jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Dimitris G. Mintis | Sotiris Kallishis | Panagiotis D. Kolokathis | Andreas Tsoumanis | Georgia Melagraki | Joris T.K. Quik | Johannes A.J. Meesters | Iseult Lynch | Dario Greco | Antreas Afantitis
Source: RSC Sustainability (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

SimpleBox4nano: Environmental Fate Modelling of Nanomaterials via the Enalos Cloud Platform This work presents the development of a web application, freely accessible through the Enalos Cloud Platform (https://www.enaloscloud.novamechanics.com/insight/simplebox4nano/), that integrates the environmental fate model SimpleBox4nano, and eliminates the complexities associated with... Cloud computing ; Work (physics) ; Web application ; Environmental systems Nanoparticles: synthesis and applications ; Chemistry and Chemical Engineering ; Graphene and Nanomaterials Applications
Authors: George W. Huber | Euncheol Ra | Yoel Cortes-Pena | David Rivera-Kohr | Lily Callen | Panzheng Zhou | Jiuling Yu | Charles Granger | Nepu Saha | Jordan Klinger | John Estela-Garcia | Fei Long | Trey K. Sato | Morgan Davies | Harrison Appiah | Kevin Nelson | Kevin S. Guigley | Bumjun Kwon | Kyuhyeok Choi | Priscilla Lee | Lewis Tanner | Brittney Vo | A. G. McDonald | Tim A. Osswald | Victor M. Zavala | Reid C. Van Lehn | Ezra Bar-Ziv | Brian G. Fox
Source: Research Square (preprint, 2026-03-13)
Field: Materials Science — Biomaterials

Upcycling Municipal Solid Waste to Polymers and Bioethanol Raw material ; Biofuel ; Municipal solid waste ; Hydrolysis ; Biorefinery ; Residue (chemistry) ; Ethanol fuel ; Polymer biodegradable polymer synthesis and properties ; Biofuel production and bioconversion ; Microplastics and Plastic Pollution
Authors: Shuhei Naganuma | Jiro Kitagawa
Source: ArXiv.org (article, 2026-03-24)
Field: Materials Science — Materials Chemistry

Experimental investigation of magnetic properties of MnFeCo$_{4}$Si$_{2}$ discovered by GNoME AI-driven inorganic materials research has garnered significant attention due to its ability to reduce the time, labor, and cost associated with experiments. An AI model known as GNoME, recently developed by Google DeepMind, is particularly fascinating because it is integrated with the Materials Project open database. The experimental verification of compounds identified by GNoME is a crucial process for advancing AI-driven materials research. Here, we focus on the magnetic compound MnFeCo$_{4}$Si$_{2}$ (Materials ID: mp-3203253), which possesses a layered-like structure. Consistent with the GNoME prediction, MnFeCo$_{4}$Si$_{2}$ crystallizes in a rhombohedral structure with a single-phase nature. We have characterized its magnetic properties and determined that MnFeCo$_{4}$Si$_{2}$ is a soft ferromagnet with a Curie temperature of 1039 K. Curie temperature ; Ferromagnetism ; Focus (optics) ; Curie ; Magnet ; Process (computing) Machine Learning in Materials Science ; Heusler alloys: electronic and magnetic properties ; Chemical and Physical Properties of Materials
Authors: M. K. Seddeek | A. M. A. Shamekh
Source: Research Square (preprint, 2026-03-08)
Field: Materials Science — Polymers and Plastics

Phosphate-Functionalized Chitosan/Ag Nanocomposites: Synergistic Optical Tunability and Enhanced Antimicrobial Activity Chitosan ; Nanocomposite ; Fourier transform infrared spectroscopy ; Amorphous solid ; Antimicrobial ; Doping ; Phosphate ; Dispersion (optics) ; Absorption spectroscopy Polymer Nanocomposite Synthesis and Irradiation ; Electrospun Nanofibers in Biomedical Applications ; Nanocomposite Films for Food Packaging
Authors: Shaolei Song | Peiqi Jiang | Yuanyuan Liu | Lei Lei | Yan Li (23143)
Source: Materials (article, 2026-02-28)
Field: Materials Science — Ceramics and Composites

Effects of Platelet TiB2 on the Formation and Mechanical Properties of (Zr,Ti)B2 Ceramics Prepared by Spark Plasma Sintering (Zr,Ti)B<sub>2</sub> ceramics with enhanced hardness and fracture toughness were prepared by spark plasma sintering using platelet TiB<sub>2</sub> and irregular ZrB<sub>2</sub> as starting powders. The effects of sintering temperature (1700-1900 °C) and platelet TiB<sub>2</sub> content (0-30 wt.%) on the sinterability, phase composition, microstructure, and mechanical properties of the (Zr,Ti)B<sub>2</sub> ceramics were investigated. With increasing sintering temperature, the relative density of the solid solution increased from 89.9 ± 0.5% at 1700 °C to 97.7 ± 0.4% at 1800 °C, followed by no significant change upon further temperature elevation; however, the relative density showed an initial increase and subsequent decrease with increasing TiB<sub>2</sub> content. Under optimized parameters (1800 °C, 3 min, 50 MPa, with a TiB<sub>2</sub> content of 30 wt.%), (Zr,Ti)B<sub>2</sub> ceramics achieve a maximum hardness of 24.9 ± 1.0 GPa, a fracture toughness of 5.0 ± 0.3 MPa·m<sup>1/2</sup>, and a relative density of 96.5 ± 0.5%. The high content of platelet TiB<sub>2</sub> refined the (Zr,Ti)B<sub>2</sub> grain size, reducing the D50 by 25.8% to 1.70 μm compared to the 20 wt.% content. This study provides a novel perspective for the design and preparation of high-performance ceramics. Spark plasma sintering ; Relative density ; Sintering ; Fracture toughness ; Ceramic ; Microstructure Advanced ceramic materials synthesis ; Boron and Carbon Nanomaterials Research ; Metal and Thin Film Mechanics
Authors: Katharina Ueltzen | Aakash A. Naik | Christina Ertural | Philipp Benner | J. George
Source: ChemRxiv (article, 2026-01-13)
Field: Materials Science — Materials Chemistry

Can simple exchange heuristics guide us in predicting magnetic properties of solids? The Kanamori-Goodenough-Anderson rules are a textbook heuristic for predicting magnetism. They connect bond angles to magnetic ordering for some transition metal compounds. Such domain knowledge is of high importance for building predictive machine learning models in scenarios with scarce data. Yet, there has been no statistical, large-scale evaluation of the heuristic. Here, we evaluate this heuristic on an experimental database of magnetic structures. We observe that the heuristic is largely satisfied, and we discuss the exceptions. We then demonstrate how integrating this heuristic into machine learning models for predicting magnetic ordering enhances prediction quality. Notably, these magnetism models are also capable of predicting if non-collinear magnetic ordering might occur. Furthermore, the heuristic provides a useful benchmark for evaluating theoretical methods that calculate magnetic properties. Heuristics ; Heuristic ; Benchmark (surveying) ; Simple (philosophy) ; Domain (mathematical analysis) ; Magnetism Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Inorganic Chemistry and Materials
Authors: Aleksandr Poliukhin | Nicola Colonna | Francesco Libbi | Samuel Poncé | Nicola Marzari
Source: npj Computational Materials (article, 2026-03-03)
Field: Materials Science — Materials Chemistry

Carrier mobilities and electron-phonon interactions beyond DFT Abstract Electron-phonon coupling is a key interaction that governs diverse physical processes such as carrier transport, superconductivity, and optical absorption. Calculating such interactions from first-principles with methods beyond density-functional theory remains a challenge. We introduce here a finite-difference framework for computing electron-phonon couplings for any electronic structure method that provides eigenvalues and eigenvectors, and showcase applications for hybrid and Koopmans functionals, and G W many-body perturbation theory. Our approach introduces a novel projectability scheme based on eigenvalue differences and bypasses many of the limitations of the direct finite difference methods. It also leverages symmetries to reduce the number of independent atomic displacements, decreasing overall computational cost. This approach enables seamless integration with established first-principles codes for generating displaced supercells, performing Wannier interpolations, and evaluating transport properties. Applications to silicon and gallium arsenide show that advanced electronic-structure functionals predict different electron-phonon couplings and modify band curvatures, resulting in much more accurate estimates of intrinsic carrier drift mobilities and effective masses. In general, our method provides a robust and accessible framework for calculating the electron-phonon properties with state-of-the-art beyond DFT methods. Eigenvalues and eigenvectors ; Coupling (piping) ; Perturbation theory (quantum mechanics) ; Gallium arsenide ; Silicon ; Scheme (mathematics) ; Wannier function Machine Learning in Materials Science ; Thermal properties of materials ; 2D Materials and Applications
Authors: Pirscoveanu Denisa Floriana Vasilica | Pluta Ion Dorin | Carmen Vladulescu | Cristina Popescu | Diana-Maria Trașcă | Kristina Radivojević | Renata Maria Varut | Ștefănița Bianca Vintilescu | Mioara Desdemona Stepan | George Alin Stoica
Source: Pharmaceutics (article, 2026-04-07)
Field: Materials Science — Biomaterials

Cyclodextrin-Based Strategies for Brain Drug Delivery: Mechanistic Insights into Blood–Brain Barrier Transport and Therapeutic Applications Cyclodextrins (CDs) have gained increasing attention as versatile platforms for enhancing drug delivery to the central nervous system, particularly in overcoming the restrictive properties of the blood–brain barrier (BBB). Owing to their unique cyclic oligosaccharide structure, CDs are capable of forming inclusion complexes with a wide range of therapeutic agents, thereby improving their solubility, stability, and bioavailability. In addition to their role as excipients, growing evidence indicates that CDs can actively modulate biological processes, including membrane fluidity and cholesterol homeostasis, which are critical factors in neurological disorders. This review explores the application of CDs in facilitating drug transport across the BBB through multiple mechanisms, including carrier-mediated transport, receptor-mediated transcytosis, and nanoparticle-based delivery systems. Special emphasis is placed on their use in the treatment of neurodegenerative and neurological diseases, such as Alzheimer’s disease, Parkinson’s disease, multiple sclerosis, Niemann–Pick type C disease, and other central nervous system disorders. In these contexts, CD-based formulations have demonstrated the ability to enhance brain targeting, reduce pathological protein aggregation, and improve therapeutic outcomes in preclinical models. This review uniquely integrates cyclodextrin’s physicochemical properties with specific blood–brain barrier transport mechanisms, proposing a structure–transport–therapy framework that enables a more predictive understanding of brain-targeted drug delivery. Drug ; Drug delivery ; Blood–brain barrier ; Central nervous system ; Targeted drug delivery ; Drug discovery Nanoparticle-Based Drug Delivery ; Barrier Structure and Function Studies ; Advanced Drug Delivery Systems
Authors: Alfonso Monzamodeth Roman Sedano | José Gonzálo González Reyes | Ignacio Alejandro Figueroa Vargas | Eliezer Hernández Mecinas
Source:  (article, 2026-02-16)
Field: Materials Science — Materials Chemistry

Materiales del futuro: La inteligencia artificial impulsando el diseño de aleaciones de alta entropía El desarrollo de aleaciones metálicas avanzadas, como las aleaciones de alta entropía, requiere explorar millones de posibles combinaciones químicas, lo cual sería inviable mediante métodos tradicionales de prueba y error. Este artículo presenta un enfoque moderno y accesible que integra algoritmos genéticos y redes neuronales artificiales para acelerar el diseño de nuevos materiales metálicos. A través de herramientas computacionales, es posible identificar composiciones óptimas que combinan alta dureza, estabilidad térmica y estructuras atómicas simples como la cúbica centrada en el cuerpo. Se demuestra cómo estas tecnologías permiten avanzar de manera más eficiente hacia materiales aplicables en sectores críticos como el aeroespacial, la energía y la biomedicina. Work (physics) ; Order (exchange) Machine Learning in Materials Science ; High Entropy Alloys Studies ; Material Selection and Properties
Authors: Paulina Piotrkiewicz | Justyna Zygmuntowicz | Marcin Wachowski | Ireneusz Szachogłuchowicz | W. Kaszuwara
Source: Materials (article, 2026-03-12)
Field: Materials Science — Ceramics and Composites

Effect of Pulse Plasma Sintering Temperature on Microstructure and Mechanical Properties of Al2O3-Cu Composites Al<sub>2</sub>O<sub>3</sub>-Cu ceramic-metal composites containing 2.5 vol.% of a metallic phase were fabricated using the Pulse Plasma Sintering (PPS) method in order to evaluate the influence of sintering temperature on densification, microstructure, and mechanical performance. Consolidation was carried out at 1200 °C, 1250 °C, 1300 °C, and 1400 °C under uniaxial pressure with a short sintering time of 3 min. Regardless of the processing temperature, all composites exhibited very high relative densities exceeding 99% of the theoretical value, indicating the high efficiency of PPS in densifying Al<sub>2</sub>O<sub>3</sub>-Cu systems while suppressing copper leakage. X-ray diffraction confirmed the presence of only two phases, Al<sub>2</sub>O<sub>3</sub> and Cu, with no secondary reaction products. Microstructural observations revealed irregular copper particles and areas of dispersed metallic phase, whose proportion decreased with increasing sintering temperature due to accelerated matrix densification and copper immobilization. Grain growth in the alumina matrix was strongly temperature-dependent, with the average equivalent grain diameter increasing from 0.49 µm at 1200 °C to 2.35 µm at 1400 °C. Hardness decreased from 19.5 ± 2.8 GPa to 12.2 ± 1.6 GPa with increasing temperature, whereas fracture toughness reached a maximum of 5.42 ± 0.65 MPa·m0.5 at 1400 °C. The highest strength under monotonic compression conditions was obtained for samples sintered at 1300 °C, indicating an optimal balance between densification and microstructural coarsening. These results demonstrate that PPS is an effective method for producing dense Al<sub>2</sub>O<sub>3</sub>-Cu composites with tailored microstructure and mechanical properties. Sintering ; Microstructure ; Relative density ; Fracture toughness ; Grain size ; Grain growth ; Copper ; Powder metallurgy Advanced ceramic materials synthesis ; Aluminum Alloys Composites Properties ; Powder Metallurgy Techniques and Materials
Authors: Jovan Louzeiro Silva | Gabriel Nascimento Tavares | Andrey Marcos Mendonça Ferreira | Romário Costa Ribeiro | Carlos Riedel Porto Carreiro | Ronan Corrêa Santos | Diego Aurélio dos Santos Cunha
Source: Revista Brasileira de Engenharia de Pesca (article, 2026-01-03)
Field: Chemistry — Analytical Chemistry

ÍNDICE DE FRESCOR OCULAR: UM MODELO MATEMÁTICO BASEADO EM IMAGENS PARA AVALIAÇÃO NÃO DESTRUTIVA DA QUALIDADE DE Pangasius sp. A avaliação do frescor do pescado é um aspecto fundamental para garantir a qualidade do produto e a segurança alimentar, porém os métodos tradicionalmente empregados apresentam limitações relacionadas à subjetividade, ao custo e à necessidade de infraestrutura laboratorial. Nesse contexto, o presente estudo teve como objetivo desenvolver e validar um modelo matemático não destrutivo, denominado Índice de Frescor Ocular (IFO), baseado na análise de imagens do olho de Pangasius sp., visando à avaliação objetiva do frescor e da qualidade do pescado ao longo do armazenamento em gelo. Foram analisados exemplares em três tempos distintos: imediatamente após a captura (frescor ideal), após 8 dias (frescor moderado) e após 22 dias de estocagem em gelo (frescor ruim). As imagens do olho foram obtidas sob condições padronizadas e submetidas a técnicas de processamento digital para extração de variáveis ópticas relacionadas ao brilho, transparência, homogeneidade da textura e reflexividade especular. Essas variáveis foram integradas em um modelo matemático contínuo, resultando no IFO. Os resultados demonstraram alterações mensuráveis nos parâmetros oculares em função do tempo de estocagem, evidenciando a sensibilidade do índice às mudanças associadas à deterioração pós-morte. O método proposto mostrou-se viável, de baixo custo e aplicável em condições reais de comercialização, configurando-se como uma ferramenta promissora para complementar os métodos tradicionais de avaliação do frescor do pescado. Quality (philosophy) ; Epidemiological surveillance Spectroscopy and Chemometric Analyses ; Edible Oils Quality and Analysis ; Sensory Analysis and Statistical Methods
Authors: Gaurav Achuda | Tamme Claus | Manuel Torrilhon | Silvia Richter
Source: EPJ Web of Conferences (article, 2026-01-01)
Field: Materials Science — Surfaces, Coatings and Films

Characterisation and Validation of Submicron Structures in a Fe–Al–Nb Alloy Using Inverse Modelling of the <i>P</i> <sub> <i>N</i> </sub> Method for EPMA Using conventional matrix correction methods, the spatial resolution of EPMA is limited by the size of the electron interaction volume. Secondary and back-scattered electron imaging techniques achieve higher spatial resolution, but can only provide structural information at the surface of the sample. In this study, we present a method for reconstructing complex three-dimensional material structures in EPMA by means of inverse modelling, based on the deterministic spherical harmonic P N method for simulating electron transport in solids. We apply this approach to characterise submicron structures in an Fe-Al-Nb alloy, where the structural features are smaller than the interaction volume. The accuracy of the reconstructed material structure is further validated by comparing with cross-sectional micro-structural information obtained via FIB/SEM tomography. Electron microprobe ; Inverse ; Alloy ; Matrix (chemical analysis) ; Electron ; Spherical harmonics ; Inverse problem Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications ; Advanced Materials Characterization Techniques
Authors: Mehmet Merkepçi
Source: Scientific Reports (article, 2026-04-10)
Field: Materials Science — Polymers and Plastics

Microscopic image dataset for air-permeability classification of patterned knitted fabrics This study presents a comprehensive multimodal dataset and an integrated analysis framework aimed at predicting key structural properties of patterned knitted fabrics using advanced image-based techniques. High-resolution microscope images were collected from forty-five fabric groups, each exhibiting different porosity levels, structural densities, and pattern configurations. For each fabric sample, twenty color images were captured under controlled lighting conditions, and 12 class labels were assigned based on expert-validated porosity categories. The dataset reflects real-world variability due to differences in yarn density, stitch geometry, and color–texture interactions, making the classification task inherently challenging. To evaluate the discriminative power of deep learning models on this fabric dataset, three widely used convolutional neural network architectures—VGG19, EfficientNet-B3, and DenseNet-121—were trained and compared using k-fold cross-validation. Class imbalance was addressed through the use of Focal Loss, and performance was assessed using accuracy, confusion matrices, and fold-averaged metrics. Among the tested architectures, VGG19 achieved the highest overall accuracy, while DenseNet-121 showed balanced performance across several porosity categories. Overall, VGG19 achieved the highest cross-validation accuracy of 87.42% and a final test accuracy of 79.01% demonstrating robust predictive performance for estimating the structural properties of patterned knitted fabrics. Discriminative model ; Convolutional neural network ; Confusion ; Pattern recognition (psychology) ; Task (project management) ; Class (philosophy) ; Porosity ; Yarn Textile materials and evaluations ; Advanced Sensor and Energy Harvesting Materials ; Industrial Vision Systems and Defect Detection
Authors: Teemu Härkönen | Hui Chen | Erik M. Vartiainen∥
Source: ArXiv.org (article, 2026-03-27)
Field: Chemistry — Analytical Chemistry

Bayesian estimation of optical constants using mixtures of Gaussian process experts We propose modeling absorption spectrum measurements as mixtures of Gaussian process experts. This enables us to construct a flexible statistical model for interpolating and extrapolating measurements, facilitating statistical integration of Kramers-Kronig relations to estimate the whole complex refractive index. Additionally, we statistically model the anchoring points used in subtractive Kramers-Kronig relations to account for possible measurement errors of the anchor point. In addition to flexible statistical modeling, the mixtures of Gaussian process formulation enables automatic selection of measurement points to use for extrapolation. We apply the method to experimental absorption spectrum measurements of gallium arsenide, potassium chloride, and transparent wood. Gaussian process ; Gaussian ; Statistical model ; Process (computing) ; Subtractive color ; Bayesian optimization ; Bayesian probability ; Estimation theory Spectroscopy and Chemometric Analyses ; Spectroscopy and Laser Applications ; Material Dynamics and Properties
Authors: Zehua Li | Guisheng Zou | Jinpeng Huo | Jin Peng | Tianming Sun | Yu Xiao | Jiali Huo | Bin Feng | Lei Liu
Source: Light Science & Applications (article, 2026-03-06)
Field: Materials Science — Materials Chemistry

Dual-mode 0D/2D spatial asymmetry optoelectronic device enabled by in situ microzone femtosecond laser deposition To develop artificial intelligence and humanoid robotics, it is crucial to fabricate advanced vision systems with high efficiency and versatility. A key challenge is the functional integration of high-speed photodetectors (PD) and neuromorphic vision sensors (NVS) into a single device, as current studies suffer from complex architectures or fabrication processes. Hence, we propose a universal Microzone Femtosecond Laser Deposition (M-FLD) technique that enables the localized, in situ deposition of zero-dimensional (0D) black phosphorus (BP) nanoparticles onto a two-dimensional (2D) MoS<sub>2</sub> channel by ablating a micro-scale solid-state target. By M-FLD and h-BN nanomask, we fabricated a spatial asymmetric 0D/2D heterostructure for highly integrated dual-mode optoelectronic device. By changing the direction of V<sub>ds</sub>, the device can be converted from PD to NVS. Under the PD mode, the device can sense high-frequency optical signals up to 3030 Hz. Under the NVS mode, the device's optical energy consumption per activity is only 191.2 pJ. Based on the sensing and memory capabilities, the device is simulated for MNIST handwritten digit recognition, achieving an accuracy of up to 96.20%. This work provides a flexible and powerful platform for fabricating complex heterostructures, paving the way for highly integrated and reconfigurable neuromorphic vision systems. Neuromorphic engineering ; Femtosecond ; Photodetector ; Laser ; Fabrication ; Heterojunction 2D Materials and Applications ; Advanced Sensor and Energy Harvesting Materials ; Neural Networks and Reservoir Computing
Authors: Lutfun Nahar
Source: eMicrobe (article, 2026-01-05)
Field: Materials Science — Biomaterials

In Silico Technologies Advancing Microbial Science: A Visionary Review Scilight Press is an international academic publisher dedicated to open-access peer-reviewed journals and books. As a global multidisciplinary publisher, we provide a platform for researchers, scholars, and academics to disseminate their work across diverse fields of study. We are committed to advancing knowledge and fostering academic discourse. Multidisciplinary approach ; Dissemination ; Work (physics) ; In silico Diatoms and Algae Research ; Silicon Effects in Agriculture ; Alexander von Humboldt Studies