jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Debela N. Gurmu | Krzysztof Wacławiak | Hirpa G. Lemu
Source: Materials (article, 2025-05-27)
Field: Materials Science — Polymers and Plastics

Predicting the Compressive Properties of Carbon Foam Using Artificial Neural Networks This article focusses on predicting the compressive properties of polyurethane-derived carbon foam using an artificial neural network (ANN) approach. To train the model, strain, pore density (20, 40, and 60 ppi), and solvents (acetone, ethanol, and methanol) were used as inputs, while compressive stress was used as output. Categorical variables like acetone, ethanol, and methanol were converted to binary form before training the ANN model by using one-hot encoding mechanism. Both inputs and outputs were normalized to prevent features with larger numerical ranges from dominating the training process. A feed-forward ANN with four hidden layers, each containing 100 neurons, was constructed. The performance of the ANN model was tested using three metrics: mean square error (MSE), mean absolute error (MAE), and coefficient of determination (R<sup>2</sup>). The Adam optimizer was used to optimize the weights and biases of the ANN. The model was trained for 10,000 epochs with a batch size of 50. Rectified Linear Unit (ReLU) and linear functions were used as activation functions for the hidden layers and the output layer, respectively. From the results, overall average MSE, MAE, RMSE, and R<sup>2</sup> values of 36.34, 4.42, 6.00, and 0.9797, respectively, were obtained. Artificial neural network ; Mean squared error ; Methanol ; Compressive strength ; Biological system ; Correlation coefficient ; Acetone ; Materials science ; Coefficient of determination ; Approximation error ; Linear density ; Mean absolute percentage error ; Mathematics ; Algorithm ; Computer science ; Composite material ; Artificial intelligence ; Chemistry ; Statistics ; Organic chemistry Polymer composites and self-healing ; Asphalt Pavement Performance Evaluation
Authors: Jingwen Liu | Kai Wang | Fengying Jin | Jiayi Li | Yuezhen Bin | Xiaofei Qian
Source: Nanomaterials (review, 2025-06-11)
Field: Materials Science — Biomaterials

Preparation Methods and Multifunctional Applications of Functionalized Electrospun Nanofibers for Biomedicine Electrospinning has emerged as a versatile and cost-effective technique for fabricating nanofibers with a high surface area, tunable morphology, and exceptional mechanical properties, demonstrating significant potential for applications in biomedicine. This review summarizes the main parameters of the electrospinning process and fabrication methods of functionalized electrospun nanofibers (FENFs) through one-step functionalization and post-functionalization. The applications of FENFs, with their antibacterial activity, anti-inflammatory effects, and tissue regenerative effects, as well as their potential in drug delivery systems and sensors, showcase their capability to address challenges in wound healing, cancer therapy, and health monitoring. Current limitations and future research directions are also identified. This review provides valuable insights for advancing research on nanofiber-based materials and their practical implementations. Electrospinning ; Nanofiber ; Surface modification ; Nanotechnology ; Materials science ; Biomedicine ; Drug delivery ; Wound dressing ; Polymer ; Engineering ; Chemical engineering ; Composite material ; Bioinformatics Electrospun Nanofibers in Biomedical Applications ; Advanced Sensor and Energy Harvesting Materials ; Conducting polymers and applications
Authors: Hèctor Prats | Michail Stamatakis
Source: ACS Catalysis (article, 2025-02-03)
Field: Materials Science — Materials Chemistry

First-Principles Kinetic Monte Carlo Simulations for Single-Cluster Catalysis: Study of CO<sub>2</sub> and CH<sub>4</sub> Conversion on Pt/HfC The deposition of small transition metal (TM) clusters on transition metal carbides (TMC) gives rise to bifunctional catalysts with multiple active sites. This family of single-cluster catalysts (SCCs) offers exciting opportunities for enabling a wider range of chemical reactions owing to their strong metal-support interactions, which drastically modify the catalytic properties of the supported metal atoms. In this work, we use first-principles Kinetic Monte Carlo (KMC) simulations to investigate the conversion of CO<sub>2</sub> and CH<sub>4</sub> on Pt/HfC, which was identified as the most promising TM/TMC combination in a previous DFT-based high-throughput screening study. We analyze the interplay between the Pt clusters and the HfC support, evaluating the catalytic activity, selectivity, and adlayer composition across a broad range of operating conditions (<i>p</i> <sub><i>A</i></sub> , <i>p</i> <sub><i>B</i></sub> , and <i>T</i>) and Pt loadings. This study evaluates five different industrial processes, including the dry reforming (DRM), steam reforming (SRM), and partial oxidation (POM) of methane, as well as the water-gas shift (WGS) reaction and its reverse (RWGS). Our results demonstrate that the deposition of Pt clusters on HfC systematically enhances the catalytic performance, even at a Pt loading as low as ∼0.02 ML. This work illustrates the extensive catalytic benefits of SCCs and highlights the importance of considering diffusion steps and lateral interactions in kinetic modeling. Kinetic Monte Carlo ; Monte Carlo method ; Kinetic energy ; Cluster (spacecraft) ; Catalysis ; Materials science ; Physical chemistry ; Chemistry ; Physics ; Statistical physics ; Computer science Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Machine Learning in Materials Science
Authors: Henrik Eliasson | Rolf Erni
Source: Nano Letters (article, 2025-01-29)
Field: Materials Science — Surfaces, Coatings and Films

Improving Nanoparticle Size Estimation from Scanning Transmission Electron Micrographs with a Multislice Surrogate Model The computational cost of simulating scanning transmission electron microscopy (STEM) images limits the curation of large enough data sets to train accurate and robust machine learning networks for deep feature extraction from atomically resolved STEM images. For nanoparticle size estimation in particular, a diverse data set is essential due to the large variations in size, shape, crystallinity, orientation, and dynamical diffraction effects in experimental data. To address this, we train a 3D convolutional neural network to predict STEM images from voxelized atomic models, achieving a 100x speed-up compared to traditional multislice simulations while maintaining high image quality. We then generate a data set of 100.000 synthetic multislice images and investigate the performance of different size-estimator architectures as a function of training set size. A ResNet18-based model trained on 4000 real and 100.000 synthetic images is found to perform the best, reducing the median size-estimation error from 9.89% without synthetic data to 5.26%. Micrograph ; Multislice ; Nanoparticle ; Materials science ; Scanning electron microscope ; Transmission electron microscopy ; Electron micrographs ; Scanning transmission electron microscopy ; Nanotechnology ; Surrogate endpoint ; Optics ; Analytical Chemistry (journal) ; Chemistry ; Electron microscope ; Nuclear magnetic resonance ; Medicine ; Physics ; Radiology ; Composite material ; Chromatography Electron and X-Ray Spectroscopy Techniques ; Machine Learning in Materials Science ; Enhanced Oil Recovery Techniques
Authors: Fatemeh Abedi Tameh | Mahmood Akbari
Source: Chemistry Africa (article, 2025-05-27)
Field: Materials Science — Materials Chemistry

Tuning the Band Gap of Cerium Oxide Nanoparticles Via Terbium (Tb) Doping: Experimental and Theoretical Insights Abstract Cerium oxide nanoparticles (CNPs) exhibit remarkable electronic and optical properties; however, their wide band gap and high recombination rates hinder their photocatalytic efficiency. In this study, we investigate the effects of terbium (Tb) doping on cubic CNPs synthesized via the hydrothermal method to overcome these limitations. Elemental analysis using Fourier Transform Infrared Spectroscopy (FTIR) and Energy-Dispersive X-ray Spectroscopy (EDX) confirms successful Tb incorporation, while structural characterization through X-ray Diffraction (XRD) and Raman spectroscopy reveals the coexistence of Tb³⁺ and Tb⁴⁺ within the CNP lattice. Scanning Electron Microscopy (SEM) and Transmission Electron Microscopy (TEM) demonstrate a reduction in nanoparticle size with increasing Tb content. Optical band gap measurements, obtained via Diffuse Reflectance Spectroscopy (DRS) and supported by Density Functional Theory (DFT) calculations, indicate a significant band gap reduction, enhancing visible light absorption. By systematically analyzing the effects of Tb doping at 8% and 12% concentrations on cubic CNPs, this study provides valuable insights into the structural, electronic, and optical modifications induced by doping. These findings suggest that Tb-doped CNPs hold great potential for improved photocatalytic and optical applications. Keywords: Cerium Oxide Nanoparticle (CNP); Doping; Band Gap; Characterizations; DFT Calculations Terbium ; Cerium oxide ; Materials science ; Doping ; Cerium ; Nanoparticle ; Band gap ; Oxide ; Nanotechnology ; Optoelectronics ; Luminescence ; Metallurgy Catalytic Processes in Materials Science ; Gas Sensing Nanomaterials and Sensors ; Electronic and Structural Properties of Oxides
Authors: Yan Zhao | Huachao Ma | Zhenglu Gao | Ziyan Huang | Yuanyuan Wu | Kuilin Lv
Source: Coatings (article, 2025-01-03)
Field: Materials Science — General Materials Science

Enhancement of the Corrosion and Wear Resistance of an Epoxy Coating Using a Combination of Mullite Powder and PVB Currently, with the gradual development of corrosion-resistant materials, coatings often exhibit ultra-high hydrophobic properties while possessing corrosion resistance, complicating the preparation of corrosion-resistant coatings. To explore a novel coating that combines high corrosion resistance with simplified preparation methods, mullite/kaolin powder was stirred using ball milling, and polyvinyl butyral was added to serve as a binder, thereby preparing a hydrophilic and highly corrosion-resistant coating. The coating was characterized using SEM, IR, XRD, and other testing methods. The results revealed that the components of the coating are connected through physical crosslinking, avoiding chemical reactions. Regarding the coating’s performance, electrochemical and salt spray tests were conducted to characterize the prepared coating. According to electrochemical impedance spectroscopy tests, after immersion for 7 days, the electrochemical impedance spectroscopy impedance value of the A4C6EP coating reached 1.13 × 108 Ω·cm2, several times higher than that of other coatings, demonstrating its superior corrosion resistance. After a salt spray test for 2000 h, the coating surface showed neither bubbles, further validating the excellent corrosion resistance of the A4C6EP coating. The A4C6EP coating underwent an abrasion test using sandpaper and, after 100 cycles, the contact angle decreased by only 2.3°, with only slight scratches appearing on the surface, indicating very high mechanical abrasion resistance. This research demonstrates the successful preparation of a hydrophilic coating with excellent corrosion resistance and ultra-high mechanical abrasion resistance through a simple method, providing new insights for the development of hydrophilic corrosion-resistant coatings and reducing the cost of such coatings. Mullite ; Epoxy ; Materials science ; Coating ; Corrosion ; Wear resistance ; Metallurgy ; Powder coating ; Composite material ; Ceramic Material Properties and Applications ; Polymer Nanocomposites and Properties ; Tribology and Wear Analysis
Authors: Wenli Wang | Yuehui Tai | Qian Wu | Shuai Shao | Yuhui Zhao | Lingxiao Cai | Qifeng Liu
Source: Applied Water Science (article, 2025-05-17)
Field: Materials Science — Materials Chemistry

Design and synthesis of g-C3N4-doped Bi-MOF composites for high-efficient catalytic ozonation of 4-Nitrophenol: mechanism, effect parameters and degradation pathways analysis 4-Nitrophenol (4-NP), a toxic and persistent pollutant in chemical wastewater, presents significant challenges in degradation and mineralization. Conventional ozone-oxidation catalysts are hindered by low efficiency, mass transfer constraints and metal leaching, necessitating the development of stable and efficient catalysts. Herein, the Bi-MOF/g-C3N4/MS composite materials were prepared by the solvothermal method and an immersion-induced phase separation strategy. The resulting materials were characterized and applied for catalytic ozonation degradation of 4-NP. Under the specific experimental conditions of the O3 + BiCN100/MS system, the total organic carbon and chemical oxygen demand removal rates of 4-NP were observed to reach 71.2% and 83.7% within 20 min, respectively. These two parameters were improved by raising the initial pH, reducing 4-NP concentration and increasing the catalyst dosage. The abundant Lewis acid sites were regarded as the pivotal catalytic site of BiCN100/MS, which were conducive to the adsorption of O3 and the acceleration of the formation of reactive oxygen species (ROS). The electron paramagnetic resonance results demonstrated that the primary ROS engaged in the degradation reaction were 1O2, ·O2− and ⋅OH. Toxicity analysis revealed that the O3 + BiCN100/MS system exhibited an effective detoxification effect. Ultimately, the primary degradation pathway of 4-NP was proposed through liquid chromatography–mass spectroscopy (LC–MS) analyses at varying reaction times. Additionally, BiCN100/MS demonstrated excellent stability and reusability in actual waste water cyclic experiments. Degradation (telecommunications) ; Nitrophenol ; Catalysis ; Doping ; Materials science ; Chemical engineering ; Mechanism (biology) ; Reaction mechanism ; Composite material ; Chemistry ; Organic chemistry ; Computer science ; Optoelectronics Catalytic Processes in Materials Science ; Advanced Photocatalysis Techniques ; Gas Sensing Nanomaterials and Sensors
Authors: Ivan Maliyov | Petr Grigorev | Thomas D. Swinburne
Source: npj Computational Materials (article, 2025-01-27)
Field: Materials Science — Materials Chemistry

Exploring parameter dependence of atomic minima with implicit differentiation Interatomic potentials are essential to go beyond ab initio size limitations, but simulation results depend sensitively on potential parameters. Forward propagation of parameter variation is key for uncertainty quantification, whilst backpropagation has found application for emerging inverse problems such as fine-tuning or targeted design. Here, the implicit derivative of functions defined as a fixed point is used to Taylor-expand the energy and structure of atomic minima in potential parameters, evaluating terms via automatic differentiation, dense linear algebra or a sparse operator approach. The latter allows efficient forward and backpropagation through relaxed structures of arbitrarily large systems. The implicit expansion accurately predicts lattice distortion and defect formation energies and volumes with classical and machine-learning potentials, enabling high-dimensional uncertainty propagation without prohibitive overhead. We then show how the implicit derivative can be used to solve challenging inverse problems, minimizing an implicit loss to fine-tune potentials and stabilize solute-induced structural rearrangements at dislocations in tungsten. Maxima and minima ; Statistical physics ; Physics ; Theoretical physics ; Mathematics ; Mathematical analysis Thermal and Kinetic Analysis ; X-ray Diffraction in Crystallography ; Machine Learning in Materials Science
Authors: Obaid Khaleifah Alhmoudi | Mahmoud Aboushanab | Muhammed Thameem | Ali Elkamel | Ali A. AlHammadi
Source: Scientific Reports (article, 2025-07-02)
Field: Materials Science — Materials Chemistry

Domain adaptation of a SMILES chemical transformer to SELFIES with limited computational resources Accurate molecular property prediction requires input representations that preserve substructural details and maintain syntactic consistency. SMILES (Simplified Molecular Input Line Entry System), while widely used, does not guarantee validity and allows multiple representations for the same compound. SELFIES (Self-Referencing Embedded Strings) addresses these limitations through a robust grammar that ensures structural validity. This study investigates whether a SMILES-pretrained transformer, ChemBERTa-zinc-base-v1, can be adapted to SELFIES using domain-adaptive pretraining without modifying the tokenizer or model architecture. Approximately 700,000 SELFIES-formatted molecules from PubChem were used for adaptation, completed within 12 h on a single NVIDIA A100 GPU. Embedding-level evaluation included t-distributed stochastic neighbor embedding (t-SNE), cosine similarity, and regression on twelve QM9 properties using frozen transformer weights. The domain-adapted model outperformed the original SMILES baseline and slightly outperformed the performance of ChemBERTa-77 M-MLM across most targets, despite a 100-fold difference in pretraining scale. For downstream evaluation, the model was fine-tuned end-to-end on ESOL, FreeSolv, and Lipophilicity, achieving root mean squared error (RMSE) values of 0.944, 2.511, and 0.746, respectively. These results demonstrate that SELFIES-based adaptation offers a cost-efficient alternative for molecular property prediction, without relying on molecular descriptors, 3D features, or large-scale infrastructure. Computer science ; Domain adaptation ; Adaptation (eye) ; Transformer ; Data science ; Computational biology ; Artificial intelligence ; Biology ; Neuroscience ; Engineering ; Electrical engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Karen L. Chong | Qusai Abdulkhaliq Alsabia | Zongyu Ye | Andrew S. McDaniel | Douglas Baumgardner | Dianne J. Xiao | Shijing Sun
Source: Journal of Materials Chemistry A (article, 2025-01-01)
Field: Chemistry — Inorganic Chemistry

Controlling metal–organic framework crystallization <i>via</i> computer vision and robotic handling A closed-loop robotic and computer vision system was developed to control and quantify MOF crystallization outcomes. Crystallization ; Computer science ; Artificial intelligence ; Computer vision ; Materials science ; Engineering ; Chemical engineering Metal-Organic Frameworks: Synthesis and Applications ; Remote-Sensing Image Classification ; Mercury impact and mitigation studies
Authors: Lifeng Sun | Yuanyuan Zeng | Zhengyu Shen | C. Y. Yue | Yahan Yang | Jia Gao | Junhao Zhang | Qi Yuan | Limei Cha
Source: Journal of Functional Biomaterials (review, 2025-08-29)
Field: Materials Science — Biomaterials

Biodegradable Metal-Based Stents: Advances, Challenges, and Prospects Cardiovascular disease is a leading cause of global mortality. Percutaneous coronary intervention, which involves the placement of stents to restore blood flow in narrowed arteries, is a widely used treatment. However, traditional stents, such as bare metal stents and drug-eluting stents, can lead to long-term complications such as restenosis, inflammation, and thrombosis. Biodegradable metallic vascular stents, with their superior mechanical properties, excellent biocompatibility, and gradual degradation in vivo, hold significant potential for the treatment of coronary artery disease. This review provides a comprehensive overview of the current research status and challenges. Firstly, it outlines the design principles and performance evaluation methods for biodegradable stents, which focus on mechanical properties, chemical characteristics, corrosion behavior, and biocompatibility. Furthermore, it summarizes the material features, degradation mechanisms, and metabolic behavior of three primary biodegradable metals-magnesium alloys, iron alloys, and zinc alloys-and discusses critical issues such as the degradation rate of different alloys and the development of zinc alloys. Finally, based on the current achievements and challenges of studies on biodegradable metal-based stents, this article proposes some optimization strategies and research prospects. Materials science ; Nanotechnology ; Metal ; Metallurgy Magnesium Alloys: Properties and Applications ; Corrosion Behavior and Inhibition ; Aluminum Alloys Composites Properties
Authors: Evans Pericoli | Viola Ferretti | Dario Verna | Luca Pasquini
Source: ACS Applied Energy Materials (article, 2025-02-06)
Field: Materials Science — Materials Chemistry

Tuning TiFe<sub>1–<i>x</i></sub>Ni<sub><i>x</i></sub> Hydride Thermodynamics through Compositional Tailoring In this study, we investigate how structural modifications induced by Fe substitution with Ni in the TiFe intermetallic alloy affect the thermodynamics of hydride formation and decomposition. The primary goal of substituting Fe with Ni was to reduce the plateau pressure of TiFe, a crucial parameter for reversible solid-state hydrogen storage applications under near-ambient conditions (below 150 °C and 50 bar). Alloy compositions TiFe<sub>1-<i>x</i></sub> Ni <sub><i>x</i></sub> with <i>x</i> ≤ 0.30 were synthesized by arc melting. The structural and morphological properties were characterized using powder X-ray diffraction and scanning electron microscopy with energy-dispersive X-ray spectroscopy. The thermodynamic properties were investigated through volumetric measurements using a Sieverts' apparatus and calorimetric analysis with a high-pressure differential scanning calorimeter. We show that Ni incorporation effectively lowers the plateau pressure, stabilizing the hydride thermodynamics due to a more negative enthalpy of hydride formation. Moreover, the entropy of hydride formation increases with the Ni content, resulting in a linear correlation between the enthalpy and entropy values determined at different compositions. The enthalpy-entropy compensation effect was analyzed to determine whether it arises from statistical artifacts or is genuine to the system, as our findings suggest. Hydride ; Thermodynamics ; Materials science ; Specific heat ; Physical chemistry ; Physics ; Chemistry ; Metallurgy ; Metal Hydrogen Storage and Materials ; Boron and Carbon Nanomaterials Research ; Inorganic Chemistry and Materials
Authors: Ye Yang | Zesheng Chen | Wenwen Cheng | Fangyuan Li | Wang Xiaolong | Zijian Wang | Weikang Hu
Source: Interdisciplinary medicine (article, 2025-05-15)
Field: Materials Science — Materials Chemistry

MXenes‐based flexible electronics for wound monitoring and treatment Abstract Wound healing is a dynamic and overlapping bio‐process with varied cellular and molecular characteristics involved. Traditional wound dressings have made significant achievements in accelerating wound healing; however, they lack the ability to monitor the dynamic changes of wounds, which limits the effectiveness of wound management. Currently, the development of flexible electronics‐based intelligent dressings that can monitor wound characteristics in a real‐time manner can make a positive impact on clinical practice. MXenes, also known as a type of 2D inorganic compound, are generally considered ideal precursors for fabricating flexible electronics. MXenes‐based flexible electronics can not only monitor the biomarkers of wounds (e.g., temperature, pH, lactic acid) but also promote wound healing by combining with other bio‐activities. This review begins with a brief introduction to the challenges associated with the early diagnosis of wounds. It then provides a comprehensive summary and discussion of the fabrication and applications of MXenes‐based flexible electronics for wound monitoring and treatment. Finally, it explores the current challenges and future directions in this cutting‐edge field. MXenes ; Electronics ; Engineering ; Electrical engineering ; Materials science ; Nanotechnology MXene and MAX Phase Materials ; Advanced Sensor and Energy Harvesting Materials ; Polydiacetylene-based materials and applications