jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Shuo Liu | Z. Chen
Source: Frontiers in Plant Science (article, 2026-01-20)
Field: Chemistry — Analytical Chemistry

Rapid detection method of soybean seed germination potential based on the PLSR-MLP fusion model In order to realize the rapid detection of soybean seed germination potential, this study designed a fusion model to solve the problem that the single model was insufficient in spectral feature analysis and the prediction performance was limited. The model combines the advantages of the Partial Least Squares Regression (PLSR) and the Multilayer Perceptron (MLP), and utilizing principal components extracted by PLSR as the input features for MLP to construct a soybean seed germination potential prediction model with both linear and nonlinear modeling capabilities. The PLSR module accurately extracts the linear features of the spectrum, and the MLP network further captures the nonlinear relationship between the spectral data and the target variable, which significantly improves the generalization ability of the model. The experimental results show that the prediction performance of the proposed PLSR-MLP fusion model (R<sub>p</sub> <sup>2</sup> = 0.9534, RMSEP = 7.3821) is significantly improved compared with the single PLSR model (R<sub>p</sub> <sup>2</sup> = 0.7284, RMSEP = 17.8154) and the single MLP model (R<sub>p</sub> <sup>2</sup> = 0.7935, RMSEP = 15.5335). In the prediction of soybean germination potential, the PLSR-MLP model also outperforms other single models (Support Vector Machine, SVM; Random Forest, RF) and other fusion models such as PLSR-SVM and PLSR-RF. The PLSR-MLP fusion model effectively addresses the limitations of a single model's performance enhancement potential and the susceptibility to overfitting. It provides a new method for the efficient evaluation of seed germination potential. It also has practical application value for precision seed selection in agriculture and offers a new idea for near-infrared spectrum modeling. Germination ; Partial least squares regression ; Multilayer perceptron ; Principal component analysis ; Fusion ; Pattern recognition (psychology) ; Artificial neural network ; Support vector machine ; Feature selection ; Generalization Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Agricultural Engineering and Mechanization
Authors: Mr. Shardul Sanjay Kalyankar | Mr. Pratham Sunil Dhanavade | Ms. Nikhita uttam Dige | Mr. Nikhil Vishwanth Hulavale | Ms. Sejal Pramod Gundaye
Source:  (article, 2026-01-05)
Field: Materials Science — Biomaterials

Petrographic and Geochemical Studies of Lateritic soils in Kwara State Polytechnic Ilorin In order to improve drug delivery methods for brain tumors, specifically glioblastoma multiforme (GBM), this study investigates the combination of artificial intelligence (AI) with nanomedicine. Because of their rapid growth, resistance to traditional treatments, and the challenge of getting medications over the blood–brain barrier, brain tumors continue to rank among the most complicated and deadly illnesses. The paper emphasizes how AI may be used to predict medication permeability, optimize nanocarrier design, increase diagnostic accuracy, and customize treatment plans. Additionally, it highlights the importance of prognosis monitoring, early biomarker discovery, and innovative computational techniques that improve the accuracy of brain tumor treatment. AI-driven nanomedicine presents new avenues for more efficient, tailored, and minimally invasive treatments, even while traditional therapies like surgery, radiation, and chemotherapy have drawbacks. Additionally, as discussed in future prospects, current innovation uses artificial intelligence for achieving the highest rate of success in medication research. Glioblastoma ; Rank (graph theory) ; Biomarker ; Nanomedicine ; Drug delivery ; Nanocarriers Clay minerals and soil interactions ; Therapeutic Uses of Natural Elements ; Geochemistry and Elemental Analysis
Authors: Fabian Hecker | Kaare Teilum | Andrea Capozzi | Mathilde H. Lerche
Source: Angewandte Chemie International Edition (article, 2026-02-23)
Field: Chemistry — Spectroscopy

Residue‐Specific Signatures of Structural Water Identified by Dissolution Dynamic Nuclear Polarization with UV‐Generated Radicals Conserved structural water molecules stabilize protein folds and modulate their function, yet remain difficult to observe in solution because exchange-based readouts favor solvent-exposed sites. Here, we introduce a protocol to detect structural water molecules under native conditions, using long-lived hyperpolarized water (HyperW) enabled by UV-induced, nonpersistent radicals. In the model protein chymotrypsin inhibitor 2, HyperW-enhanced two-dimensional NMR correlation spectra across pD 5.5-8.4 reveal strong exchange-driven enhancements at solvent-exposed residues. By contrast, a distinct group of four residues shows hyperpolarized amide signals, which disappear when through-space polarization transfer via nuclear Overhauser effect (NOE) is suppressed using a CLEANEX-PM experiment. The CLEANEX-negative/HyperW-positive signature, together with the spatial proximity of these residues to crystallographically conserved water molecules, supports NOE-mediated transfer from long-residence internal water, not distinguishable by standard NMR methods. The combined observables establish HyperW NMR as a residue-specific reporter of structural hydration and hydration-coupled dynamics under native conditions, providing a route to link conserved water observed in crystals to their roles in solution. Two-dimensional nuclear magnetic resonance spectroscopy ; Molecule ; Nuclear Overhauser effect ; Polarization (electrochemistry) ; NMR spectra database ; Nuclear magnetic resonance spectroscopy ; Spectral line ; Molecular dynamics Advanced NMR Techniques and Applications ; Protein Structure and Dynamics ; Solid-state spectroscopy and crystallography
Authors: Amalia Hidayah | Maulana Firmansyah | Dwi Teguh Rahardjo | Anif Jamaluddin
Source: Journal of Environment and Sustainability Education (article, 2026-04-01)
Field: Chemistry — Physical and Theoretical Chemistry

IoT-based mobile data acquisition for pH and temperature monitoring as an enrichment material for high school energy transformation book Monitoring pH and temperature is critical in various fields, yet integrating such technical concepts into high school education remains a challenge. This study aims to develop and validate an IoT-based mobile data acquisition system as a practical case study for an enrichment book on the topic of energy transformation for high school students. Using a Research and Development (R&amp;D) approach, an IoT device consisting of a pH sensor, a K-type thermocouple, and a NodeMCU ESP8266 was built. An accompanying enrichment book was developed using Canva and Heyzine Flipbook. The IoT system demonstrated good accuracy, achieving R² values for pH measurement of 0.9526 (acidic) and 0.8843 (basic). The enrichment book was validated by experts and practitioners, receiving high feasibility ratings of 92.58% and 83.01%, respectively. These findings indicate that the developed enrichment book is a highly feasible supplementary teaching resource. This study demonstrates the significant potential of integrating hands-on IoT projects into science curricula to enhance students' understanding of abstract concepts like energy transformation. Data acquisition ; Transformation (genetics) ; Curriculum ; Mobile device ; Energy (signal processing) ; Internet of Things Various Chemistry Research Topics ; Science Education and Pedagogy ; Innovative Teaching Methods
Authors: Federica Franzetti | Nicole Giugni | Manuel Airoldi | Heather Bondi | Tiziana Alberio | Mauro Fasano
Source: Proteomes (article, 2026-04-07)
Field: Chemistry — Spectroscopy

Beyond Reanalysis: Critical Issues in Data Reuse for Solid Tumor Proteomics Proteomics represents a fundamental layer for understanding the molecular complexity of solid tumors by quantifying protein abundance and capturing proteoforms and post-translational modifications undetected in genomics or transcriptomics analyses. As mass spectrometry-based technologies and public proteomics repositories have expanded, opportunities for large-scale data reuse have grown accordingly. Nevertheless, data availability has not been translated into straightforward reuse: differences in experimental design, acquisition strategies, quantification workflows and metadata quality still limit the reproducibility and cross-study comparability. In this review, proteomics data reuse is defined as the systematic reanalysis and integration of publicly available datasets to support precision oncology applications such as biomarker assessment and antibody–drug conjugate target prioritization. We discuss reuse as an end-to-end analytical process, focusing on data analysis workflows, harmonization strategies, and the impact of heterogeneous experimental and analytical choices on interoperability. The increased application of artificial intelligence in proteomics data integration and reuse is also addressed, highlighting its analytical potential while underscoring the risks of overinterpretation when biological context and data structure are not adequately considered. Using colorectal and prostate cancer as representative examples, we illustrate how proteomics data reuse can support biological discovery and translational research, while critically examining the factors that limit robustness and clinical relevance. Proteomics ; Reuse ; Context (archaeology) ; Metadata ; Workflow ; Raw data ; Data integration ; Data quality Advanced Proteomics Techniques and Applications ; Advanced Biosensing Techniques and Applications ; Single-cell and spatial transcriptomics
Authors: Hawkins S. Shepard | Jody C. May | John A. McLean
Source: The Analyst (article, 2026-01-01)
Field: Chemistry — Spectroscopy

Ambient ionization strategies for the characterization of microbial systems <i>via</i> mass spectrometry Ambient ionization sources enable analysis <i>via</i> mass spectrometry (MS) with minimal sample handling and without vacuum-based ionization/sampling. The versatile nature of ambient ionization techniques makes them well suited for both high-throughput analyses and <i>in situ</i> spatial characterization. Ambient MS platforms such as desorption electrospray ionization (DESI-MS), direct analysis in real time (DART-MS), paper spray (PS-MS), and secondary electrospray ionization (SESI-MS) are particularly amenable for microbial analysis and have recently been utilized for rapid profiling of microorganisms and imaging of fragile substrates with complex biochemistries. This minireview aims to provide an overview of contemporary ambient ionization technologies coupled with MS and summarize the recent application areas of these strategies in the characterization of microbial systems <i>via</i> mass spectrometry over the past five years. Desorption electrospray ionization ; Ambient ionization ; Mass spectrometry ; Extractive electrospray ionization ; Direct electron ionization liquid chromatography–mass spectrometry interface ; Ionization ; Electrospray ionization ; Characterization (materials science) Mass Spectrometry Techniques and Applications ; Radioactive contamination and transfer ; Ion-surface interactions and analysis
Authors: Antoine Cornet | Thierry Deschamps | Floch, Sylvie Le | Dominique de Ligny | Christine Martinet
Source: ArXiv.org (article, 2026-01-06)
Field: Materials Science — Ceramics and Composites

Multiscale Experimental Evidence of a Transitory State in the HDA-LDA Transition in Alumino-sodo-silicate Glasses The structural evolution with temperature of pure silica (SiO2), sodium-silicate (5Na2O-95SiO2, 10Na2O-90SiO2 and 25Na2O-75SiO2) and albite (15Na2O-15Al2O3-75SiO2) glasses previously densified from hot compression is monitored with a combination of small and wide angle x-ray scattering. Transient scattering maxima in the length scales associated with both techniques indicate the presence of a transitory state, suggesting the classification of the initial and final states as distinct glass states in the context of polyamorphism. In all glasses, a transient intensity peak in the small angle scattering is observed, consistent with the nucleation of a new amorphous state. Based on a comparison with the phenomenology observed in supercooled and glassy water, we propose that this structural evolution provide a strong evidence of the glass polyamorphism as the consequence of the existence of an underlying but thermodynamically defined liquid-liquid transition, independent on the polymerization degree of the system. Polyamorphism ; Supercooling ; Nucleation ; Scattering ; Amorphous solid ; Glass transition ; Context (archaeology) ; Softening ; Small-angle scattering Glass properties and applications ; Material Dynamics and Properties ; Clay minerals and soil interactions
Authors: Wenchao Ma | Raphaël Pestourie | Zin Lin | Steven G. Johnson
Source: Nanophotonics (article, 2026-03-28)
Field: Chemistry — Analytical Chemistry

Inverse Design for Robust Inference in Integrated Computational Spectrometry ABSTRACT We propose an inverse‐design approach for computational spectrometers in which the scattering media are topology‐optimized to achieve better performance in inference of unknown spectra. Unlike traditional end‐to‐end approaches, our inverse design of the scattering media does not need a training set of spectra, a distribution of detector noise, or an inference algorithm. Our approach allows the selection of the inference algorithm to be decoupled from that of the scatterer. For smooth spectra, we additionally devise a regularized reconstruction algorithm based on Chebyshev interpolation, which yields higher accuracy compared with conventional methods in which the spectra are sampled at equally spaced frequencies or wavelengths with equal weights. Our approaches are numerically demonstrated via inverse design of integrated computational spectrometers and reconstruction of example spectra. The inverse‐designed spectrometers exhibit significantly better performance in the presence of noise than their counterparts with random scatterers. Our method provides a useful complement to end‐to‐end co‐design methods. Inference ; Inverse problem ; Spectrometer ; Complement (music) ; Inverse ; Inverse scattering problem ; Detector ; Noise (video) Spectroscopy and Chemometric Analyses ; Fault Detection and Control Systems
Authors: Amirali Bajgholi | Reza Jafari | Alireza Saidi
Source: Textiles (article, 2026-01-27)
Field: Materials Science — Biomaterials

Advancing Greenhouse Air Filtration: Biodegradable Nanofiber Filters with Sustained Antimicrobial Performance Air quality management in greenhouses is critical to safeguarding plant health and occupational safety, yet conventional filtration methods often fall short in performance and sustainability. These enclosed environments are prone to the accumulation of bioaerosols, including fungi, bacteria, pollen, and dust particles, which can compromise crop productivity and pose health risks to workers. This review explores recent advancements in air filtration technologies for controlled environments such as greenhouses, where airborne particulate matter, bioaerosols, and volatile organic compounds (VOCs) present ongoing challenges. Special focus is given to the development of filtration media based on electrospun nanofibers, which offer high surface area, tunable porosity, and low airflow resistance. The use of biodegradable polymers in these systems to support environmental sustainability is examined, along with electrospinning techniques that enable precise control over fiber morphology and functionalization. Antimicrobial enhancements are discussed, including inorganic agents such as metal nanoparticles and bio-based options like essential oils. Essential oils, known for their broad-spectrum antimicrobial properties, are assessed for their potential in long-term, controlled-release applications through nanofiber encapsulation. Overall, this paper highlights the potential of integrating sustainable materials, innovative fiber fabrication techniques, and nature-derived antimicrobials to advance air filtration performance while meeting ecological and health-related standards. Filtration (mathematics) ; Nanofiber ; Air filtration ; Reusability ; Electrospinning ; Air filter ; Antimicrobial Electrospun Nanofibers in Biomedical Applications ; Plant Disease Management Techniques ; Polymer-Based Agricultural Enhancements
Authors: Nor Surayahani Suriani | Norzali Hj Mohd | Shaharil Mohd Shah | Siti Zarina Muji | Fadilla Atyka Nor Rashid
Source: International Journal of Advanced Computer Science and Applications (article, 2026-01-01)
Field: Chemistry — Analytical Chemistry

A Feasibility Study on Synthetic RGB-NIR Image Generation for Oil Palm Fresh Fruit Bunch Grading Accurate ripeness of grading oil palm fruit bunches (FFBs) is essential for optimizing oil quality and harvesting decisions. While near-infrared (NIR) imaging provides useful spectral cues for ripeness assessment, its adoption in field conditions is limited by sensor cost and system complexity. This study presents a low-cost alternative by generating synthetic NIR images from RGB inputs using a U-Net-based image translation model and integrating the generated NIR with RGB channels for ripeness classification. Five deep learning models, including a custom CNN, ResNet-50, EfficientNet-B0, DenseNet-201 and MobileNetV3, were evaluated under RGB-only and RGB + synthetic NIR configurations using identical training protocols. Experimental results demonstrate consistent performance improvements when synthetic NIR was incorporated. EfficientNet-B0 achieved the highest overall accuracy of 90.3%, while MobileNetV3 obtained the highest macro-averaged F1-score of 85.4%, indicating strong and balanced classification across ripeness classes. Confusion matrix analysis further revealed complementary strengths between the models, where EfficientNet-B0 showed stronger robustness in late-stage maturity detection, and MobileNetV3 provided improved discrimination of early-stage ripeness. The results demonstrate that synthetic NIR augmentation enhances classification performance and training stability without requiring specialized imaging hardware. Ripeness ; RGB color model ; Palm oil ; Robustness (evolution) ; Confusion matrix ; Image translation ; Pattern recognition (psychology) Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Plant Surface Properties and Treatments
Authors: Wenjuan Wang | Yufen Zhang | Haoyi Huang | T. Liu | Minyue Zeng | YouQiang FU | Hua Shu | Jianyuan Yang | Long Yu
Source: Agronomy (article, 2026-01-05)
Field: Chemistry — Analytical Chemistry

Monitoring Rice Blast Disease Progression Through the Fusion of Time-Series Hyperspectral Imaging and Deep Learning Rice blast, caused by Magnaporthe oryzae, is a devastating disease that jeopardizes global rice production and food security. Precision agriculture demands timely and accurate monitoring tools to enable targeted intervention. This study introduces a novel deep learning framework that fuses time-series hyperspectral imaging with an advanced Autoformer model (AutoMSD) to dynamically track rice blast progression. The proposed AutoMSD model integrates multi-scale convolution and adaptive sequence decomposition, effectively decoding complex spatio-temporal patterns associated with disease development. When deployed on a 7-day hyperspectral dataset, AutoMSD achieved 86.67% prediction accuracy using only 3 days of historical data, surpassing conventional approaches. This accuracy at an early infection stage underscores the model’s strong potential for practical field deployment. Our work provides a scalable and robust decision-support tool that paves the way for site-specific disease management, reduced pesticide usage, and enhanced sustainability in rice cultivation systems. Hyperspectral imaging ; Deep learning ; Scalability ; Convolutional neural network ; Precision agriculture ; Plant disease ; Magnaporthe grisea ; Field (mathematics) Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Remote Sensing in Agriculture
Authors: Yonghwan Hwang | Junseok Kim
Source: Numerical Mathematics Theory Methods and Applications (article, 2026-01-28)
Field: Materials Science — Materials Chemistry

Maximum Principle-Preserving Explicit Scheme for the Ternary Allen-Cahn Equation We investigate the stability of the ternary Allen-Cahn (tAC) equation solved by the fully explicit Euler method. The tAC equation models interface dynamics and phase separation in three-component systems, and is formulated as an $L^2$-gradient flow of the Ginzburg-Landau energy. We derive a sufficient condition on the time step size that satisfies the discrete maximum principle and guarantees the numerical stability of the fully explicit Euler method. Through detailed analysis, we confirm that the derived condition provides the largest possible time step size among those that preserve stability. Numerical experiments are conducted to verify the theoretical results, and the numerical solution violates the maximum principle and becomes unstable when the time step size exceeds the derived bound. Conversely, when the time step condition is satisfied, the computational solution preserves the maximum principle and maintains the energy dissipation property of the tAC equation. These results provide a rigorous theoretical foundation for determining the stability limit of the explicit Euler method applied to the tAC equation. Limit (mathematics) ; Dissipation ; Stability (learning theory) ; Backward Euler method ; Ternary operation ; Euler equations ; Flow (mathematics) ; Euler's formula Solidification and crystal growth phenomena ; Nonlinear Dynamics and Pattern Formation ; Fluid Dynamics and Thin Films