jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Xin Jiang | Ruizhi Yang | Jia Liang | Bin Zheng | Meiling Chen | Bin Zhang | Tao Han | Ya-Dong Zhao | Soottawat Benjakul
Source: Food Frontiers (article, 2025-09-15)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Application of Surface‐Enhanced Raman Spectroscopy (SERS) in Histamine Analysis of Aquatic Products: Principles, Applications, Challenges, and Prospects ABSTRACT Histamine, produced through the decarboxylation of histidine by microbial histidine decarboxylase during the transportation and processing of aquatic products, serves as a critical biomarker for evaluating spoilage and freshness. However, conventional detection methods for histamine, such as HPLC and ELISA, are often hampered by lengthy procedures, complex protocols, and specialized equipment requirements. These limitations underscore the urgent need for rapid, sensitive, and field‐deployable alternatives. Surface‐enhanced Raman spectroscopy (SERS) has emerged as a transformative solution, offering unparalleled advantages including single‐molecule sensitivity, minimal sample preparation, and rapid analysis, thereby positioning it as a leading nondestructive tool for real‐time food safety monitoring. This review systematically examines recent progress in SERS‐based histamine detection for aquatic products, detailing the fundamental principles, substrate design innovations, and synergistic integration with complementary technologies (e.g., microfluidics, machine learning) to enhance reproducibility and reduce detection limits. By critically analyzing current challenges—such as substrate uniformity and matrix interference—we propose future directions for SERS technology, including scalable substrate fabrication, portable device development, and standardized protocols. These advancements hold significant potential to revolutionize quality control across the aquatic product supply chain, bridging the gap between laboratory research and industrial implementation. Bioanalysis ; Food spoilage ; Substrate (aquarium) ; Sample preparation ; Raman spectroscopy ; Analyte ; Complex matrix Gold and Silver Nanoparticles Synthesis and Applications ; Identification and Quantification in Food ; Melamine detection and toxicity
Authors: Yanhong Wang | Tim Snow | Nick Terrill | Himadri Shikhar Gupta
Source: IUCrJ (article, 2025-11-19)
Field: Materials Science — Biomaterials

Anisotropic diffraction of materials with fibre symmetry: application to chitin cuticle Understanding the structure-function relationships in anisotropic fibre-symmetric materials is critical for both biological insight and bioinspired design. We present a generalized analytical model for X-ray diffraction intensity from nanofibrillar materials with fibre symmetry, accommodating arbitrary diffraction rings beyond prior axial and equatorial limits. This model integrates 3D orientation, strain heterogeneity and angular misalignment effects, and is validated using wide-angle X-ray diffraction (WAXD) from the Bouligand-structured cuticle of the mantis shrimp (Odontodactylus scyllarus). Using scanning synchrotron WAXD, we extract depth-averaged and sub-lamellar information on 3D fibre orientation and crystalline parameters from 2D scans. Model simulations and experimental fits show accurate reconstruction of the Bouligand texture and reveal spatial gradients in orientation, strain and angular dispersion. By fitting multiple reflections - axial (002), equatorial (110) and intermediate (013) - we improve the robustness in parameter extraction, especially in regions where the Ewald condition is partially satisfied. Our framework enhances the interpretation of WAXD in heterogeneous fibre-based materials and can be embedded into advanced tomographic or machine-learning workflows. This approach is applicable to a broad class of biological and synthetic composites, facilitating high-throughput structural characterization in scenarios where rotation is impractical or impossible. Fiber diffraction ; Diffraction ; Anisotropy ; Robustness (evolution) ; Orientation (vector space) ; Rotation (mathematics) ; X-ray crystallography ; Stacking Calcium Carbonate Crystallization and Inhibition ; Enzyme Structure and Function ; Paleontology and Evolutionary Biology
Authors: Young-Hun Kim | Wonseok Lee | Jihan Kim
Source: ACS Applied Materials & Interfaces (article, 2025-08-22)
Field: Chemistry — Inorganic Chemistry

Amorphous Metal–Organic Framework Database for Amorphization Prediction and CO<sub>2</sub> Direct Air Capture Screening The amorphous phases of crystalline metal-organic frameworks (MOFs), known as amorphous metal-organic frameworks (AMOFs), offer a vast yet underexplored search space for numerous applications. To efficiently navigate this chemical space, data-driven methodologies are essential for elucidating structure-property relationships. In this study, a novel approach to explore this space is introduced by utilizing chemically accurate reactive force field (ReaxFF) to first create the AMOF database. Through melt-quenching simulations performed using ReaxFF, a database consisting of 487 AMOF structures was constructed. From this database, machine learning models trained on the generated AMOF database accurately predicted which MOFs are more likely to go through thermal amorphization based on key structural features. Furthermore, promising candidates for CO<sub>2</sub> direct air capture (DAC) applications were identified by running Widom insertion calculation, discovering three AMOF structures exhibiting DAC-favorable features after thermal amorphization. These findings highlight the effectiveness of this new data-driven approach for exploring the amorphous phases of MOFs beyond existing databases. Materials science ; Amorphous solid ; Metal-organic framework ; Nanotechnology ; Physical chemistry ; Crystallography ; Adsorption Metal-Organic Frameworks: Synthesis and Applications ; Carbon dioxide utilization in catalysis ; Machine Learning in Materials Science
Authors: Kamal Choudhary
Source: The Journal of Physical Chemistry Letters (article, 2025-10-17)
Field: Materials Science — Materials Chemistry

SlaKoNet: A Unified Slater-Koster Tight-Binding Framework Using Neural Network Infrastructure for the Periodic Table Accurate and efficient prediction of electronic band structures is essential for designing materials with targeted properties. However, existing machine learning models often lack universality and struggle to predict detailed electronic structures, while traditional tight-binding models based on the Slater-Koster (SK) formalism suffer from (i) limited transferability, (ii) the need for manual parametrization, and (iii) training on low-fidelity electronic structure data. To address these challenges, I introduce SlaKoNet, a parameter optimization framework that learns SK-based Hamiltonian matrix elements across 65 elements of the periodic table using automatic differentiation. SlaKoNet is trained on density functional theory data from the JARVIS-DFT database using the Tran-Blaha modified Becke-Johnson (TBmBJ) functional, encompassing over 20000 materials. The framework achieves a mean absolute error (MAE) of 0.74 eV for bandgap predictions against experimental data, representing a reasonable improvement over standard GGA functionals (MAE = 1.14 eV) while preserving the computational advantages and physical interpretability of tight-binding methods. SlaKoNet demonstrates promising scalability with up to 8.4× speedup on GPUs, enabling rapid electronic structure screening for materials discovery. SlaKoNet is publicly available at the Web site https://github.com/atomgptlab/slakonet. Interpretability ; Scalability ; Universality (dynamical systems) ; Artificial neural network ; Formalism (music) ; Speedup ; Classifier (UML) ; Electronic structure Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Topic Modeling
Authors: Y. R. Chen | Zhongli Yang | Jiaxuan Wang | Kun Han | Zhijing Zhu | Chenglong Wang | Min Xu | Guang Yang | Likun Pan
Source: Nanoscale (article, 2025-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A data-driven machine learning approach for predictive modeling of transition metal dichalcogenide/carbon composite supercapacitor electrodes Transition metal dichalcogenide (MS<sub>2</sub>) materials possess unique pseudocapacitive characteristics but typically suffer from poor electrical conductivity and volume expansion during cycling, limiting their practical applications. Combining MS<sub>2</sub> with conductive carbon materials has proved to be a common and effective strategy, yielding MS<sub>2</sub>/carbon composites with significantly improved supercapacitor performance. However, their development often relies on empirical trial-and-error methods, limiting systematic progress. Machine learning (ML) is revolutionizing materials science by enabling the rapid screening and prediction of material properties. In this study, we present a comprehensive ML framework to predict the electrochemical performance of MS<sub>2</sub>/carbon composite supercapacitor electrodes. Four ML models were evaluated, with the transformer-based TabPFN model achieving the highest predictive accuracy (<i>R</i><sup>2</sup> = 0.988 and RMSE = 32.15 F g<sup>-1</sup>). SHapley Additive exPlanations (SHAP) identified covalent radius, specific surface area, and current density as critical factors governing the specific capacitance (<i>C</i><sub>s</sub>). Density functional theory (DFT) calculations were performed to evaluate the adsorption energies of potassium ions on various MS<sub>2</sub> slabs, and the agreement with the ML results confirms the reliability of the ML predictions. This work establishes a data-driven ML approach to guide the design of advanced pseudocapacitive materials, significantly accelerating their development. Supercapacitor ; Composite number ; Limiting ; Transition metal ; Capacitance ; Electrical conductor ; Work (physics) ; Reliability (semiconductor) ; Current density Supercapacitor Materials and Fabrication ; Fuel Cells and Related Materials ; Conducting polymers and applications
Authors: Junning Mei | Yu Chen | Zefen Li | Jiaxin Wu | Shuangxing Zhu | Ruan Zhang | Ying Liu | Demin An | Gang Qiao | S. Li | Kenji Watanabe | Takashi Taniguchi | Fucai Liu | Xinghan Cai
Source: ACS Photonics (article, 2025-03-17)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Tunable Photoconductivity Polarity in a Two-Dimensional Ferromagnet for Enhanced Image Recognition Exploring the optoelectronic characteristics of magnetic materials has the potential to merge the optical and magnetic functionalities within a spintronic system. This could pave the way for advancements in all-optical storage, photonic integrated circuits, and improved performance of photodetectors. In this work, we conduct a systematic analysis of the photoresponse in the two-dimensional ferromagnetic material Fe3GeTe2 (FGT) and discover the coexistence of positive photoconductivity (PPC) and negative photoconductivity (NPC), which can be toggled by adjusting either the light illumination power, bias voltage, or temperature. By modeling the photoresponse as it relates to various physical parameters, we determine that the photocurrent generation in the FGT nanoflake is driven by the bolometric effect, and the shift in photoconductivity polarity corresponds to the nonmonotonic resistance–temperature relationship of the conductive channel. Furthermore, we propose an enhanced image recognition vision system utilizing the FGT photodetector for extreme conditions, integrating the convolutional neural network algorithm to improve the image recognition accuracy in low-brightness and noise scenarios. Our findings delve into the combined optical, electrical, and magnetic modulation of two-dimensional ferromagnetic systems, potentially paving the way for new optoelectronic devices in the post-Moore era. Photoconductivity ; Polarity (international relations) ; Ferromagnetism ; Materials science ; Optoelectronics ; Optics ; Condensed matter physics ; Physics ; Chemistry Multiferroics and related materials ; Advanced Memory and Neural Computing ; 2D Materials and Applications
Authors: Mauro Pulzone | Natalya S. Fedorova | Hugo Aramberri | Jorge Íñiguez-González
Source: Physical review. B./Physical review. B (article, 2025-11-25)
Field: Materials Science — Materials Chemistry

Machine learning Landau free-energy potentials for third-principles simulations We show how to construct Landau-like free-energy potentials using a machine learning approach. For concreteness, we focus on perovskite oxide ${\mathrm{PbTiO}}_{3}$, representative of a large class of materials that undergo nonreconstructive structural phase transitions; in this case, a proper ferroelectric transformation with improper ferroelastic features. We work with a training set obtained from Monte Carlo simulations based on an atomistic ``second-principles'' potential for ${\mathrm{PbTiO}}_{3}$. We rely exclusively on data that would be experimentally accessible---i.e., temperature-dependent polarization and strain, both with and without external electric fields and stresses applied---to explore scenarios where the training set could be obtained from laboratory measurements. We introduce a scheme that allows us to identify optimal polynomial models of the temperature-dependent free-energy surface, mapped as a function of the homogeneous electric polarization and homogeneous strain. Typically, our method evaluates thousands of possible models, ranking them by accuracy and predictive power. Our results for ${\mathrm{PbTiO}}_{3}$ show that a very simple polynomial---where only two parameters depend linearly on temperature---is sufficient to yield a correct description of the material's behavior. We thus validate the usual approximations made in phenomenological studies of phase transitions of this kind. Remarkably, the obtained models also capture the subtle couplings by which elastic strain controls key features of ferroelectricity in ${\mathrm{PbTiO}}_{3}$, such as the symmetry of the polar phase and the discontinuous character of the transition, despite the fact that no effort was made to include such information in the training set. We emphasize the distinctive aspects of our methodology (which relies on an original form of validation step) by comparing it with the usual machine learning approach for model construction. Our results illustrate how physically motivated models can have remarkable predictive power, even if they are derived from a limited amount of data. We argue that such ``third-principles'' models can be the basis for predictive macroscopic or mesoscopic simulations of ferroelectrics and other materials undergoing nonreconstructive structural transitions. Polarization (electrochemistry) ; Ferroelectricity ; Monte Carlo method ; Ranking (information retrieval) ; Computation ; Phase transition ; Set (abstract data type) Machine Learning in Materials Science ; Ferroelectric and Piezoelectric Materials ; Model Reduction and Neural Networks
Authors: Liang Shan | Xin Xu | Lin Huang | Dan Li | Yujiao Deng | Xiangfei Xue | Susu Guo | Yiman Huang | Xiao Zhang | Yongchun Yu | Lifang Ma | Kun Qian | Jiayi Wang
Source: Advanced Science (article, 2025-06-25)
Field: Chemistry — Spectroscopy

A Novel nor@DHB Matrix for Direct Microbial Analysis in Lung Cancer Tissues Dynamic changes occurring in the lung microbiota can impact the initiation, progression, and prognosis of lung cancer (LC). Consequently, the development of suitable intratumoral microbiota analysis methods is crucial. Although matrix-assisted laser desorption/ionization mass spectrometry (MALDI MS) involves straightforward operations and provides precise results, the "direct smear method" limits the identification of bacterial subspecies. Furthermore, the issue of inadequate quantification with MALDI MS renders it unsuitable for direct analysis of intratumoral bacteria. To address these challenges, a novel ionic liquid in this study is employed, called norharmane conjugated to 2,5-dihydroxybenzoic acid (Nor@DHB) for the direct detection of intratumoral bacteria using MALDI MS. Because gram-negative bacteria are dominant within cancer cells, lipid A is selected as the chemical fingerprint for bacterial identification. The results demonstrated that using Nor@DHB can enhance the lipid A signal by an order of magnitude and achieved a good linear relationship within a concentration range of 0.01-80 ng mL<sup>-1</sup>. Here, this method is successfully applied to the direct analysis of lipid A in actual clinical samples. Subsequent machine learning and nomogram models further confirmed the correlation between characteristic lipid A ions and LC patient clinicopathological features, which are further validated through both in vitro and in vivo experiments. Matrix-assisted laser desorption/ionization ; Mass spectrometry ; In vivo ; Chemistry ; Bacteria ; Chromatography ; Desorption ; Biology ; Adsorption Mass Spectrometry Techniques and Applications ; Genomics and Phylogenetic Studies ; Metabolomics and Mass Spectrometry Studies
Authors: Waseem Akhtar | Ishfaq Ahmed | Zheng Lu | Shanza Mubashir | Yong Liu | Nan Qu | Jingchuan Zhu
Source: Materials Science and Engineering B (article, 2025-09-30)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Design of novel full Heusler alloys Mn2YAl (Y Fe, Sc, Ni, co): A combined machine learning and DFT study of magnetic and electronic properties This study presents a novel approach for predicting the magnetic and electronic properties of Full Heusler alloys (FHAs) by employing eXtreme Gradient Boosting (XGBoost), a machine learning technique, alongside Density Functional Theory (DFT) calculations. The model employs feature engineering, k-fold cross-validation, and Bayesian optimization (BO) to precisely predict the material properties derived from an extensive library of Heusler alloy compositions. we employed rigorous assessment measures, resulting in a high performance on the unseen test set with R 2 = 0.90, MSE = 0.13, and MAE = 0.21 for the magnetic and R 2 = 0.87, MSE = 0.14, and MAE = 0.26 for the electronic properties. Compared to DFT, the XGBoost model produces very precise predictions with negligible error from −12.4 to 9.6 %, thus increasing the rate at which new materials are discovered. This work demonstrates the application of machine learning (ML) in materials science and facilitates further exploration of HAs, which are characterized by their magnetic and spintronic properties. • Machine learning regression model predicts Heusler alloy magnetic and electronic properties. • XGBoost attained a high level of precision (R 2 > 0.87) vs DFT calculations. • Bayesian optimization (BO) designed new Mn₂YAl (Y Fe,Sc,Ni,Co) alloys. • DFT confirmed ML predictions with errors under 12.4 %. Bayesian optimization ; Gradient boosting ; Test set ; Density functional theory ; Spintronics ; Boosting (machine learning) ; Feature (linguistics) ; Bayesian probability Heusler alloys: electronic and magnetic properties ; Advanced Thermoelectric Materials and Devices ; Advanced Welding Techniques Analysis
Authors: Sohail Ahmad | Hessa A. Alsalmah
Source: Case Studies in Thermal Engineering (article, 2025-07-28)
Field: Materials Science — Materials Chemistry

Thermal optimization of nickel-carbon nanodots using neural network and numerical simulation The nano-composition of nickel Ni and carbon nanodots CNDs in glycerol C 3 H 8 O 3 can serve as nanocarriers for targeted drug delivery or as photodynamic therapy agents due to their ability to generate reactive oxygen species. The energy systems involving Ni-CNDs/C 3 H 8 O 3 play an eminent role in supercapacitors and batteries, benefiting from the high conductivity of carbon nanodots and nickel's redox activity. The glycerol can act as a binder or stabilizer to enhance durability. The ongoing research presents an in-depth analysis of the dynamics of nickel-carbon nanodots suspended in glycerol. The governing Navier-Stokes’ system is amended by the activation energy and heat generation. The dynamial and thermal aspects of Ni-CNDs/C 3 H 8 O 3 are assessed by employing a convolutional neural network ( CNN ) alongside a finite difference numerical scheme. The nonlinear governing problem is solved through the finite difference method and then further optimized and validated with a CNN -based model for enhanced accuracy and computational efficiency. The CNN model is trained on a data generated from numerical simulations, enabling efficient prediction of flow and thermal characteristics. It is deduced that the volume concentrations of nickel and carbon nanodots have high impact on the temperature in either CNDs/C 3 H 8 O 3 and Ni-CNDs/C 3 H 8 O 3 case. Nickel ; Nanodot ; Materials science ; Artificial neural network ; Thermal ; Computer science ; Carbon fibers ; Computer simulation ; Biological system ; Chemical engineering ; Nanotechnology ; Simulation ; Composite material ; Thermodynamics ; Artificial intelligence ; Metallurgy ; Physics ; Composite number Thermal properties of materials ; Nanofluid Flow and Heat Transfer ; Power Transformer Diagnostics and Insulation
Authors: Vivian Chinekwu Onwujiogu | Samuel Ibezim Orjiocha | Eze Faith Chinonso | Mansour A.S. Salem | Anushree S. Bhat | Rabiya Mehandi | Abugu Hillary Onyeka
Source: Archives of Case Reports (article, 2025-12-01)
Field: Chemistry — Spectroscopy

Chromatography Hyphenated Techniques for the Analysis of Natural Products (A Review) Hyphenated techniques for natural product analysis significantly enhance the separation and identification of complex mixtures. Among these, Gas Chromatography-Mass Spectrometry (GC-MS) excels at analyzing volatile and semi-volatile compounds. Food safety, forensics, medicines, environmental analysis, and other fields depend on it heavily, and new technical developments are always improving its abilities and extending its uses. Sensitivity and selectivity of GC-MS have greatly increased in recent years. Automated sample preparation methods improve efficiency, decrease human error, and streamline operations. Machine learning techniques also improve data analysis by allowing automated peak detection, quantification, and compound property prediction. High-throughput analysis is facilitated by ultra-high-performance liquid chromatography (UHPLC) and high-performance liquid chromatography-mass spectrometry (HPLC-MS). When it comes to locating and measuring the bioactive substances in medicinal plants, HPLC-MS is quite useful. Liquid Chromatography-Fourier Transform Infrared Spectroscopy (LC-FTIR) provides complementary benefits by combining liquid chromatography’s separation capabilities with FTIR’s structural elucidation. LC-FTIR improves molecular composition comprehension and quantitative capabilities, as chemometrics advances. Combining LC-FTIR with additional methods, such as LC-MS, offers a full perspective of sample composition. Along with these technological advancements, green chromatography methods such as eco-friendly solvents, supercritical fluid chromatography (SFC), energy-efficient apparatus, and low-waste sample preparation are gaining popularity for decreasing the ecological impact. This study aims at understanding the principles, advantages, limits, scientific evaluation, applications and differences in modern hyphenated chromatographic technique which have found relevance in natural products analysis. Chemometrics ; Sample preparation ; Supercritical fluid chromatography ; Sample (material) ; High-performance liquid chromatography ; Mass spectrometry ; Supercritical fluid extraction Analytical Chemistry and Chromatography ; Chromatography in Natural Products ; Plant-Derived Bioactive Compounds