jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Nayef Ghasem
Source: Eng—Advances in Engineering (article, 2025-09-03)
Field: Materials Science — Materials Chemistry

AI-Driven Predictive Modeling of Nanoparticle-Enhanced Solvent-Based CO2 Capture Systems: Comprehensive Review and ANN Analysis Designing efficient nanoparticle-enhanced CO2 capture systems is challenging due to the diversity of nanoparticles, solvent formulations, reactor configurations, and operating conditions. This study presents the first ANN-based meta-analysis framework developed to predict CO2 absorption enhancement across multiple reactor systems, including batch reactors, packed columns, and membrane contactors. A curated dataset of 312 experimental data points was compiled from literature, and an artificial neural network (ANN) model was trained using six input variables: nanoparticle type, concentration, system configuration, base fluid, pressure, and temperature. The proposed model achieved high predictive accuracy (R2 &gt; 0.92; RMSE: 4.2%; MAE: 3.1%) and successfully captured complex nonlinear interactions. Feature importance analysis revealed nanoparticle concentration (28.3%) and system configuration (22.1%) as the most influential factors, with functionalized nanoparticles such as Fe3O4@SiO2-NH2 showing superior performance. The model further predicted up to 130% enhancement for ZnO in optimized membrane contactors. This AI-driven tool provides quantitative insights and a scalable decision-support framework for designing advanced nanoparticle–solvent systems, reducing experimental workload, and accelerating the development of sustainable CO2 capture technologies. Nanoparticle ; Computer science ; Nanotechnology ; Artificial intelligence ; Materials science Catalytic Processes in Materials Science ; Carbon Dioxide Capture Technologies ; Gas Sensing Nanomaterials and Sensors
Authors: Rajat Goel | Nir Goldman | Ambarish Kulkarni
Source: ChemRxiv (preprint, 2025-02-17)
Field: Materials Science — Materials Chemistry

Electronic and Geometric contributors to Hydrogen Binding in Uranium Oxide Grain Boundaries Hydrogen induced corrosion of uranium, which leads to the formation of toxic and pyrophoric UH3, raises significant safety concerns for long-term storage of nuclear materials. Previous work suggests hydrogen diffuses through the top passivating oxide layer to initiate hydriding reactions within the underlying grain boundaries (GB). However, the atomistic mechanisms underlying this phenomenon and the structural factors that control its initiation are not well understood. To address this knowledge gap, here we use a high-throughput density function theory (DFT) workflow to investigate the adsorption of H and H2 in the defective bulk UO2. Specifically, we exhaustively investigated the adsorption of H (107 sites) and H2 (26 sites) in three different coincidence site lattice (CSL) GBs: Σ3, Σ5, and Σ9. Compared to the binding energies in pristine UO2, we observe significantly stronger hydrogen adsorption at these GB sites. Interestingly, we find that the trends in H and H2 adsorption vary considerably across the three GB models. In particular, while a small number of sites in Σ5 and Σ9 show exothermic adsorption of H and H2, respectively, no such sites are found in Σ3. Further statistical analysis of these trends suggests that H adsorption, which is adsorbed as a hydride anion (i.e., H-), is positively correlated with the value of its negative charge and depends on the number of neighboring oxygen and uranium atoms. Together, these results provide fundamental atomistic insights that could guide the development of future corrosion mitigation strategies for the storage of nuclear materials. Grain boundary ; Uranium ; Hydrogen ; Uranium oxide ; Oxide ; Materials science ; Metallurgy ; Chemistry Nuclear Materials and Properties ; Radioactive element chemistry and processing ; Nuclear reactor physics and engineering
Authors: Minghao Xu | Jingyi Song | Kongming Wu | Xiangxin Zhou | Bin Cui | Wentao Zhang
Source: ArXiv.org (preprint, 2025-06-02)
Field: Materials Science — Biomaterials

Modeling All-Atom Glycan Structures via Hierarchical Message Passing and Multi-Scale Pre-training Understanding the various properties of glycans with machine learning has shown some preliminary promise. However, previous methods mainly focused on modeling the backbone structure of glycans as graphs of monosaccharides (i.e., sugar units), while they neglected the atomic structures underlying each monosaccharide, which are actually important indicators of glycan properties. We fill this blank by introducing the GlycanAA model for All-Atom-wise Glycan modeling. GlycanAA models a glycan as a heterogeneous graph with monosaccharide nodes representing its global backbone structure and atom nodes representing its local atomic-level structures. Based on such a graph, GlycanAA performs hierarchical message passing to capture from local atomic-level interactions to global monosaccharide-level interactions. To further enhance model capability, we pre-train GlycanAA on a high-quality unlabeled glycan dataset, deriving the PreGlycanAA model. We design a multi-scale mask prediction algorithm to endow the model about different levels of dependencies in a glycan. Extensive benchmark results show the superiority of GlycanAA over existing glycan encoders and verify the further improvements achieved by PreGlycanAA. We maintain all resources at https://github.com/kasawa1234/GlycanAA Glycan ; Monosaccharide ; Graph ; Benchmark (surveying) ; Encoder ; Data modeling Supramolecular Self-Assembly in Materials ; Glycosylation and Glycoproteins Research ; Machine Learning in Bioinformatics
Authors: William Paul Flynn | Sean Garnsey | A. S. Bhalla | Ruyan Guo
Source: Journal of Composites Science (article, 2025-05-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Finite Element Analysis of Strain-Mediated Direct Magnetoelectric Coupling in Multiferroic Nanocomposites for Material Jetting Fabrication of Tunable Devices Magnetoelectric composites enable strain-mediated coupling between magnetic and electric fields, supporting applications in sensors, actuators, and tunable devices. This study presents a finite element modeling framework for simulating the direct magnetoelectric effect in core–shell and layered nanocomposites fabricated by material jetting (inkjet printing). The model incorporates nonlinear magnetostrictive behavior of cobalt ferrite nanoparticles and size-dependent piezoelectric properties of barium titanate, allowing efficient simulation of complex interfacial strain transfer. Results show a strong dependence of coupling on field orientation, particle arrangement, and interfacial geometry. Simulations of printed droplet geometries, including coffee ring droplet morphologies, reveal enhanced performance through increased surface area and directional alignment. These findings highlight the potential of material jetting for customizable, high-performance magnetoelectric devices and provide a foundation for simulation-guided design. Multiferroics ; Fabrication ; Materials science ; Nanocomposite ; Finite element method ; Coupling (piping) ; Strain (injury) ; Composite material ; Element (criminal law) ; Optoelectronics ; Structural engineering ; Ferroelectricity ; Engineering ; Dielectric Multiferroics and related materials ; Ferroelectric and Piezoelectric Materials ; Electromagnetic wave absorption materials
Authors: Mukhethwa Netshia | Maje Phasha | Bongani Ngobe | Peter Apata Olubambi
Source: MATEC Web of Conferences (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Effect of cooling mediums on mechanical properties and wear behaviour of binary β-type Ti-Mo alloy for bio-implant applications Orthopaedic implants are increasingly used to correct skeletal dysfunctions by replacing or repairing damaged bone tissue with appropriate biomaterials. This study investigates the effect of cooling medium on the mechanical properties and wear behaviour of the solution heat-treated β-type Ti-Mo alloy. Samples were solution heat-treated at 900℃ and cooled in different cooling media, such as water quenching, air, and furnace cooling. The phases, morphology, and microstructural analyses were performed using X-ray diffraction (XRD), optical microscope, and electron backscatter diffraction (OM and EBSD). The mechanical and nanomechanical properties of the alloys were analysed using Vickers hardness and nanoindentation machines. The wear test analyses of the alloys were conducted using a tribometer. XRD and microstructural characterization showed the crystalline phase, and the microstructure of the alloys was affected by the cooling medium. The furnace-cooled sample yielded high micro-hardness (454 HV) due to the grain refinement attributed to the presence of α phase. Nanoindentation ; Microstructure ; Electron backscatter diffraction ; Alloy ; Vickers hardness test ; Diffraction ; Grain size ; Characterization (materials science) Titanium Alloys Microstructure and Properties ; Orthopaedic implants and arthroplasty ; Advanced materials and composites
Authors: Olimpo Sierra-Hernandez | Oscar Saurith-Coronell | Jackson J. Alcázar | Eliceo Cortés | Máryury Flores-Sumoza | Juan David Rodríguez Macías | José R. Mora | Yovani Marrero‐Ponce | Ernesto Contreras‐Torres
Source: Research Square (preprint, 2025-12-17)
Field: Chemistry — Organic Chemistry

In Silico Discovery of Thioglycoside Analogues as Donor-Site Inhibitors of Glycosyltransferase LgtC Docking (animal) ; In silico ; Glycosyltransferase ; Enzyme ; Virtual screening ; Binding site ; Active site ; Rational design Carbohydrate Chemistry and Synthesis ; Escherichia coli research studies ; Glycosylation and Glycoproteins Research
Authors: Yuanming Song
Source: ChemRxiv (preprint, 2025-05-18)
Field: Materials Science — Biomaterials

Evaluating SIRAH Force Field for Modeling Spontaneous and Redox-Responsive Peptide Self-Assembly Peptide self-assembly encompasses both spontaneous organization of molecules into stable nanostructures and active assembly controlled by external stimuli. Studying spontaneous assembly is crucial for understanding inherent peptide aggregation mechanisms and designing robust materials, while active, stimuli-responsive assembly enables dynamic control over material properties for applications in drug delivery and smart biomaterials. This study evaluates the performance of the SIRAH coarse-grained force field in simulating both spontaneous and redox-responsive peptide assemblies. Using experimentally characterized sequences and comparisons with established MARTINI simulations, preliminary results reveal significant limitations in SIRAH’s predictive capabilities: it exhibits overly high aggregation tendencies and fails to distinguish aggregating from non-aggregating peptides reliably. These findings suggest that, despite its detailed interaction mapping, SIRAH requires further parameter refinement before it can be used effectively for accurate screening and design of peptide-based self-assembling systems. Redox ; Field (mathematics) ; Force field (fiction) ; Peptide ; Nanotechnology ; Chemistry ; Computer science ; Materials science ; Biochemistry ; Artificial intelligence ; Mathematics Supramolecular Self-Assembly in Materials ; Photoreceptor and optogenetics research ; Polydiacetylene-based materials and applications
Authors: Dilan Sarpkaya | Müge Kesici | Zeynep Tacer-Caba | Fatih Kahraman | Gül Tekin Temur
Source: Uludağ Üniversitesi Ziraat Fakültesi dergisi/Bursa uludağ üniversitesi ziraat fakültesi dergisi (article, 2025-12-01)
Field: Chemistry — Organic Chemistry

Use of machine learning for olive cultivation: A review Together with the globalization and climate change, rapid technological developments are also driving research into agriculture. Considering the current state of the world's population, agricultural sustainability is one of the key factors for minimizing food waste and shortages. Thanks to technology improvements, smart agricultural practices can produce a range of chances in this context, promoting the notion of regional goods being producible in different regions. In this study, studies were carried out in the field of machine learning of the cultivation of olive fruit. There are detailed analyses of the existing studies based on the year, region and methodologies used. While commonly referred to be a Mediterranean product in literature, it may flourish with same quality anywhere with a technological tweak. Furthermore, apart from emphasizing the significance of machine learning in optimizing product quality, it is anticipated that various forms of learning will be more effectively controlled with social and governmental support. Product (mathematics) ; Sustainability ; Agriculture ; Field (mathematics) ; Quality (philosophy) ; State (computer science) ; Production (economics) Edible Oils Quality and Analysis ; Smart Agriculture and AI ; Spectroscopy and Chemometric Analyses
Authors: Shiva Ghaemi | Rehenuma Tasmin Rodoshi | Shahana Shultana | Amarda Shehu | Daniel Barbará
Source:  (article, 2025-10-11)
Field: Materials Science — Materials Chemistry

From Contrast to Control: Domain Conditioned Masking for Small Molecule Generation of Quaternary Ammonium Compounds Generative models for chemistry benefit from molecular representations that reflect domain-specific targets, yet most contrastive learning (CL) pretraining remains domain-agnostic. We survey a range of CL models for molecular representation and introduce Domain-Conditioned Masking (DCM), a simple, label-aware masking strategy that integrates seamlessly into existing CL pipelines. Using binary labels (target vs. non-target), DCM guides view construction to emphasize domain-relevant features. We implement DCM in the setting of CONSMI, a generative model that uses standard CL techniques and SMILES representations to generate molecules. We pair CONSMI with MolGPT, a generative model that also uses SMILES representations. DCM produces embeddings with much clearer separation between target and non-target molecules than those produced by the standard CONSMI model, as evidenced by histogram shifts, increased centroid distances, and Silhouette scores. As molecular targets we choose Quaternary Ammonium Compounds (QACs) a rare and underrepresented molecular type with antibacterial properties. Injecting MolGPT with DCM-trained QAC embeddings improves generation validity over MolGPT and CONSMI baselines. Our results show that minimal domain supervision combined with targeted masking during CL reliably shapes representations for domain-aware molecular generation. We will expand our results into other generative models (SimSon, MolMCL) in the future. Masking (illustration) ; Generative model ; Binary number ; Generative grammar ; Domain (mathematical analysis) ; Representation (politics) ; Contrast (vision) ; Pattern recognition (psychology) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Domain Adaptation and Few-Shot Learning
Authors: Islam yahya Dawood | Mohammed Mahbubul Islam | Attallah ibrahim ekhlayef
Source: International Journal of Research Publication and Reviews (article, 2025-09-01)
Field: Materials Science — Biomaterials

Next-Generation Scaffolds for Tissue Engineering: Smart Materials, Fabrication Strategies, and Biomedical Applications Tissue engineering has emerged as a revolutionary method in regenerative medicine.The structural and functional framework for guiding tissue maturation, cell adhesion, and proliferation is provided by scaffolds.Although early scaffold methods offer passive support, their poor bioactivity, mechanical mismatch, and poor host tissue integration have hindered clinical translation.As a result, advanced fabrication methods and intelligent materials are being used to develop nextgeneration scaffolds that actively regulate biological processes and more closely mimic the complexity of natural extracellular matrices.This review highlights the key design principles of next-generation scaffolds, such as mechanical integrity, biodegradability, vascularization potential, and immunomodulatory properties.Particular focus is placed on smart materials that enable dynamic interactions with cells and tissues, such as stimuli-responsive polymers, conductive nanocomposites, and bioactive hybrids.It is critically examined how fabrication techniques, such as electrospinning, freeze-drying, additive manufacturing, and 4D printing, can be used to customize scaffold design, bio functionality, and patient-specific adaptability.In the discussion of biomedical applications across bone, cartilage, cardiovascular, neural, skin, and organ engineering, examples are given of how scaffold developments enhance tissue regeneration, accelerate healing, and reduce immune rejection.Despite these advancements, a number of challenges remain, including the need for scale-up production, long-term stability, regulatory approval, and reasonably priced clinical solutions.The integration of bioelectronics, computational modelling, and artificial intelligence into scaffold design is expected to accelerate the development of customized, multipurpose platforms in the future.In addition to showing promise for improving current tissue engineering techniques, these advancements pave the way for next-generation therapeutic options in precision and regenerative medicine. Fabrication ; Biocompatible material ; Process (computing) ; Limiting Electrospun Nanofibers in Biomedical Applications ; Tissue Engineering and Regenerative Medicine
Authors: Eemeli A. Eronen | Johannes Niskanen
Source: ArXiv.org (preprint, 2025-05-05)
Field: Materials Science — Materials Chemistry

Statistics within UV-Visible Absorption Spectrum of Ethanolic Azobenzene We report a statistical simulation of the UV--visible absorption spectrum of {\it trans}-azobenzene in ethanol solution. Due to intermolecular interactions, the used explicit solvent environment necessitates accounting for numerous transitions for a spectrum covering the two energetically lowest lines, S$_1$ and S$_2$. Furthermore, the spectrum manifests vast variation as a function of the underlying local structure, in conjunction with previous observations for spectra of liquids in the X-ray regime. We disentangle the complex structure--spectrum relationship using a machine learning-based method known as the emulator-based component analysis. This structural decomposition outperforms commonly used principal component analysis in explained spectral variation and reveals a small subset of latent structural variables responsible for the total spectral variance. Among other structural characteristics, blueshifting of the S$_2$ peak occurs with fewer hydrogen bonds with the ethanol solvent, and a contracted N=N bond within the C--N=N--C bridge. The observed structural dependence of the absorption spectrum thus implies an overrepresentation of certain structural classes after a photoexcitation, potentially significant for the subsequent nuclear dynamics, photophysics, and photochemistry. Principal component analysis ; Intermolecular force ; Absorption spectroscopy ; Absorption (acoustics) ; Azobenzene ; Spectrum (functional analysis) ; Hydrogen bond ; Spectral line ; Component (thermodynamics) Photochromic and Fluorescence Chemistry ; Advanced Chemical Sensor Technologies
Authors: Tae-Soo Kwak
Source: Modern Concepts in Material Science (article, 2025-12-01)
Field: Materials Science — Ceramics and Composites

Trends in Removal Machining Technology for Silicon Nitride Ceramics in ball bearings As advanced technology develops, high-functional ceramics are attracting attention as high-performance parts materials due to their excellent physical properties such as high strength, heat resistance, wear resistance, functional surface, and corrosion resistance. However, ceramics are brittle materials and are very difficult to machining, so there are many restrictions on making shapes by machining. In order to meet the demand for highfunctional ceramics, ultra-precision machining and high-efficiency machining technologies are required. Silicon nitride ceramics are highly regarded as functional precision parts, but the process of making parts with high reliability and precision is never easy due to their unique characteristics of ceramics, brittleness. This review focuses on the latest convergence research incorporating optimization technology as well as ceramic machining technology that induces ductile fracture and identification of the microstructure failure mechanism in relation to the trend of removal machining technology, focusing on silicon nitride ceramics. The development of ceramic removal machining technology is essential for all ceramic materials, not just silicon nitride ceramics, to be widely applicable as functional high-tech materials. Machining ; Ceramic ; Silicon nitride ; Brittleness ; Microstructure ; Ball (mathematics) ; Silicon Advanced ceramic materials synthesis ; Advanced Surface Polishing Techniques ; Metal and Thin Film Mechanics
Authors: Thaís Ferreira da Silva | Ticiana da Silva | Rodrigo Correa | Rui Ribeiro | Guilherme Ferreira de Melo Morgado | Larissa Stieven Montagna | B. E. B. Uribe | Maraísa Gonçalves | Michelle Leali Costa | Fábio Roberto Passador | María C. Paiva | Edson Cocchieri Botelho
Source: C – Journal of Carbon Research (article, 2025-11-19)
Field: Materials Science — Materials Chemistry

Synergistic Effects in Hybrid Buckypapers of Graphene Nanoplatelets and Carbon Nanotubes: Processing and Performance Hybrid buckypapers (BPs) composed of graphene nanoplatelets (GNPs) and carbon nanotubes (CNTs) hold great potential for applications in flexible electronics, electromagnetic shielding, and energy storage. In this study, hybrid BPs were fabricated and characterized to evaluate their structural, thermal, and electrical properties. Hybrid BPs with varying GNP/CNT mass ratios (0/100, 25/75, 50/50, 75/25, 85/15, 90/10, and 95/5 wt%) were prepared via vacuum-assisted filtration of well-dispersed aqueous suspensions stabilized by surfactants. The resulting hybrid GNP/CNT BPs were dried and subjected to post-treatment processes to enhance structural integrity and electrical performance. Characterization techniques included scanning electron microscopy (SEM), X-ray diffraction (XRD), Fourier transform infrared (FT-IR), Raman spectroscopy, thermogravimetric analysis (TGA), nitrogen adsorption/desorption isotherms, and impedance spectroscopy (IS). The hybrid GNP/CNT BPs exhibited electrical conductivities comparable to conventional CNT-based BPs. At GNP concentrations of 25 to 50 wt%, electrical conductivity values approached those of CNT-based BPs, while at GNP concentrations between 75 and 90 wt%, a slight increase in conductivity was observed (171%). These results highlight a synergistic effect at lower CNT concentrations, where the combination of CNTs and GNPs enhances conductivity. The findings suggest that optimal conductivity is achieved through a balanced incorporation of both materials, offering promising prospects for advanced BP applications. Graphene ; Carbon nanotube ; Thermogravimetric analysis ; Fourier transform infrared spectroscopy ; Scanning electron microscope ; Hybrid material ; Electrical resistivity and conductivity ; Raman spectroscopy ; Conductivity Graphene research and applications ; Carbon Nanotubes in Composites ; Electromagnetic wave absorption materials