jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Massimo Rippa | Alessia Milano | Valentina Marchesano | Domenico Sagnelli | Bryan Guilcapi | Amalia D’Avino | Giovanna Palermo | Giuseppe Strangi | Luciano Consagra | Maurizio Brigotti | Stefano Morabito | Joseph Zyss | Lucia Petti
Source: Nanophotonics (article, 2025-04-17)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Diagnostic oriented discrimination of different Shiga toxins via PCA‐assisted SERS‐based plasmonic metasurface Plasmonic biosensors are powerful platforms for detecting various types of analytes. Specifically, surface-enhanced Raman spectroscopy (SERS) can enable label-free and selective detection. Shiga toxin-producing <i>Escherichia coli</i> (STEC) represents zoonotic pathogens that cause severe diseases, such as hemolytic uremic syndrome (HUS), the most important cause of acute renal failure in children. To date, there are no effective therapies for STEC infection, and the available diagnostic methods are complex and inconclusive. Here, a novel nanopattern fabricated by electron beam lithography with remarkable plasmonic properties is employed as SERS substrate for realizing the specific recognition of Stx1a, Stx2a, and of a third variation of the latter. A limit of detection (LOD) of 6.8 pM for Stx1a and 2 pM for Stx2a was achieved. Our approach supported using the principal component analysis (PCA) appears to be a valid alternative to conventional methods, allowing real-time and fast <i>in situ</i> analysis. Plasmon ; Surface-enhanced Raman spectroscopy ; Materials science ; Detection limit ; Nanotechnology ; Raman spectroscopy ; Shiga toxin ; Biology ; Optoelectronics ; Chemistry ; Escherichia coli ; Raman scattering ; Optics ; Chromatography ; Physics Gold and Silver Nanoparticles Synthesis and Applications ; Biosensors and Analytical Detection ; Advanced biosensing and bioanalysis techniques
Authors: Koichi Hashiguchi | Yuki Yamakawa | Masami Ueno
Source: Archives of Computational Methods in Engineering (article, 2025-06-18)
Field: Materials Science — Materials Chemistry

Elasto–Viscoplastic Constitutive Formulation with Temperature-Dependence for General loading Process Including Monotonic and Cyclic Loading Processes: Extended Subloading-Overstress Model Abstract The exact formulation of the subloading surface model is provided in this article for the description of the elastoplastic and elasto–viscoplastic deformations not only for the monotonic but also the cyclic loading processes at general deformation rate ranging from the quasi-static to the impact loading in a unified manner by the subloading-overstress model. Here, it is noteworthy that even the elastoplastic deformation can be described more exactly by the present elasto–viscoplastic constitutive equation, noting that the elastoplastic constitutive equation is limited to the description of the quasi-static deformation behavior, but the purely quasi-static deformation does not exist actually. Therefore, the elastoplastic constitutive equation can be disused only by using the subloading-overstress model. It will be extended to describe the temperature-dependence for metals, since the elasto–viscoplastic deformation behavior is influenced by the temperature in general. Then, the validity of the extended subloading-overstress model for the prediction of the temperature-dependent elasto–viscoplastic deformation of metals will be verified by the comparisons with the test data of metals for various isothermal and/or non-isothermal deformations in the monotonic and the cyclic loading processes. Viscoplasticity ; Constitutive equation ; Monotonic function ; Isothermal process ; Deformation (meteorology) ; Materials science ; Mechanics ; Structural engineering ; Thermodynamics ; Mathematics ; Composite material ; Mathematical analysis ; Physics ; Engineering ; Finite element method High-Velocity Impact and Material Behavior ; Fatigue and fracture mechanics ; Metal Forming Simulation Techniques
Authors: Lapo Renai | Viktoriia Turkina | Tobias Hulleman | Alexandros Nikolopoulos | Andrea Gargano | Elvio D. Amato | Massimo Del Bubba | Saer Samanipour
Source: Environmental Science & Technology Letters (article, 2025-08-18)
Field: Chemistry — Analytical Chemistry

A Novel Chemical-Space-Dependent Strategy for Compound Selection in Non-target LC-HRMS Method Development Using Physicochemical and Structural Data The virtual chemical space of substances, including emerging contaminants relevant to the environment and exposome, is rapidly expanding. Non-targeted analysis (NTA) by liquid chromatography-high-resolution mass spectrometry (LC-HRMS) is useful in measuring broad chemical space regions. Internal standards are typically used to optimize the selectivity and sensitivity of NTA LC-HRMS methods, assuming a linear relationship between structure and behavior across all analytes. However, this assumption fails for large, heterogeneous chemical spaces, narrowing measurable coverage to structurally similar compounds. We present a data-driven strategy for unbiased sampling of candidate structures for NTA LC-HRMS method development from extensive chemical spaces, such as the U.S. EPA's CompTox (>1 million chemicals). The workflow maximizes physicochemical/structural diversity using precomputed PubChem descriptors (e.g., molecular weight, XLogP) and grants LC-HRMS compatibility thanks to predicted mobility and ionization efficiency from molecular fingerprints. The resulting measurable compound lists (MCLs) provide broad, heterogeneous coverage for NTA method development, validation, and boundary assessment. Applied to the CompTox space, the approach yielded MCLs with greater chemical coverage and broader predicted LC-HRMS applicability than conventional "watch list" contaminants, offering a robust framework for enhancing NTA's measurable chemical space while preserving diversity. Chemical space ; Selection (genetic algorithm) ; Biochemical engineering ; Space (punctuation) ; Biological system ; Chemistry ; Computer science ; Chromatography ; Artificial intelligence ; Drug discovery ; Biology ; Engineering ; Biochemistry Analytical chemistry methods development ; Pesticide Residue Analysis and Safety ; Analytical Chemistry and Chromatography
Authors: Matthew E. Carroll | Ashlyn Rairdin | Liza Van der Laan | Antonella Ferela | Sahishnu Hanumolu | Soumik Sarkar | Baskar Ganapathysubramanian | Arti Singh | Asheesh K. Singh | Asheesh K Singh
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-06-02)
Field: Chemistry — Analytical Chemistry

Time Series GWAS for Iron Deficiency Chlorosis Tolerance in Soybean using Aerial Imagery The use of drones has become a commonly used tool by plant scientists to aid in plant phenotyping endeavors. Iron deficiency chlorosis (IDC) is a commonly observed abiotic stress in soybean fields with high soil pH levels. IDC severity is visually classified, and recent work has shown that digital imaging techniques using both ground and UAS-acquired imagery can be utilized for automated severity ratings. In our study, we compared the classification accuracy of two flight altitudes to determine the optimal flight parameters for IDC phenotyping. In addition to this, we investigated the ability to use image-predicted scores for genome wide association study (GWAS), as well as the effect of IDC on traits such as canopy area and canopy growth and development. We also report a tool for semi-automated plot extraction from orthomosaic images that can be easily integrated with UAS. We noted that 43 days after planting was an ideal time for IDC severity ratings as the highest number of significant SNPs were reported at this timepoint. Chlorosis ; Aerial imagery ; Series (stratigraphy) ; Artificial intelligence ; Computer science ; Horticulture ; Biology Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Remote Sensing and Land Use
Authors: Xinyu Chen | Zhilong Song | Shuaihua Lu | Yilei Wu | Minggang Ju | Qionghua Zhou | Jinlan Wang
Source: Scientia Sinica Chimica (article, 2025-04-16)
Field: Materials Science — Materials Chemistry

AI-driven materials design: paradigm shift from small data to big data 人工智能（AI）的崛起正推动材料科学迈入高效设计与发现的新时代, 其核心在于通过数据驱动的模型揭示材料构效关系的深层规律. 然而, 与自然语言处理、计算机视觉等领域相比, 材料数据的规模受限于实验合成表征以及高精度计算的高成本, 呈现出典型的“小数据”特征, 这为AI模型的泛化能力与预测精度带来严峻挑战. 本文系统综述了面向小样本材料数据的高效学习策略与技术路径，并介绍了课题组在该领域的研究成果: 首先, 针对材料数据的高维度、稀疏性和强关联特性, 重点探讨基于迁移学习、主动学习与针对材料的特征工程来突破小样本约束; 其次, 剖析材料数据的演进趋势, 主要聚焦高通量计算、生成模型与标准化数据库的协同发展; 最后, 展望材料大数据与AI深度融合的前沿方向, 如基于大语言模型的材料设计, 跨材料体系的预训练大模型构建. 本文重点介绍作者在小数据场景下开发的AI驱动的材料设计方法, 同时探讨材料数据与AI模型协同发展为材料科设计范式带来的转变. Computer science Machine Learning in Materials Science ; Nanoporous metals and alloys ; Material Selection and Properties
Authors: Satya Pratik Srivastava | Rohan Gorantla | Sharath Krishna Chundru | Claire J.R. Winkelman | Antonia S. J. S. Mey | Rajeev Kumar Singh
Source: Digital Discovery (article, 2025-12-23)
Field: Materials Science — Materials Chemistry

Explainable active learning framework for ligand binding affinity prediction Active learning (AL) guides the selection of which compounds to evaluate next for protein–ligand binding affinity when assay or simulation budgets are limited. Active learning (machine learning) ; Ligand (biochemistry) ; Selection (genetic algorithm) ; Identification (biology) ; Feature selection ; Affinity chromatography Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Gaussian Processes and Bayesian Inference
Authors: Nikolina Poranek | Marcin Marczak | Agata Wajda | Krzysztof Pikoń
Source: Materials (article, 2025-06-23)
Field: Materials Science — Polymers and Plastics

The Effect of the Addition of Sage (Salvia officinalis) and Lucerne (Medicago sativa) on the Strength Parameters of a Polymer-Based Composite and Socio-Economic Analysis Polymer composites are of considerable interest due to the possibility of improving the performance parameters of plastics. The filler is a component whose introduction into the rubber mixture can affect the physicochemical and functional properties of the composite. It is present in the largest quantity in the mixture, so its type is of significant importance in the polymer composite production process. Currently, much attention is being paid to the potential use of various materials as fillers to improve the properties of composites. These materials should, among other things, exhibit good adhesion to the polymer matrix and a high degree of dispersion. One example of such a material is dried plant material. In this group, dried leaves of two plants-sage (<i>Salvia officinalis</i>) and lucerne (<i>Medicago sativa</i>)-were introduced into a rubber mixture in several different content variants. The mixtures were subjected to durability and aging tests and the results were compared with a mixture without any plant additives. Of all the test variants with plant filler, the best results were obtained with the lowest proportion of dried plant material, which was 5 Parts per Hundred Rubber (PHR). In this case, most parameters remained at a level similar to the variant without additives. A slight improvement was observed in elongation at break for the mixture with sage (from 550% to 559%), while in the case of the mixture with lucerne, the color improved (from 1.21 to 0.94). Some parameters of vulcanization characteristics and tensile strength deteriorated. For the latter parameter, a decrease of 11% was noted for the mixture with sage (from 4.65 MPa to 4.13 MPa) and 18% for the mixture with lucerne (to 3.82 MPa). Interestingly, as a result of the ageing of the samples, a significant part of the mixtures with dried plants obtained better results in the case of tensile strength than before ageing. This applies especially to the following variants: 30 PHR for the mixture with sage (an increase of 48%) and 5 PHR for the mixture with alfalfa (an increase of 15%). In general, it should be noted that the functional parameters deteriorated with the increase in the proportion of plant additives. SAGE ; Salvia officinalis ; Medicago sativa ; Composite number ; Materials science ; Composite material ; Economic analysis ; Botany ; Biology ; Economics ; Agricultural economics ; Physics Natural Fiber Reinforced Composites ; Nanocomposite Films for Food Packaging ; Textile materials and evaluations
Authors: Teja Potočnik | Oliver J. Burton | Suman Chakraborty | Purbasha Ray | Ralf Mouthaan | Peter J. Christopher | Zeinab Tirandaz | Xi–Wei Lin | Hannah J. Joyce | Stephan Hofmann | Prasana K. Sahoo | Jack Alexander-Webber
Source: Small Methods (article, 2025-07-09)
Field: Materials Science — Materials Chemistry

High‐Throughput Ellipsometric Contrast Microscopy of Lateral 2D Heterostructures for Optoelectronics Covalently-bonded lateral 2D heterostructures offer unique (opto)electronic functionalities and can be deposited during a single growth process. However, the position of lateral junctions is typically uncontrolled due to random nucleation processes, which necessitates post-growth identification of suitable heterojunction regions for device integration. Here, ellipsometric contrast microscopy (ECM) is demonstrated to evaluate 2D lateral monolayer MoSe<sub>2</sub>-WSe<sub>2</sub> and MoS<sub>2</sub>-WS<sub>2</sub> heterostructures, which enables rapid imaging with high material-contrast down to sub-nanometer thickness for high-throughput characterisation of heterostructure domains. In addition, a computer vision algorithm provides precise identification of individual monolayer heterostructure junctions and their integration into rectifying devices and photodetectors. These results establish the advantages of ECM for reliable, fast characterization and large-scale integration of atomically thin 2D heterostructures into advanced optoelectronic devices, with potential extension to other nanomaterials. Heterojunction ; Materials science ; Monolayer ; Nucleation ; Optoelectronics ; Nanotechnology ; Characterization (materials science) ; Photodetector ; Microscopy ; Optics ; Chemistry ; Physics 2D Materials and Applications ; Perovskite Materials and Applications ; Quantum Dots Synthesis And Properties
Authors: Debarati Nath | Pamulapati Soujanya | Joydeep Roy | Debajit Deb | Sandip Swarnakar
Source: physica status solidi (b) (article, 2025-05-19)
Field: Materials Science — Materials Chemistry

Effect of Spin‐Split Band and its Machine Learning Prediction on Tunnel Magnetoresistance across Organic Magnetic Tunnel Junctions The effect of spin‐split band energy on tunnel magnetoresistance (TMR) of rubrene‐based magnetic tunnel junction devices (x/rubrene/Co, x = La 2 O 3 , LaMnO 3 , La 0.7 Ca 0.3 MnO 3 (LCMO), La 0.7 Sr 0.3 MnO 3 (LSMO)) using nonequilibrium Green's function is investigated. Parallel ( R P ) and antiparallel ( R AP ) resistances have been observed to be affected by spin‐split band energy (Δ) due to modified spin‐dependent scattering at density of states of corresponding spin orientation. The sharp rise of TMR in La 2 O 3 has been attributed to the inhibition of R P by R AP with exchange energy. Spin‐transfer torque (STT) results indicate minimum torque induced spin damping leading to enhanced TMR at higher Δ values. Machine learning‐based models have been prepared to predict the behavior of different parameters ( R P , R AP , TMR (%), STT‐IN, STT‐OUT) versus voltage graphs with varying spin‐split energy. Among various models, Gaussian process regressor and polynomial regression achieve the highest predicting power capability with lease root mean square error. All the parameters associated with LCMO and LaMnO 3 are supposed to be correctly predicted. TMR (%) and STT‐OUT parameters of LSMO device have been correctly predicted as these values are highly correlated with Δ. Condensed matter physics ; Tunnel magnetoresistance ; Magnetoresistance ; Spin (aerodynamics) ; Tunnel junction ; Materials science ; Physics ; Engineering ; Quantum mechanics ; Quantum tunnelling ; Magnetic field ; Mechanical engineering ; Ferromagnetism Machine Learning in Materials Science ; Surface and Thin Film Phenomena ; Advanced Physical and Chemical Molecular Interactions
Authors: Yuheng Chen | Vaishnavi Iyer | Alexander V. Kildishev | Vladimir M. Shalaev | Alexandra Boltasseva
Source: Advanced Photonics (article, 2025-08-04)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Learning deeply to vanish light: intelligent metasurface design for next-generation antireflection The commentary discusses recently published design for antireflective coating on solar cells. The proposed free-form inverse design, based on single-layer silicon metasurfaces, dramatically suppresses broadband optical reflection from silicon surfaces. Optics ; Computer science ; Optoelectronics ; Physics Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Optical Wireless Communication Technologies
Authors: M. Amrita | Nageswara Rao. D | K.V. Sathish | M. Rakesh Vimal | B. Venkatesh
Source: Journal of Vibration Engineering & Technologies (article, 2025-09-22)
Field: Materials Science — Polymers and Plastics

Vibration Analysis of Graphene Reinforced Hybrid Jute/Basalt Natural Nanocomposite Beam Systematic experimental assessment of graphene—enhanced natural fiber hybrid composites for vibration performance is a relatively unexplored domain. The aim is to experimentally investigate the influence of graphene nanoplatelet inclusion, boundary condition and impact position on vibrational behavior of hybrid jute basalt natural fiber reinforced composite beam. Natural frequency, damping factor and mode shapes are evaluated. Seven samples of composite beams were prepared- pure basalt, pure jute, hybrid jute/ basalt and hybrid jute /basalt with graphene nanoplatelets of varying composition 0.2wt. %, 0.4wt.%, 0.6wt.% and 1wt.%. Natural frequency test was performed on all beams experimentally using fast fourier transform (FFT) analyzer at two boundary conditions: clamped- clamped and clamped free condition using moving rover method. Basalt fibre composite showed highest natural frequency and least damping factor and jute fibre composite showed least natural frequency and highest damping factor. Concentration of graphene significantly influenced natural frequency and damping factor. Hybrid jute/basalt composite beam with 0.6wt% graphene showed highest natural frequency and composite with 0.4wt% graphene showed highest damping factor. The hybrid jute-basalt fiber composite exhibited a balanced performance, demonstrating intermediate values of both natural frequency and damping factor—positioning it effectively between its individual constituents in terms of dynamic behavior. The incorporation of graphene significantly influenced the natural frequency and damping properties of hybrid natural composites. Natural frequency ; Composite number ; Graphene ; Natural fiber ; Vibration ; Basalt fiber ; Beam (structure) ; Fiber Natural Fiber Reinforced Composites ; Mechanical Engineering and Vibrations Research ; Tribology and Wear Analysis
Authors: Lincoln Harris | Michael Riffle | William Stafford Noble | Michael J. MacCoss | Christine C. Wu | Danielle P. Johnson Erickson | Alec Morimoto | Benjamin A. Shaver | Taylor N. Stein | Ning Cao | Eric C. Ford
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-05-31)
Field: Chemistry — Spectroscopy

Improved quantitation in data-independent acquisition proteomics via retention time boundary imputation The traditional approaches to handling missing values in DIA proteomics are to either remove high-missingness proteins or impute them with statistical procedures. Both have their disadvantages-removal can limit statistical power, while imputation can introduce spurious correlations or dilute signal. We present an alternative approach based on imputing peptide retention times (RTs) rather than quantitations. For each missing value, we impute the RT boundaries, then obtain a quantitation by integrating the chromatographic signal within the imputed boundaries. Our method yields more accurate quantitations than existing proteomics imputation methods. RT boundary imputation also identifies differentially abundant peptides from key Alzheimer's genes that were not identified with library search alone. RT boundary imputation improves the ability to estimate radiation exposure in biological tissues. RT boundary imputation significantly increases the number of peptides with quantitations, leading to increases in statistical power. Finally, RT boundary imputation better quantifies low abundance peptides than library search alone. Our RT boundary imputation method, called Nettle, is available as a standalone tool. Imputation (statistics) ; Computer science ; Retention time ; Data retention ; Data mining ; Chromatography ; Missing data ; Machine learning ; Chemistry Advanced Proteomics Techniques and Applications ; Advanced Biosensing Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Filip Duša | Jiří Šalplachta | Marie Horká | Kamila Lunerová | Veronika Čermáková | Michal Dřevínek | Oldřich Kubíček
Source: Electrophoresis (article, 2025-01-02)
Field: Chemistry — Spectroscopy

Isoelectric Focusing Fractionation Method for Signal Enhancement in Detection of Inactivated Biological Agents Using Matrix‐Assisted Laser Desorption/Ionization Mass Spectrometry Timely identification of highly pathogenic bacteria is crucial for efficient mitigation of the connected harmful health effects. Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) of intact cells enables fast identification of the microorganisms based on their mass spectrometry protein fingerprint profiles. However, the MALDI-TOF MS examination must be preceded by a time-demanding cultivation of the native bacteria to isolate representative cell samples to obtain indicative fingerprints. Isoelectric focusing (IEF) is capable of separating bacterial cells according to their isoelectric point while effectively removing other non-focusing compounds from sample matrix. In this work, we present a divergent-flow IEF chip (DF-IEF chip) fractionation as an alternative way for sample clean-up and concentration of bacterial cells to prepare samples usable for following MALDI-TOF MS analysis without the need of time-demanding cultivation. By means of DF-IEF chip method, we processed four species of highly pathogenic bacteria (Bacillus anthracis, Brucella abortus, Burkholderia mallei, and Yersinia pestis) inactivated with H<sub>2</sub>O<sub>2</sub> vapors or by heat treatment at 62.5°C for 24 h. The DF-IEF chip method continually separated and concentrated the inactivated bacterial cells for subsequent detection using MALDI-TOF MS. The content of the inactivated bacteria in the DF-IEF chip fractions was evaluated with the MS analysis, where inactivated Y. pestis was found to be the most efficiently focusing species. Sensitivity analysis showed limits as low as 2 × 10<sup>5</sup> colony forming units per mL for inactivated B. anthracis. Mass spectrometry ; Fractionation ; Isoelectric focusing ; Desorption ; Chemistry ; Chromatography ; Ionization ; Matrix-assisted laser desorption/ionization ; Matrix (chemical analysis) ; Analytical Chemistry (journal) ; Surface-enhanced laser desorption/ionization ; Sample preparation in mass spectrometry ; Electrospray ionization ; Ion ; Adsorption ; Biochemistry Analytical Chemistry and Chromatography ; Mass Spectrometry Techniques and Applications ; Inhalation and Respiratory Drug Delivery