jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Andri Pramesyanti Pramono | Retno Yulianti | Muhamad Alif Razi | Karina | Shaik Ling Ang | Kumar Sudesh | Hanan | Yuni Cahya Endarwati | Muhammad Radian Ghoza
Source: HAYATI Journal of Biosciences (article, 2026-01-13)
Field: Materials Science — Biomaterials

Preparation and Characterization of P(3HB-co-HHX)/Silk Fibroin Nanofiber for Chondrogenic Differentiation on Human Adipose Mesenchymal Stem Cell This study explored the proliferative and chondrogenic diﬀerentiation capacities of nanofiber containing silk fibroin hybrid Bombyx mori silkworm cocoons (Japan-China SP-01 variant from Indonesia) and PHA P(3HB-co-3HHx) on human adipose mesenchymal stem cells. First, the scaffolds were prepared for electrospinning by combining two distinct biomaterials, consisting of silk fibroin derived from hybrid Bombyx mori silkworm cocoons, with a combination of 3-hydroxybutyrate (3HB) and 3-hydroxyhexanoate (3HHx). The effects of various ratios of P(3HB-co-3HHx)/Silk Fibroin nanofiber mixture on proliferative and differentiation capacity were then investigated. Following that, the morphology, chemical compositions, contact angle, tensile strength, roughness, cell viability, and human adipose mesenchymal stem cell differentiation of the nanofiber were investigated by collagen type 2 gene expression. The results of scanning electron microscopy showed the mean diameter of the nanofiber ranged from 370-600 nm. Following that, 16,000 volts was prominent for nanofiber manufacture in all ratios. It was also demonstrated that the nanofiber has significant mechanical properties, acceptable hydrophilicity and smoothness, and appropriate cell viability (up to 99.1% compared to the control on silk fibroin nanofiber). Although PHA increased tensile strength, silk fibroin administration to the mixture predominantly enhanced chondrogenic differentiation, as evidenced by modulation of chondrogenic collagen type 2 (up to 8.718-fold) gene markers. Furthermore, the physicochemical characteristics of the nanofiber mixture significantly influenced the proliferation and differentiation of human adipose mesenchymal stem cells. The results of the tests showed that silk fibroin administration into a nanofiber mixture has improved chondrogenesis and showed great potential as a cartilage tissue scaffold. Fibroin ; Nanofiber ; Bombyx mori ; Chondrogenesis ; Mesenchymal stem cell ; Adipose tissue ; Electrospinning ; Cartilage Silk-based biomaterials and applications ; Electrospun Nanofibers in Biomedical Applications ; Hydrogels: synthesis, properties, applications
Authors: Xichen Sun | Wentao Wei | Jiahua Rao | Jiancong Xie | Yuedong Yang
Source: ArXiv.org (article, 2026-02-02)
Field: Chemistry — Spectroscopy

De Novo Molecular Generation from Mass Spectra via Many-Body Enhanced Diffusion Molecular structure generation from mass spectrometry is fundamental for understanding cellular metabolism and discovering novel compounds. Although tandem mass spectrometry (MS/MS) enables the high-throughput acquisition of fragment fingerprints, these spectra often reflect higher-order interactions involving the concerted cleavage of multiple atoms and bonds-crucial for resolving complex isomers and non-local fragmentation mechanisms. However, most existing methods adopt atom-centric and pairwise interaction modeling, overlooking higher-order edge interactions and lacking the capacity to systematically capture essential many-body characteristics for structure generation. To overcome these limitations, we present MBGen, a Many-Body enhanced diffusion framework for de novo molecular structure Generation from mass spectra. By integrating a many-body attention mechanism and higher-order edge modeling, MBGen comprehensively leverages the rich structural information encoded in MS/MS spectra, enabling accurate de novo generation and isomer differentiation for novel molecules. Experimental results on the NPLIB1 and MassSpecGym benchmarks demonstrate that MBGen achieves superior performance, with improvements of up to 230% over state-of-the-art methods, highlighting the scientific value and practical utility of many-body modeling for mass spectrometry-based molecular generation. Further analysis and ablation studies show that our approach effectively captures higher-order interactions and exhibits enhanced sensitivity to complex isomeric and non-local fragmentation information. Fragmentation (computing) ; Tandem ; Mass spectrometry ; Tandem mass spectrometry ; Pairwise comparison ; Molecule ; Molecular dynamics ; Cleavage (geology) Mass Spectrometry Techniques and Applications ; Protein Structure and Dynamics ; Metabolomics and Mass Spectrometry Studies
Authors: Abdul Salam | Ali Raza Khan | Muhammad Zeeshan | Muḥammad Z̲ākir | Liupeng Yang | Qun Zheng | Zaid Ulhassan | Ruifei Wang | Zhixiang Zhang | Chen Zhao
Source: Plants (article, 2026-02-08)
Field: Materials Science — Materials Chemistry

Nano-Enabled Solutions for Plant Abiotic Stress Tolerance and Soil Contaminant Remediation: A Review Global agriculture and food security are under serious threat from abiotic stresses, including salinity, drought, heavy metals, and extreme temperatures. While industrial development has driven progress, it has also intensified environmental challenges and contributed to declining crop productivity. Tackling these issues demands innovative and sustainable solutions. Nanotechnology has emerged as an effective approach to enhance stress tolerance, improve nutrient use efficiency, and increase crop yield and quality. This review critically examines the expanding role of nanoparticles (NPs) in mitigating abiotic stresses and promoting sustainable agricultural systems. While several studies have investigated the use of NPs in stress mitigation, ongoing research continues to reveal novel mechanisms and applications, highlighting the untapped potential of nanotechnology in plant science. The review discusses the impact of abiotic stress on plant growth and physiology, followed by a detailed analysis of the mechanisms through which NPs confer stress tolerance. Particular attention is given to the interaction of NPs with phytohormones and other growth regulators, as well as their role in the remediation of contaminated soils. Furthermore, the review highlights the dual role of NPs in stress alleviation and environmental remediation, while also considering emerging concerns about their potential ecological and toxicological impacts. Emphasis is placed on the need for risk assessments and effective management strategies. The review also identifies key knowledge gaps and methodological limitations, offering recommendations to guide future research in this emerging field. Agriculture ; Abiotic stress ; Food security ; Abiotic component ; Sustainable agriculture ; Environmental remediation ; Sustainable development Nanoparticles: synthesis and applications ; Plant Stress Responses and Tolerance ; Silicon Effects in Agriculture
Authors: Radja Nour El Imene Bennoui
Source: Metallurgical and Materials Data (article, 2026-03-03)
Field: Materials Science — Electronic, Optical and Magnetic Materials

First-principles investigation of the structural, elastic, and electronic properties of Half-Heusler compounds LiTiB and LiTiAl This study presents a first-principles investigation based on density functional theory (GGA) of the Half-Heusler compounds LiTiB and LiTiAl. Structural stability was evaluated through total energy optimization of three possible atomic configurations, with the Type-II phase identified as the energetically most favorable. Mechanical stability is confirmed by the calculated elastic constants, which satisfy the Born stability criteria. The derived mechanical parameters indicate that LiTiAl exhibits greater ductility, whereas LiTiB demonstrates higher stiffness. Both compounds exhibit metallic behavior, as evidenced by their electronic band structures and density of states. The electronic states near the Fermi level are predominantly governed by Ti d-states, while contributions from B p- or Al p-states are minor, and Li shows negligible participation. These findings indicate that LiTiB and LiTiAl are structurally stable and mechanically robust Half-Heusler metallic alloys with potential relevance for structural and functional applications. Density functional theory ; Structural stability ; Electronic structure ; Stability (learning theory) ; Fermi level ; Metal ; Total energy ; Electronic band structure Heusler alloys: electronic and magnetic properties ; Boron and Carbon Nanomaterials Research ; MXene and MAX Phase Materials
Authors: Veton Haziri | Avni Berisha | Klemen Bohinc
Source: Colloids and Interfaces (article, 2026-03-09)
Field: Materials Science — Materials Chemistry

Computational Insights into Carbon Nanocones as Sorption Materials for Nerve Agent The dangerous potential of chemical warfare requires immediate development of new materials capable of detecting and efficiently adsorbing the toxic nerve agents VX and Novichok (A-234). The current adsorbents fail to achieve sufficient detection efficiency and specific binding capabilities. Our research, conducted through advanced computational modeling, predicts that carbon nanocones (CNCs) could function as effective molecular traps for these toxic substances. The research combines density functional theory (DFT) with molecular dynamics (MD) and Monte Carlo (MC) simulations to explain the basic principles of molecular trapping by these agents. The nanocone shape produces two distinct and selective binding areas. MC shows preferential trapping VX molecules within the internal concave surface (P1), while A-234 molecules are strongly adsorbed on the external convex surface (P2). Docking results complement this by showing that A-234 exhibits stronger single-molecule binding on the more open surface, consistent with its preference for P2. The nanocone captures molecules through van der Waals forces, which produce measurable electronic changes that modify its electronic signature. The research demonstrates that carbon nanocones represent a promising candidate material for the future development of chemical defense systems, potentially including sensitive detection systems and advanced filtration technologies. Adsorption ; Molecule ; van der Waals force ; Density functional theory ; Molecular dynamics ; Monte Carlo method ; Carbon fibers ; Nerve agent ; Trapping Carbon and Quantum Dots Applications ; Graphene and Nanomaterials Applications ; Carbon Nanotubes in Composites
Authors: O. Coquand | Davide Salvatore Paolino | Ludovic Berthier
Source: ArXiv.org (article, 2026-03-12)
Field: Materials Science — Materials Chemistry

Direct Boltzmann inversion method from particle configurations at arbitrary state points We introduce a direct Boltzmann inversion method to infer the interaction potential in particle systems using as input particle configurations generated at an arbitrary state point of the system. Unlike iterative Boltzmann inversion, the proposed method does not require performing a new Monte Carlo simulation at each step of the iteration process. It relies instead on enforcing consistency between two independent estimates of the pair correlation function, respectively obtained from interparticle distances and from pairwise forces. As a result, the approach is computationally inexpensive and straightforward to implement. Because it relies on the sole expression of interparticle forces, our method naturally applies to any state point, including when the density is large and alternative methods may fail. Here we present the basic principles of the method and benchmark its performance on a diverse set of test potentials studied using computer simulations. Practical aspects and detailed implementation of the method are also discussed. Owing to its simplicity and generality, the method should be broadly applicable, from the construction of coarse-grained interaction potentials to the inference of effective interactions in non-equilibrium systems. Pairwise comparison ; Consistency (knowledge bases) ; Boltzmann constant ; Monte Carlo method ; Benchmark (surveying) ; Inversion (geology) ; Set (abstract data type) ; Inference Block Copolymer Self-Assembly ; Material Dynamics and Properties ; Nanopore and Nanochannel Transport Studies
Authors: Habib Ur Rahman | Abid Hussain | Muhammad ilyas | Manzoor Ahmed | Muhammad Saad Qayyum
Source: Research Square (preprint, 2026-04-07)
Field: Materials Science — Materials Chemistry

Development of a Convective–Diffusion Physics-Informed Neural Network for Thermal Analysis of Lithium Film Flow on the Surface of a Tokamak Divertor Heat transfer ; Divertor ; Artificial neural network ; Tokamak ; Thermal conduction ; Perceptron ; Collocation (remote sensing) ; Flow (mathematics) ; Transfer function ; Surface (topology) Fusion materials and technologies ; Nuclear reactor physics and engineering ; Nuclear Engineering Thermal-Hydraulics
Authors: Kewei Fang | Yan Liu | Hongqun Liu | Jian Shen | Jiachang Lu | Yuan Fei Li | Jian Cheng | Enze Xu | Yanxin Qiao
Source: Metals (article, 2026-01-08)
Field: Materials Science — Materials Chemistry

Effect of Dissolved Oxygen and Amino Acid Corrosion Inhibitor on Corrosion of Carbon Steel Firewater Pipeline This paper investigates the effects of dissolved oxygen and an amino acid corrosion inhibitor on the corrosion of 20# carbon steel in tap water and deionized water. The corrosion behavior was systematically analyzed using electrochemical impedance spectroscopy (EIS), potentiodynamic polarization, weight loss measurements, and scanning electron microscopy (SEM). The results show that deoxygenation is the most effective way to inhibit the carbon steel corrosion in two media, and the increase in dissolved oxygen concentration will make the carbon steel in the tap water corrosion resistance decline, but additional oxygen will enhance its corrosion resistance compared with the natural state without deoxygenation and oxygen addition in deionized water. Moreover, the effectiveness of the corrosion inhibitor in inhibiting carbon steel corrosion in tap water is significantly lower than that in deionized water. These results offer valuable insights for the operation of carbon steel firewater pipelines in media containing dissolved oxygen and serve as a practical guide to enhancing their service life. Corrosion ; Carbon steel ; Deoxygenation ; Oxygen ; Tap water ; Corrosion inhibitor Corrosion Behavior and Inhibition ; Hydrogen embrittlement and corrosion behaviors in metals ; Surface Modification and Superhydrophobicity
Authors: Liang Shi | Jiarui Lu | Junqi Liu | Chence Shi | Zhi Qi Yang | Tang, Jian, 1968-
Source: ArXiv.org (article, 2026-03-18)
Field: Materials Science — Materials Chemistry

Atomic Trajectory Modeling with State Space Models for Biomolecular Dynamics Understanding the dynamic behavior of biomolecules is fundamental to elucidating biological function and facilitating drug discovery. While Molecular Dynamics (MD) simulations provide a rigorous physical basis for studying these dynamics, they remain computationally expensive for long timescales. Conversely, recent deep generative models accelerate conformation generation but are typically either failing to model temporal relationship or built only for monomeric proteins. To bridge this gap, we introduce ATMOS, a novel generative framework based on State Space Models (SSM) designed to generate atom-level MD trajectories for biomolecular systems. ATMOS integrates a Pairformer-based state transition mechanism to capture long-range temporal dependencies, with a diffusion-based module to decode trajectory frames in an autoregressive manner. ATMOS is trained across crystal structures from PDB and conformation trajectory from large-scale MD simulation datasets including mdCATH and MISATO. We demonstrate that ATMOS achieves state-of-the-art performance in generating conformation trajectories for both protein monomers and complex protein-ligand systems. By enabling efficient inference of atomic trajectory of motions, this work establishes a promising foundation for modeling biomolecular dynamics. Trajectory ; Molecular dynamics ; State space ; Function (biology) ; Basis (linear algebra) ; Inference ; Biomolecular structure ; Autoregressive model ; Generative model Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Zeno Romero | Kerstin Münnemann | Hans Hasse | Fabian Jirasek
Source: arXiv (Cornell University) (article, 2026-02-26)
Field: Chemistry — Spectroscopy

Prediction of Diffusion Coefficients in Mixtures with Tensor Completion Predicting diffusion coefficients in mixtures is crucial for many applications, as experimental data remain scarce, and machine learning (ML) offers promising alternatives to established semi-empirical models. Among ML models, matrix completion methods (MCMs) have proven effective in predicting thermophysical properties, including diffusion coefficients in binary mixtures. However, MCMs are restricted to single-temperature predictions, and their accuracy depends strongly on the availability of high-quality experimental data for each temperature of interest. In this work, we address this challenge by presenting a hybrid tensor completion method (TCM) for predicting temperature-dependent diffusion coefficients at infinite dilution in binary mixtures. The TCM employs a Tucker decomposition and is jointly trained on experimental data for diffusion coefficients at infinite dilution in binary systems at 298 K, 313 K, and 333 K. Predictions from the semi-empirical SEGWE model serve as prior knowledge within a Bayesian training framework. The TCM then extrapolates linearly to any temperature between 268 K and 378 K, achieving markedly improved prediction accuracy compared to established models across all studied temperatures. To further enhance predictive performance, the experimental database was expanded using active learning (AL) strategies for targeted acquisition of new diffusion data by pulsed-field gradient (PFG) NMR measurements. Diffusion coefficients at infinite dilution in 19 solute + solvent systems were measured at 298 K, 313 K, and 333 K. Incorporating these results yields a substantial improvement in the TCM's predictive accuracy. These findings highlight the potential of combining data-efficient ML methods with adaptive experimentation to advance predictive modeling of transport properties. Diffusion ; Binary number ; Dilution ; Experimental data ; Tensor (intrinsic definition) ; Diffusion MRI ; Binary data Diffusion Coefficients in Liquids ; Phase Equilibria and Thermodynamics ; Carbon Dioxide Capture Technologies
Authors: Ya Xu | K. Oyoshi | Haruka Yoshikawa | Hiroyuki Takeya | H. Amekura | Takafumi D. Yamamoto | Yoshitaka Matsushita | Alexei A. Belik | Miyoko Tanaka | Akiko T. Saito | K. Kamiya | Yoshihiko Takeda
Source: Nature Communications (article, 2026-04-07)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Enhancing hydrogen permeation barrier performance of ErCo2 magnetic refrigeration material via surface oxide layer formation The ErCo<sub>2</sub> intermetallic compound exhibits a significant magnetocaloric effect at approximately 32 K and has potential applications as a magnetic refrigeration material for hydrogen liquefaction. However, exposure to a hydrogen atmosphere may lead to hydride formation, which weakens the magnetocaloric effect. Thus, preventing hydrogen permeation into ErCo<sub>2</sub> is crucial. Herein, we enhance the hydrogen permeation barrier (HPB) performance of ErCo<sub>2</sub> particles by using electroless Cu plating followed by oxidation treatment to form a CuO layer with a thickness of a few micrometers. In experiments, ErCo<sub>2</sub> particles, with a 1.5- to 5-µm-thick CuO surface layer, exhibited a large magnetic entropy change of 24 J kg⁻¹ K⁻¹ even after exposure to a H<sub>2</sub> atmosphere at 1.27 MPa and 296 K for 7 d. Experimental analyses and first-principles calculations revealed the potential of CuO as an HPB material for magnetic refrigeration. Magnetic refrigeration ; Intermetallic ; Hydrogen ; Oxide ; Permeation ; Hydride Magnetic and transport properties of perovskites and related materials ; Hydrogen Storage and Materials ; Magnetic properties of thin films
Authors: Berna Yıldız Akdağ
Source: Journal of the Turkish Chemical Society Section A Chemistry (article, 2026-01-15)
Field: Materials Science — Ceramics and Composites

Phase Separation Challenges in Borosilicate Nuclear Glasses and Strategies for Vitrification Improvement This review highlights three main topics: advancements in vitrification technology and associated issues like melter corrosion in global facilities, the chemistry and networking of glassy and frit forms of waste, and improvements in vitrified structures that enhance the properties of glass formulations. Borosilicate glass formulations offer various technical advantages for nuclear waste management, including effective bonding with fission products and actinides, resistance to radiation, simple and safe technology, and low leaching tendencies in aqueous environments. The maximum results indicate that the vitrification facility in Tarapur, India, 43.8% waste loadings with 6.4% B2O3 in the glass composition, while Savannah River Site in the USA processes waste loadings of up to 50 wt% with 6% B2O3 in the glass composition. In the IAEA-TECDOC, it is stated that considering spent fuel borosilicate glasses are suitable matrices for the immobilising up to 13 wt % of UO2 or 6 wt % of PuO2. However, borosilicate glasses may face issues when the molybdenum ratio in the waste exceeds certain limits, potentially leading to phase separation in the vitrified network. This review covers the importance of waste management policies and provides a historical overview of nuclear waste glass in different countries. Borosilicate glass ; Vitrification ; Frit ; Radioactive waste ; Savannah River Site ; Leaching (pedology) ; High-level waste ; Glass recycling ; Fission products ; Nuclear fuel cycle Glass properties and applications ; Nuclear materials and radiation effects ; Graphite, nuclear technology, radiation studies
Authors: Bayezid Baten | M. Ayyan Iqbal | Sebastian Ament | Julius Kusuma | Nishant Garg
Source: ArXiv.org (article, 2026-03-23)
Field: Materials Science — Materials Chemistry

BOxCrete: A Bayesian Optimization Open-Source AI Model for Concrete Strength Forecasting and Mix Optimization Modern concrete must simultaneously satisfy evolving demands for mechanical performance, workability, durability, and sustainability, making mix designs increasingly complex. Recent studies leveraging Artificial Intelligence (AI) and Machine Learning (ML) models show promise for predicting compressive strength and guiding mix optimization, but most existing efforts are based on proprietary industrial datasets and closed-source implementations. Here we introduce BOxCrete, an open-source probabilistic modeling and optimization framework trained on a new open-access dataset of over 500 strength measurements (1-15 ksi) from 123 mixtures - 69 mortar and 54 concrete mixes tested at five curing ages (1, 3, 5, 14, and 28 days). BOxCrete leverages Gaussian Process (GP) regression to predict strength development, achieving average R$^2$ = 0.94 and RMSE = 0.69 ksi, quantify uncertainty, and carry out multi-objective optimization of compressive strength and embodied carbon. The dataset and model establish a reproducible open-source foundation for data-driven development of AI-based optimized mix designs. Probabilistic logic ; Compressive strength ; Gaussian process ; Bayesian optimization ; Kriging ; Bayesian probability ; Process (computing) ; Bayesian inference Machine Learning in Materials Science ; Innovative concrete reinforcement materials ; Infrastructure Maintenance and Monitoring