jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Gena Mahato | Ryan C. Graebner | Mattew Hunt | Grayson F. Namdar | Rachael Plunkett | D. W. Wood | Margaret Krause | Surendra P. Singh | Francisco J. Calderón | Christina H. Hagerty
Source: Research Square (preprint, 2026-02-19)
Field: Chemistry — Analytical Chemistry

Comparative analysis of wheat grain protein content: wet chemistry and NIR based methods Grain size ; Analytical Chemistry (journal) ; Spectrum analyzer ; Wheat grain ; Ranging ; Accuracy and precision Spectroscopy and Chemometric Analyses ; Wheat and Barley Genetics and Pathology ; Food composition and properties
Authors: Swaraj Pathak | Swrangsi Goyary | N. B. DEB NATH
Source: Magnetic Resonance in Chemistry (article, 2026-01-07)
Field: Chemistry — Spectroscopy

C <i>i</i> SE: Chirality In Silico Structure Elucidation for Stereochemical Assignment of Natural and Synthetic Organic Compounds Stereochemical elucidation of synthetic and natural compounds is a rate-determining step in the structure determination process. In the present work, we introduce a method called Chirality In Silico Structure Elucidation (CiSE), which employs <sup>1</sup>J<sub>CC</sub> coupling constants to determine the correct stereochemistry of natural products. To determine the correct stereochemistry, the proposed method assigns a probability to all possible structures of a compound in question. For calculating these probabilities, the errors between the experimental and density functional theory (DFT) computed <sup>1</sup>J<sub>CC</sub> couplings of a large number of compounds are fitted into Student's t-distribution, from which statistical parameters such as mean, standard deviation, and degrees of freedom are determined. Afterward, the probabilities for all possible candidate structures are calculated using Bayes's theorem, with the correctly assigned stereoisomer typically exhibiting a probability exceeding 95%. In silico ; Chirality (physics) ; Stereoisomerism ; Density functional theory ; Degrees of freedom (physics and chemistry) ; Coupling constant Molecular spectroscopy and chirality ; Chemistry and Stereochemistry Studies ; History and advancements in chemistry
Authors: Javier Álvarez | Álvaro Romero Barriuso | Blasa María Villena-Escribano | Ángel Rodríguez-Sáiz
Source: Preprints.org (preprint, 2026-03-06)
Field: Materials Science — Polymers and Plastics

Prevention of Explosive Atmospheres Through the Controlled Application of Flammable Products on Surfaces In architecture and construction, it is common to use acrylic products with a high flammable content, from lacquers to improve the curing of concrete and mortar to resins that offer protection, sealing, flexibility and elasticity properties. The drying process of the treated surface involves the formation of volatile organic compound (VOC) vapours. To prevent these from degenerating into a potentially dangerous flammable atmosphere, a procedure is presented that establishes the maximum application yield for solvent-based products, providing equations that relate the maximum application surface area and minimum drying time to the air velocity available in the work area. The results are provided for both indoor and outdoor applications. A maximum application speed is established to prevent the generation of areas classified as fire or explosion hazards: 1.5 m²/h indoors and 1 m²/h outdoors. When this is carried out at an ambient temperature of 20°C, and above 40°C, it is not possible to apply varnishes without generating a flammable atmosphere. Flammable liquid ; Explosive material ; Curing (chemistry) ; Mortar ; Work (physics) Flame retardant materials and properties ; Fire dynamics and safety research ; Fire effects on concrete materials
Authors: Anjana Elapavalore | Parviel Chirsir | Emma Palm | Martin Jakubec | Emma L Schymanski
Source: ChemRxiv (article, 2026-02-03)
Field: Chemistry — Spectroscopy

pyMSscreen: An Interactive Interface for Automated Quality Control and Prescreening in Mass Spectrometry Workflows Reliable quality control of mass spectrometry data remains a major bottleneck in target, suspect and nontarget screening workflows. In non-target analysis, thousands of features can be extracted through automated peak picking, many of which require manual verification to exclude artefacts arising from noise, co-elution, inconsistent chromatographic behavior or statistical prioritization. Similarly, suspect screening workflows depend on effective chromatographic and spectral quality control to support confidence in annotated features. However, existing quality control practices are often fragmented across vendor software and custom scripts, limiting reproducibility and scalability. Scientific contribution statement: pyMSscreen presents a unified framework for chromatographic quality control that integrates automated quality metrics with interactive visual inspection of mass spectrometry data. The central contribution is the consistent evaluation of chromatographic peak quality and spectral alignment across target, suspect, and non-target screening workflows within a single methodology. This work builds on earlier developments in interactive mass spectrometry data review while establishing a scalable and reproducible approach to feature-level quality control. Workflow ; Bottleneck ; Quality (philosophy) ; Quality assurance ; Software ; Data quality ; Vendor ; Interface (matter) Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Advanced Proteomics Techniques and Applications
Authors: Tailin Zhou | Zhilin Chen | Wenlong Lyu | Zhitang Chen | Danny H. K. Tsang | Jun Zhang
Source: npj Artificial Intelligence (article, 2026-01-07)
Field: Materials Science — Materials Chemistry

Learning design-score manifold to guide diffusion models for offline optimization Optimizing complex systems—from discovering therapeutic drugs to designing high-performance materials—remains a fundamental challenge across science and engineering, as the underlying rules are often unknown and costly to evaluate. Offline optimization aims to optimize designs for target scores using pre-collected datasets without system interaction. However, conventional approaches may fail beyond training data, predicting inaccurate scores and generating inferior designs. This paper introduces ManGO, a diffusion-based framework that learns the design-score manifold, capturing the design-score interdependencies holistically. Unlike existing methods that treat design and score spaces in isolation, ManGO unifies forward prediction and backward generation, attaining generalization beyond training data. Key to this is its derivative-free guidance for conditional generation, coupled with adaptive inference-time scaling that dynamically optimizes denoising paths. Extensive evaluations demonstrate that ManGO outperforms 24 single- and 10 multi-objective optimization methods across diverse domains, including synthetic tasks, robot control, material design, DNA sequence, and real-world engineering optimization. Generalization ; Key (lock) ; Interdependence ; Minification ; Robot ; Stability (learning theory) ; Optimization problem Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Multi-Objective Optimization Algorithms
Authors: Teng Wan | Fengchen Qi | Yanna Yang | Ying Qi | Qiang Zhang | Shaoyi Du
Source: npj Heritage Science (article, 2026-02-13)
Field: Chemistry — Organic Chemistry

Adaptive multi-feature fusion for visible-infrared image registration and character enhancement of bamboo slips Ancient bamboo/wooden slips suffer severe character degradation after millennia of burial, requiring infrared imaging for text identification. This work proposes a multimodal coarse-to-fine registration method to fuse visible and infrared images while preserving texture/color and restoring degraded inscriptions. The approach comprises: (1) Coarse registration using edge-feature-priority strategy, leveraging stable slip contours for global alignment via downsampling; (2) Fine registration with improved ICP algorithm incorporating weighted features and dynamic weight adjustment, transitioning from edge-dominance to corner-dominance for precise local registration; (3) Multi-stage hybrid optimization combining gradient methods with multi-restart simulated annealing, maximizing mutual information for optimal transformation matrices. The method addresses weak texture, modal differences, and severe character degradation by selecting appropriate registration strategies and feature weights at different stages. Experiments demonstrate superior performance over existing methods in visual quality and quantitative metrics. Difference fusion based on registered multimodal images achieves effective degraded character restoration, significantly improving inscription readability. Fuse (electrical) ; Image registration ; Transformation (genetics) ; Character (mathematics) ; Fusion ; Image fusion ; Pattern recognition (psychology) ; Modal Wood and Agarwood Research ; Handwritten Text Recognition Techniques ; Image Retrieval and Classification Techniques
Authors: Sunkyu Yu | Xianji Piao | Namkyoo Park
Source: arXiv (Cornell University) (article, 2026-03-11)
Field: Materials Science — Materials Chemistry

Topological heavy-tailed networks Although two-dimensional periodic structures have functioned as the primary platform for exploring topological phenomena, recent advances have substantially expanded this research boundary to include more intricate, aperiodic structures: quasicrystals, fractals, non-Euclidean lattices, and disordered materials. A network-based perspective not only offers a unified framework for classifying these diverse platforms based on their network connectivity but also unveils unexplored regimes of topological phenomena in complex networks. Here, we implement topological heavy-tailed networks, as an example of high-degree complex networks exhibiting topological phases. By developing a tight-binding model for the Apollonian network and a deterministic algorithm to assign nontrivial gauge fields to this aperiodic geometry, we compute the magnetic-flux-dependent energy spectrum: the Apollonian butterfly. Using spectral localizers, we characterize the topological features of the Apollonian butterfly, whose sensitivity is governed by lower-degree nodes, analogous to the controllability of complex networks. Our framework bridges topological physics and network science, introducing a connectivity-driven paradigm for the control of topological waves. Aperiodic graph ; Topology (electrical circuits) ; Boundary (topology) ; Controllability ; Perspective (graphical) ; Complex network ; Homeomorphism (graph theory) Quasicrystal Structures and Properties ; Topological Materials and Phenomena ; Quantum many-body systems
Authors: Ana Carolina Maldonado da Costa e Silva | Samara Maria Piccirillo de Brito | Jhuan Luiz Silva | Alex Luiz Pereira | Giulia Maria Camara Leme | Luiz Henrique Cruz | Isabela Cristina Gomes Honório | Juliana da Silva Coppede | Silvio de Almeida-Junior
Source:  (article, 2026-01-26)
Field: Materials Science — Biomaterials

Bioinformatics Screening of Phenylpropanoids from Pyrostegia venusta in ER+ Breast Cancer This study investigated the cytotoxic, antiproliferative, and molecular interaction profiles of the phenylpropanoids verbascoside and isoverbascoside from Pyrostegia venusta using in silico approaches. Computational predictions suggested differential cytotoxicity trends between tumor and non-tumor breast cell models compared with tamoxifen. QSAR analyses indicated antiproliferative potential, while docking studies revealed stable ligand–protein interactions with estrogen-related targets and PTEN. ADMET predictions suggested favorable metabolic characteristics, including limited CYP3A4 interaction. Overall, these results provide predictive insights that support further experimental investigation of these phenylpropanoids in ER+ breast cancer models. In silico ; Breast cancer ; Cytotoxicity ; Verbascoside ; Virtual screening ; Quantitative structure–activity relationship ; Human breast ; Phenylpropanoid Phytochemistry and Bioactive Compounds ; Phytochemical compounds biological activities ; Phytochemistry and Bioactivity Studies
Authors: Yan Oueis | Akilan Ramasamy | James W. Furness | Jamin Kidd | Timo Lebeda | Jianwei Sun
Source: ArXiv.org (article, 2026-03-12)
Field: Materials Science — Materials Chemistry

Meta-generalized gradient approximation made in the Hartree gauge In density functional theory (DFT), exact constraints, fundamental mathematical properties of the exchange-correlation (XC) energy and its underlying XC hole, along with paradigm systems such as the uniform electron gas and the hydrogen atom have been instrumental in developing exchange- correlation (XC) density functional approximations (DFAs). However, since the spatial XC energy density is not uniquely defined, its exact constraints can only be formulated within a chosen gauge and are therefore seldom utilized in DFA construction. Here, we propose a meta-generalized gradient approximation for the exchange energy, explicitly constructed within the Hartree gauge, using the hydrogen atom's exchange energy density for gauge alignment in core and asymptotic regions. By formulating DFAs at the XC energy density level, this approach expands reference datasets for machine learning and establishes a foundation for more accurate nonlocal density functionals requiring gauge alignment. Gauge (firearms) ; Hartree ; Gauge theory ; Energy (signal processing) ; Density functional theory ; Local-density approximation ; Hybrid functional ; Total energy Machine Learning in Materials Science ; Cold Atom Physics and Bose-Einstein Condensates ; Quantum many-body systems
Authors: Yingying Tang | Bin Zhang | ZHEQIANG ZHONG | Meihong Rao | Pengyu Zhu | Jiawei Guo | Liancong Gao | He Cai | Dongdong Wang | Hai‐Zhi Song | You Wang
Source: Nanomaterials (article, 2026-03-12)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Theoretical Study of Polarization Holographic Encryption via a Nano-Structural Metasurface Metasurface is a kind of artificial structure which can efficiently control the amplitude, phase, frequency, and polarization of the light field. Metasurface polarization holographic encryption is a holographic encryption technology with the polarization state as a key, which has been widely concerned in recent years with advantages such as sub-wavelength pixels, precision adjustment, and high security factor. In this paper, the design and optimization of the unit structure of metasurface have been carried out, and the clear double-channel holographic image reproduction and good encryption effects have been realized afterwards. The results show that the relatively good polarization holographic encryption can be achieved by employing the designed Si nanorods with the length of 148 nm and width of 55 nm, respectively, which have been beforehand grown on SiO<sub>2</sub> substrates. Note that the periodic angle deflection around the Z axis was adopted by using the dual-channel optical rotation incidence with the wavelength of 632.8 nm. It has been theoretically demonstrated that information transmittance loss should be less and the image restoration effect should be satisfactory. A novel encryption method has also been proposed for the optical information processing and optical encryption, and the huge application potential of our theme has been revealed as the next-generation optical control platform in the near future. Holography ; Encryption ; Polarization (electrochemistry) ; Diffraction efficiency ; Transmittance ; Holographic display Metamaterials and Metasurfaces Applications ; Chaos-based Image/Signal Encryption ; Advanced Wireless Communication Technologies
Authors: Weihao Zheng | Sai Xu | Xin Liang | Huazhong N A Lu | Penghao Wu
Source: Foods (article, 2026-01-20)
Field: Chemistry — Analytical Chemistry

Non-Destructive Detection of Internal Quality of Sanhua Plum Based on Multi-Source Information Fusion This research addresses the limitations of traditional assembly line equipment, which is costly and impractical for narrow terrains, as well as the challenges of portable devices in large-scale detection. We propose a non-destructive testing method for assessing the internal quality of Sanhua Plums using a free-fall approach that integrates near-infrared spectroscopy and images. Through analysis of models created from spectral data collected under optimal conditions (motor speed: 6.6 r/min, integration time: 14 ms, spot diameter: 20 mm), we processed near-infrared data from 120 plums. The spectral data underwent preprocessing with polynomial smoothing (SG) and Standard Normal Variate (SNV) calibration, followed by feature extraction using Competitive Adaptive Reweighted Sampling (CARS), resulting in a prediction model for soluble solid content with R<sup>2</sup> of 0.8374 and RMSE of 0.5014. Simultaneously, a prediction model based solely on visual image data achieved an R<sup>2</sup> of 0.3341 and RMSE of 1.0115. We developed a multi-source information fusion model that incorporated Z-score normalization, linear weighted fusion, and Partial Least Squares Regression (PLSR), resulting in an R<sup>2</sup> of 0.8871 and RMSE of 0.4141 for the test set. This model outperformed individual spectroscopy and visual models, supporting the development of an automated non-destructive system for evaluating Sanhua Plum's internal quality. Preprocessor ; Smoothing ; Partial least squares regression ; Pattern recognition (psychology) ; Mean squared error ; Sensor fusion ; Feature extraction ; Sampling (signal processing) ; Linear regression Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Remote Sensing in Agriculture
Authors: Stefano Baroni
Source: The Journal of Chemical Physics (article, 2026-01-02)
Field: Materials Science — Materials Chemistry

The nuts and bolts of gauge invariance of heat transport In this study, I revisit the notion of gauge invariance in thermal transport and show, in the simplest and most general possible terms, why heat conductivity is unaffected by the specific choice of energy density. I provide the minimal and general conditions under which any two energy densities, though differing locally, lead to the same heat conductivity within the Green-Kubo framework. The relevance of gauge invariance in heat-transport simulations performed with machine-trained neural-network potentials is also briefly highlighted. Gauge (firearms) ; Thermal conductivity ; Gauge theory ; Energy (signal processing) ; Energy transport ; Heat kernel Thermal properties of materials ; Quantum many-body systems ; Machine Learning in Materials Science
Authors: İrfan Baştürk | İbrahim Sani Özdemir | Hande Gülcan | Selda Murat Hocaoglu | Recep Partal | Burak Bozcelik | Saamantha Charuka Meegoda | Harsha Ratnaweera | Zakhar Maletskyi
Source: Preprints.org (preprint, 2026-02-11)
Field: Chemistry — Analytical Chemistry

FT-NIR-Based Sludge Moisture Prediction: Spectral Variability and Implications for On-Site Application in WWTPs Accurate and rapid determination of moisture content in waste sludge is essential for optimizing dewatering processes, reducing disposal costs, and minimizing environmental impact. This study investigates the use of Fourier Transform Near-Infrared (FT-NIR) spectroscopy combined with Partial Least Squares Regression (PLS-R) for predicting the moisture content of dewatered sludge. A total of 96 sludge samples, with dry matter contents ranging from 12.4% to 24.6%, were collected from two treatment plants. FT-NIR spectra were acquired over the 800–2500 nm range, and chemometric models were developed to correlate spectral information with gravimetrically determined moisture content. The optimized PLS-R model demonstrated strong predictive performance, achieving a cross-validated coefficient of determination (R²CV) of 0.87, a root mean square error of cross-validation (RMSECV) of 0.92%, and a residual predictive deviation (RPD) of 2.73. Independent test set validation confirmed the robustness of the model (R²test = 0.88, RMSEP = 0.88%, RPD = 2.92), supported by strong calibration results (R² = 0.95, RMSEE = 0.60%, RPD = 4.46). Principal component analysis indicated that spectral variability observed in sludge samples was primarily associated with WWTP-specific characteristics, reflecting moisture–organic matter interactions. These results demonstrate that FT-NIR spectroscopy is a promising tool for sludge moisture prediction. Water content ; Moisture ; Partial least squares regression ; Principal component analysis ; Mean squared error ; Dewatering ; Linear regression ; Residual Spectroscopy and Chemometric Analyses ; Wastewater Treatment and Nitrogen Removal ; Water Quality Monitoring and Analysis
Source: Proceedings of the National Academy of Sciences (article, 2026-02-03)
Field: Materials Science — Biomaterials

In This Issue Nucleofection ; Gestational period ; TSG101 ; Dysgeusia ; Liquation ; Diafiltration ; Emperipolesis ; Triacetin ; Durvalumab Silk-based biomaterials and applications ; Adhesion, Friction, and Surface Interactions ; Laser and Thermal Forming Techniques
Authors: Haojun Ma | Junnan Lv | Yubo Zhou | Yingxuan Dong | Yingzheng Song | Qun Li
Source: Scientific Reports (article, 2026-01-24)
Field: Materials Science — Ceramics and Composites

Determination of mechanical properties of ceramic microspheres using an improved flat-plate crushing test and global cohesive zone modeling Ceramic microspheres are widely used in various applications, such as nuclear fuel particles in reactors, ZrO2 particles for bone fillers, and SiC particles for precision grinding media. To improve the mechanical performance and enhance the safety of these microspheres, the ability to rapidly and accurately determine their mechanical properties is of critical importance. However, due to the close relationship between the fabrication process, microstructure, and internal defect configuration of ceramic microspheres, their mechanical characterization cannot be effectively conducted using conventional methods that ignore the spherical geometry. At present, no standardized experimental technique or computational model exists for such evaluation. This study investigates the crushing mechanics of ceramic microspheres by combining an improved flat-plate crushing test with numerical simulations. Polycrystalline diamond (PCD) was adopted to enhance the conventional flat-plate crushing setup, which typically exhibits low sensitivity to specimen size but is not suitable for high-hardness materials. A dedicated high-precision experimental device was developed for testing sub-millimeter ceramic microspheres. Six groups of ZrO2 microspheres with varying diameters were tested, yielding precise force–displacement curves that captured the complete crushing process. In parallel, numerical simulations based on Voronoi tessellation and global cohesive elements were conducted to replicate the crushing process. By calibrating the model to match the experimental force–displacement curves, the mechanical parameters of the microspheres were determined in a scientifically reliable and precise manner. This integrated approach provides a new perspective for evaluating the mechanical properties of ceramic microspheres. Ceramic ; Microsphere ; Grinding ; Replicate ; Fabrication ; Voronoi diagram Advanced ceramic materials synthesis ; Advanced Surface Polishing Techniques ; Electrophoretic Deposition in Materials Science