jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Merve Fedai | Albert L. Kwansa | Yaroslava G. Yingling
Source: Data (article, 2026-01-13)
Field: Materials Science — Materials Chemistry

A Comprehensive Dataset and Workflow for Building Large-Scale, Highly Oxidized Graphene Oxide Models Graphene (GRA) and graphene oxide (GO) have drawn significant attention in materials science, chemistry, and nanotechnology because of their tunable physicochemical properties and wide range of potential uses in biomedical and environmental applications. Building reliable, large-scale molecular models of GRA and GO is essential for molecular simulations of wetting, adsorption, and catalytic behavior. However, current methods often struggle to generate large, chemically consistent sheets at high oxidation levels. In addition, the resulting structures are frequently incompatible across different simulation packages. This work introduces a step-by-step protocol with custom Tool Command Language (Tcl) and modified Python version 3.12 scripts for building large-scale, AMBER-compatible GO structures with oxidation levels from 0% to 68%. The workflow applies a systematic surface modification strategy combined with post-processing and atom-type assignment routines to ensure chemical accuracy and force field consistency. The dataset includes fifteen MOL2 format files of 20 × 20 nm2 GO sheets, ranging from pristine to highly oxidized surfaces, each validated through oxidation-ratio analysis and structural integrity checks. Together, the dataset and protocol provide a design of scalable and chemically reliable GO molecular models for molecular dynamics simulations. Workflow ; Graphene ; Scripting language ; Oxide ; Scalability ; Python (programming language) ; Molecular dynamics ; Protocol (science) Machine Learning in Materials Science ; Graphene and Nanomaterials Applications ; Graphene research and applications
Authors: David Herrera-Poyatos | Andrés Herrera-Poyatos | Rosana Montes | Paloma de Palacios | Luis García Esteban | Alberto García‐Iruela | Francisco García Fernández | Francisco Herrera
Source: Applied Intelligence (article, 2026-03-05)
Field: Chemistry — Organic Chemistry

Deep learning methodology for the identification of wood species using high-resolution macroscopic images and patch-voting Tools for automatic wood species identification are needed worldwide in order to support sustainable timber trade. This work explores the application of computer vision techniques to classify high-resolution macroscopic images of timber. The main challenge of this problem is that fine-grained patterns in timber are crucial in order to accurately identify wood species, and these patterns are not learned by convolutional neural networks (CNNs) trained on low resolution images. This work introduces the Timber Deep Learning Identification with Patch-based Inference Voting methodology, abbreviated TDLI-PIV methodology. This methodology exploits the concept of patching and the availability of high-resolution macroscopic images of timber in order to overcome the inherent challenges that CNNs face in timber identification. The TDLI-PIV methodology is able to capture fine-grained patterns in timber and, moreover, boosts robustness and prediction accuracy via a collaborative voting inference process. In this work we also introduce a new data set of marcroscopic images of timber, called GOIMAI-Phase-I, which has been obtained using optical magnification, and is openly published online in zenodo. Our experiments have assessed the performance of the TDLI-PIV methodology, including a comparison with other methodologies available in the literature, an exploration of data augmentation methods and the effect that the dataset size has on the accuracy of TDLI-PIV. Convolutional neural network ; Robustness (evolution) ; Inference ; Deep learning ; Identification (biology) ; Exploit ; Voting ; Artificial neural network Wood and Agarwood Research ; Wood Treatment and Properties ; Advanced Neural Network Applications
Authors: Robin Turlin | Thibaut Hirschler | Théo Calais | Hichem Seriket | Gaël Chevallier | Mahdi Bodaghi | Frédéric Demoly
Source: International Journal of Mechanical Sciences (article, 2026-03-03)
Field: Materials Science — Materials Chemistry

Large-scale computational design and simulation of viscoelastic metastructures for vibration attenuation Low-frequency vibration generated by rotating machinery in submarines pose a persistent challenge for acoustic stealth, as they are readily detected by passive sonar and are poorly mitigated by conventional passive or active control technologies under variable operating conditions. This paper introduces a scalable computational framework that couples shape-grammar-driven generative design with finite element simulations to systematically explore viscoelastic metastructures for vibration attenuation. The generative design method is defined by six dimension-independent geometric parameters, enabling automated synthesis of a broad and non-intuitive design space beyond traditional unit-cell parameterizations. Vibration transmissibility of each metastructure is quantified through numerical simulations, which are validated against experimental measurements on representative specimens. The results reveal viscoelastic metastructures exhibiting pronounced and tunable low-frequency attenuation bandwidths, therefore providing enhanced attenuation compared to conventional designs. The resulting dataset establishes a structured mapping between geometry and dynamic response, offering new insight into geometry-driven vibration mitigation mechanisms. Beyond forward analysis, the proposed framework provides a scalable foundation for data-driven and inverse design of metastructures targeting robust low-frequency vibration attenuation. • Scalable generative design of sophisticated metastructures for vibration attenuation. • Automated design-simulation enables large-scale metastructure discovery. • Data-driven links between geometry and damping reveal high-performance designs. Attenuation ; Vibration ; Finite element method ; Viscoelasticity ; Scalability ; Vibration control ; Generative Design ; Parametric statistics Nonlocal and gradient elasticity in micro/nano structures ; Composite Material Mechanics ; Acoustic Wave Phenomena Research
Authors: Tingting Liu | Yunqiang Bian | Siyu Wang | Yang Li | Yi Cao | Yonghua Jiao | Hai Pan
Source: Biomolecules (article, 2026-01-08)
Field: Materials Science — Surfaces, Coatings and Films

Residue-Specific Dock-Loosen-Unfold Mechanism of GB1 on Nanoparticle Surfaces Revealed by Kinetic and Φ-Value Analysis Nanoparticles interact dynamically with proteins, often leading to adsorption-induced conformational changes that alter protein function and contribute to corona formation. Here we investigated the adsorption and unfolding of a model protein GB1 on latex nanoparticle surfaces using a combination of mutational analysis, equilibrium binding assays, stopped-flow kinetics and Φ-value interpretation. Seven site-directed variants of GB1 were studied to dissect residue-specific contributions to adsorption energetics. Fluorescence binding isotherms revealed that D46A and T53A mutations weakened surface affinity, while kinetic analysis demonstrated that D46A reduced adsorption rate by ~6-fold and produced a dramatic unfolding/refolding shift, identifying Asp46 as a key docking site. Φ-value analysis further highlighted Asp46 and Thr53 as central residues in the adsorption transition state, whereas mutations in the hydrophobic core or distal loops had negligible effects. These results support a dock-loosen-unfold mechanism in which electrostatic recognition initiates binding, followed by hydrophobic exposure and hairpin stabilization. This residue-level sampling of key sites advances mechanistic understanding of protein-nanoparticle interactions and suggests strategies for tuning surface charge to control corona formation. Our approach provides a generalizable method to map adsorption transition states, with implications for designing safer nanomaterials, predicting protein corona composition, and harnessing protein unfolding in biosensing applications. Adsorption ; Nanoparticle ; Kinetics ; Docking (animal) ; Static electricity ; Surface charge ; Plasma protein binding ; Protein adsorption ; Fluorescence Polymer Surface Interaction Studies ; Nanoparticle-Based Drug Delivery ; Proteins in Food Systems
Authors: Ashwini Chhatre | Nitesh Saini | Abhijeet Kumar Parmar | Pushpendra Rana | Mayank Kumar Jain
Source: Scientific Data (article, 2026-02-03)
Field: Chemistry — Organic Chemistry

BarkVisionAI: Novel dataset for rapid tree species identification Tree species identification and mapping is crucial for forest management, biodiversity conservation, and ecological research. Bark images can be captured easily from the ground-level and can provide large amount of information about the tree species and its health. Yet, existing datasets for tree bark images are often limited in scope, lacking diversity in species representation and temporal attributes. To address these limitations, we present BarkVisionAI, a comprehensive dataset of 156001 tree bark images for 13 species collected from diverse forest types across India. Each image is labeled with location, species name, device attributes, and timestamp, providing a robust foundation for studying species identification and the variability of bark characteristics. We are providing detailed metadata information about each image, encouraging its use in ecological research, machine learning model training, and environmental monitoring. Benchmarking experiments using standard image classification models demonstrate the dataset's utility and effectiveness, highlighting its potential as a valuable resource for developing reliable, real-world applications in automated tree species identification and environmental change monitoring. Identification (biology) ; Metadata ; Tree (set theory) ; Bark (sound) ; Biodiversity ; Benchmarking Wood and Agarwood Research ; Smart Agriculture and AI ; Advanced Neural Network Applications
Authors: Run-Yang Zhang | Deng-Ju Cai | L Wu | Wen-Ting Yin | Yu-Xiang Ma | Shu-Guang Gao | Hua-Min Liu
Source: Food Chemistry X (article, 2026-03-08)
Field: Chemistry — Organic Chemistry

Isothermal-microwave pretreatment enhances the quality and oxidative stability of cold-pressed oil from short-term germinated sesame seeds Enhancing the oxidative stability of cold-pressed sesame oil using green methods without high-temperature treatments remains challenging. Short-term germination is a common but limited solution. In this study, isothermal-microwave pretreatment (80 °C, 0-60 min) was applied to short-term germinated sesame seeds, followed by cold pressing. Results showed that moderate-duration pretreatments (10-30 min) yielded higher oil extraction rates (81.06%-82.22%) than the 0-min pretreatment (79.71%). Moreover, the β-tocopherol, γ-tocopherol, sesamolin, and total phenolics contents of the oils increased by 17.22%-35.14%, 10.90%-17.28%, 16.18%-25.60%, and 7.24%-26.19%, respectively, resulting in enhanced radical scavenging capacity and oxidative stability. This was because isothermal-microwave pretreatment promoted their migration into the oil bodies. Furthermore, the oil obtained from short-term germinated seeds pretreated with isothermal-microwave for 15 min also exhibited better frying stability than oil from seeds without pretreatment. Thus, this work presents a promising approach for producing high-quality cold-pressed oil with enhanced oxidative stability. Germination ; Oxidative phosphorylation ; Sesame oil ; Extraction (chemistry) ; Sesame seed ; Yield (engineering) Edible Oils Quality and Analysis ; Sesame and Sesamin Research ; Biodiesel Production and Applications
Authors: Badamasi Haruna | Ibrahim Abdullahi | Umma Abdullahi
Source: Journal of Engineering and Applied Science (article, 2026-01-14)
Field: Materials Science — Polymers and Plastics

A review on the use of agricultural and animal byproducts in polymer composites: a sustainable approach to materials reinforcement Abstract The increasing need for eco-friendly replacements to synthetic materials has motivated wide research into polymer biocomposites reinforced with natural byproducts. This review investigates the use of agricultural wastes and animal byproducts, specifically rice husk (RH) and cow bone particles (CBP), as reinforcements in polymer matrices for sustainable material development. RH, rich in silica, enhances thermal stability and stiffness, while CBP, primarily composed of calcium phosphate and collagen, improves toughness, bioactivity, and mechanical strength. The review summarizes recent studies on both single and hybrid composites, signifying that RH-CBP hybrid systems can offer synergetic enhancements in flame resistance, strength, thermal behavior, and biodegradability. These hybrid biocomposites find applications in automotive parts, structural panels, packaging materials, biomedical, and consumer goods. Comparative assessments reveal that, despite challenges such as moisture absorption and dispersion, bio-fillers are a cost-effective and eco-friendly substitute to traditional synthetic fibers, meeting the increasing need for sustainable materials. The paper also highlights research gaps, specifically in optimizing hybrid bio-fillers ratios, certifying durability under diverse environmental settings, and providing standardized processing techniques, stressing the need for further research into hybridization strategies for multifunctional applications. Durability ; Husk ; Agriculture ; Natural polymers ; Thermal stability ; Raw material ; Sustainability ; Automotive industry Natural Fiber Reinforced Composites ; Flame retardant materials and properties ; Nanocomposite Films for Food Packaging
Authors: Mikhail Frolov | Trofim A. Lozhkarev | Elmira A. Vasilieva | Л. А. Васильева | Almaz A. Zagidullin | L. Ya. Zakharova | Galim Alimzhanovich Kungurov | Natalia Trachtmann | Shamil Validov
Source: Preprints.org (preprint, 2026-01-06)
Field: Materials Science — Materials Chemistry

Novel Silicone-Polyol Antifoam Emulsions: Impact on Foam Control and Physiology of Diverse Microbial Cultures The selection of an optimal antifoam is critical for efficient fermentation, as industrial agents often have detrimental side effects like growth inhibition, while some can enhance productivity. This study presents a rational approach to developing and screening novel silicone-polyol antifoam emulsions. A key finding was the discovery of selective antibacterial activity in agent 3L10, which strongly inhibited Gram-positive bacteria (especially Corynebacterium glutamicum) but not Gram-negative strains. This specificity, likely mediated by interaction with the mycolic acid layer of C. glutamicum, highlights the necessity for strain-specific antifoam testing. A comprehensive evaluation protocol—combining chemical design, cytotoxicity screening across diverse microorganisms, determination of minimum effective concentrations (MEC), and validation in model bioreactor fermentations—was established. Through this process, agent 6T80 was identified as a promising candidate. It exhibited low MEC, high emulsion stability, no cytotoxicity, and did not impair growth or recombinant protein production in B. subtilis or P. putida fermentations. The study concludes that agent 6T80 is suitable for further application in processes involving Gram-negative and certain Gram-positive hosts, whereas agent 3L10 serves as a valuable tool for studying surfactant-membrane interactions. The developed methodology enables the targeted selection of highly efficient and biocompatible antifoams for specific biotechnological processes. Defoamer ; Bioreactor ; Corynebacterium glutamicum ; Bacteria ; Emulsion ; Mycolic acid Pickering emulsions and particle stabilization ; Biopolymer Synthesis and Applications ; Bacterial biofilms and quorum sensing
Authors: Kyle A. Vanderschoot | Jorge Padilla | Kelli A. Steineman | Christopher M. De Caro | Yongheng Wang | A Wang | Marie C. Heffern | Elizabeth K. Neumann
Source: Analytical Chemistry (article, 2026-03-19)
Field: Chemistry — Spectroscopy

Spatial Multiomics Combining Lipids and Gene Expression Using MALDI ISH MSI Current spatial gene expression techniques rely upon DNA microarrays, next-generation sequencing, and fluorescence microscopy, which are often costly, time-consuming, and/or restricted in the sampling area. Matrix-assisted laser desorption/ionization <i>in situ</i> hybridization (MALDI ISH) mass spectrometry imaging (MSI) combines the spatiotemporal capabilities of <i>in situ</i> hybridization with MSI using photocleavable peptide mass tags and nucleic acid probes to enable serial detection of lipidomic and gene expression data. We synthesized a copper-catalyzed azide-alkyne cycloaddition (CuAAC) <i>o</i>-nitrobenzylic azide linker that is compatible with solid phase peptide synthesis (SPPS) to enable a flexible and modular conjugation platform between DNA sequences and peptide mass tags. After conjugation of the mass tag and hybridization to the native RNA target, the triazole-functionalized <i>o</i>-nitrobenzyl linker can undergo photolytic cleavage to separate the hybridized nucleic acid sequence from its corresponding peptide mass tag. Using this approach, we synthesized 33 unique photocleavable mRNA probes and successfully validated 12 distinct gene expression patterns across sagittal sections of fresh-frozen murine brain of both wild-type and a Canavan's disease model. This method enables codetection of native RNA and lipidomic signals from the same tissue section, providing an alternative strategy for spatial multiomic analysis. While this study demonstrates a 12-plex implementation, the platform is scalable and may support expanded multiplexing with continued optimization of peptide design and instrumentation. This approach is broadly applicable across disease models and offers new insights into transcriptome-metabolome interactions. Peptide nucleic acid ; Linker ; Peptide ; Nucleic acid ; Gene expression ; DNA ; RNA ; Oligonucleotide Mass Spectrometry Techniques and Applications ; Click Chemistry and Applications ; Molecular Biology Techniques and Applications
Authors: WAN NUR AZRINA WAN MUHAMMAD | ZAKIAH KAMDI | AINUN RAHMAHWATI AINUDDIN | MD.NOR ANUAR MOHAMAD
Source: Research Square (preprint, 2026-01-27)
Field: Materials Science — Materials Chemistry

Bibliometric Analysis of Shielded Metal Arc Welding for Hardfacing Applications Hardfacing ; Shielded metal arc welding ; Welding ; Submerged arc welding ; Arc welding ; Gas tungsten arc welding Metal Alloys Wear and Properties ; High Entropy Alloys Studies ; Engineering and Environmental Studies
Authors: Michele Ceriotti | David E. Manolopoulos | Thomas E. Markland | Mariana Rossi
Source: arXiv (Cornell University) (article, 2026-03-30)
Field: Materials Science — Materials Chemistry

Path Integral Methods in Atomistic Modelling: An Introduction This book provides an introduction to path integral methods and their application to modeling atomistic processes. The book covers both the foundational theory and recently developed simulation techniques. The text provides a self-contained resource and was originally developed for the CECAM schools on Path Integral Methods. Path integral formulation ; Path (computing) ; Calculus (dental) ; Integral equation ; Line integral Block Copolymer Self-Assembly ; Advanced Physical and Chemical Molecular Interactions ; Nanopore and Nanochannel Transport Studies
Authors: Antoine Monnet | Teresa Frąś | Slim Bahi | R. Bernier | Yaël Demarty | Anthony Bracq | Antoine Guitton | A. Rusinek
Source: SPIRE - Sciences Po Institutional REpository (preprint, 2026-03-13)
Field: Materials Science — Materials Chemistry

Microstructure-driven mechanical response and constitutive modeling of a Fe–Cr–C hardfacing deposit under quasi-static and dynamic loading Fe-Cr-C hardfacing alloys are widely used to protect components in severe and complex environments, yet their intrinsic mechanical response under controlled loading remains insufficiently documented. This work presents a combined experimental–numerical investigation of a highly alloyed Fe–Cr–C hardfacing deposit welded onto an S355MC base substrate, with emphasis on the coupling between microstructural gradients, strain-rate effects, and damage-driven behavior. Compression samples extracted at several distances from the substrate–deposit interface were tested in compression from quasi-static to dynamic regimes using a universal quasi-static press and a Split Hopkinson Pressure Bar (SHPB) apparatus, complemented by high-speed imaging and digital image correlation. The deposit exhibits a high-strength, low-ductility response with compressive strengths up to 2.5 GPa and failure governed by early damage and brittle fragmentation. Strong differences are observed between carbide-rich upper layers and more diluted interfacial regions, highlighting the key role of local microstructure and defect populations. Experimental-mechanical results were numerically modeled using the non-linear explicit code IMPETUS Afea Solver. A Johnson–Holmquist (JH-2) constitutive model was calibrated through an analytical identification, optimization, and inverse numerical simulations. The model successfully reproduced the deposit stress–strain relationship and main fracture features for different microstructural states. The proposed framework provides a practical approach for integrating experimentally derived material laws into more severe loading numerical scenarios (impact, blast, etc.) involving Fe–Cr–C hardfacings. Hardfacing ; Split-Hopkinson pressure bar ; Compression (physics) ; Microstructure ; Brittleness ; Constitutive equation ; Welding ; Dynamic range compression ; Fracture (geology) High-Velocity Impact and Material Behavior ; Metal Alloys Wear and Properties ; Surface Treatment and Residual Stress
Authors: Purusottam Mishra | Payal Gupta | Aleksandra Markowska | Saeid Ghavami | Jarosław Markowski | Marek J. Łos
Source: International Journal of Molecular Sciences (article, 2026-02-28)
Field: Materials Science — Biomaterials

Advancements in Drug Delivery Systems in Glioblastoma Therapy Glioblastoma (GB) is one of the most aggressive brain tumours, with a high mortality rate. Tumour heterogeneity, GB's invasive nature, the blood-brain barrier (BBB) and resistance development offer significant challenges in devising an effective strategy to manage GB. Clinicians rely on tumour resection, radiotherapy and temozolomide (TMZ) chemotherapy, but their efficacy is hindered due to poor BBB penetration. EGFR (epidermal growth factor receptor), NF-κB, angiogenic pathways, RAS/RAF/MAPK, PI3K/Akt/mTOR, etc., play an important role in GB progression. Development in nanotechnology, pharmaceutical science and genetic engineering enables the design of drug candidates with superior efficacy and safety profiles. This review delves into recent advancements in nanoparticles, hydrogels, extracellular vesicles, microneedles and other drug delivery platforms used in GB treatment. These novel drug delivery systems achieved superior BBB penetration, tumour targeting, and controlled release and better survival outcomes in preclinical setups. This review also discusses the major translational challenges, including those of large-scale production, tumour heterogeneity, off-target effects and M2 macrophage induction. Innovative strategies focusing on drug delivery as a biological decision-making process, integrating tumour stress responses into drug carrier and system-level design principles, are discussed, outlining future prospects. Glioblastoma ; Temozolomide ; Drug delivery ; Drug ; Targeted drug delivery ; Drug resistance ; Radiation therapy ; Drug development Nanoparticle-Based Drug Delivery ; Nanoplatforms for cancer theranostics ; Glioma Diagnosis and Treatment