jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Nurit Manukovsky | Noy Vaisleib | Michal Arbel Haddad | Amir Goldbourt
Source: Physical Chemistry Chemical Physics (article, 2025-01-01)
Field: Chemistry — Spectroscopy

Prediction of NMR parameters and geometry in <sup>133</sup>Cs-containing compounds using density functional theory The need to immobilize low-level nuclear waste, in particular <sup>137</sup>Cs-bearing waste, has led to a growing interest in geopolymer-based waste matrices, in addition to optimization attempts of cement matrix compositions for this specific application. Although the overall phase composition and structure of these matrices are well characterized, the binding sites of Cs in these materials have not been clearly identified. Recent studies have suggested that combining the sensitivity of solid-state nuclear magnetic resonance (SSNMR) to the local atomic structure with other structural techniques provides insights into the mode of Cs binding and release. Density functional theory (DFT) can provide the connection between spectroscopic parameters and geometric properties. However, the reliability of DFT results strongly relies on the choice of a suitable exchange-correlation functional, which for <sup>133</sup>Cs, the NMR surrogate for such studies, is not well-established. In this work we benchmark various functionals against their performance in predicting the geometry of various simple Cs compounds, their NMR quadrupolar coupling constants, and their chemical shift values, while prioritizing the ability to incorporate dispersion interactions and maintaining low computational cost. We examined Cs salts, Cs oxides, perovskites, caged materials, a borate glass and a cesium fluoroscandate. While no single functional performs equally well for all parameters, the results show rev-vdW-DF2 and PBEsol+D3 to be leading candidates for these systems, in particular with respect to geometry and chemical shifts, which are of high importance for Cs-immobilization matrices. Density functional theory ; Computational chemistry ; Materials science ; Geometry ; Chemistry ; Mathematics Advanced NMR Techniques and Applications
Authors: Caitlin E. Shields | Abbie M. Scholes | Anna G. Slater
Source: Chemical Science (review, 2025-01-01)
Field: Materials Science — Biomaterials

Enabling technology for accelerated discovery of supramolecular materials Organic supramolecular materials, defined by their discrete, modular nature, promise to deliver flexible solutions to pressing challenges, including separations, storage, sensing, and catalysis. The absence of strong metallic or covalent bonding within their solid-state structures enables fine-tuning and post-synthetic processing to tailor properties towards specific applications. However, their production suffers from poor reproducibility, scalability, and sustainability; as a result, translation of these materials from the lab to real-world situations is rare. In this perspective, we discuss how the field stands to benefit from the emergence of 'enabling technologies', such as high-throughput screening (HTS), automation, and flow chemistry. We summarise recent advances and consider the opportunities these technologies present for the accelerated discovery, optimisation, and translation of supramolecular materials. Supramolecular chemistry ; Modular design ; Supramolecular assembly ; Translation (biology) ; Field (mathematics) Supramolecular Self-Assembly in Materials ; Calcium Carbonate Crystallization and Inhibition ; Gas Sensing Nanomaterials and Sensors
Authors: Ega Abdillah Ghaniya | Daffa Hadyan Akmal | Muhammad Dzaki Adlil Zulbadri | Septia Eka Marsha Putra
Source: Greensusmater (article, 2025-07-02)
Field: Materials Science — Materials Chemistry

Assessing k‑Point Mesh Density for Accurate DFT Modeling (1x1) unit cell of Graphene This study systematically evaluated the influence of k-points mesh density and offset conditions on the structural accuracy, total energy convergence, and computational efficiency of a pristine graphene system. The total energy results indicate that convergence is achieved at a k-points mesh of 12×12×1, with negligible variations up to 20×20×1. Similarly, structural parameters, including lattice constants and C–C bond lengths, demonstrate minimal deviation at higher mesh densities. However, computational time increases non-linearly with k-points density, especially under offset conditions, highlighting the trade-off between precision and computational cost. Based on a comprehensive assessment of energy stability, structural consistency, and time efficiency, the 16×16×1 no-offset k-points mesh emerges as the most balanced and reliable configuration. It yields the lowest total energy, exhibits excellent agreement with established structural benchmarks, and avoids excessive computational demand. This makes it particularly suitable as a reference system for future ab initio studies, such as H₂O adsorption on graphene, where accurate baseline energies are critical for computing adsorption energetics. The findings underscore the importance of k-points convergence testing in density functional theory (DFT) simulations and support prior literature emphasizing the balance between computational accuracy and efficiency. Future adsorption studies can confidently adopt the identified k-points mesh to ensure both reliable results and computational feasibility. Graphene ; Point (geometry) ; Unit (ring theory) ; Materials science ; Computer science ; Mathematics ; Nanotechnology ; Geometry Graphene research and applications
Authors: Jing Cao | Huilin Li | Jingjing Cui | Mengmeng Gao | Jingming Sun | Mingfei Pan
Source: Foods (article, 2025-09-24)
Field: Chemistry — Analytical Chemistry

Synthesis of Magnetic Core–Shell Materials and Their Application in Detection of Food Contaminants Food contamination poses a significant global public health challenge, necessitating the accurate detection of hazardous substances within complex food matrices. Magnetic core-shell nanomaterials have emerged as critical materials for trace contaminant analysis due to their efficient magnetic separation capabilities, excellent adsorption performance, and tunable surface functionalities. By encapsulating magnetic cores with functional shells, these nanomaterials combine rapid magnetic responsiveness with advantageous shell properties, including target-specific recognition, enhanced dispersibility, colloidal stability, and high surface area. This enables a comprehensive detection approach encompassing target adsorption, rapid separation, and signal amplification. Magnetic core-shell nanomaterials have been effectively integrated with techniques including magnetic solid-phase extraction (MSPE), fluorescence (FL) assays, and lateral flow immunoassays (LFIAs), demonstrating broad applicability in food safety monitoring and detection. This review outlines synthesis strategies for magnetic core-shell nanomaterials, highlights their applications for food contaminant detection, and discusses future challenges and prospects in the field of food safety analysis. Magnetic nanoparticles ; Nanomaterials ; Magnetic separation ; Contamination ; Food safety ; Hazardous waste ; Adsorption ; Zerovalent iron Analytical chemistry methods development ; Biosensors and Analytical Detection ; Mercury impact and mitigation studies
Authors: Onur Boyar | Yanheng Gu | Yuji Tanaka | Shunsuke Tonogai | Tomoya Itakura | Ichiro Takeuchi
Source: Neural Computation (article, 2025-07-08)
Field: Materials Science — Materials Chemistry

Crystal-LSBO: Automated Design of De Novo Crystals With Latent Space Bayesian Optimization Generative modeling of crystal structures is significantly challenged by the complexity of input data, which constrains the ability of these models to explore and discover novel crystals. This complexity often confines de novo design methodologies to merely small perturbations of known crystals and hampers the effective application of advanced optimization techniques. One such optimization technique, latent space Bayesian optimization (LSBO), has demonstrated promising results in uncovering novel objects across various domains, especially when combined with variational autoencoders (VAEs). Recognizing LSBO's potential and the critical need for innovative crystal discovery, we introduce Crystal-LSBO, a de novo design framework for crystals specifically tailored to enhance explorability within LSBO frameworks. Crystal-LSBO employs multiple VAEs, each dedicated to a distinct aspect of crystal structure-lattice, coordinates, and chemical elements-orchestrated by an integrative model that synthesizes these components into a cohesive output. This setup not only streamlines the learning process but also produces explorable latent spaces thanks to the decreased complexity of the learning task for each model, enabling LSBO approaches to operate. Our study pioneers the use of LSBO for de novo crystal design, demonstrating its efficacy through optimization tasks focused mainly on formation energy values. Our results highlight the effectiveness of our methodology, offering a new perspective for de novo crystal discovery. Bayesian optimization ; Bayesian probability ; Space (punctuation) ; Computer science ; Crystal (programming language) ; Artificial intelligence ; Algorithm ; Programming language Machine Learning in Materials Science ; Drilling and Well Engineering ; Calcium Carbonate Crystallization and Inhibition
Authors: Asset M Kabyshev | N. K. Aidarbekov | Кенжебатыр Бекмырза | К. А. Кутербеков | М. М. Кубенова | A. Baratova | Natei Ermias Benti
Source: International Journal of Low-Carbon Technologies (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

A review on thermal management of solid oxide fuel cells Abstract Safe operation of fuel cells is highly associated with their operating temperature. Efficient cooling system is required to keep the temperature of these devices in an allowable range. Solid Oxide Fuel Cells (SOFCs) have high operating temperature and their proper thermal management has high importance. Different techniques are applicable for cooling of these power generation systems and their effectiveness is impacted by a variety of factors and items. In this article, research studies centered on the thermal management units of SOFCs are reviewed and presents their significant findings. Configuration of the thermal management unit greatly affects cooling effectiveness. Thermal management of electronic devices and systems ; Thermal ; Fuel cells ; Operating temperature ; Oxide ; Water cooling Advancements in Solid Oxide Fuel Cells ; Chemical and Environmental Engineering Research ; Thermal Expansion and Ionic Conductivity
Authors: Xu Yang | Hao Cui | Zhongchao Liu | Yun Liu
Source: Sensors (article, 2025-07-03)
Field: Materials Science — Materials Chemistry

DFT Exploration of a Pd-Doped InSe Monolayer as a Novel Gas Sensing Candidate upon SF6 Decomposition: SO2, SOF2, and SO2F2 Monitoring SF<sub>6</sub> decomposition gases has emerged as a vital diagnostic technique for evaluating insulation conditions and identifying faults in SF<sub>6</sub>-based electrical equipment. This study comprehensively explores the adsorption properties and sensing capabilities of a Pd-doped InSe (Pd-InSe) monolayer for SF<sub>6</sub> decomposition gases, including SO<sub>2</sub>, SOF<sub>2</sub>, and SO<sub>2</sub>F<sub>2</sub>, through density functional theory calculations. The Pd-InSe monolayer is constructed by substituting one Se atom with a Pd atom in the pristine InSe structure. Then, the Pd doping effect on the InSe monolayer and the adsorption behaviors of the Pd-InSe monolayer for three gases are thoroughly examined. The adsorption configurations, charge density differences, and electron localization functions are scrutinized to elucidate the gas adsorption mechanisms of the Pd-InSe monolayer; and the band structures, along with the density of states, are analyzed to gain insights into the resistive gas sensing mechanisms for detecting these three gases. Finally, the temperature-dependent recovery characteristics are evaluated to assess the reusability of the monolayer. These findings not only underscore the potential of the Pd-InSe monolayer for sensing SF<sub>6</sub> decomposition gases but also open new avenues for the development of next-generation 2D materials in gas sensing applications within the field of electrical engineering. Monolayer ; Adsorption ; Doping ; Density functional theory ; Decomposition ; Materials science ; Sulfur hexafluoride ; Nanotechnology ; Atom (system on chip) ; Chemical physics ; Fermi level ; Chemistry ; Computational chemistry ; Physical chemistry ; Optoelectronics ; Electron ; Organic chemistry ; Physics ; Computer science 2D Materials and Applications ; Ga2O3 and related materials ; Gas Sensing Nanomaterials and Sensors
Authors: Vlad-Gabriel Vasilescu | Lucian Toma Ciocan | Andreea Mihaela Custura | Miruna Stan | Florin Miculescu | Cosmin Mihai Cotrut | Diana Maria Vranceanu | Elisabeta VASILESCU | Marina Imre | Silviu Mirel Pițuru
Source: Dentistry Journal (article, 2025-12-22)
Field: Materials Science — Materials Chemistry

Biocompatibility Evaluation of Novel Experimental Titanium Alloys for Dental Implants <b>Background/Objectives</b>: The purpose of this study was to assess the in vitro biocompatibility and corrosion resistance of five titanium alloys that have been recently developed for dental implant applications, whose compositions were designed to align with current approaches in the development of novel biomaterials. Priority was given to limiting the harmfulness associated with specific chemical elements present in common conventional alloys and increasing corrosion resistance to improve the biomaterial-tissue cellular interaction. <b>Methods</b>: For this purpose, five types of titanium alloys with original chemical compositions (Ti1-Ti5) were developed. The electrochemical behavior of the alloys was analyzed by evaluating the corrosion resistance in environments that simulate the oral environment, as well as the cellular behavior, by evaluating the viability, growth, and proliferation of human cells on osteoblasts and gingival fibroblasts. Detailed analysis of the chemical composition by scanning electron microscope (SEM/EDS) methods was used. The corrosion rate of the alloys in artificial saliva was tested using the polarization resistance technique (Tafel). Human osteoblasts (hFOB cell line) and human gingival fibroblasts (hFIB-G cell line) were used to measure biocompatibility in vitro. <b>Results</b>: The Ti5 alloy demonstrated the highest cell viability and the lowest corrosion rate (0.114 μm/year) among all tested compositions, with the Ti3 alloy containing Mo and Zr following closely behind. The Ti2 alloy exhibited reduced biocompatibility because of the inclusion of Ni and Fe in its composition. <b>Conclusions:</b> Taken together, the results of this study provide useful information on the basic characteristics of titanium alloys with original chemical compositions. The titanium alloys were analyzed in comparison with common conventional alloys (Cp-Ti and Ti6Al4V) as well as alloys such as Ti-Zr, Ti-Nb, and Ti-Nb-Zr-Ta, which are considered to be viable alternatives to conventional materials for making dental implants. Biocompatibility ; Corrosion ; Titanium ; Titanium alloy ; Scanning electron microscope ; Alloy ; Dental implant Titanium Alloys Microstructure and Properties ; Bone Tissue Engineering Materials ; Dental Implant Techniques and Outcomes
Authors: Titouan Simonnet | Mame Diarra Fall | Sylvain Grangeon | Bruno Galerne
Source:  (article, 2025-03-12)
Field: Materials Science — Materials Chemistry

Vision Transformers for X-ray Diffraction Patterns Analysis Understanding materials properties depends largely on the ability to determine its components, and in particular its mineral phases. Powder X-ray diffraction (XRD) is a powerful tool for such purposes. This paper presents a Transformer-based vision model (ViT) for mineral phase identification, and proportion inference to quantify the mineral phases present in a material. Our analysis shows that the tokenization strategy is a critical step for XRD pattern analysis. The results obtained for both tasks are excellent and more robust than those obtained with a CNN. The proposed approach also makes it possible to introduce visualization tools for signal analysis, to better understand how information flows through the model and how data is classified or quantified. Computer science ; Transformer ; Diffraction ; Optics ; Electrical engineering ; Engineering ; Physics ; Voltage X-ray Diffraction in Crystallography
Authors: Mohammad Maroufkhani | Alireza Khodabandeh | Iulian Radu | Elmira Moosavi‐Khoonsari | Mohammad Jahazi
Source: Results in Materials (article, 2025-10-04)
Field: Materials Science — Materials Chemistry

Thermodynamic and kinetic analyses of high temperature oxidation of 316L stainless steel This study investigates the thermodynamic behavior and non-isothermal oxidation kinetics of 316L stainless steel in the temperature range of 1100 K - 1373 K, with relevance to the heat-affected zone during welding in oil and gas pipeline applications. Thermogravimetric analysis was performed at heating rates of 5, 10, 15, 20, and 25 K/min study the high-temperature oxidation kinetics of AISI 316L stainless steel welds. Kinetic analysis was conducted using Kennedy-Clark and Coats-Redfern methods as well as Friedman, Starink, Kissinger-Akahira-Sunose, and Flynn-Wall-Ozawa model-free isoconversional methods. Activation energies determined using isoconversional models ranged from 224.79 to 233.81 kJ/mol. The second-order (F2) and third-order (F3) reaction models provided the best fit to the experimental data, as confirmed by Criado master plot analysis. Thermodynamic properties (ΔH ≠ . , ΔS ≠ . , ΔG ≠ ) were also calculated for isocoversional models. FactSage thermochemical simulations revealed the formation of a dual-layer protective oxide scale primarily composed of spinel and corundum phases. These oxide layers enhance oxidation resistance at high temperatures. The findings contribute to a mechanistic and kinetic understanding of high-temperature oxidation in 316L stainless steel, supporting its reliable application in demanding oil and gas environments. • Kinetic analysis of 316LSS oxidation using isoconversional and model-fitting methods. • Identification of solid-state reaction mechanisms through Criado master plot analysis. • Thermochemical validation of oxidation products using FactSage simulations • Integrating multiple kinetic approaches to enhance the accuracy of E a determination. Thermogravimetric analysis ; Kinetic energy ; Kinetics ; Oxide ; Atmospheric temperature range ; Corundum ; Spinel Thermal and Kinetic Analysis ; Energetic Materials and Combustion ; High-Temperature Coating Behaviors
Authors: Yusuke Sasano | Hideya Tanaka | Silvia Natsuko Akutsu | Rui Yamakawa | Shu Saito | Kanta Kido | Tsubasa Suzuki | Yuki Tateishi | Shota Nagasawa | Yoshiharu Iwabuchi | Tomoyuki Miyao
Source: ChemRxiv (preprint, 2025-05-15)
Field: Chemistry — Organic Chemistry

Quantitative Structure–Reactivity Relationship-Guided Mechanistic Study Enables the Optimization of Nitroxyl Radical-Catalyzed Alcohol Oxidation Alcohol oxidation is a fundamental transformation in organic chemistry, playing a central role in the synthesis of valuable intermediates and products. Herein, the mechanism of alcohol oxidation using a nitroxyl radical catalyst and a hypervalent iodine reagent was investigated through kinetic isotope effect experiments and data-driven quantitative structure–reactivity relationship analyses to identify the rate-determining step and guide reaction optimization. Among the molecular representations evaluated, the percent buried volume: %Vbur at 2.5 Å centered on the α-carbon of the alcohol showed the strongest correlation with the initial reaction rate across a diverse substrate set, revealing a key structural factor governing the reaction rate of alcohols. These mechanistic insights, which suggested that the addition of the alcohol to the catalyst is likely the rate-determining step, motivated the investigation of potential additives. The addition of water was found to accelerate the reaction, leading to a more efficient catalytic system. This study offers mechanistic insights and predictive tools to guide the design of more efficient nitroxyl radical-catalyzed alcohol oxidations. Nitroxyl ; Reactivity (psychology) ; Alcohol ; Alcohol oxidation ; Chemistry ; Catalysis ; Combinatorial chemistry ; Organic chemistry ; Photochemistry ; Medicine Oxidative Organic Chemistry Reactions ; Electron Spin Resonance Studies
Authors: Alexander J. Heilman | Weiyi Gong | Qimin Yan
Source: npj Computational Materials (article, 2025-11-18)
Field: Materials Science — Materials Chemistry

Crystal hypergraph convolutional networks Graph representations of solid state materials that encode only interatomic-distance information lack geometrical resolution, resulting in degenerate representations that may map distinct structures to equivalent graphs. Here, we propose a hypergraph representation scheme for materials that allows for the association of higher-order geometrical information with hyperedges. Hyperedges generalize edges to connected sets of more than two nodes, and may be used to represent triplets and local environments of atoms in materials. This generalization of edges requires a different approach in graph convolution, which is developed in this work. These crystal hypergraph convolutional networks are trained based on various property prediction tasks for a vast set of solid-state materials available via MatBench. Results presented here focus on the improved performance of models based on both pair-wise edges and local environment hyperedges. These results demonstrate that hypergraphs are an effective and efficient method for incorporating geometrical information in material representations. Hypergraph ; Graph ; Generalization ; Representation (politics) ; ENCODE ; Set (abstract data type) ; Focus (optics) ; Property (philosophy) Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Graph Theory and Algorithms
Authors: Fernando Gabriel Benitez Jara | Jorge David Barrios Lémus | Jan Peter Embs | Heloisa N. Bordallo | Rubens Nunes de Faria
Source: Journal of Power Sources (article, 2025-12-04)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Hierarchical porosity and electrolyte compatibility in carbon-based supercapacitors: Structure-performance relationship Supercapacitor ; Electrolyte ; Capacitance ; Porosity ; Energy storage ; Electrochemistry ; Ion ; Anode Supercapacitor Materials and Fabrication ; Microbial Fuel Cells and Bioremediation ; Advanced battery technologies research