors: Juerg Leuthold | Ping Ma | Xinzhi Zhang | Wolfgang Heni | Nikolaus Floery | Tobias Blatter | Yannik Horst | T. Watanabe | Chaohui Wang | Teng Wu | Bo Li | Maorong Sun | Zixiao Wang | Killian Keller | Maurizio Burla | Alexandros Emboras | Lukáš Novotný | Delwin L. Elder | Larry R. Dalton
Source: Research Square (preprint, 2025-06-23)
Field: Materials Science — Materials Chemistry

Compact, Low-Loss, High-Speed Graphene Hybrid Modulator Graphene ; Materials science ; Optoelectronics ; Nanotechnology Graphene research and applications ; Quantum-Dot Cellular Automata ; Molecular Junctions and Nanostructures
Authors: Carlos Vonessen | Charles B. Harris | Miruna Cretu | Píetro Lió
Source: ArXiv.org (preprint, 2025-07-01)
Field: Materials Science — Materials Chemistry

TABASCO: A Fast, Simplified Model for Molecular Generation with Improved Physical Quality State-of-the-art models for 3D molecular generation are based on significant inductive biases, SE(3), permutation equivariance to respect symmetry and graph message-passing networks to capture local chemistry, yet the generated molecules still struggle with physical plausibility. We introduce TABASCO which relaxes these assumptions: The model has a standard non-equivariant transformer architecture, treats atoms in a molecule as sequences and reconstructs bonds deterministically after generation. The absence of equivariant layers and message passing allows us to significantly simplify the model architecture and scale data throughput. On the GEOM-Drugs benchmark TABASCO achieves state-of-the-art PoseBusters validity and delivers inference roughly 10x faster than the strongest baseline, while exhibiting emergent rotational equivariance despite symmetry not being hard-coded. Our work offers a blueprint for training minimalist, high-throughput generative models suited to specialised tasks such as structure- and pharmacophore-based drug design. We provide a link to our implementation at github.com/carlosinator/tabasco. Inference ; Equivariant map ; Permutation (music) ; Transformer ; Graph ; Representation (politics) ; Symmetry (geometry) ; Benchmark (surveying) ; Generative model Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Zhi Gong | Zuqi Tang | Abdelkader Bénabou
Source: IET Electric Power Applications (article, 2025-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Physics‐Informed Neural Network for Magnetization Distribution Estimation ABSTRACT Accurately estimating the magnetization distribution in permanent magnets is critical for optimising their performance in various applications, such as electric motors, generators and magnetic sensors, where precise magnetic field control is essential. A physics‐informed neural network (PINN) is demonstrated to solve the inverse problem of magnetization distribution within the volume of permanent magnets. A neural network is constructed to model the spatially dependent magnetization in the magnet. The physical model, based on the Biot–Savart law, is integrated into the PINN framework. The discrepancy between the magnetic field calculated by the physical model and the externally observed one is used to guide the network training, exhibiting both the model‐based and data‐driven characteristics of the PINN. The accuracy and robustness of the proposed PINN are demonstrated through numerical experiments with both uniform and nonuniform magnetization scenarios, as well as both noise‐free and noisy observation data. This study provides a new approach for solving magnetization distribution estimation problems, benefiting the development of high‐quality permanent magnets for electrical engineering applications. Artificial neural network ; Magnetization ; Distribution (mathematics) ; Statistical physics ; Estimation ; Physics ; Condensed matter physics ; Computer science ; Artificial intelligence ; Mathematics ; Engineering ; Mathematical analysis ; Magnetic field ; Quantum mechanics ; Systems engineering Magnetic Properties and Applications ; Model Reduction and Neural Networks ; Non-Destructive Testing Techniques