jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Mario Laudato
Source: ArXiv.org (preprint, 2025-12-16)
Field: Materials Science — Materials Chemistry

Neural-Network Closures for Complex-Shaped Particles in the Force-Coupling Method A data-driven surrogate framework to accelerate particle-resolved modelling of quasi-dilute suspensions of rigid, non-spherical particles in Stokes flow is introduced. A regularized-Stokeslet boundary element method (BEM) is implemented to compute hydrodynamic responses in canonical linear flows, focusing on the particle stresslet and angular velocity for spheroids, and additionally the chiral thrust for helicoidal particles. For spheroids, the BEM solver is validated against available analytical benchmarks (Faxen-type relations for the stresslet and Jeffery's theory for rotation), and parameter choices for surface discretization and regularization are selected through systematic convergence studies. For helicoidal particles, where no analytical solutions exist, accuracy is quantified via Richardson-style self-convergence, complemented by tests of linearity, frame objectivity, and chirality-dependent symmetries. The resulting datasets are used to train a neural-operator surrogate that maps local flow descriptors and particle configuration to the corresponding stresslet, rotation, and thrust at negligible evaluation cost. Across independent test sets spanning random orientations and flow types, the surrogate achieves median relative errors below 1% for the deviatoric stresslet (95th percentile below 3%) and comparable accuracy for angular velocity and thrust. The combination of validated BEM generation and fast inference provides a practical route to coupling complex particle shapes into mesoscale solvers such as the force-coupling method, enabling large-ensemble studies of microstructure and suspension rheology. Solver ; Discretization ; Flow (mathematics) ; Regularization (linguistics) ; Discrete element method ; Stokes flow ; Convergence (economics) ; Angular velocity ; Coupling (piping) ; Mesoscale meteorology Block Copolymer Self-Assembly ; Model Reduction and Neural Networks ; Composite Material Mechanics
Authors: Marisa Asadian | Seth Croslow | Timothy J. Trinklein | Stanislav S. Rubakhin | Fan Lam | Jonathan V. Sweedler
Source: ChemRxiv (preprint, 2025-04-08)
Field: Chemistry — Spectroscopy

High-throughput Fluorescence-guided Sequential Single-Cell MALDI-ICC Mass Spectrometry Lipids are a diverse class of biomolecules essential for brain function, yet their cell-type-specific distributions remain underexplored, presenting significant knowledge gaps in the era of single-cell biology. Traditional bulk measurements provide valuable insights into lipid composition across brain regions but lack the resolution to distinguish lipid profiles at the single-cell level. To address this, we introduce fluorescence-guided sequential single-cell mass spectrometry (SSMS), an automated workflow combining untargeted lipid profiling with antibody-targeted protein detection via photocleavable mass tags, enabling neurolipidomic classification of cell types and cell states. We applied this approach to rodent hippocampal cells, analyzing over a thousand single cells and annotating more than a hundred lipid species with complementary LC-MS/MS measurements. Our findings show that phosphatidylcholine (PC) species are predominantly enriched in oligodendrocytes and neurons compared to astrocytes, while hexosylceramide (HexCer) species are differentially expressed across these cell types. Furthermore, neuronal state analysis revealed an enrichment of phosphatidylethanolamines (PEs) in presynaptic neurons, while non-presynaptic neurons exhibited a more diverse lipid composition, including HexCer, PC, sphingomyelin, triacyl-glycerol, and PE. Our findings provide new insights into brain lipid heterogeneity with cell-type specificity and extend ca-pabilities of next-generation single-cell mass spectrometry to map brain biochemistry. Mass spectrometry ; Fluorescence ; Throughput ; MALDI imaging ; Chemistry ; Mass spectrometry imaging ; Chromatography ; Matrix-assisted laser desorption/ionization ; Computer science ; Physics ; Organic chemistry ; Optics ; Desorption Mass Spectrometry Techniques and Applications ; Analytical chemistry methods development ; Ion-surface interactions and analysis
Authors: Zhengyang Wang | Jiahui Wang | Haowen Liang | Juntao Li
Source: Chinese Optics Letters (article, 2025-12-12)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Trans-reflective RGB achromatic metalens for wide field-of-view and high readability augmented reality imaging Augmented reality (AR) has garnered significant attention in recent years for its advanced display capabilities.Highperforming AR demands lightweight and integrated optical components.Metalenses offer a solution, as they can meet these requirements through their multifunctional and integrative potential.In this work, we propose a trans-reflective red-green-blue (RGB) achromatic AR metalens composed of titanium dioxide (TiO 2 ) nanopillars and a multiwavelength notch filter.The AR metalens achieves balanced focusing efficiencies for obliquely incident light, with a field of view exceeding 90, which ensures excellent readability in various scenarios for AR devices.Additionally, ambient light can pass efficiently through the metalens.This design functions both as an eyepiece and an optical combiner, demonstrating strong potential for AR applications. Achromatic lens ; RGB color model ; Augmented reality ; Readability ; Integral imaging Metamaterials and Metasurfaces Applications ; Advanced optical system design ; Transition Metal Oxide Nanomaterials
Authors: Talía Tene | Marco Guevara | Santiago López | Diego Mayorga | Alex Buñay Caizaguano | Juan Carlos Chimbo Pilco | Cristian Vacacela Gómez
Source: Photonics (article, 2025-11-04)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Optical Sensor Based on Carbon Nanomaterials for UGLU Detection This study develops an optical surface plasmon resonance (SPR) biosensing platform for non-invasive glucose detection directly in urine and examines how two-dimensional (2D) nanomaterials modulate sensing performance. Angular interrogation at 633 nm is modeled using a transfer-matrix framework for Au/Si3N4 stacks capped with graphene, semiconducting single-walled carbon nanotubes (s-SWCNTs), graphene oxide (GO), or reduced graphene oxide (rGO). Urine–glucose (UGLU) refractive indices spanning clinically relevant concentrations are used to evaluate resonance angle shifts and line-shape evolution. Sensor metrics—sensitivity, detection accuracy, figure of merit, quality factor, and limit of detection—are computed to compare architectures and identify thickness windows. Across all designs, increasing glucose concentration produces monotonic angle shifts, while the 2D overlayer governs dip depth and full width at half maximum. Graphene- and s-SWCNT-capped stacks yield the lowest limits of detection and the most favorable figures of merit, particularly at higher concentrations where narrowing improves the quality factor. rGO exhibits a thin, low-loss regime that provides large shifts with acceptable broadening, whereas thicker films degrade detectability; GO offers stable line shapes suited to metrological robustness. These results indicate that nanoscale optical engineering of 2D overlayers can meet practical detectability targets in urine without biochemical amplification, supporting compact, label-free platforms for routine glucose monitoring. Graphene ; Nanomaterials ; Overlayer ; Surface plasmon resonance ; Carbon nanotube ; Detection limit ; Biosensor ; Refractive index Gold and Silver Nanoparticles Synthesis and Applications ; Electrochemical sensors and biosensors ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Xiaohui Yang | Lei Yang | Xinhui Lu | Yu Guo
Source: ArXiv.org (preprint, 2025-06-05)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Spherical Phase Metalenses: Intrinsic Suppression of Spherical Aberration via Equiphase Surface Modulation Recent progress in large-scale metasurfaces requires phase profiles beyond traditional hyperbolic designs. We show hyperbolic phase distributions cause spherical aberration from mismatched light propagation geometry and unrealistic phase assumptions. By analyzing metalens fundamentals via isophase surfaces, we develop a spherical phase profile based on spherical wavefront theory. This method prevents spherical aberration, essential for wide-aperture metalenses. Simulations prove superior focusing: spherical phase reduces FWHM by 7.3% and increases peak intensity by 20.4% versus hyperbolic designs at 31.46 micron radius. Spherical phase maintains consistent focusing across radii, while hyperbolic phase shows strong correlation (R squared = 0.95) with aberration. We also propose a normal vector tracing metric to measure design aberrations. This work establishes a scalable framework for diffraction-limited metalenses. Spherical aberration ; Wavefront ; Phase (matter) ; Spherical model ; Metric (unit) ; Spherical wave ; Measure (data warehouse) ; Spherical shell ; Phase modulation Metamaterials and Metasurfaces Applications ; Orbital Angular Momentum in Optics ; Optical Coatings and Gratings
Authors: J. Maxwell | James Brock | C. Cuevas | Hai Dong | C. Keith | J. Pierce
Source: ArXiv.org (preprint, 2025-06-17)
Field: Chemistry — Spectroscopy

A New cw-NMR Q-meter for Dynamically Polarized Targets for Particle Physics Polarized solid targets produced via Dynamic Nuclear Polarization rely on Continuous-Wave Nuclear Magnetism Resonance measurements to accurately determine the degree of polarization of bulk samples polarized to nearly 100%. Since the late 1970's phase sensitive detection methods have been utilized to observe the magnetization of a sample as a small change in inductance under RF excitation near the Larmor frequency of the nuclear species of interest, using a device known as a Q-meter. Liverpool Q-meters, produced in the UK in the 80's and 90's, have been the workhorse devices for these targets for decades, however their age and scarcity has meant new systems are needed. We describe a Q-meter system designed and built at Jefferson Lab in the Liverpool style to have comparable electronic performance with several improvements to update and adapt the devices for modern use. Polarization (electrochemistry) ; Excitation ; Magnetism ; Magnetization ; Larmor precession ; Magnetic field ; Particle accelerator Advanced NMR Techniques and Applications ; Atomic and Subatomic Physics Research ; Nuclear Physics and Applications
Authors: Najoua ZEFFATE | Mohammed El Amine BECHAR | Jean-Marie Guyader | Nathalie Douet‐Guilbert | Marwa El Bouz | Marie‐Bérengère Troadec
Source: Research Square (preprint, 2025-09-11)
Field: Chemistry — Analytical Chemistry

Exploring HSI imagery for overlapped chromosomes segmentation Hyperspectral imaging ; Segmentation ; Pattern recognition (psychology) ; RGB color model ; Benchmark (surveying) ; Image segmentation ; Metaphase Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Ahmet Çetkin
Source: Pamukkale University Journal of Engineering Sciences (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Flow stress and fracture strain behavior of galvanized low carbon EREGLI 1312 steel Galvanization ; Fracture (geology) ; Carbon steel ; Flow stress ; Dynamic strain aging ; Strain (injury) Metal Alloys Wear and Properties ; Metallurgy and Material Forming
Authors: Yanwei Ma | Chunge Nie | Siqun Ma
Source: Journal of Electronic Research and Application (article, 2025-09-26)
Field: Materials Science — General Materials Science

Optimization of Laminating Angles for Skirt Panels of EMUs Front Using Composite Materials Based on the Cheetah Optimizer With the development of composite materials, their lightweight and high-strength characteristics have caused more widespread use from aerospace applications to automotive and rail transportation sectors, significantly reducing the energy consumption during the operation of EMUs (Electric Multiple Units). This study aims to explore the application of composite materials in the lightweight design of EMU front skirts and proposes a design method based on three-dimensional Hashin failure criteria and the Cheetah Optimizer (CO) to achieve maximum lightweight efficiency. The UMAT subroutine was developed based on the three-dimensional Hashin failure criteria to calculate failure parameters, which were used as design parameters in the CO. The model calculations and result extraction were implemented in MATLAB, and the Cheetah Optimizer iteratively determined the optimal laminating angle design that minimized the overall failure factor. After 100 iterations, ensuring structural integrity, the optimized design reduced the weight of the skirt panel by 60% compared to the original aluminum alloy structure, achieving significant lightweight benefits. This study provides foundational data for the lightweight design of EMUs. Aerospace ; Composite number ; Automotive industry ; Subroutine ; Optimal design ; Energy consumption ; Advanced composite materials Material Properties and Applications
Authors: Oliwia Frankiewicz | Maciej Skowron | Jeremi Jan Jarosz | Sebastien Weisse | Jerome Valire | Krzysztof Szabat
Source: Energies (article, 2025-05-20)
Field: Materials Science — Materials Chemistry

Classification of Rolling Bearing Defects Based on the Direct Analysis of Phase Currents Electric machines are gaining popularity in transport and replacing internal combustion engines. However, the diagnosis of their faults remains an ongoing problem. Traditional diagnostic methods, such as vibration, sound, and temperature analysis, have limitations in practical applications, particularly because of external interference and the need for additional sensors. This paper presents a new diagnostic approach based on convolutional neural networks (CNNs) and direct analysis of current signals. The proposed solution allows for a significant reduction in the number of samples required for effective diagnostics. The neural network, operating on 500 signal samples, achieved a classification efficiency of 99.85–100% for each category of damage investigated. Tests were conducted to determine the effect of noise on the accuracy of the system. This study compares applications based on mechanical vibration signals and the proposed algorithm based on phase current signals. The results indicate that the proposed approach can be successfully applied to real-world monitoring systems for electrical machinery, offering a high-efficiency diagnostic tool while fulfilling the limitations of demanding measurement systems. Bearing (navigation) ; Phase (matter) ; Three-phase ; Direct current ; Materials science ; Structural engineering ; Automotive engineering ; Engineering ; Computer science ; Electrical engineering ; Physics ; Artificial intelligence ; Voltage Metal Alloys Wear and Properties ; Material Properties and Failure Mechanisms ; Microstructure and Mechanical Properties of Steels
Authors: Emily J. Gaizley | Silvia Šurinová
Source: Communications Biology (article, 2025-11-22)
Field: Chemistry — Spectroscopy

The pursuit of ultrasensitive phosphoproteomics to unravel signalling in rare cells Mass spectrometry-based phosphoproteomic workflows enable routine study of signalling dynamics in contexts where cells are abundant. However, the analysis of cell signalling governed by phosphorylation requires an additional enrichment step which poses a significant challenge when dealing with limited material. The development of sensitive techniques that allow phosphoproteomic analysis of a few hundred cells is increasingly enabling researchers to disambiguate the complexity of protein signalling networks within heterogeneous cell populations. The imminent task at hand is to apply these techniques in research contexts guided by the available biological material to address complex questions about cellular function. Examples range from characterising differential treatment responses in distinct cell populations to investigating rare cell types from primary patient material or in vivo models. To achieve this, adapted protocols need to consider appropriate isolation of specific cells, simplified sample processing to avoid losses, labelling and multiplexing, and optimised analytical methodologies. Here, we discuss these aspects of the workflow, highlighting how innovations from low-input and single-cell proteomics can be adapted to drive low-input phosphoproteomics forward. Phosphoproteomics ; Signalling ; Workflow ; Proteomics ; Proteome ; Signalling pathways ; Cell type ; Phosphorylation Advanced Proteomics Techniques and Applications ; Single-cell and spatial transcriptomics ; Mass Spectrometry Techniques and Applications
Authors: Олександр Росляков | Сергій Єрмоленко
Source: Молодий вчений (article, 2025-06-30)
Field: Chemistry — Analytical Chemistry

БАГАТОХВИЛЬОВИЙ NIR-МЕТОД ДЛЯ НЕІНВАЗИВНОГО ВИЗНАЧЕННЯ ВОЛОГОСТІ ЗЕРНА У роботі запропоновано компактну оптико-електронну систему для неінвазивного визначення вологості зернових культур методом багатохвильової ближньоінфрачервоної (NIR) трансмісійної спектроскопії. Основою запропонованого підходу стала побудова багатоканальної регресійної моделі на базі трьох фізично інтерпретованих спектральних індексах — NDWI, RI та FDR, які відповідають зональним максимумам поглинання води в діапазоні 970–1450 нм. Вибрані індекси дозволили досягти високої точності передбачення вмісту вологи у зерні (RMSE = 0,30%, R² = 0,993) зі збереженням обчислювальної ефективності та простоти фізичної інтерпретації моделі. У результаті серії експериментів було визначено оптимальну товщину оптичного прошарку (10–14 мм), що забезпечував найкращий баланс між чутливістю до вологи та лінійністю спектрального відгуку для різних типів зерна (пшениця, кукурудза, соя). Конструкція сенсорної частини реалізована у вигляді модульного блоку, який можна інтегрувати у проточні конвейерні діагностичні пристрої, автоматизовані лінії сортування та зберігання зерна. Computer science Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Pesticide Residue Analysis and Safety
Authors: C. Igathinathane | R. Visvanathan | Ganesh C. Bora | Shafiqur Rahman
Source: AgriEngineering (article, 2025-07-01)
Field: Chemistry — Analytical Chemistry

Computer Vision-Based Multiple-Width Measurements for Agricultural Produce The most common size measurements for agricultural produce, including fruits and vegetables, are length and width. While the length of any agricultural produce can be unique, the width varies continuously along its length. Single-width measurements alone are insufficient for accurately characterizing varying width profiles, resulting in an inaccurate representation of the shape or mean dimension. Consequently, the manual measurement of multiple mean dimensions is laborious or impractical, and no information in this domain is available. Therefore, an efficient alternative computer vision measurement tool was developed utilizing ImageJ (Ver. 1.54p). Twenty sample sets, comprising fruits and vegetables, with each representing different shapes, were selected and measured for length and multiple widths. A statistically significant minimum number of multiple widths was determined for practical measurements based on an object’s shape. The “aspect ratio” (width/length) was identified to serve as an effective indicator of the minimum multiple width measurements. In general, 50 multiple width measurements are recommended; however, even 15 measurements would be satisfactory (1.0%±0.6% deviation from 50 widths). The developed plugin was fast (734 ms ± 365 ms CPU time/image), accurate (&gt;99.6%), and cost-effective, and it incorporated several user-friendly and helpful features. This study’s outcomes have practical applications in the characterization, quality control, grading and sorting, and pricing determination of agricultural produce. Length measurement ; Sample (material) ; Grading (engineering) ; Standard deviation ; Sample size determination ; Representation (politics) ; Quality (philosophy) Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Industrial Vision Systems and Defect Detection
Authors: C. P. Singh | Sajid Ali
Source: Journal of Neonatal Surgery (article, 2025-02-06)
Field: Chemistry — Analytical Chemistry

Signal processing techniques to monitor and predict medication response It is noteworthy that biomedical indicators provide important information about how biological systems behave. Physiological and clinical data are improved when these signals are handled properly. Modern physiological frameworks and idiosyncrasies are investigated both subjectively and quantitatively using sophisticated signal processing and example acknowledgment processes. Even if a clinical professional's interpretation and analysis of a sign is emotional in nature, it reflects the depth of their expertise and experience. When done with the right justification, PC analysis of biological indicators may contribute objective solidarity to the master's comprehension. Furthermore, it provides more advanced diagnosis and online monitoring of individuals who are essentially ill. Also, it offers better diagnostics and online patient checking for patients in horrible shape. In the field of clinical imaging, attractive reverberation imaging, or MR imaging, is a notable and state of the art innovation that offers accuracy and reliable information. Since attractive reverberation imaging (X-ray) is harmless and delivers no ionizing radiation, it is inclined toward above other imaging strategies for clinical imaging. In MR picture examination, division is a urgent step. The method of giving every pixel in an image a name so pixels with a similar mark have specific properties is known as picture division. A computerized picture is partitioned into various segments, each with similar pixels. The fundamental goal of picture division, which enables handling for a variety of purposes, is to deconstruct and transform an image's representation into a more rational and sensible arrangement. Medicine ; SIGNAL (programming language) ; Signal processing ; Computer science ; Computer hardware ; Digital signal processing Spectroscopy and Chemometric Analyses