jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jibao Liu | Bei Zhang | Qing‐Long Fu | Toshihiro Isobe | Rongjun Gao | Yuansong Wei | Eunsang Kwon | Zhineng Hao | Wei An | Qi Rong | Manabu Fujii
Source: Environmental Science & Technology Letters (article, 2025-05-09)
Field: Chemistry — Analytical Chemistry

MoleTrans: Browser-Based Webtool for Postanalysis on Molecular Chemodiversity and Transformation of Dissolved Organic Matters via FT-ICR MS Dissolved fractions of organic matter in natural and anthropogenic sources, also known as dissolved organic matter (DOM), play crucial roles in natural and engineering processes. Application of the Fourier transform ion cyclotron resonance mass spectrometer (FT-ICR MS) has expanded dramatically for the molecular characterization of DOM in various research communities. However, data processing/mining on the detected tens of thousands of molecular formulas is complex. Here, we introduced a browser-based webtool (MoleTrans) for flexible and comprehensive postanalysis on a formula-assigned data set from FT-ICR MS. MoleTrans includes the classical but fundamental analysis/visualization techniques (e.g., chemodiversity, multivariate statistics, van Krevelen diagrams, formula composition plots, Kendrick Mass Defect (KMD) based homologous series analysis). Users can explore the putative molecular transformation relations in single or multiple samples using a Paired Mass Distance (PMD) network in a flexible manner (e.g., user-defined mass errors and reference transformation groups, etc.). This unique tool also supports the machine learning (ML) workflow (data processing and training models) for explaining the molecular transformation behaviors between samples (e.g., exploring the disappeared, resistant, and newly appeared formulas during the transformation). Therefore, MoleTrans provides the opportunity to unravel the molecular fingerprint in DOM mixtures in a comprehensive, flexible, and reproducible way. Transformation (genetics) ; Chemistry ; Computer science ; World Wide Web ; Biochemistry Analytical chemistry methods development ; Analytical Chemistry and Chromatography ; Water Quality Monitoring and Analysis
Authors: Rui Guo | Shutong Li | Jianhui Li | Lianjun Wang | Wan Jiang
Source: Advances in Colloid and Interface Science (review, 2025-07-21)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Advances and mechanistic insights into low-frequency (Sub-8 GHz) microwave-absorbing materials: A critical review bridging conventional architectures and emerging frontiers Bridging (networking) ; Nanotechnology ; Microwave ; Materials science ; Engineering physics ; Computer science ; Engineering ; Telecommunications Electromagnetic wave absorption materials ; Advanced Antenna and Metasurface Technologies ; Microwave Dielectric Ceramics Synthesis
Authors: Liu Xiong | He Zhang | Wei Zhou | Yuying Zhou | Yuexin Zhang | Xiaoliang Cao | Muqing Liu | Yingzi Peng
Source: Journal of Chromatography A (article, 2025-03-23)
Field: Chemistry — Spectroscopy

Machine learning for predicting retention times of chiral analytes chromatographically separated by CMPA technique Chemistry ; Chromatography ; Analyte ; Retention time Analytical Chemistry and Chromatography ; Mass Spectrometry Techniques and Applications ; Crystallization and Solubility Studies
Authors: Haoyun Fang | Alin Rai | Seyed Sadegh Eslami | Kevin Huynh | Hsiao-Chi Liao | Agus Salim | David W. Greening
Source: Molecular & Cellular Proteomics (article, 2025-03-18)
Field: Chemistry — Spectroscopy

Proteomics and Machine Learning–Based Approach to Decipher Subcellular Proteome of Mouse Heart Protein compartmentalization to distinctive subcellular niches is critical for cardiac function and homeostasis. Here, we employed a rapid and robust workflow based on differential centrifugal-based fractionation with mass spectrometry-based proteomics and bioinformatic analyses for systemic mapping of the subcellular proteome of mouse heart. Using supervised machine learning of 450 hallmark protein markers from 16 subcellular niches, we further refined the subcellular information of 2083 proteins with high confidence. Our data validation focused on specific subcellular niches such as mitochondria, cell surface, cardiac dyad, myofibril, and nuclear, unfolding dominant subcellular localization of proteins in their native environment of mouse heart. We further provide targeted nuclear enrichment and co-immunoprecipitation-based proteomic validation from the heart of nuclear-localizing protein networks. This study provides novel insights into the molecular landscape of different subcellular niches of the heart and serves as a draft map for heart subcellular proteome. DECIPHER ; Proteome ; Proteomics ; Computational biology ; Computer science ; Chemistry ; Biology ; Cell biology ; Bioinformatics ; Biochemistry Advanced Proteomics Techniques and Applications ; Machine Learning in Bioinformatics ; Metabolomics and Mass Spectrometry Studies
Authors: Yinsheng Liu | Rui Xue | Kai Zhu | Bing Yan
Source: Angewandte Chemie International Edition (article, 2025-02-12)
Field: Materials Science — Materials Chemistry

σ‐π Hyperconjugation as a Design Strategy for High‐Performance Fluorescent Covalent Organic Frameworks Realizing efficient solid-state fluorescence in covalent organic frameworks (COFs) represents a persistent and fundamental challenge in materials science, hindering their application in next-generation optoelectronics. Herein, a transformative strategy leveraging σ-π hyperconjugation is introduced to enhance the fluorescence properties of COFs. The incorporation of a methyl group into a pyrene-based COF monomer yields an unprecedented fluorescence quantum yield increase of more than 100-fold, accompanied by significantly enhanced optoelectronic performance. This enhancement arises from σ-π hyperconjugation, which redistributes electron density, converting pyrene from an electron donor to an electron acceptor and facilitating efficient electronic transitions. Additionally, a COF-based fluorescent acoustic sensor, fabricated via electrospinning, demonstrates real-time speech pattern recognition when integrated with machine learning algorithms, showcasing potential in language-assistive technologies. This study pioneers the exploration of hyperconjugation in COFs, offering a new paradigm for the precise modulation of optoelectronic properties and bridging fundamental research with real-world applications. Hyperconjugation ; Fluorescence ; Pyrene ; Nanotechnology ; Covalent bond ; Materials science ; Density functional theory ; Acceptor ; Chemistry ; Computer science ; Computational chemistry ; Physics ; Molecule ; Organic chemistry ; Optics Covalent Organic Framework Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Luminescence and Fluorescent Materials
Authors: Pooja Prajapati | Sanketsinh Thakor | Prince Jain | V. A. Rana | T.R. Pandit
Source: Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy (article, 2025-08-05)
Field: Chemistry — Analytical Chemistry

Investigation of dielectric studies of paracetamol-diethylamine solutions: Experimental and machine learning approach Chemistry ; Diethylamine ; Dielectric ; Organic chemistry ; Optoelectronics Analytical Methods in Pharmaceuticals ; Analytical Chemistry and Sensors ; Thermodynamic properties of mixtures
Authors: Chandra Sekhar Espenti | Kummara Madhusudana Rao | K.S.V. Krishna Rao | Maheshwar Reddy Mettu | Sung Soo Han
Source: Applied Materials Today (article, 2025-07-08)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Advancements in transmission and scanning electron microscopy for nanomaterials: Insights into structural, morphological, and functional characteristics Nanomaterials ; Nanotechnology ; Transmission electron microscopy ; Scanning electron microscope ; Materials science ; Scanning transmission electron microscopy ; Scanning confocal electron microscopy ; Composite material Gold and Silver Nanoparticles Synthesis and Applications ; Quantum Dots Synthesis And Properties ; Copper-based nanomaterials and applications
Authors: Tong Zhai | Wentao Gu | Miao Yu | Yi Shen | Jing‐Min Liu | Shuo Wang
Source: ACS Sensors (article, 2025-05-19)
Field: Chemistry — Analytical Chemistry

Machine Learning-Assisted Multicolor Fluorescence Assay for Visual Data Acquisition and Intelligent Inspection of Multiple Food Hazards Regardless of Matrix Interference Regarding the significant health risks of pesticide residue in foods, while current sensors still suffer from limited efficiency and stability, as well as difficulties in qualitative identification and quantitative detection of mixtures, development of innovative detection techniques combined with advanced methodology holds great research value. Herein, a highly efficient intelligent food risk evaluation system was proposed by integrating a multicolor fluorescent responsive assay with machine learning (ML) algorithms for the identification and quantification of multiple pesticides, carbendazim (CBZ), heptachlor (HEP), chlordimeform (CDF), and their mixtures. This method leveraged the color changes generated from the interaction between multicolor carbon dots (CDs) and target pesticide molecules. By extracting color signal feature values from these reactions and integrating the visual data acquisition with ML models, this method enables efficient qualitative identification and quantitative detection of multiple pesticides, regardless of matrix interference through a dual-source data acquisition strategy without large instruments. The developed evaluation system via a ″stepwise prediction″ strategy automatically demonstrated robust qualitative identification capability with a discrimination accuracy of 99.3% for pesticide categorization while achieving robust quantitative prediction accuracy (<i>R</i><sup>2</sup> ≥ 0.8946) for pesticide concentration detection, verified in six kinds of food matrix. This method significantly improves the detection stability and efficiency, providing a promising tool for food safety monitoring. Interference (communication) ; Computer science ; Matrix (chemical analysis) ; Fluorescence ; Artificial intelligence ; Computer vision ; Data acquisition ; Machine learning ; Pattern recognition (psychology) ; Chemistry ; Optics ; Chromatography ; Telecommunications ; Physics Dye analysis and toxicity ; Biosensors and Analytical Detection ; Carbon and Quantum Dots Applications
Authors: Naim Ben Ali | Raj Agravat | Shobhit K. Patel | Ammar Armghan | Marouan Kouki | Om Prakash Kumar
Source: Scientific Reports (article, 2025-03-26)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Optimization of ultra-wideband metamaterial MXene-based polarization-insensitive solar absorber using machine learning for solar heater application The aim was to optimize energy production and minimize energy losses with regard to sources of sustainable energy, particularly solar energy, by examining a variety of solar absorber designs developed from various materials. Metamaterial MXene/W-based Resonator Solar Absorber (MMRSA) using MXene and Tungsten material utilized in a resonator, which has a tiny wire and cylindrical ring-shaped geometry. The MgF<sub>2</sub> was utilized as a substrate and MXene was used as the ground layer of the suggested solar absorber. The MMRSA worked at the 200-3000 nm range and gained more than 94% absorptance. This MMRSA has a polarization-insensitive and ultra-wideband absorber, their wideband bandwidth is 1730 and 690 nm at 440 to 1930 nm and 1150 to 1840 nm. The negative metamaterial response such as permittivity, permeability, and refractive index given by the MMRSA increased the stability and absorptance of the absorber. To examine and optimize the MMRSA's different parameters and structure by examining the Transverse Electric and Magnetic properties. Optimized the MMRSA using machine learning which gives the higher value of R<sup>2</sup> is 0.97779 and mean square error is 6.869962 × 10<sup>-5</sup>. Aims to reduce other simulation requirements thus minimizing simulation time by 25% when compared to previous approaches. Additionally, at last observed the MMRSA Electric and Magnetic intensity, and compared it with the previously studied absorber. The significant amount of absorptance with ultra-wideband this MMRSA is used for solar water heaters. Absorptance ; Materials science ; Metamaterial ; Wideband ; Optoelectronics ; Optics ; Resonator ; Permittivity ; Metamaterial absorber ; Physics ; Reflectivity ; Dielectric Metamaterials and Metasurfaces Applications ; Energy Harvesting in Wireless Networks ; Antenna Design and Analysis
Authors: Ke Li | Hao Ma
Source: Materials Today Physics (article, 2025-04-09)
Field: Materials Science — Materials Chemistry

Decoding the thermal conductivity of ionic covalent organic frameworks: Optical phonons as key determinants revealed by neuroevolution potential Ionic covalent organic frameworks (ICOFs) are a unique subclass of covalent organic frameworks (COFs) that combine the advantages of metal-organic frameworks (MOFs) and COFs through the integration of ionic and covalent bonds . Using ICOF-10n-Li/Na as examples, we trained a machine learning-based neuroevolution potential (NEP) function and conducted a comprehensive study of the thermal transport properties of ICOFs through large-scale molecular dynamics simulations . We found that the thermal conductivity perpendicular to the pore channels (x-direction) reaches a maximum of 4.04 ± 0.20 W m −1 K −1 at room temperature , primarily driven by high-frequency optical phonons (contributing ∼94 %). In contrast, the thermal conductivity along the pore channels (z-direction) is 0.74 ± 0.02 W m −1 K −1 , dominated by low-frequency acoustic phonons (contributing ∼67 %). Further analysis reveals that linker types strongly influence phonon lifetimes of optical phonons in the x-direction, while interlayer ions significantly impact group velocities of acoustic phonons in the z-direction. This work highlights the critical role of optical phonons in determining the thermal behavior of ICOFs and provides deep insights into the influence of linkers and interlayer ions on thermal transport properties. The superior thermal conductivity (4.04 ± 0.20 W m −1 K −1 ) achieved in the x-direction underscores the unique synergistic effects of ionic and covalent bonding in ICOFs, making them highly promising for applications requiring efficient thermal management and molecular separation. Materials science ; Neuroevolution ; Decoding methods ; Key (lock) ; Phonon ; Ionic bonding ; Thermal conductivity ; Covalent bond ; Optoelectronics ; Ion ; Artificial neural network ; Condensed matter physics ; Computer science ; Artificial intelligence ; Composite material ; Biology ; Telecommunications ; Ecology ; Physics Thermal properties of materials ; Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science
Authors: Ezhil Vilian | Jenna Kim Ahn | Ali Mohammadi | Saeed Reza Hormozi Jangi | Leila Mohammadzadeh | Jaebum Choo | Yun Suk Huh | Youngkyu Han
Source: Biosensors and Bioelectronics (article, 2025-12-02)
Field: Materials Science — Materials Chemistry

An AI-assisted smartphone platform for H2O2 and glutathione detection using a dual-functional Pd-ReS2-MXene nanoprobes Glutathione ; Catalysis ; Detection limit ; Oxidative stress ; Nanoparticle ; In situ ; Peroxidase ; Spectrum analyzer Advanced Nanomaterials in Catalysis ; Sulfur Compounds in Biology ; Nanoplatforms for cancer theranostics
Authors: Chi Zhang | Jinyuan Yang | Tao Sun | Huai Zhang | John P. Brodholt
Source: Proceedings of the National Academy of Sciences (article, 2025-01-29)
Field: Materials Science — Materials Chemistry

Strong precursor softening in cubic CaSiO <sub>3</sub> perovskite CaSiO[Formula: see text] perovskite (CaPv) is the last major mineral in the Earth's lower mantle whose elasticity remains largely unresolved. Here, we investigate the elasticity of CaPv using ab initio machine-learning force fields (MLFF). At room temperature, the elasticity of tetragonal CaPv determined by MLFF molecular dynamics (MD) agrees well with experimental measurements after considering temperature induced variations in the hydrostatic structure, proving the effectiveness of the method. We use the MLFF MD in the [Formula: see text] ensemble to establish the tetragonal-cubic phase boundary and confirm that in the lower mantle CaPv is in the cubic phase. The elasticity of cubic CaPv shows distinct temperature dependence at different ranges: it is linear at high temperatures, whereas it exhibits anomalous precursor softening near the tetragonal-cubic phase boundary. The temperature interval of precursor softening widens as the pressure increases and overlaps with the temperature profile of subducted cold slabs near the core-mantle boundary. While cubic CaPv is seismically invisible along the average mantle geotherm, it may induce low-velocity zones with negative temperature anomaly, leading to the view that the large low shear velocity provinces (LLSVPs) may be caused by subducted oceanic crust rich in CaPv with temperature lower than ambient mantle. A cool, rigid LLSVP may help explain the preferential formation of mantle plumes at its margins, as well as its weaker seismic anisotropy. Softening ; Perovskite (structure) ; Materials science ; Geology ; Composite material ; Chemical engineering ; Engineering Electronic and Structural Properties of Oxides ; High-pressure geophysics and materials ; X-ray Diffraction in Crystallography
Authors: Zhen Guo | Haifang Wang | Fernando Auat Cheein | Zhishang Ren | Lianming Xia | Ibrahim A. Darwısh | Yemin Guo | Xia Sun
Source: Postharvest Biology and Technology (article, 2025-03-08)
Field: Chemistry — Analytical Chemistry

Detection of Aspergillus flavus contamination in peanut kernels using a hybrid convolutional transformer-feature fusion network: A macro-micro integrated hyperspectral imaging approach and two-dimensional correlation spectroscopy analysis Hyperspectral imaging ; Aspergillus flavus ; Contamination ; Pattern recognition (psychology) ; Convolutional neural network ; Artificial intelligence ; Biological system ; Macro ; Feature (linguistics) ; Fusion ; Environmental science ; Computer science ; Biology ; Botany ; Ecology Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Robabeh Bashiri | Preston S. Lawson | Stewart He | Sadisha Nanayakkara | Kwangnam Kim | Nicholas S. Barnett | Vitalie Stavila | Farid El Gabaly | Jaydie Lee | Eric Ayars | Monica C. So
Source: Chemistry of Materials (article, 2025-01-21)
Field: Chemistry — Inorganic Chemistry

Discovery of Dual Ion-Electron Conductivity of Metal–Organic Frameworks via Machine Learning-Guided Experimentation Identifying conductive metal-organic frameworks (MOFs) with a coupled ion-electron behavior from a vast array of existing MOFs offers a cost-effective strategy to tap into their potential in energy storage applications. This study employs classification and regression machine learning (ML) to rapidly screen the CoREMOF database and experimental methodologies to validate ML predictions. This process revealed the structure-property relationships contributing to MOFs' bulk ion-electron conductivity. Among the 60 conductive compounds predicted, only two p-type conductive MOFs, [Cu<sub>3</sub>(μ<sub>3</sub>-OH) (μ<sub>3</sub>-C<sub>4</sub>H<sub>2</sub>N<sub>2</sub>O<sub>2</sub>)<sub>3</sub>(H<sub>3</sub>O)]·2C<sub>2</sub>H<sub>5</sub>OH·4H<sub>2</sub>O <b>(1)</b> and NH<sub>4</sub>[Cu<sub>3</sub>(μ<sub>3</sub>-OH)(μ<sub>3</sub>-C<sub>4</sub>H<sub>2</sub>N<sub>2</sub>O<sub>2</sub>)<sub>3</sub>]·8H<sub>2</sub>O or <b>(2)</b> (C<sub>4</sub>H<sub>2</sub>N<sub>2</sub>O = 1H-pyrazole-4-carboxylic acid), were validated for their coupled electron-ion behavior. MOFs utilize earth-abundant copper and pyrazoles as ligands, demonstrating significant potential following thorough electrochemical characterization. Further analysis confirmed the critical role of strong σ-donating, π-accepting, and redox-active ligands in promoting electron mobility. In-depth structural investigations revealed that the presence of the O-Cu-N chain significantly influences conductivity, outperforming MOFs with only Cu-N or Cu-O bonds. Additionally, this study highlights how higher ionic conductivity is correlated with the ion mobility through linkers in <b>1</b> or the presence of ammonium ions in <b>2</b>. These structure-property relationships offer valuable insights for future research in using ML coupled with experimentation to design MOFs containing earth-abundant reagents for ion-electron conductivity without employing a host-guest MOF strategy. Conductivity ; Ion ; Metal-organic framework ; Reagent ; Electrochemistry ; Redox ; Chemistry ; Ionic bonding ; Metal ; Materials science ; Electrical conductor ; Inorganic chemistry ; Nanotechnology ; Chemical physics ; Physical chemistry ; Electrode ; Organic chemistry ; Adsorption ; Composite material Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Covalent Organic Framework Applications
Authors: Bingxu Wang | Shisheng Zheng | Jie Wu | Jingyan Li | Feng Pan
Source: npj Computational Materials (article, 2025-05-24)
Field: Materials Science — Materials Chemistry

Inverse design of catalytic active sites via interpretable topology-based deep generative models Abstract The rational design of catalyst structures tailored to target performance is an ambitious and profoundly impactful goal. Key challenges include achieving refined representations of the three-dimensional structure of active sites and imbuing models with robust physical interpretability. Herein, we developed a topology-based variational autoencoder framework (PGH-VAEs) to enable the interpretable inverse design of catalytic active sites. Leveraging high-entropy alloys as a case, we demonstrate that persistent GLMY homology, an advanced topological algebraic analysis tool, enables the quantification of three-dimensional structural sensitivity and establishes correlations with adsorption properties. The multi-channel PGH-VAEs illustrate how coordination and ligand effects shape the latent space and influence the adsorption energies. Building on the inverse design results from PGH-VAEs, the strategies to optimize the composition and facet structures to maximize the proportion of optimal active sites are proposed. This interpretable inverse design framework can be extended to diverse systems, paving the way for AI-driven catalyst design. Inverse ; Generative grammar ; Topology (electrical circuits) ; Computer science ; Generative model ; Artificial intelligence ; Mathematics ; Combinatorics ; Geometry Machine Learning in Materials Science ; Catalysis and Hydrodesulfurization Studies ; Catalysis and Oxidation Reactions