jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Syam Prasad Ammineni | Sampath Kumar Siddam | Md. Aleem Pasha | V. R. Manthena | D. Madhavi | T. Anuradha | G. Prakasham | P. S. V. Ramana Rao
Source: Polymers for Advanced Technologies (article, 2026-04-01)
Field: Materials Science — Polymers and Plastics

A Hybrid Machine Learning Approach to Shelflife Prediction of <scp>NBR</scp> for Long‐Term Storage Applications ABSTRACT This study examines the thermal degradation kinetics and shelf‐life prediction of Nitrile Butadiene Rubber (NBR) using thermogravimetric analysis and a hybrid machine learning framework. Both virgin and two‐year‐aged NBR samples were assessed with model‐free kinetic methods: Ozawa–Flynn–Wall (OFW), Kissinger–Akahira–Sunose, and Kissinger. The activation energy for virgin samples ranged from 67.26 to 73.33 kJ/mol; for aged samples, it was slightly lower (66.9–72.57 kJ/mol), indicating molecular degradation. Shelf life estimated at 40°C with Toop's equation decreased from 111.29 to 89.01 years under OFW kinetics. An Arrhenius‐type exponential degradation model, using a Random Forest (RF)–AdaBoost algorithm (80/20 train‐test split), achieved high accuracy ( R 2 &gt; 0.999, RMSE &lt; 1%, MAPE &lt; 2%). The fitted parameters (Ψ ≈0.043°C −1 ; Υ = 0.08–0.11; Φ ≈1) and deviations of less than 1% from the experimental shelf life confirm the robustness of the nonlinear degradation prediction. Thermogravimetric analysis ; Robustness (evolution) ; Degradation (telecommunications) ; Exponential function ; Nonlinear system ; Thermal ; Kinetic energy ; Kinetics ; Natural rubber Polymer Nanocomposites and Properties ; Fiber-reinforced polymer composites ; Epoxy Resin Curing Processes
Authors: Haixia Kong | Wenbo Liu | Yan Wang | Bing Liu | Pu Zhang | Yi Peng Wang | Yi Wang
Source: Chemical Engineering Journal (article, 2026-04-01)
Field: Materials Science — Materials Chemistry

Machine learning-assisted dual-mode sensing array: NIR-enhanced Cu-Co3O4 nanozymes for high-throughput antioxidant discrimination Antioxidant ; Antioxidant capacity ; Nanoparticle Advanced Nanomaterials in Catalysis ; Nanoplatforms for cancer theranostics ; Nanocluster Synthesis and Applications
Authors: Mao Chen | Jiacong Li | Yong Li | Liangjun Yang | Chuncan Zhou | Yihui Yang | Wen Li | Youqian Chen | Gang Logan Liu | Wenjun Hu
Source: Analytical Chemistry (article, 2026-03-12)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Direct Visualization and Quantitative Modeling of Mass Transport Limitation in Biomolecular Kinetics via Imaging-Based Meta-SPR Directly observing how mass transport limitation (MTL) distorts biomolecular kinetics has long remained out of reach. Here, we harness an imaging-based metasurface plasmon resonance (Meta-SPR) platform to both visualize and chemically quantify MTL effects in real time, revealing a pronounced flow-path signal decay beyond bulk depletion. This spatially resolved capability uniquely links optical contrast to the local analyte concentration, enabling quantitative mapping of MTL severity. Inspired by neural network loss-landscape theory, we show that the traditional mass transport coefficient (<i>k</i><sub>m</sub>) is fundamentally unidentifiable, exhibiting flat loss valleys that prevent robust fitting. To address this, we introduce two practical alternatives: a pseudoactivity model replacing <i>k</i><sub>m</sub> with a single factor (α<sub>p</sub>) for intuitive MTL quantification and a free-<i>R</i><sub>max</sub> model that extracts robust kinetic parameters without known ligand density. Both strategies are compatible with standard fitting software and validated across Meta-SPR, SPR, and BLI platforms, offering broadly applicable tools for accurate affinity and kinetics determination. Kinetics ; Analyte ; Mass transport ; Visualization ; Surface plasmon resonance ; Contrast (vision) ; Plasmon ; Mass spectrometry Gold and Silver Nanoparticles Synthesis and Applications ; Advanced Fluorescence Microscopy Techniques ; Plasmonic and Surface Plasmon Research
Authors: Haiting Yu | Ming Xu | Lin Nie | Yukui Tong | Miaomiao Tian
Source: Talanta (article, 2026-03-20)
Field: Materials Science — Materials Chemistry

Deep learning-assisted Ce/Zr-MOF nanozyme hydrogel sensor for colorimetric/photothermal dual-mode detection of glyphosate Glyphosate ; Quantitative analysis (chemistry) ; Fluorescence ; Colorimetry Advanced Nanomaterials in Catalysis ; Pesticide and Herbicide Environmental Studies ; Carbon and Quantum Dots Applications
Authors: Didar Üçüncüoğlu | Vedat Arda Küçük | Selim Buyrukoğlu
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Chemistry — Organic Chemistry

Discrimination of RBD Vegetable Oils Using ATR-FTIR Spectra and a PCA Based Stacked Ensemble Approach Principal component analysis ; Pattern recognition (psychology) ; Support vector machine ; Random forest ; Artificial neural network ; Fourier transform ; Gaussian ; Feature (linguistics) ; Ensemble learning ; Receiver operating characteristic Edible Oils Quality and Analysis ; Spectroscopy and Chemometric Analyses ; Polymer composites and self-healing
Authors: Yun Luo | Xinyuan Yang | Rongjun Liu | Hongxiang Zheng | Wenchun Jiang
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

Multi-objective Optimization of Solid Oxide Fuel Cell with Interfacial Delamination Cracking Delamination (geology) ; Power density ; Renewable energy ; Durability ; Work (physics) ; Solid oxide fuel cell ; Oxide ; Power (physics) Advancements in Solid Oxide Fuel Cells ; Catalysts for Methane Reforming ; Fuel Cells and Related Materials
Authors: Changping Li | Hui Liao | Xiuzhen Yang | Ying Yi
Source: Sensors and Actuators A Physical (article, 2026-03-24)
Field: Materials Science — Polymers and Plastics

ANN-assisted GO-CNT-PTFE photochromic patch for ultraviolet radiation monitoring Ultraviolet ; Photochromism ; Ultraviolet radiation ; Wearable computer ; Software deployment ; Radiation ; Visible spectrum ; Sunlight Transition Metal Oxide Nanomaterials ; Skin Protection and Aging ; Advanced Sensor and Energy Harvesting Materials
Authors: William Sandholt | Nicolas Gauquelin | John Mangeri | Edwin Dollekamp | Gyanendra Panchal | Tamazouzt Chennit | Annick De Backer | Arno Annys | Nikolas Vitaliti | Andrea Roberto Insinga | Jonas Mejlby Hansen | Rajesh Mandal | Davi R. Rodrigues | Sandra Van Aert | Katja Wurster | Arghya Bhowmik | Ivano E. Castelli | So̷ren Bredmose Simonsen | Thomas S. Jespersen | Richard D. James | Bharat Jalan | Jo Verbeeck | Juan Maria Garcia Lastra | Nini Pryds
Source: arXiv (Cornell University) (preprint, 2026-03-28)
Field: Materials Science — Materials Chemistry

Designing dislocation-driven polar vortex networks in twisted perovskites Twisting two atomic layers produces a geometric moire pattern, but bonding-induced interfacial reconstruction fundamentally transforms this into an ordered dislocation network - a distinction obscured in weakly-bonded van der Waals systems. Although in-plane topological vortex nanostructures arising from twisting-induced lateral strain modulation have been linked to periodic moire patterns in freestanding perovskite layers and 2D bilayers, their coupling to the interfacial dislocation network in twisted layers remains unresolved. Here we demonstrate that twisting freestanding SrTiO3 layers undergo interfacial reconstruction into a network of screw dislocations, accompanied by the emergence of in-plane topological vortices. Unlike in previous reports, these vortices are associated with the periodicity of the dislocation network rather than with geometric moire patterns. Four-dimensional scanning transmission electron microscopy (4D-STEM) reveals long-range ordered vortex-antivortex arrays with nearly continuous polarisation rotation. A machine-learning interatomic potential, trained on first-principles calculations, together with phase-field modelling, confirms that competing strains within the dislocation network stabilize polar vortex-antivortex pairs and drive the emergence of an electronic superlattice with a well-defined periodicity. Our results establish twist-controlled dislocation networks as a new and versatile route to designing local polar and electronic structures in oxide materials. Superlattice ; Dislocation ; Vortex ; Moiré pattern ; Polar ; van der Waals force ; Perovskite (structure) ; Topological defect Electronic and Structural Properties of Oxides ; Topological Materials and Phenomena ; Chemical and Physical Properties of Materials
Authors: Cong Liu | Huabo Huang | Dr X. Yu | Yuanhang Luo | Yongnan Jiang | Yong Li | Xi Zhang | Zhaoyang Wan | Jiayou Ji | Yijun Yu | Liang Li
Source: ACS Applied Polymer Materials (article, 2026-02-20)
Field: Materials Science — Biomaterials

Machine Learning-Validated Silk Fibroin Composite Hydrogels: Robust, Antifreezing, and Conductive Sensors Owing to their renewable nature and biocompatibility, silk fibroin composite hydrogels have been widely employed as flexible strain sensors in recent studies. However, three major limitations hindering the application of flexible devices include prolonged dialysis-induced undesirable ion loss, elevated interfacial impedance due to spatially heterogeneous distribution of exogenous ions, and dysfunction caused by low temperature. This study proposes a simple in situ ion preloading method for fabricating the PZS composite hydrogel composed of silk fibroin (SF), poly(vinyl alcohol) (PVA), zinc chloride (ZnCl2), and ethylene glycol (EG). This strategy significantly increases the cross-linking sites within hydrogel networks while retaining uniform ion distribution. The resulting hydrogel architecture exhibits excellent mechanical properties (tensile stress exceeding 1.92 MPa at 500% strain), outstanding conductivity (up to 12 S/m), and retainable functionality at low temperature (without freezing at −120 °C). The hydrogel can serve as an effective strain sensor with the ability to detect multiple types of human movements. Notably, the strain sensor exhibits 99.06% accuracy in motion digit recognition through machine learning-assisted verification. This research presents a significant advancement by circumventing the intricate procedures of conventional approaches. Through strategically optimized material compositions and fabrication techniques, the synergistic enhancement of antifreezing, mechanical, and conductive properties is accomplished, providing an idea for designing future flexible electronics. Fibroin ; Composite number ; Fabrication ; Self-healing hydrogels ; Electrical conductor ; Ethylene glycol ; SILK Silk-based biomaterials and applications ; Advanced Sensor and Energy Harvesting Materials ; Hydrogels: synthesis, properties, applications
Authors: Zhiyuan Gao | Xiaomo Jiang
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

Bridging Physics and Data: A Transfer Learning Transformer-Kolmogorov-Arnold Networks Hybrid Paradigm for Fatigue Damage Prediction with Scarce Experiments Bridging (networking) ; Encoder ; Transfer of learning ; Experimental data ; Artificial neural network ; Transformer ; Nonlinear system ; Reliability (semiconductor) ; Training set Machine Learning in Materials Science ; Gaussian Processes and Bayesian Inference ; Model Reduction and Neural Networks
Authors: Alexander Weilacher | A. Schneider | Tobias Reichenstein | J. Franke
Source: Procedia CIRP (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Towards Machine Learning for Quality-Driven Production Optimization of Optical Thin-Film Coatings Machine learning (ML) is increasingly applied across various industrial domains; however, its adoption in the field of industrial production optimization of optical thin-film coatings remains limited. Existing research primarily focuses on the design of optical multilayer systems or the modeling of material properties to enhance application-specific performance. In contrast, the application of ML for quality-driven production process optimization and control remains largely unexplored due to complex interactions among coating and machining parameters. This paper addresses this gap by outlining a roadmap for implementing ML-based quality optimization in optical multilayer thin-film coating processes, using ion beam-assisted physical vapor deposition (IBAD-PVD) as a representative case. The investigated use case involves the coating of optical lenses, where ML models are trained on time-series data of machine parameters to predict spectral performance metrics relevant to quality assessment. The paper first presents experimental results from initial studies which, although preliminary, show promising trends. The highest achieved coefficient of determination (R²) in these experiments is ~0.5. Based on a feature importance analysis and other empirical findings, the paper proposes new conceptual approaches and outlines future research directions aimed at advancing ML integration into industrial thin-film production workflows. Coating ; Machining ; Production (economics) ; Field (mathematics) ; Quality (philosophy) ; Process (computing) ; Industrial production ; Design of experiments Machine Learning in Materials Science ; Optical Coatings and Gratings ; Laser Material Processing Techniques
Authors: Shenbaga Moorthy Balakrishnan | Hifzur Rahman Ansari | Taoufik Nedjadi
Source: Biophysical Journal (article, 2026-04-01)
Field: Chemistry — Spectroscopy

Proteome-Wide Structural and Interaction Analysis Using Cross-Linking Mass Spectrometry and its Applications Mass spectrometry ; Analytical Chemistry (journal) ; Quantitative analysis (chemistry) ; Protein mass spectrometry ; Ion-mobility spectrometry–mass spectrometry Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Advanced Biosensing Techniques and Applications
Authors: G. Arunachalam | Anand Karuppannan | Sandeep Prabhu | U. Suresh Kumar
Source: Results in Physics (article, 2026-02-17)
Field: Materials Science — Materials Chemistry

Machine learning enhanced terahertz metasurface biosensor based on MXene–Graphene–Black phosphorus heterostructure for peptide detection Terahertz metasurface biosensors offer a promising platform for label-free and non-invasive biomolecular detection.However, many existing designs rely on limited material platforms and operate over narrow refractive-index ranges, restricting their practical applicability in complex biological environments. In this work, a hybrid MXene–graphene–black phosphorus (BP) based terahertz metasurface biosensor is proposed for the quantitative detection of peptide biomolecules. The sensor is systematically analyzed using finite element method simulations in COMSOL Multiphysics to optimize the resonator geometry and investigate its electromagnetic response. The influence of key parameters, including graphene Fermi level, incident wave angle, and structural dimensions, on the transmission spectra is comprehensively examined. The optimized design achieves a high refractive-index sensitivity of 500 GHz/RIU over a broad operating range of 1.606–1.706 RIU, which is well suited for peptide sensing applications. To accelerate performance evaluation, a machine-learning framework based on Bayesian Ridge Regression is employed to predict resonance frequency shifts and transmission characteristics, yielding a strong predictive accuracy with R2 = 0.94. Comparative analysis confirms the competitive sensitivity and wide dynamic range of the proposed sensor. Overall, the results demonstrate the strong potential of the proposed metasurface platform for rapid, label-free, and quantitative peptide biomarker detection in terahertz diagnostic applications. Terahertz radiation ; Biosensor ; Heterojunction ; Peptide ; Phosphorus MXene and MAX Phase Materials ; Terahertz technology and applications ; 2D Materials and Applications
Authors: Raphael M. Tromer | Isaac M. Félix | Rafael Besse | Marcelo L. Pereira Junior | Marcos G. E. da Luz
Source: Open MIND (preprint, 2026-02-26)
Field: Materials Science — Materials Chemistry

A Comparative Study of Structural Representations for 2D Materials: Insights from Dynamic Collision Fingerprint and Matminer In materials science, the selection of structural descriptors for machine learning protocols strongly influences predictive performance and the degree of physical interpretability that can be achieved from the derived models. Although more complex descriptors may improve numerical accuracy, they often represent extra computational load, also reducing transparency into the underlying structural information. A framework called the Dynamic Collision Fingerprint (DCF) was recently proposed with the goal of producing concise, physically significant representations, generating descriptors via dynamical probing of atomic structures. In this work, we benchmark DCF using a dataset composed of 120 two-dimensional carbon allotropes and compare its performance with the widely considered Matminer library. The analysis employs three regression models, linear regression, decision tree, and XGBoost, evaluated over train and test partitions ranging from 10\% to 90\% and repeated over multiple random seeds in order to characterize statistical variability. The obtained results demonstrate that DCF easily matches Matminer in terms of predicting accuracy across all learning algorithms. However, it accomplishes this using descriptors that are significantly lower dimensional, pointing to manageable computing costs. Moreover, compared to the rather technical Matminer descriptions, the DCF exhibits considerably clearer physical interpretability. These findings suggest that DCF is a significant substitute for high-dimensional descriptor libraries as structural representation since it is both computationally flexible and physically grounded. Interpretability ; Representation (politics) ; Benchmark (surveying) ; Collision ; Feature selection ; Fingerprint (computing) ; Selection (genetic algorithm) ; Regression Machine Learning in Materials Science ; Nanopore and Nanochannel Transport Studies ; Advanced Graph Neural Networks
Authors: Mohd Taukeer Khan | Saleh O. Allehabi
Source: Journal of Materials Science (article, 2026-04-10)
Field: Materials Science — Materials Chemistry

Interpretable machine learning analysis of stability mechanisms and feature interactions in halide perovskite solar cells Perovskite (structure) ; Halide ; Stability (learning theory) ; Feature (linguistics) ; Pattern recognition (psychology) ; Polymer Machine Learning in Materials Science ; Perovskite Materials and Applications ; Explainable Artificial Intelligence (XAI)
Authors: N Roshini | Sahil Kumar | Mudit Dixit | Sujata Mandal
Source: Langmuir (article, 2026-03-04)
Field: Materials Science — Materials Chemistry

<i>In Situ</i> -Grown Zeolitic Imidazolate Framework-8@Mg–Fe Layered Double-Hydroxide Heterostructure with Enhanced Adsorption Properties: Insights from Experimental and Computational Investigations The removal of organic pollutants, especially dyes, from wastewater remains a critical environmental challenge. Metal-organic frameworks (MOF) and layered double hydroxides (LDH) have individually demonstrated a strong adsorption potential for organic and inorganic contaminants in aqueous media. However, integrating their complementary properties into heterostructures offers a promising route to synthesize efficient adsorbent materials for water/wastewater treatment. In this work, a hierarchical zeolitic imidazolate framework-8@Mg-Fe layered double-hydroxide (ZIF-8@Mg-Fe-LDH) heterostructure was synthesized by <i>in situ</i> growth of ZIF-8 on Mg-Fe LDH, and its structure-activity relationship was investigated for the adsorption of model organic dyes, acid orange 7 (anionic) and rhodamine B (cationic). The ZIF-8@Mg-Fe-LDH heterostructure exhibited both micro- and mesopores, with a specific surface area that is larger than that of the parent LDH. First-principles density functional theory (DFT) calculations, combined with machine-learning interatomic potential (MLIP) studies, provided atomic-level insights into the adsorption mechanism. These findings provide insights into the structure-function relationship between ZIF-8@Mg-Fe LDH and organic pollutants (specifically dyes) and provide a pathway for designing new metal-organic framework@layered double-hydroxide heterostructures for the efficient remediation of aqueous pollutants. Heterojunction ; Adsorption ; Imidazolate ; Aqueous solution ; Rhodamine B ; Zeolitic imidazolate framework ; Methyl orange ; Layered double hydroxides Layered Double Hydroxides Synthesis and Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Adsorption and biosorption for pollutant removal
Authors: N Roshini | Sahil Kumar | Mudit Dixit | Sujata Mandal
Source: Figshare (article, 2026-03-04)
Field: Materials Science — Materials Chemistry

InSitu-Grown Zeolitic ImidazolateFramework-8@Mg–Fe Layered Double-Hydroxide Heterostructurewith Enhanced Adsorption Properties: Insights from Experimental andComputational Investigations The removal of organic pollutants, especially dyes, from wastewater remains a critical environmental challenge. Metal–organic frameworks (MOF) and layered double hydroxides (LDH) have individually demonstrated a strong adsorption potential for organic and inorganic contaminants in aqueous media. However, integrating their complementary properties into heterostructures offers a promising route to synthesize efficient adsorbent materials for water/wastewater treatment. In this work, a hierarchical zeolitic imidazolate framework-8@Mg–Fe layered double-hydroxide (ZIF-8@Mg–Fe-LDH) heterostructure was synthesized by <i>in situ</i> growth of ZIF-8 on Mg–Fe LDH, and its structure–activity relationship was investigated for the adsorption of model organic dyes, acid orange 7 (anionic) and rhodamine B (cationic). The ZIF-8@Mg–Fe-LDH heterostructure exhibited both micro- and mesopores, with a specific surface area that is larger than that of the parent LDH. First-principles density functional theory (DFT) calculations, combined with machine-learning interatomic potential (MLIP) studies, provided atomic-level insights into the adsorption mechanism. These findings provide insights into the structure–function relationship between ZIF-8@Mg–Fe LDH and organic pollutants (specifically dyes) and provide a pathway for designing new metal–organic framework@layered double-hydroxide heterostructures for the efficient remediation of aqueous pollutants. Heterojunction ; Adsorption ; Imidazolate ; Aqueous solution ; Rhodamine B ; Zeolitic imidazolate framework ; Methyl orange ; Density functional theory Layered Double Hydroxides Synthesis and Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Adsorption and biosorption for pollutant removal
Authors: Behnam Talari | Kimia Masoudifar | Sayem Ul Alam
Source: Food Analytical Methods (article, 2026-03-04)
Field: Chemistry — Analytical Chemistry

Applications of Raman and FTIR Spectroscopy in Monitoring Antibiotic Residues in Bovine Milk: A Critical Overview Raman spectroscopy ; Tetracycline ; Residue (chemistry) ; Veterinary drug ; Antibiotics ; Spectroscopy ; Residual ; Tetracycline antibiotics ; Fourier transform infrared spectroscopy Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Listeria monocytogenes in Food Safety
Authors: Incé A. Husain | Amer Marwan El-Samman | Stijn De Baerdemacker
Source: Theoretical Chemistry Accounts (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Occupancies vs embeddings: internal representations of Ab Initio and graph probabilistic models. Ab initio ; Probabilistic logic ; Observable ; Representation (politics) ; Quantum ; Embedding ; Graph ; Wave function Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Advanced Chemical Physics Studies