jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: D. S. Kiran | Susheela K. Lenkennavar
Source: Prabha Materials Science Letters (article, 2026-03-01)
Field: Materials Science — Materials Chemistry

Theoretical Evaluation of Density and Radiation Shielding Performance of Fluoride Glasses This work investigated an empirical equation to predict the density of different fluoride glass compositions. The calculated values showed a close match with the theoretical density model proposed by Makishima and Mackenzie (1973), showed strong agreement with the experimental values. The calculations were used some compositional factors including molecular weight, molecular fractions, and the ionic radii of different elements mixed with fluoride, the values of ionic radii derived from "Revised Effective Ionic Radii and Systematic Studies of Interatomic Distances in Halides and Chalcogenides" (Shannon, 1976). In order to theoretically evaluate the radiation shielding properties of fluoride glasses, such as the Mean Free Path (MFP), Half-Value Layer (HVL), Tenth-Value Layer (TVL), Linear Attenuation Coefficient (LAC), and Mass Attenuation Coefficient (MAC), the density value is essential (Inaba and Fujino, 2010). Fluoride ; Attenuation ; Electromagnetic shielding ; Attenuation coefficient ; Ionic bonding ; Ionic radius ; Mass attenuation coefficient ; Work (physics) Radiation Shielding Materials Analysis ; Graphite, nuclear technology, radiation studies ; Thermal Expansion and Ionic Conductivity
Authors: Michael Ringleb | Michael Streiber | Theresa Lutz | Lena Rother | Elisa-Maria Bachinger | Jonas De Breuck | Christopher Kuenneth | Ulrich S. Schubert | Stefan Zechel | Anja Traeger | Meike N. Leiske
Source: RSC Applied Polymers (article, 2026-01-01)
Field: Materials Science — Surfaces, Coatings and Films

Design of tuneable, biocompatible anionic polymers by <scp>l</scp> -norvaline amino acid post-polymerisation modification of pentafluorophenyl acrylate copolymers PFPA copolymers were modified with l -norvaline to tune the hydrophobicity. The resulting biocompatible, non-haemolytic polyanions associated with mammalian cells in the investigated cases, highlighting their potential for delivery applications. Copolymer ; Biocompatible material ; Polymer ; Amino acid ; Acrylate Polymer Surface Interaction Studies ; Antimicrobial agents and applications ; Advanced Polymer Synthesis and Characterization
Authors: Hong Li | Xi Wu | Doudou Tang | Peiru Zhang | Junjie Deng | M. Li | Jian Zhuo | Hanjie Shao | J Li | Jie Ma | Huigang Li | Xin Zhou | Yanhua Wan | Haili Li | Wei Jian | Y. Liu | Xiao Xu | Di Lu
Source: Research Square (preprint, 2026-03-10)
Field: Materials Science — Surfaces, Coatings and Films

Nanoengineered Dual-Functional Protein Coatings Conferring Antithrombotic and Antimicrobial Protection to Vascular Stents Antithrombotic ; Coating ; Coagulation ; Platelet ; Adhesion ; Thrombosis ; Nanofiber ; Biofilm ; In vitro Polymer Surface Interaction Studies ; Electrospun Nanofibers in Biomedical Applications ; Hemoglobin structure and function
Authors: Shaymaa E. Sorour | A. E. Amin
Source: Research Square (preprint, 2026-03-10)
Field: Materials Science — Polymers and Plastics

AI-Powered Industrial Quality Assurance System for Fancy Yarn Using Computer Vision and 3D Visualization Yarn ; Quality assurance ; Visualization ; Reliability (semiconductor) ; Quality (philosophy) ; Feature (linguistics) ; Machine vision ; Textile Textile materials and evaluations ; Industrial Vision Systems and Defect Detection ; Dyeing and Modifying Textile Fibers
Authors: Kacper Drużbicki | Rafał Dolot | Sławomir Kaźmierski | Marek J Potrzebowski
Source: ChemRxiv (article, 2026-02-13)
Field: Materials Science — Biomaterials

Self-Assembly of Binary Peptide System in the Solid State Induced by the Presence of Environmental Water - An Unusual Case of Ex-Vivo Peptides Cooperation Peptide ; Solid-state ; State (computer science) ; Peptide sequence ; Component (thermodynamics) Supramolecular Self-Assembly in Materials ; Polydiacetylene-based materials and applications ; Cephalopods and Marine Biology
Authors: J Ma | Ying Yu | Jin Liu
Source: Light Science & Applications (article, 2026-01-04)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Harnessing optical bound states in the continuum for ultrafast, reconfigurable, long-range photonic networks Bound states in the continuum (BICs) provide a route to strong, long-range photonic coupling with dynamic tunability. Recent advances demonstrate that BIC metasurfaces enable reconfigurable two-dimensional coupling between arbitrarily positioned resonators, with the added capability of ultrafast all-optical control. Photonics ; Ultrashort pulse ; Coupling (piping) ; Bound state ; Coupling strength ; Quantum optics ; Optical communication ; Integrated optics Metamaterials and Metasurfaces Applications ; Neural Networks and Reservoir Computing ; Plasmonic and Surface Plasmon Research
Authors: Md Farhadul Islam | Saeed Arabha | Ray Hixon | Reza Rizvi
Source: Advanced Engineering Materials (article, 2026-02-17)
Field: Materials Science — Materials Chemistry

Compressible Flow Exfoliation of Two‐Dimensional Nanomaterials: Insights Into Layer Separation Informed by Gas Dynamics As the technological implications of 2D nanomaterials like graphene, hexagonal boron nitride (h‐BN), and molybdenum disulfide (MoS 2 ) continue to expand, a critical need arises for the streamlined production of these materials. Their characteristics such as high mechanical robustness, exceptional electrical and thermal conductivities, and substantial surface area effects underscore this urgency. This study delves into an innovative technique, the compressible flow exfoliation (CFE) method, for the aim of achieving scalable production of 2D h‐BN nanosheets. The CFE technique capitalizes on the properties of high‐pressure gas, laden with 2D nanomaterials, as it traverses an orifice, acting as a converging–divergent (CD) nozzle. In such situations, the supersonic flow results in the formation of shock waves, exerting a sudden drag force on the nanomaterials. However, it is revealed that such shock events alone are not sufficient for separating the 2D layers in nanomaterials. Through detailed particle laden‐flow analysis and experiments, we reveal that the key to initiating exfoliation lies in the presence of a thrust force, which is critical for effectively separating these layers. The ensuing flow structure understanding is validated using a CD nozzle and a shock tube experiment, yielding the insight that a thrust force, originating from acceleration at the nozzle throat, is essential for material exfoliation. The rapid and continuous nature of the CFE process signifies its superiority over conventional 2D material exfoliation methods, attributing to environmentally friendly processing, decreased defect emergence, and adaptability to a wide variety of 2D layered materials utilizing a gaseous medium. Exfoliation joint ; Shock (circulatory) ; Drag ; Flow (mathematics) ; Nozzle ; Molybdenum disulfide ; Layer (electronics) ; Boron nitride ; Supersonic speed ; Hexagonal boron nitride Graphene research and applications ; Thermal properties of materials ; Solar-Powered Water Purification Methods
Authors: Yajie Hao | Qi-Ming Ding | Xiaoting Wang | Xiao Yuan
Source: Journal of Chemical Theory and Computation (preprint, 2026-01-13)
Field: Materials Science — Materials Chemistry

Large-Scale Efficient Molecule Geometry Optimization with Hybrid Quantum–Classical Computing Accurately and efficiently predicting the equilibrium geometries of large molecules remains a central challenge in quantum computational chemistry, even with hybrid quantum-classical algorithms. Two major obstacles hinder progress: the large number of qubits required and the prohibitive cost of conventional nested optimization. In this work, we introduce a co-optimization framework that combines Density Matrix Embedding Theory (DMET) with Variational Quantum Eigensolver (VQE) to address these limitations. This approach substantially reduces the required quantum resources, enabling the treatment of molecular systems significantly larger than previously feasible. We first validate our framework on benchmark systems, such as H<sub>4</sub> and H<sub>2</sub>O<sub>2</sub>, before demonstrating its efficacy in determining the equilibrium geometry of glycolic acid (C<sub>2</sub>H<sub>4</sub>O<sub>3</sub>)─a molecule of a size previously considered intractable for quantum geometry optimization. Our results show the method achieves high accuracy while drastically lowering computational cost. This work thus represents a significant step toward practical, scalable quantum simulations, moving beyond the small, proof-of-concept molecules that have historically dominated the field. More broadly, our framework establishes a tangible path toward leveraging quantum advantage for the in silico design of complex catalysts and pharmaceuticals. Embedding ; Quantum ; Scalability ; Qubit ; Benchmark (surveying) ; Quantum computer ; Quantum algorithm ; Density matrix ; Topology (electrical circuits) ; Energy minimization Machine Learning in Materials Science ; Quantum Computing Algorithms and Architecture ; Advanced Chemical Physics Studies
Authors: Louis Bastogne | Lukas Korosec | Evgenios Stylianidis | Daniel G. Porter | Gareth Nisbet | Clémentine Thibault | Jean-Marc Triscone | Marios Hadjimichael | Philippe Ghosez
Source: arXiv (Cornell University) (article, 2026-03-29)
Field: Materials Science — Electronic, Optical and Magnetic Materials

First-order polarization process as an alternative to antiferroelectricity Antiferroelectrics generate significant interest since their polarization versus electric field (PE) curves show typical double-hysteresis loops appealing for various applications. Unfortunately, antiferroelectrics are rare. In magnetic compounds, magnetization versus magnetic field (M-H) curves can show analogous double hysteresis loops not only in antiferromagnets but also in systems exhibiting field-induced first-order reorientation of the magnetization through a so-called first-order magnetization process. Here, we show that appealing double-hysteresis P-E loops can also appear from an unprecedented first-order polarization process. Focusing on non-polar CaTiO3, which can be turned ferroelectric under tensile strain, we study epitaxial thin films on differently-oriented NdGaO3 substrates using a combination of theoretical and experimental techniques. We uncover that a certain configuration exhibits double-hysteresis P-E loops that we rationalize from a field-induced abrupt rotation of the polarization. Such a first-order polarization process establishes a promising alternative pathway to achieve double hysterisis P-E loops appealing for practical applications. Magnetization ; Antiferroelectricity ; Polarization (electrochemistry) ; Ferroelectricity ; Hysteresis ; Magnetic field ; Magnetic hysteresis Multiferroics and related materials ; Ferroelectric and Piezoelectric Materials ; Ferroelectric and Negative Capacitance Devices
Authors: Marina Valentini Arf | Ênio Antônio Manfroi | Nairiane dos Santos Bilhalva | Dthenifer Cordeiro Santana | Paulo Eduardo Teodoro | Larissa Pereira Ribeiro Teodoro | Paulo Carteri Coradi
Source: International Agrophysics (article, 2026-02-06)
Field: Chemistry — Analytical Chemistry

Spectral signatures of the physicochemical quality of white, black, red, and parboiled rice processed using non-destructive technologies (VIS/SWIR) combined with machine learning algorithms Ab s t r a c t.This study aimed to correlate the physicochemical attributes of rice grains using non-destructive techniques combined with machine learning algorithms.Samples of white, black, red, and parboiled rice were analyzed using hyperspectral spectroscopy (350-2 500 nm) and subjected to linear regression (LR), random forest (RF), gradient boosting (GB), support vector machine (SVM), convolutional neural networks (CNN), and recurrent neural networks (RNN) prediction.Spectral and physicochemical data were examined through multivariate analysis (PCA) and cross-validation using performance metrics such as R, R, MAE, and RMSE.The results indicated that SVM, RF, and GB outperformed the other algorithms, showing higher accuracy and lower variability in the prediction, SVM reached R = 0.952 and R = 0.904, with MAE = 0.409 and RMSE = 0.582, followed closely by RF with R = 0.950 and MAE = 0.416, and GB with R = 0.947 and MAE = 0.431.Black rice stood out for its high protein, lipid, and ash contents.Parboiled rice showed higher fiber content, and white rice was notable for its elevated starch content.Hyperspectral spectroscopy proved effective in differentiating rice types, enabling the identification of relevant spectral bands for optimized sensors.Overall, integrating non-destructive technologies with machine learning shows strong potential for industrial applications.K e y w Quality (philosophy) ; Spectral signature ; Pattern recognition (psychology) ; Hyperspectral imaging ; Random forest ; Signature (topology) Spectroscopy and Chemometric Analyses ; GABA and Rice Research ; Water Quality Monitoring and Analysis
Authors: Hajime Sugita | Akira Kodaka | Akira Takahashi | K. Mikami | Hideyuki Otsuka
Source: Polymer Chemistry (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Design of a thermally stable succinonitrile mechanophore featuring electron-withdrawing triazine rings for radical polymerization A triazine-based succinonitrile mechanophore was designed using radical stabilization energy calculations and simulations. It shows high thermal stability and mechanochromism via C–C bond cleavage, enabling its use in radical polymerization. Succinonitrile ; Thermal stability ; Radical polymerization ; Polymerization ; Triazine ; Thermal Luminescence and Fluorescent Materials ; Force Microscopy Techniques and Applications ; Supramolecular Chemistry and Complexes
Authors: Ali Yasar | Adem Gölcük
Source: Multimedia Tools and Applications (article, 2026-02-02)
Field: Chemistry — Analytical Chemistry

Performance analysis of deep feature extraction, feature fusion and feature selection with machine learning techniques in classification of chickpea seeds Abstract Chickpeas are an essential food source rich in carbohydrates and proteins. However, distinguishing among different chickpea varieties remains challenging due to their high morphological similarity. Most recent studies rely on handcrafted or traditional feature-based methods, which are time-consuming, sometimes destructive, and often lack generalization capability. To overcome these limitations, we present a non-destructive computer vision and machine learning framework for efficient chickpea seed classification. Deep features were extracted using convolutional neural networks, fused to capture complementary representations, and reduced through the Tree–Seed Algorithm (TSA), a metaheuristic optimization method for feature selection. The selected features were classified using various machine learning algorithms, achieving a maximum accuracy of 95.6%. TSA reduced the feature dimensionality by approximately 60%, significantly decreasing training time while preserving high accuracy. Compared with existing studies reporting accuracies between 83% and 94%, the proposed approach improves classification performance by up to 12%. To the best of our knowledge, this is the first study to integrate deep feature fusion with TSA-based feature selection for chickpea seed classification. The results demonstrate a robust, efficient, and non-destructive alternative to conventional approaches. Feature selection ; Feature (linguistics) ; Convolutional neural network ; Pattern recognition (psychology) ; Generalization ; Curse of dimensionality ; Deep learning ; Dimensionality reduction Spectroscopy and Chemometric Analyses ; Genetic and Environmental Crop Studies ; Agricultural Engineering and Mechanization
Authors: Saurabh Tiwari | Ambika Gupta
Source: Materials (article, 2026-03-14)
Field: Materials Science — Polymers and Plastics

A Machine Learning Framework for the Reconstruction of Composite Fatigue and Fracture Properties: A Synthetic Data Study This study presents a machine learning framework for the reconstruction of fatigue life and fracture toughness in natural fiber-reinforced composites, evaluating the predictive accuracy of six regression algorithms-Random Forest, Gradient Boosting, Support Vector Machine, Neural Network, Ridge Regression, and Lasso Regression-using a controlled synthetic dataset of 600 samples generated from established Basquin fatigue and Rule of Mixtures fracture equations, incorporating stochastic noise calibrated to experimental scatter (CV = 15-50%), with log-normal noise standard deviation of 0.20 for fatigue life and Gaussian noise standard deviation of 0.15 for fracture toughness. The dataset encompasses eight natural fiber types (flax, jute, sisal, hemp, bamboo, coconut, banana, and pineapple) and five matrix systems (epoxy, polyester, PLA, vinyl ester, and polyurethane). Models were evaluated using a 70-15-15 train-validation-test split with 5-fold cross-validation and exhaustive grid search hyperparameter optimisation. Gradient Boosting achieved R<sup>2</sup> = 0.93 for fatigue life and Stacking Ensemble achieved R<sup>2</sup> = 0.87 for fracture toughness, representing 97% and 89% of their respective noise-ceiling values (theoretical maximum R<sup>2</sup> of 0.96 and 0.98 given the programmed noise levels). The ML models perform supervised function approximation-learning to reconstruct the programmed generation equations rather than discovering novel physical composite behaviour-and function as automated surrogates for the governing equations. Feature importance analysis identified engineered composite indicators, stress amplitude, and fiber length as the most influential parameters. The framework provides a reproducible ML evaluation pipeline as a methodological template for future experimental composite studies. Hyperparameter optimization ; Hyperparameter ; Support vector machine ; Standard deviation ; Fracture (geology) ; Noise (video) ; Gradient boosting ; Synthetic data ; Experimental data Natural Fiber Reinforced Composites ; Mechanical Behavior of Composites ; Composite Material Mechanics
Authors: Eslam Syala | W. Sadik | Abdel-Ghaffar M. El-Demerdash | Waffa Mekhamer | M. Essam El-Rafey
Source: Scientific Reports (article, 2026-01-06)
Field: Materials Science — Polymers and Plastics

Impact of partial cement dust replacement in unsaturated polyester: assessing material performance and waste valorization for sustainable management This study is a continuation of the earlier studies (The effective treatment of dye‑containing simulated wastewater by using the cement kiln dust as an industrial waste adsorbent) and (The effective remediation of heavy metal-laden wastewater by employing cement dust derived from industrial activities as a sorbent) as an attempt to provide a comprehensive image of the possible useful uses of cement dust. In this research, a polymer composite consisting of an unsaturated polyester (UP) thermoset matrix and micro-sized cement kiln dust (CKD) as filler with an addition percentage range of 0-10% was synthesized to achieve maximum utilization of this harmful waste that influences the environment and public health. Studying the structure revealed the presence of characteristic UP and CKD peaks in all the XRD and IR spectra, confirming the physical interaction between the filler and the matrix. The inclusion of CKD up to 10% decreased the degradation temperature of the UP resin. The water absorption data for the UP-CKD composites revealed a maximum water uptake of 0.55% with nonlinear behavior compared to UP doped with other inorganic fillers. An increase in the CKD content to 10% increased the limiting oxygen index (LOI) of UP above the oxygen percentage in the air (21%), promoting the fire resistance properties of the system. Prime mechanical properties in terms of ultimate tensile strength, Young's modulus, bending (flexural) strength, and flexural modulus decreased from 27.54 to 7.57 MPa, and from 1118.4 to 260.45 MPa, and from 25 to 11.49 MPa, and from 4.68 to 2.15 MPa, respectively, while both elongation (%) at break and hardness revealed fluctuating behavior with increasing CKD content. Poor dispersion and agglomeration, and hence low adhesion and poor bonding between the CKD filler and the UP, were the main reasons for this observed declining behavior, as exhibited from SEM illustrations. The as-prepared UP-CKD can be used in various applications where there is no need for distinctive mechanical performance, such as tables and benches manufacturing. Cement ; Flexural strength ; Ultimate tensile strength ; Cement kiln ; Wastewater ; Absorption of water ; Filler (materials) ; Composite number Flame retardant materials and properties ; Natural Fiber Reinforced Composites ; Fiber-reinforced polymer composites
Authors: I. Festi | E. Alfinelli | D. Bessas | F. Caporaletti | A. I. Chumakov | M. Moratalla | M. A. Ramos | M. Rodríguez-López | C. Rodríguez-Tinoco | J. Rodríguez-Viejo | G. Baldi
Source: ArXiv.org (article, 2026-02-25)
Field: Materials Science — Materials Chemistry

Effect of glass stability on the low frequency vibrations of vapor deposited glasses Ultra-stable glasses prepared from the physical vapor deposition of organic molecules present a very low density of two-level states, the kind of glass defects that determine their peculiar low temperature thermal properties. Numerical simulations suggest that quasi-localized harmonic vibrational modes emerge in the soft regions associated with two-level states. However, the connection between the low frequency vibrational modes and the local structural instabilities of glasses remains unexplained. Here we exploit a recently developed spectrograph for nuclear resonant analysis of inelastic X-ray scattering to probe the density of vibrational states of amorphous thin films of ultra-stable and conventional glasses down to an exceptionally low frequency of $\sim 70$ GHz. We show that the glass stability does not affect the harmonic vibrational modes at the lowest frequencies, despite a reduction of almost an order of magnitude in the density of two-level states. At the same time, the vibrational modes at higher frequencies, around the boson peak maximum, are extremely sensitive to the glass stability. Although we cannot exclude the possible existence of quasi-localized modes in glasses, we show that their presence is not strictly necessary to describe the measured density of low frequency vibrations. The experimental developments here presented pave the way to the solution to the long-standing debate on the low frequency vibrations in glasses. Low frequency ; Molecular vibration ; Vibration ; Phonon ; Harmonic ; Polyamorphism ; Thermal stability ; Amorphous solid ; Stability (learning theory) Material Dynamics and Properties ; Advanced Physical and Chemical Molecular Interactions ; Spectroscopy and Quantum Chemical Studies