jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Liuqun Gu
Source: ChemRxiv (preprint, 2024-08-05)
Field: Chemistry — Organic Chemistry

An Integrated Study of Pd(0)-Halide Interactions in Pd Oxidative Addition to Organic Halides: Uncovering the “Super Iodine” Character of Pd(0) Pd(0) oxidative addition (OA) to organic halides is a fundamental step in many catalytic organic transformations. Previous mechanistic studies have shown that the electronic/steric nature of ligand, metal coordination unsaturation, halide identity and the presence of an additive all have sizable influences on reaction reactivity and selectivity. A single parameter-based rational design fully integrating all important factors in a quantitative manner remains elusive, due to the complex nature of Pd(0) OA. To address this challenge, a study of Pd(0)-halide interactions which are crucial for the 3-centered concerted mechanism, is initiated in this work by integrating the key progress in organometallic chemistry and the relatively well-established halogen bonding knowledge, mimicking the “data-driven” method. The achieved advanced understanding of the mechanisms in this work includes five key parts: 1) uncovering the “super iodine” character of Pd(0), 2) uncovering the bonding nature of Pd-halide (organic halide) interaction and its essential role in Pd(organic halide) precomplex before OA; 3) a molecular mechanism for the additive effect, and a theoretical prediction of a halogen transfer pathway for OA to specific organic halides; 4) demystifying the known super reactivity of the ligand-controlled Pd catalysis with inactive aryl chlorides; and ultimately 5) proposing a proof-of-concept of surface molecule electrostatic potential-based rational design of OA. Halide ; Chemistry ; Oxidative addition ; Reactivity (psychology) ; Halogen ; Steric effects ; Catalysis ; Aryl halide ; Ligand (biochemistry) ; Rational design ; Molecule ; Palladium ; Combinatorial chemistry ; Computational chemistry ; Organic chemistry ; Nanotechnology ; Materials science ; Alkyl Catalytic C–H Functionalization Methods ; Catalytic Cross-Coupling Reactions ; Asymmetric Hydrogenation and Catalysis
Authors: You Li | Yi Wan | Yuhai Lin | Ting Hu | Mingyan Liu | Yibin Zhao | Yunwei Yang | Changting Wei | Fang Li | Chengxi Huang | Fang Wu | Xiaoming Li | Xiufeng Song | Erjun Kan
Source: Nano Research (article, 2024-12-16)
Field: Materials Science — Materials Chemistry

Enabling room-temperature ferromagnetism and p-type conductance in MoS <sub>2</sub> monolayers by substitutional doping of vanadium The integrated circuits based on carrier charge have neared their physical limits, which are constrained by the von Neumann architecture and incapable of meeting the demands for information storage, processing, and transmission from the rapid advancements in mass data and artificial intelligence. Recently discovered two-dimensional magnets, with their strengths in multi-field manipulation and high-density heterogeneous integration, have presented significant opportunities for the development of novel devices such as compute-in-memory. However, generating stable magnetic order in two-dimensional systems at room temperature is still difficult. Here, we have devised a liquid-phase precursor-assisted chemical vapor deposition methodology for the synthesis of vanadium-doped MoS<sub>2</sub> monolayers. Magnetic measurements showed an augmentation of magnetic signals concurrent with an increase in growth temperature and doping concentration. Field-effect transistor assessments using the synthesized V-doped MoS<sub>2</sub> monolayer as the conducting channel indicated a transition from n-type semiconducting to p-type at a doping concentration of around 6.8%, further corroborated by theoretical computations. Our study not only presents a novel synthetic approach toward the production of two-dimensional magnetic materials but also elucidates the potential of doping procedures for modulating electrical characteristics. Vanadium ; Ferromagnetism ; Doping ; Monolayer ; Materials science ; Conductance ; Condensed matter physics ; Vanadium dioxide ; Nanotechnology ; Thin film ; Optoelectronics ; Metallurgy ; Physics 2D Materials and Applications ; Chalcogenide Semiconductor Thin Films ; Molecular Junctions and Nanostructures
Authors: Soochan Lee | Hyein Jeong | Sungyeop Jung | Yeong-Jin Kim | Eunchan Cho | Joohan Nam | D. ChangMo Yang | Hyunchul Oh | Wonyoung Choe
Source: ChemRxiv (preprint, 2024-08-06)
Field: Chemistry — Inorganic Chemistry

Data-Driven Search Algorithm for Discovery of Synthesizable Zeolitic Imidazolate Frameworks Zeolitic imidazolate frameworks (ZIFs), metal-organic analogues of zeolites, hold great potential for carbon-neutral applications due to their exceptional stability and porosity. However, ZIF discovery has been hindered by the limited topologies resulting from a mismatch between numerous predicted and few synthesized zeolitic networks. To address this, we propose a data-driven search algorithm using structural descriptors of known materials as a screening tool. From over 4 million zeolite structures, we identified potential ZIF candidates based on O−T−O angle differences, vertex symbols, and T−O−T angles. Energy calculations facilitated the ranking of ZIFs by their synthesizability, leading to the successful synthesis of three ZIFs with two novel topologies: UZIF-31 (uft1) and UZIF-32, -33 (uft2). Notably, UZIF-33 exhibited remarkable CO2 selective adsorption. This study highlights the synergistic potential of combining structural predictions with chemical intuition to advance material discovery. Zeolitic imidazolate framework ; Network topology ; Imidazolate ; Materials science ; Computer science ; Intuition ; Adsorption ; Metal-organic framework ; Algorithm ; Chemistry ; Topology (electrical circuits) ; Nanotechnology ; Mathematics ; Combinatorics ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Zeolite Catalysis and Synthesis ; Covalent Organic Framework Applications
Authors: Palak Sharma | Atul Kumar Dubey
Source: IP International Journal of Forensic Medicine and Toxicological Sciences (review, 2024-07-06)
Field: Chemistry — Spectroscopy

Advances in the detection of explosives and chemical weapons: A comprehensive review The persistent threat of explosives and chemical weapons necessitates continual advancements in detection technologies to ensure public safety and national security. This review paper provides a comprehensive overview of recent developments in the field of explosive and chemical weapon detection. Emphasizing the interdisciplinary nature of this research, the paper covers a range of sensing technologies, including ion mobility spectrometry, mass spectrometry, infrared spectroscopy, and biosensors. The review explores the integration of nanotechnology to enhance the sensitivity and selectivity of detection methods, alongside the application of advanced imaging technologies such as X-ray and Terahertz imaging for non-contact identification of concealed threats. Additionally, it delves into the utilization of biological sensing elements in biosensor platforms, employing enzymes and antibodies to detect specific chemical compounds. Standoff detection capabilities, crucial for ensuring the safety of personnel, are examined, and the paper discusses the ongoing developments in chemical forensics, aiming to determine the origin and composition of explosive or chemical materials. The challenges faced in this field, including the need for high sensitivity, adaptability to different environments, and the detection of emerging threats, are thoroughly addressed. The review concludes by outlining future directions, highlighting the importance of miniaturization, artificial intelligence, and global collaboration in shaping the next generation of detection technologies. This thorough analysis provides insights into current trends, obstacles, and potential solutions in the crucial field of explosives and chemical weapons detection, making it an invaluable tool for scholars, decision-makers, and security experts. Explosive material ; Chemical warfare ; Environmental science ; Political science ; History ; Archaeology ; Law Mass Spectrometry Techniques and Applications ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies
Authors: Carlos Serrano | Néstor Ulloa | Christian Quintero Flores | Fausto Caicedo
Source: Mathematical Modelling and Engineering Problems (article, 2024-05-30)
Field: Materials Science — Materials Chemistry

Artificial Intelligence for Mechanical Properties Prediction of Polyethylene-Carbon Nanotube Composites The novel class of composite materials known as polyethylene-carbon nanotube composites (PECNTs) has attracted significant interest from scientists.In this study, authors investigated how artificial intelligence (AI) is employed to calculate the elastic modulus of PECNTs.For the first time, an AI-based modeling methodology replaces nanoindentation techniques like depth sensing indentation (DSI).This study highlights the complexities inherent in traditional methods, where the proposed methodology utilizes a gene expression programming (GEP) model, addressing challenges associated with accuracy in PECNT simulation.The proposed AI model test uses 135 input/output data pairs taken from the literature and randomly split into 82 training and 53 testing sets.The elastic modulus (EM) whichever dynamic E′ or quasi-static E) employs as an output factor in the models created, with the method of analysis, matrix type, processing technique, nanofiller type, and its content serving as inputs.Though the modeling progression is complete with results from the training and testing sets, the nanometer sensitivity of the prominent designs of the AI model displayed significant promise for the effective application of artificial intelligence methods in measuring the elastic modulus of PECNTs through non-destructive testing. Composite material ; Carbon nanotube ; Materials science ; Polyethylene ; Carbon nanotube metal matrix composites ; Nanotube Carbon Nanotubes in Composites ; Polymer Nanocomposites and Properties ; Additive Manufacturing and 3D Printing Technologies
Authors: Ziheng Xiao | Yu Jiang | Zhigang Yao | Yi Tang
Source:  (article, 2024-05-17)
Field: Materials Science — Materials Chemistry

AI-assisted Peak Efficiency Searching of CLLC Resonant Converters in Step-down Conditions The CLLC resonant converter exhibits significant suitability for application in battery chargers and energy storage systems, thanks to its zero-voltage switching-on (ZVS-on) capability, which can be regulated by adjusting two control parameters: the switching frequency and the duty cycle. While pulse frequency modulation (PFM) yields high efficiency under nominal or boost conditions, in buck conditions, both control variables are adjusted to minimize power loss. Conventional strategies for diminishing power loss often depend on idealized, lossless, or overly simplistic analytical models, which can result in discrepancies between theoretical calculation and experimental results, arising from neglected or inadequately represented parameters. To address this issue, the present study introduces a methodology assisted by artificial intelligence (AI) for the precise calculation of power loss and the identification of peak efficiency points. This approach was applied to a 3 kW CLLC prototype, yielding an accurate determination of peak efficiency and an average discrepancy in power loss of 11.3% between theoretical predictions and practical observations. Converters ; Materials science ; Computer science ; Electronic engineering ; Optoelectronics ; Electrical engineering ; Engineering ; Voltage Copper-based nanomaterials and applications ; ZnO doping and properties ; Gas Sensing Nanomaterials and Sensors
Authors: Chenyang Li | Ganglong Cui | Xiangjian Shen
Source: Scientia Sinica Chimica (article, 2024-10-21)
Field: Chemistry — Organic Chemistry

Challenges and opportunities in electronic structure theory <p indent="0mm">This article summarizes the academic discussions on the symposium entitled “Challenges and Opportunities in Electronic Structure Theory” supported by the National Science Foundation of China. We first introduce some recent advancements of electronic structure theory. In particular, we focus on the following fields: (1) methods for strongly correlated systems, (2) density and density-matrix functional theory, (3) low-scaling electronic structure methods, (4) methods for condensed matter systems, (5) quantum computational chemistry, (6) methods driven by artificial intelligence, (7) methods enriched by emerging computing power, and (8) high-performance algorithms and software. We point out a number of challenges and opportunities in developing new electronic structure methods, such as improving the accuracy and efficiency under current frameworks, leveraging new computing technologies to develop efficient algorithms and software, and deepening interdisciplinary research. Lastly, we suggest some key scientific problems for future developments in the next <sc>5−10 years.</sc> Computer science Synthesis and Properties of Aromatic Compounds ; Advanced Physical and Chemical Molecular Interactions ; Rare-earth and actinide compounds
Authors: Ercüment Öztürk | Ayfer Dönmez Çavdar | Tuğrul Çavdar
Source: Archives of Computational Methods in Engineering (article, 2024-08-27)
Field: Materials Science — Materials Chemistry

Artificial Intelligence Applications in Composites: A Survey Production (economics) ; Context (archaeology) ; Raw material ; Quality (philosophy) ; Process (computing) ; Computer science ; Point (geometry) ; Product (mathematics) ; Manufacturing engineering ; Industrial production ; Process engineering ; Risk analysis (engineering) ; Engineering ; Mathematics ; Business Machine Learning in Materials Science ; Industrial Vision Systems and Defect Detection ; Advanced Chemical Sensor Technologies
Authors: Shih-Hung Cheng | Ting-I Kuo | Er-Feng Hsieh | Wen–Jeng Hsueh
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Materials Chemistry

Gate-Controllable Two-Dimensional Transition Metal Dichalcogenides for Spintronic Memory Spintronics ; Transition metal ; Materials science ; Nanotechnology ; Optoelectronics ; Engineering physics ; Condensed matter physics ; Ferromagnetism ; Engineering ; Physics ; Chemistry 2D Materials and Applications ; Perovskite Materials and Applications ; Heusler alloys: electronic and magnetic properties
Authors: Kelsey Horace-Herron | Naren Vikram Raj Masna | Swarup Bhunia | Soumyajit Mandal | Sandip Ray
Source: IEEE Access (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Nuclear Quadrupole Resonance for Substance Detection This tutorial paper provides a comprehensive overview of recent advances in nuclear quadrupole resonance (NQR) spectroscopy for substance detection, highlighting its principles, methodologies, and applications. The paper elucidates the fundamental physics underlying NQR spectroscopy, emphasizing the interaction between nuclear quadrupole moments and electric field gradients. It explores the various experimental techniques and instrumentation developments that have enabled the sensitive detection and precise characterization of substances containing quadrupolar nuclei. A significant portion of the survey is dedicated to discussing the diverse applications of NQR spectroscopy, including the detection of explosives, drug pharmaceuticals, and material authentication. Furthermore, the survey examines the challenges and limitations associated with NQR spectroscopy, including issues related to signal-to-noise ratio (SNR), temperature dependency, and substance restrictions. Strategies to overcome these challenges are discussed, offering insights into the future directions of NQR spectroscopy research that includes artificial intelligence (AI), internet of things (IoT) integration, incorporating a cloud database for NQR parameter storage, and multi-modal analysis. Nuclear quadrupole resonance ; Spectroscopy ; Quadrupole ; Computer science ; Explosive detection ; Explosive material ; Nuclear magnetic resonance ; Physics ; Materials science ; Nanotechnology ; Chemistry ; Atomic physics Solid-state spectroscopy and crystallography ; Advanced NMR Techniques and Applications ; Advanced Chemical Sensor Technologies
Authors: A. Matsumoto | Shigeyuki Matsunami | Hiroshi Narazaki | Shinya Kawashima | Masayoshi Inoue
Source: IEEJ Transactions on Fundamentals and Materials (article, 2024-08-31)
Field: Materials Science — Materials Chemistry

Example of Data-driven Superconducting Wire Research The data-driven research has progressed in recent years. Particularly in the field of superconducting materials, there has been active research into the search for new materials, especially by materials informatics (MI). On the other hand, in a superconducting material that has three critical points, the critical current density of the three critical points changes greatly depending on the material manufacturing process. The critical current density properties greatly depend on the material structure, and there are many parameters to obtain the appropriate mictrostructures for various applications. Therefore, these data will be useful to a wide range of users, including materials researchers and even superconducting wire suppliers and a field of application. In this commentary, we provided an overview of data collection, storage, and use, and then presented the progress of some ongoing research and discussed future development possibilities. Superconductivity ; Critical current ; Field (mathematics) ; Process (computing) ; Nanotechnology ; Materials science ; Informatics ; Engineering physics ; Computer science ; Systems engineering ; Engineering ; Physics ; Electrical engineering ; Condensed matter physics Machine Learning in Materials Science ; Physics of Superconductivity and Magnetism ; Superconductivity in MgB2 and Alloys
Authors: Lingqi Quan | Zhiqi Wang | Yu Zhong | Yiqiang Wu | Hui Zhao | Yan Mi | Fangchao Cheng
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Chemistry — Organic Chemistry

Rapid Photochromism and Water-Driven Ultra-Fast Fading of Cellulose-Based Composite Hydrogels Enabled by Sodium Tungstate Photochromism ; Self-healing hydrogels ; Tungstate ; Sodium tungstate ; Composite number ; Cellulose ; Chemical engineering ; Materials science ; Chemistry ; Polymer chemistry ; Composite material ; Photochemistry ; Inorganic chemistry ; Organic chemistry ; Tungsten Polydiacetylene-based materials and applications ; Photochromic and Fluorescence Chemistry ; Photonic Crystals and Applications
Authors: Yi-Wen Liao | Raphaël Latty | Bin Yang
Source: IEEE Transactions on Semiconductor Manufacturing (article, 2024-03-06)
Field: Materials Science — Materials Chemistry

Experts in the Loop: Conditional Variable Selection Based on Deep Learning for Accelerating Post-Silicon Validation Post-silicon validation is one of the most critical processes in modern semiconductor manufacturing. Specifically, correct and deep understanding in test cases of manufactured devices is key to enable post-silicon tuning and debugging. This analysis is typically performed by experienced human experts. However, with the fast development in semiconductor industry, test cases can contain hundreds of variables. The resulting high-dimensionality poses enormous challenges to experts. Thereby, some recent prior works have introduced data-driven variable selection algorithms to tackle these problems and achieved notable success. Nevertheless, for these methods, experts are not involved in training and inference phases, which may lead to bias and inaccuracy due to the lack of prior knowledge. Hence, this letter for the first time aims to design a novel conditional variable selection approach while keeping experts in the loop. In this way, we expect that our algorithm can be more efficiently and effectively trained to identify the most critical variables under certain expert knowledge. Extensive experiments on both synthetic and real-world datasets from industry have been conducted and shown the effectiveness of our method. Selection (genetic algorithm) ; Artificial intelligence ; Computer science ; Machine learning ; Feature selection ; Loop (graph theory) ; Variable (mathematics) ; Silicon ; Silicon valley ; Engineering ; Reliability engineering ; Data mining ; Mathematics ; Materials science ; Economics ; Optoelectronics Machine Learning in Materials Science
Authors: Anqi Lin | Richard J. Sheridan | Bingyin Hu | L. Catherine Brinson
Source: Journal of the Mechanics and Physics of Solids (article, 2024-10-19)
Field: Materials Science — Polymers and Plastics

ViscoNet: A lightweight FEA surrogate model for polymer nanocomposites viscoelastic response prediction Viscoelasticity ; Materials science ; Finite element method ; Surrogate model ; Composite material ; Nanocomposite ; Structural engineering ; Engineering ; Mathematics ; Mathematical optimization Polymer crystallization and properties ; Polymer Nanocomposites and Properties ; Natural Fiber Reinforced Composites
Authors: Ruirui Niu | Zhuoxian Li | Xiangyan Han | Qianling Liu | Zhuangzhuang Qu | Zhiyu Wang | Chunrui Han | Kenji Watanabe | Takashi Taniguchi | Kaihui Liu | Jinhai Mao | Wu Shi | Bo Peng | Zheng Han | Zizhao Gan | Jianming Lü
Source: arXiv (Cornell University) (preprint, 2024-03-26)
Field: Materials Science — Materials Chemistry

Unveiling the origin of unconventional moire ferroelectricity Interfacial ferroelectricity emerges in heterostructures consisting of nonpolar van der Waals (vdW) layers, greatly expanding the scope of two dimensional ferroelectrics. In particular, the unconventional moire ferroelectricity observed in bilayer graphene/boron nitride (BN) heterostructures, exhibits promising functionalities with topological current, superconductivity and synaptic responses. However, the debate about its mechanism - correlation driven charge transfer between two graphene layers - limits device reproducibility and hence large-scale production. Here by designing a single-layer graphene encapsulated by lattice-mismatched WSe2, we identify the ferroelectricity as stemming from - instead of graphene moire bands - the particular BN, where interfacial sliding ferroelectricity must play a role. With similar structures, multilayer twisted MoS2 is found to reproduce the ferroelectricity. The key is a conductive moire ferroelectric, where the screened gate and the pinned domain wall together result in unchanged electronic states, i.e. anomalous screening. The intimate connection to interfacial sliding ferroelectricity thus provides advantages of diverse choices of constituent materials and robust polarization switching while preserving the unique anomalous screening, paving the way to reproducible and reliable memory-based devices in artificial intelligence. Moiré pattern ; Ferroelectricity ; Materials science ; Physics ; Optics ; Optoelectronics Solid-state spectroscopy and crystallography