jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Christian Barker
Source: Zenodo (CERN European Organization for Nuclear Research) (preprint, 2025-12-21)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Tabletop Testbed for Low-Power Metamaterial Field Manipulation: A YIG Based Prototype with Retrocausal Control This paper presents a fully buildable tabletop-scale testbed for investigating low-power spacetime eld manipulation using a ceramic-YIG (yttrium iron garnet) metamaterial hull integrated with neuromorphic control architecture. The design combines hexagonal YIG tiling optimized via golden-ratio spacing for spin-wave resonance uniformity with a Raspberry Pi-based spiking neural network implementing Sutherland action-reaction dynamics through forward-backward temporal propagation. No exotic matter or negative energy is required; measurable e ects arise from coherent spin-wave interference in YIG under controlled microwave drive at 2.4 GHz (5–10 W continuous). Predicted outcomes include micro-displacement of 0.05–0.1 mm and associated phase anomalies, testable via laser interferometry at 0.01 mm resolution. The prototype is buildable with o -the-shelf components (total cost < £500, assembly timeline 6 months) and provides a falsiable experimental platform for probing eld-control mechanisms relevant to unidenti ed aerial phenomena (UAP) observations. Simulation code (Python, EveNN neuromorphic framework) is provided for pre-build validation and parameter optimization. Keywords: metamaterial propulsion, YIG spin-wave coupling, low-power eld manipulation, neuromorphic control, Sutherland retrocausality, tabletop testbed, falsi able experiment Neuromorphic engineering ; Testbed ; Metamaterial ; Energy (signal processing) ; Field (mathematics) ; Microwave ; Phase (matter) ; Gigabit Metamaterials and Metasurfaces Applications ; Magnetic properties of thin films ; Neural Networks and Reservoir Computing
Authors: Fahimeh Saberi | Ahmad Gharzi | Ashraf Jazayeri | Vahid Akmali | Khosrow Chehri | Naser Karimi | Nasser Babajani | Muhammad Rizwan | Elahe Baratalipour
Source: Journal of Drug Delivery Science and Technology (article, 2025-03-29)
Field: Materials Science — Materials Chemistry

Eco-friendly synthesis and multifunctional evaluation of silver nanoparticles using Boleophthalmus dussumieri mucus: Antibacterial, anticancer, and predictive modeling applications Mucus ; Silver nanoparticle ; Nanotechnology ; Nanoparticle ; Materials science ; Biology ; Ecology Nanoparticles: synthesis and applications ; Medicinal Plants and Neuroprotection ; Advanced Nanomaterials in Catalysis
Authors: Mohammad Mahmudul Alam Mia | Shahid Ahmed | Md. Eyakub Ali Md. Eyakub | Md. Ruhul | Jannatul Nayem Novera
Source: Research Square (preprint, 2025-11-04)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Triple-Band Terahertz Metamaterial Absorber for Label-Free Detection of Industrial Contaminants with Machine Learning Driven Technique Terahertz radiation ; Resonator ; Metamaterial ; Parametric statistics ; Sensitivity (control systems) ; Figure of merit ; Metamaterial absorber Metamaterials and Metasurfaces Applications ; Electromagnetic wave absorption materials ; Plasmonic and Surface Plasmon Research
Authors: Poornima Ramesh (10586884) | Shubham Sukerndeo Upadhyay (22262812) | Sonet Daniel Thomas (21025133) | Chandrashekar Jeevaraj Sorake (22262815) | Ganesh M. K. (22262818) | Vijith Vittal Shetty (20673734) | Prashant Kumar Modi (7359551) | Rohan Shetty (22262821) | Manavalan Vijayakumar (7474721) | Jalaluddin Akbar Kandel Codi (21708094) | Thottethodi Subrahmanya Keshava Prasad (3846439)
Source: Figshare (article, 2025-09-17)
Field: Chemistry — Spectroscopy

Differentiating Gastric Cancers from Acid Peptic Diseasesthrough Integrative Targeted Proteomics and Machine Learning Approaches Gastric cancers (GCs) are often diagnosed in advanced stages owing to nonspecific early symptoms resembling Acid Peptic Diseases (APDs). Despite recent efforts, a simple, liquid biopsy-based multiprotein panel prediagnostic assay capable of differentiating GCs from APDs is lacking. Mass spectrometry (MS)-based targeted proteomics methods, including Multiple Reaction Monitoring (MRM), are utilized as the method of choice to develop Laboratory Developed Tests (LDTs) that revolutionize GC early diagnosis and screening. In this study, a 22-min MS-MRM LDT was developed and tested to quantify a serum protein panel in 135 serum samples from treatment-naive cases of GCs, APDs, and healthy individuals. Notably, a novel Deep Neural Network (DNN)-based pattern recognition scoring architecture, integrated with a model explainability tool (SHAP), was developed to score and categorize GCs. The MRM-MS assay produced minimal carryover and matrix effects, with adequate limits of detection/quantification. Quantities of SAA1 and IGFBP2, as determined through ELISA, demonstrated similar sensitivity compared to the LDT. Importantly, the DNN-based scoring architecture efficiently differentiated GCs from the rest of the samples (AUROC = 0.95), with average precision marking >0.90 and minimal bias in protein expression affecting model performance. This LDT can serve as a prediagnostic screening method to distinguish GCs from APDs, guiding clinicians and patients in proceeding with a confirmatory diagnosis. Proteomics ; Categorization ; Quantitative proteomics ; Biomarker ; Artificial neural network ; Mass spectrometry Advanced Proteomics Techniques and Applications ; Helicobacter pylori-related gastroenterology studies ; Machine Learning in Bioinformatics
Authors: Maheswar Chaudhary | Ashok Thapa | Shalabh C. Maroo
Source: AI Thermal Fluids (article, 2025-10-10)
Field: Materials Science — Surfaces, Coatings and Films

Flat superhydrophilic coating and machine learning of co-sputtered coatings for wettability prediction Co-sputtering is a widely adopted technique for producing flat coatings with tailored wettability; however, it typically requires time-consuming and iterative tuning of deposition parameters through trial-and-error experiments. In this study, machine learning (ML) models are employed to predict the water contact angle (WCA) of co-sputtered coatings. A dataset comprising 86 samples was used to train and evaluate four ML models; of these, 73 data points were extracted from the literature, and 13 were experimentally created by depositing thin films on silicon wafers through co-sputtering. During the experimental data generation, a novel flat coating composed of silicon (Si) and magnesium (Mg) was developed, exhibiting superhydrophilic behavior with a WCA below 5°. The coating demonstrated short- and long-term durability, retaining its superhydrophilic characteristics for up to 30 days under ambient laboratory conditions. Remarkably, even after one year, the surface could recover its superhydrophilic state following basic chemical cleaning. To enhance model performance, the hyperparameters of each ML model—linear regression, decision tree, random forest, and gradient boosting regression—were systematically optimized. Hold-out validation as well as 10-fold cross validation approaches are used to investigate the performance of each ML model. Among the models tested, random forest exhibited the highest prediction accuracy. Superhydrophilicity ; Coating ; Wetting ; Hyperparameter ; Gradient boosting ; Deposition (geology) ; Random forest ; Wafer Surface Modification and Superhydrophobicity ; Surface Roughness and Optical Measurements ; Adhesion, Friction, and Surface Interactions
Authors: Giri Prasad Gorumutchu
Source: Asian Journal of Pharmaceutics (article, 2025-03-01)
Field: Chemistry — Analytical Chemistry

Developing and Validating a Stability- Indicating Ultra Performance Liquid Chromatography Method for the Concurrent Measurement of Estradiol and Dydrogesterone in Bulk and Tablet Doses Pharmacovigilance (PV) is the science of drug safety, now established as a separate discipline driven by modern technologies such as digital health tools, big data, artificial intelligence (AI), and machine learning (ML). Legacy PV methods relied on manual reporting and spontaneous submissions of adverse drug reactions (ADRs), but these were hindered by delays in submissions, signal detection, and data quality issues. Emerging technologies such as AI, digital health, and big data play a critical role in drug safety and risk mitigation. Digital health tools, including wearable monitors, patient engagement modules, and electronic health records, generate Real- World Data that help healthcare professionals track patient reactions to drugs over extended periods, providing insights into genomics, vitals, and ADRs. Big data allows PV practitioners to handle complex, heterogeneous datasets, including patient reviews, which are analyzed using natural language processing to extract insights from social media, reports, and clinical data. ML algorithms automate signal detection, predictive modeling, and casualty assessment, significantly improving the speed and accuracy of ADR identification. Technologies such as AI-driven platforms (e.g., World Health Organization VigiBase and Food and Drug Administration Sentinel initiative) demonstrate how large-scale, real-time data can enhance risk signal identification, while digital health devices assist in monitoring patient vitals for early risk detection. Despite these advancements, challenges persist, including ethical concerns, data inoperability, algorithm bias, and regulatory issues. This review underscores the need for global collaboration, standardized reporting methods, and robust regulatory guidelines to address these challenges. Emerging technologies like the internet of medical things pave the way for personalized and ML-driven predictive PV. Dydrogesterone ; Chromatography ; Chemistry ; Medicine ; Estrogen ; Internal medicine Analytical Methods in Pharmaceuticals ; Analytical Chemistry and Chromatography ; Estrogen and related hormone effects
Authors: Yuchen Tang | Xuting Guo | Jianqiang Zhao | Dandan Ma | Meiqi Liu | Xuebin Yue | Yunfei Hu | Wenlong Li
Source: Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy (article, 2025-11-20)
Field: Chemistry — Analytical Chemistry

Intelligent species identification of Fritillaria spp. based on multispectral feature fusion Pattern recognition (psychology) ; Identification (biology) ; Sensor fusion ; Support vector machine ; Feature selection ; Feature (linguistics) ; Multispectral image Spectroscopy and Chemometric Analyses ; Traditional Chinese Medicine Analysis ; Phytochemical Studies and Bioactivities
Authors: Reem Abukmeil | Ahmad Al-Mallahi | Felipe Campelo
Source: Artificial Intelligence in Agriculture (article, 2025-10-04)
Field: Chemistry — Analytical Chemistry

Multivariate stacked regression pipeline to estimate correlated macro and micronutrients in potato plants using visible and near-infrared reflectance spectra The ability to sense nutrient status in potato plants using spectroscopy has several merits including the ability to proactively respond to deficiencies of certain elements. While research so far has focused on finding spectral signatures of elements based on their foliar reflectance, the influence of the spectral signatures of the elements on each other in estimating their concentrations in the plant has not been investigated. This work presents a pipeline of stacked regression models capable of accurately estimating nutrient concentrations based on the foliar reflectance. A data set was built from 179 samples of petioles collected across two growing seasons, consisting of the chemical concentrations of 11 nutrients with spectral reflectance values between 400 and 2500 nm. The pipeline consisted of a base layer composed of a multiple univariate linear Lasso regression models to find the initial independent signatures of each nutrient, followed by a layer of nonlinear models to correlate these signatures and account for their interdependencies before finalizing the estimation. The results show that adding this second layer improved estimation performance for 10 and 9 nutrients out of 12 in the dried and fresh mode, respectively, with large improvements in predictive performance for some critical micronutrients such as Zn, Fe, and Al. • Intricate biological relationships of nutrients overlapped reflectance spectra. • Multivariate stacked regression was developed to estimate correlated nutrients. • Ensemble machine learning Combining Lasso and XGB provided optimum results. • The developed pipeline improved the estimation of all nutrients of fresh and dried leaves. Pipeline (software) ; Nutrient ; Univariate ; Multivariate statistics ; Linear regression ; Partial least squares regression ; Reflectivity ; Regression ; Lasso (programming language) Spectroscopy and Chemometric Analyses ; Potato Plant Research ; Water Quality Monitoring and Analysis
Authors: Diego
Ulysses Melo (17676683) | Leonardo Martins Carneiro (21668282) | Mauricio Domingues Coutinho-Neto (17271623) | Paula Homem-de-Mello (1803232) | Fernando Heering Bartoloni (2015380)
Source: Figshare (article, 2025-07-07)
Field: Materials Science — Materials Chemistry

Developing a MachineLearning Model for Hydrogen BondAcceptance Based on Natural Bond Orbital Descriptors This study employs machine learning (ML) to assess the predictive power of electronic descriptors derived from natural bond orbital (NBO) analysis for hydrogen bond acceptance. Using a data set of 979 hydrogen bond complexes, each formed by a hydrogen bond acceptor and 4-fluorophenol as the donor, we optimized geometries via GFN2-xTB, followed by DFT single-point calculations. From these, NBO analysis was used to extract intramolecular donor–acceptor interactions, particularly the orbital stabilization energies (<i>E</i><sup>(2)</sup>), which reflect electron delocalization and relate to canonical resonance structures. The <i>E</i><sup>(2)</sup> values served as features to train seven ML models, based on different techniques: KNN, Decision Tree, SVM, RF, MLP, XGBoost, and CatBoost. To our knowledge, this is the first work that uses <i>E</i><sup>(2)</sup> as a standalone ML descriptor for hydrogen bond acceptance. Even with a small set of descriptors, we achieved high predictive performance, with errors below 0.4 kcal mol<sup>–1</sup>, surpassing previous studies that used heterogeneous descriptors, including quantum-chemical data. Our results highlight the utility of NBO-based features in building accurate, physically meaningful, and generalizable ML models for p<i>K</i><sub>BHX</sub> prediction. Natural bond orbital ; Intramolecular force ; Hydrogen bond ; Set (abstract data type) ; Resonance (particle physics) ; Test set ; Bond ; Bond order ; Delocalized electron Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Jin-Liang Han | Yongzheng Sun | Qihui Shen | Yichen Dai | J. D. Shao | Ying Lin | Liang Xu
Source: APL Photonics (article, 2025-08-01)
Field: Chemistry — Spectroscopy

Electrohydrodynamic excimer laser: Ultra-compact system and laser energy performance dynamics Excimer lasers, essential deep ultraviolet gas light sources, have consistently encountered significant challenges in achieving miniaturization while maintaining high performance. These challenges have limited their broader application and development in complex scenarios. Here, we present a novel ultra-compact excimer laser design based on electrohydrodynamic principles and develop an efficient non-invasive flow velocimetry technique to quantify its medium circulation capabilities. This excimer laser achieves remarkable compactness (130 mm diameter × 300 mm length) with a medium flow velocity of 1.27 m/s, effectively overcoming deployment limitations in complex environments. Notably, we observe a unique explosive transition phenomenon involving pulse energy and stability in this electrohydrodynamic excimer laser. Through high-dimensional XeCl reaction network dynamics analysis, we demonstrate that these transition characteristics originate from threshold-driven photon flux bursts, thereby revealing, for the first time, the microscopic mechanisms behind macroscopic laser energy fluctuations. In addition, we develop an interpretable machine learning framework to accurately predict energy explosive transition in ultra-compact excimer lasers, facilitating effective laser system regulation and optimization. This pioneering study carries substantial implications for both promoting the development of excimer lasers and deepening the understanding of their underlying mechanisms. Laser ; Excimer ; Miniaturization ; Excimer laser ; Electrohydrodynamics ; Materials science ; Velocimetry ; Explosive material ; Optics ; Optoelectronics ; Nanotechnology ; Physics ; Chemistry Spectroscopy and Laser Applications ; Laser Design and Applications ; Atomic and Subatomic Physics Research
Authors: Khairulazhar Jumbri | Noor Fazrieyana Hamidon | Hayati Mukhair | Nurizyan Najiha Lizman | Nor Ain Fathihah Abdullah | Md. Sazzad Hossain | Hayyiratul Fatimah Mohd Zaid | Hamza Ahmad Isiyaka | Roswanira Abdul Wahab | Mohd Basyaruddin Abdul Rahman
Source: International Journal of Environmental Science and Technology (article, 2025-06-03)
Field: Chemistry — Inorganic Chemistry

Experimental design and modelling of adsorption process for organic pollutants removal by NH2-UiO-66(Zr) metal–organic framework Pollutant ; Adsorption ; Metal-organic framework ; Process (computing) ; Environmental chemistry ; Environmental science ; Ecotoxicology ; Waste management ; Chemistry ; Environmental engineering ; Chemical engineering ; Engineering ; Computer science ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Adsorption and biosorption for pollutant removal ; Nanomaterials for catalytic reactions
Authors: Farzaneh Shalileh | Morteza Hosseini | Mohammad Golbashy | Mehdi Dadmehr | Hossein Sabahi | Chang Xu | Li Fu | Hassan Karimi‐Maleh
Source: Microchemical Journal (article, 2025-10-04)
Field: Materials Science — Materials Chemistry

Shining light on pathogen detection: a comprehensive review of metallic nanocluster-based optical biosensors Nanoclusters ; Biosensor ; Bimetallic strip ; Multiplex ; Optical sensing ; Foodborne pathogen Nanocluster Synthesis and Applications ; Advanced Nanomaterials in Catalysis ; Gold and Silver Nanoparticles Synthesis and Applications
Authors: Zhuofan Shen | Yang Yang | Zachary M. Sparrow | Brian G. Ernst | Trine Kay Quady | Richard Kang | J.-H. Lee | Yanyu Yang | Lijie Tu | Robert A. DiStasio
Source: The Journal of Physical Chemistry Letters (article, 2025-12-17)
Field: Materials Science — Materials Chemistry

Learning Molecular Conformational Energies Using Semilocal Density Fingerprints We present a general theoretical framework for transforming the information contained in semilocal descriptors of the electron density (i.e., ρ(<b>r</b>) and ∇ρ(<b>r</b>))─the quantum mechanical objects at the very heart of density functional theory (DFT)─into feature vectors that can be used for machine learning (ML). As a proof of principle, we consider the task of learning molecular conformational energies and introduce the semilocal density fingerprint (SLDF) descriptor, which transforms the most energetically relevant information in ρ(<b>r</b>) into compact fixed-size feature vectors that are invariant to translations, rotations, and atomic permutations, unique (at least up to the semilocal DFT exchange-correlation energy), smooth with respect to changes in the atomic positions, and more transferable to systems containing elements from wider swaths of the periodic table. We demonstrate that SLDF-based ML models can predict benchmark conformational energies that are often >100 times more accurate than semilocal DFT, with some predictions approaching spectroscopic accuracy (≈1 cm<sup>-1</sup>). Under the most stringent learning strategy─wherein the training set does not include any conformations of the molecules in the test set─SLDF-based ML models exhibit a sizable degree of transferability and generate conformational energies that are 10-fold more accurate than semilocal DFT for molecules containing first- and second-row main-group elements. As a final assessment of transferability, we demonstrate that SLDF-based ML models can rectify the qualitatively incorrect semilocal DFT description of the oxirene potential energy surface without seeing a single oxirene conformation. Density functional theory ; Invariant (physics) ; Transferability ; Electron density ; Molecule ; Feature (linguistics) ; Energy (signal processing) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Physics Studies
Authors: Hadi Esmaeili Khoshmardan | Maede Esmaeili Khoshmardan | Behnam Khoshandam | Saeed Motesaddi Zarandi | Hassan Abdoos | Mohamadreza Massoudinejad | Milad Darboui | Khashayar Partovi
Source: Nanotechnology for Environmental Engineering (article, 2025-11-15)
Field: Materials Science — Materials Chemistry

Nanomaterials at the forefront of vehicular emission mitigation: a critical review on mechanistic advances, performance breakthroughs, and sustainable engineering strategies Air quality index ; Sustainability ; Sustainable development ; Work (physics) ; Quality (philosophy) ; Environmental quality ; Pollutant Catalytic Processes in Materials Science ; Advanced Photocatalysis Techniques ; Gas Sensing Nanomaterials and Sensors
Authors: Peng Xiong (1459000) | Hongtao Xu (164285) | Haoran Zheng (388894)
Source: Figshare (article, 2025-09-13)
Field: Chemistry — Analytical Chemistry

SupervisedContrastive Learning Leads to More ReasonableSpectral Embeddings Over the past decades, mass spectrometry has served as a fundamental technique for molecular identification in the field of metabolomics, widely applied to the analysis and characterization of biomolecules. However, the complexity of experimental conditions and the structural similarities among compounds pose significant challenges for accurate identification. Recent advancements in deep learning have offered new solutions to address these challenges, particularly demonstrating great potential in generating high-quality spectral embeddings. In this study, we propose a novel method, SpecEmbedding, which leverages a transformer encoder architecture and employs replicated spectra of compounds as positive samples, trained under a supervised contrastive learning framework. By mapping complex mass spectra into low-dimensional vector representations, SpecEmbedding significantly enhances the comparability between spectra, thereby improving identification accuracy. We trained SpecEmbedding on the GNPS training subset and evaluated its performance on the GNPS test subset, the MoNA data set, and the MTBLS1572 data set. Experimental results show that SpecEmbedding achieves a Top-1 hit ratio of 81.73% on the GNPS test subset, substantially outperforming MSBERT (77.81%) and DreaMS (71.90%). The source code for this study is publicly available at https://github.com/sword-nan/SpecEmbedding. A web service is provided at https://huggingface.co/spaces/xp113280/SpecEmbedding. Comparability ; Encoder ; Identification (biology) ; Deep learning ; Support vector machine ; Autoencoder ; Supervised learning Spectroscopy and Chemometric Analyses ; Metabolomics and Mass Spectrometry Studies ; Remote-Sensing Image Classification