jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yu Guan | Guoqiang Song | Claudia Li | Kang Hui Lim | Bo Wang | Lianghui Xia | Huchao Song | Yinhe Liu | Chunfei Wu | Sibudjing Kawi
Source: Carbon Capture Science & Technology (review, 2024-02-20)
Field: Materials Science — Materials Chemistry

Ni-based core-shell structured catalysts for efficient conversion of CH4 to H2: A review Methane (CH4) serves as a primary constituent of natural gas and a potent greenhouse gas. Catalytic conversion of CH4 into H2 presents a promising avenue for mitigating CO2 emissions from direct CH4 combustion, while also facilitating the transition towards cleaner energy sources. Substantial efforts have been dedicated to enhancing the efficiency of methane conversion and the selectivity for hydrogen in catalyst design. Among these endeavors, core-shell structured Ni-based catalysts have emerged as highly advantageous catalysts for CH4 conversion, with a multitude of benefits including high catalytic activity, coking resistance, and longtime stability. In this review, we provide an overview of Ni-based core-shell catalysts. Furthermore, the synthesis methods of core-shell Ni-based catalysts are explored. We then delve into the myriad advantages these core-shell structured catalysts confer upon methane conversion, specifically emphasizing their remarkable stability and resistance to sintering, and CO2 activation capacity in dry reforming of methane. Moreover, this review comprehensively outlines the versatile applications of Ni-based core-shell structured catalysts in processes such as steam reforming of methane (SRM), dry reforming of methane (DRM), partial oxidation of methane (POM), and catalytic methane decomposition (CMD) for H2 production. Additionally, we delve into the recent advancements in the field of computational chemistry, with a particular focus on density functional theory (DFT) and machine learning (ML). These techniques play pivotal roles in screening Ni-based catalysts and offer insights into the intricate reaction mechanisms underlying methane conversion. Finally, we summarize the key findings of this review and provide forward-looking perspectives. This comprehensive review serves as a valuable reference in the realm of hydrogen production from methane on core-shell structured catalysts, fostering a deeper understanding of this promising area. Methane ; Catalysis ; Carbon dioxide reforming ; Syngas ; Nanotechnology ; Natural gas ; Steam reforming ; Materials science ; Chemical engineering ; Hydrogen production ; Chemistry ; Environmental science ; Process engineering ; Engineering ; Organic chemistry Catalytic Processes in Materials Science ; Catalysts for Methane Reforming ; Electrocatalysts for Energy Conversion
Authors: Kevizali Neikha | Амрит Пузари
Source: Langmuir (review, 2024-10-09)
Field: Chemistry — Inorganic Chemistry

Metal–Organic Frameworks through the Lens of Artificial Intelligence: A Comprehensive Review Metal-organic frameworks (MOFs) are a class of hybrid porous materials that have gained prominence as a noteworthy material with varied applications. Currently, MOFs are in extensive use, particularly in the realms of energy and catalysis. The synthesis of these materials poses considerable challenges, and their computational analysis is notably intricate due to their complex structure and versatile applications in the field of material science. Density functional theory (DFT) has helped researchers in understanding reactions and mechanisms, but it is costly and time-consuming and requires bigger systems to perform these calculations. Machine learning (ML) techniques were adopted in order to overcome these problems by implementing ML in material data sets for synthesis, structure, and property predictions of MOFs. These predictions are fast, efficient, and accurate and do not require heavy computing. In this review, we discuss ML models used in MOF and their incorporation with artificial intelligence (AI) in structure and property predictions. The advantage of AI in this field would accelerate research, particularly in synthesizing novel MOFs with multiple properties and applications oriented with minimum information. Metal-organic framework ; Lens (geology) ; Nanotechnology ; Materials science ; Chemistry ; Adsorption ; Optics ; Organic chemistry ; Physics Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Pratik Madhukar Gorde | Dibya Ranjan Dash | Sushil Kumar Singh | Poonam Singha
Source: Sustainable Chemistry and Pharmacy (review, 2024-05-22)
Field: Materials Science — Biomaterials

Advancements in sustainable food packaging: A comprehensive review on utilization of nanomaterials, machine learning and deep learning Nanomaterials ; Food packaging ; Artificial intelligence ; Computer science ; Engineering ; Nanotechnology ; Materials science ; Mechanical engineering Nanocomposite Films for Food Packaging ; Microplastics and Plastic Pollution ; Advanced Cellulose Research Studies
Authors: Juan A. Allegretto | Diego Onna | Sara A. Bilmes | Omar Azzaroni | Matías Rafti
Source: Chemistry of Materials (article, 2024-05-20)
Field: Chemistry — Inorganic Chemistry

Unified Roadmap for ZIF-8 Nucleation and Growth: Machine Learning Analysis of Synthetic Variables and Their Impact on Particle Size and Morphology Metal-organic frameworks (MOFs) have settled in the scientific community over the last decades as versatile materials with several applications. Among those, zeolitic imidazolate framework 8 (ZIF-8) is a well-known MOF that has been applied in various and diverse fields, from drug-delivery platforms to microelectronics. However, the complex role played by the reaction parameters in controlling the size and morphology of ZIF-8 particles is still not fully understood. Even further, many individual reports propose different nucleation and growth mechanisms for ZIF-8, thus creating a fragmented view for the behavior of the system. To provide a unified view, we have generated a comprehensive data set of synthetic conditions and their final outputs and applied machine learning techniques to analyze the data. Our approach has enabled us to identify the nucleation and growth mechanisms operating for ZIF-8 in a given sub-space of synthetic variables space (chemical space) and to reveal their impact on important features such as final particle size and morphology. By doing so, we draw connections and establish a hierarchy for the role of each synthetic variable and provide with rule of thumb for attaining control on the final particle size. Our results provide a unified roadmap for the nucleation and growth mechanisms of ZIF-8 in agreement with mainstream reported trends, which can guide the rational design of ZIF-8 particles which ultimately determine their suitability for any given targeted application. Altogether, our work represents a step forward in seeking control of the properties of MOFs through a deeper understanding of the rationale behind the synthesis procedures employed for their synthesis. Nucleation ; Morphology (biology) ; Particle size ; Materials science ; Particle (ecology) ; Nanotechnology ; Chemical engineering ; Computer science ; Physics ; Engineering ; Thermodynamics ; Geology Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Xue Bai | Sen Lu | Pei Song | Zepeng Jia | Zhikai Gao | Tiren Peng | Zhiguo Wang | Qi Jiang | Hong Cui | Weizhi Tian | Rong Feng | Zhiyong Liang | Qin Kang | Hongkuan Yuan
Source: Journal of Colloid and Interface Science (article, 2024-03-11)
Field: Materials Science — Materials Chemistry

Heterojunction of MXenes and MN4–graphene: Machine learning to accelerate the design of bifunctional oxygen electrocatalysts Bifunctional ; Overpotential ; Oxygen evolution ; Catalysis ; Density functional theory ; Chemistry ; Graphene ; Heterojunction ; Electrochemistry ; Materials science ; Chemical engineering ; Physical chemistry ; Nanotechnology ; Computational chemistry ; Optoelectronics ; Electrode MXene and MAX Phase Materials ; Advanced Photocatalysis Techniques ; Electrocatalysts for Energy Conversion
Authors: Guobin Zhao | Yongchul G. Chung
Source: Journal of Chemical Theory and Computation (article, 2024-06-01)
Field: Materials Science — Materials Chemistry

PACMAN: A Robust Partial Atomic Charge Predicter for Nanoporous Materials Based on Crystal Graph Convolution Networks We report a fast and easy method (PACMAN) to assign partial atomic charges on metal-organic framework (MOF) and covalent-organic framework (COF) crystal structures based on graph convolution networks (GCNs) trained on >1.8 million high-fidelity partial atomic charge data obtained from the Quantum Metal-Organic Framework (QMOF) database. The developed model shows outstanding performance, achieving a mean absolute error (MAE) of 0.0055 e (test set performance) while maintaining consistency with DDEC6, Bader, and CM5 charges across diverse chemistry and topologies of MOFs and COFs. We find that the new method accurately assigns partial atomic charges for ion-containing nanoporous materials, which has not been possible in previous machine learning (ML) models. Grand canonical Monte Carlo (GCMC) simulation results for CO<sub>2</sub> and N<sub>2</sub> uptakes and the Widom particle insertion calculation for Henry's law constant of water results based on PACMAN and the original DDEC6 charges show excellent agreements compared to other ML models reported in the literature. The runtime analysis of the new method demonstrates that the partial atomic charges of MOF and COF structures with up to 500 atoms can be obtained in less than 10 s. An easy-to-use web interface has been developed to facilitate the adoption of the developed model. Nanoporous ; Charge (physics) ; Crystal (programming language) ; Graph ; Convolution (computer science) ; Computer science ; Materials science ; Nanotechnology ; Theoretical computer science ; Physics ; Artificial intelligence ; Artificial neural network ; Quantum mechanics Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; X-ray Diffraction in Crystallography
Authors: Siyang Nie | Yan Xiang | Liang Wu | Guang Lin | Qingda Liu | Shengqi Chu | Xun Wang
Source: Journal of the American Chemical Society (article, 2024-10-17)
Field: Materials Science — Materials Chemistry

Active Learning Guided Discovery of High Entropy Oxides Featuring High H<sub>2</sub>-production High entropy oxides (HEOs) represent a class of solid solutions comprising multiple elements, offering significant scientific potential. Due to the enormous combination types of elements, the design of HEOs with desirable properties within high-dimensional composition spaces has traditionally relied heavily on knowledge and intuition. In this study, we present an active learning (AL) strategy tailored to efficiently explore the vast compositional space of HEOs. Our approach operates as a closed-loop system, iteratively cycling through "Training, Prediction, and Experiment" stages. Across multiple AL iterations, we have successfully identified four novel HEOs from a vast array of potential compositions. These newly discovered materials exhibit exceptional stability and demonstrate outstanding performance in H<sub>2</sub> evolution rate (251 μmol g<sub>cat</sub><sup>-1</sup> min<sup>-1</sup>) during the water-gas shift reaction, surpassing benchmarks set by established catalysts such as Pt/γ-Al<sub>2</sub>O<sub>3</sub> (135 μmol g<sub>cat</sub><sup>-1</sup> min<sup>-1</sup>) and Cu/ZnO/Al<sub>2</sub>O<sub>3</sub> (81 μmol g<sub>cat</sub><sup>-1</sup> min<sup>-1</sup>). X-ray photoelectron spectroscopy and density functional theory calculations revealed a loss of elemental identity in the selected HEOs. This catalyst discovery process underscores the efficacy of Machine Learning in accelerating the identification of HEOs with unique characteristics by effectively leveraging insights from limited experimental data. Chemistry Electronic and Structural Properties of Oxides ; Machine Learning in Materials Science ; Advancements in Solid Oxide Fuel Cells
Authors: Simone Perego | Luigi Bonati | S. K. Tripathi | Michele Parrinello
Source: ACS Catalysis (article, 2024-09-18)
Field: Materials Science — Materials Chemistry

How Dynamics Changes Ammonia Cracking on Iron Surfaces Being rich in hydrogen and easy to transport, ammonia is a promising hydrogen carrier. However, a microscopic characterization of the ammonia cracking reaction is still lacking, hindered by extreme operando conditions. Leveraging state-of-the-art molecular dynamics, machine learning potentials, and enhanced sampling methods, we offer an atomistic view of the adsorption, diffusion, and dehydrogenation processes of a single NHx (x = 1, 3) molecule on two representative surfaces at the operando temperature of 700 K. We elucidate the effects of the dynamics on all the steps of decomposition. On the stable (110) surface, we found that the reaction intermediate diffusions are favored over dehydrogenation, with non-negligible effects on the reactivity for one intermediate. The role is even more dramatic on the (111) surface, where the mobility of Fe surface atoms introduces unexplored adsorption sites and significantly alters the dehydrogenation barriers. In both cases, a detailed analysis of reactive events shows that there is never a single transition state, but it is always an ensemble. Notwithstanding, a unified mechanism can be identified by following the charge transfer along the different reaction pathways. Cracking ; Ammonia ; Catalysis ; Ammonia production ; Chemistry ; Fluid catalytic cracking ; Metallurgy ; Materials science ; Chemical engineering ; Physical chemistry ; Organic chemistry ; Engineering Nuclear Materials and Properties ; Ammonia Synthesis and Nitrogen Reduction ; Fusion materials and technologies
Authors: Hang Xu | Yue Xue | Zhenqi Liu | Qing Tang | Tianyi Wang | Xichan Gao | Yaping Qi | Yong P. Chen | Chunlan Ma | Yucheng Jiang
Source: Small Science (article, 2024-02-14)
Field: Materials Science — Materials Chemistry

Van der Waals Heterostructures for Photoelectric, Memory, and Neural Network Applications A van der Waals (vdW) heterostructure is formed by combining multiple materials through vdW bonds. It can combine the advantages of electronic, optical, thermal, and magnetic properties of different 2D materials and has the potential to develop into the next generation of high-performance functional devices. Herein, the current research advances of vdW heterostructures are reviewed. First, current fabrication methods and physical structures of vdW heterostructures are summarized. The 2D/nD (<i>n</i> = 0,1,2,3) mixed-dimensional heterostructures are discussed in detail. Second, a new type of vdW heterostructure is introduced based on two-dimensional electron gas with a nanoscale junction interface. Finally, the application prospects of vdW heterostructures in photoelectric and memory devices are further outlined by combing new applications in the neural networks. This review shows that vdW heterostructures have great advantages in high integration, energy harvesting, and logical operations, and it provides directions and suggestions for the future research and application of environmentally friendly, high-performance, and smart functional devices. Heterojunction ; van der Waals force ; Materials science ; Photoelectric effect ; Nanotechnology ; Optoelectronics ; Nanoscopic scale ; Interface (matter) ; Fabrication ; Computer science ; Engineering physics ; Physics ; Molecule 2D Materials and Applications ; MXene and MAX Phase Materials ; Perovskite Materials and Applications
Authors: Zhiling Li | Shengfang Li | Zimeng Zhang | Xueqi Chen | Xi-Qi Li | Yunxia Zu | Fan Chen | Aimei Wang
Source: Water Research (review, 2024-12-13)
Field: Chemistry — Inorganic Chemistry

Extracellular electron transfer-dependent bioremediation of uranium-contaminated groundwater: Advancements and challenges Bioremediation ; Environmental remediation ; Geobacter ; Uranium ; Groundwater remediation ; Environmental science ; Context (archaeology) ; Biochemical engineering ; Contamination ; Engineering ; Materials science ; Ecology ; Biology ; Bacteria Radioactive element chemistry and processing ; Microbial Fuel Cells and Bioremediation ; Metal Extraction and Bioleaching
Authors: Yuan Guo | Allam Maalla | Mostafa Habibi | Zohre Moradi
Source: Mechanical Systems and Signal Processing (article, 2024-04-30)
Field: Materials Science — Materials Chemistry

Electroelastic wave dispersion in the rotary piezoelectric NEMS sensors/actuators via nonlocal strain gradient theory Nonlinear system ; Finite element method ; Piezoelectric sensor ; Piezoelectricity ; Actuator ; Quantum nonlocality ; Mechanics ; Physics ; Control theory (sociology) ; Mathematics ; Engineering ; Acoustics ; Computer science ; Structural engineering Nonlocal and gradient elasticity in micro/nano structures ; Composite Structure Analysis and Optimization ; Thermoelastic and Magnetoelastic Phenomena
Authors: Xiaoya Cui | Yanchang Liu | Yanan Chen
Source: National Science Review (article, 2024-01-27)
Field: Materials Science — Materials Chemistry

Ultrafast micro/nano-manufacturing of metastable materials for energy The structural engineering of metastable nanomaterials with abundant defects has attracted much attention in energy-related fields. The high-temperature shock (HTS) technique, as a rapidly developing and advanced synthesis strategy, offers significant potential for the rational design and fabrication of high-quality nanocatalysts in an ultrafast, scalable, controllable and eco-friendly way. In this review, we provide an overview of various metastable micro- and nanomaterials synthesized via HTS, including single metallic and bimetallic nanostructures, high entropy alloys, metal compounds (e.g. metal oxides) and carbon nanomaterials. Note that HTS provides a new research dimension for nanostructures, i.e. kinetic modulation. Furthermore, we summarize the application of HTS-as supporting films for transmission electron microscopy grids-in the structural engineering of 2D materials, which is vital for the direct imaging of metastable materials. Finally, we discuss the potential future applications of high-throughput and liquid-phase HTS strategies for non-equilibrium micro/nano-manufacturing beyond energy-related fields. It is believed that this emerging research field will bring new opportunities to the development of nanoscience and nanotechnology in both fundamental and practical aspects. Nanotechnology ; Materials science ; Nanomaterials ; Nanomaterial-based catalyst ; Nanostructure ; Ultrashort pulse ; Bimetallic strip ; Nanoparticle ; Metal ; Physics Thermal properties of materials ; Quantum Dots Synthesis And Properties ; MXene and MAX Phase Materials
Authors: Raghvendra Kumar Mishra | Jayati Sarkar | Kartikey Verma | Iva Chianella | Saurav Goel | Hamed Yazdani Nezhad
Source: Open Ceramics (article, 2024-04-25)
Field: Materials Science — Materials Chemistry

Exploring transformative and multifunctional potential of MXenes in 2D materials for next-generation technology MXenes, a rapidly growing family of two-dimensional (2D) transition metal carbides, nitrides, or carbonitrides (Mn+1XnTx, where M is a transition metal, X is carbon, nitrogen, or both, and T represents surface functional groups), have captured the scientific community's interest due to their exceptional physicochemical properties and diverse technological applications. This comprehensive review explores the latest breakthroughs in MXene synthesis and characterisation, emphasising their multifaceted applications in energy storage, catalysis, sensing, and other cutting-edge domains. This review examines the most widely used MXene synthesis strategies, including selective etching and delamination, and highlight recent advancements in controlling surface terminations, composition, and morphology. The influence of these synthetic parameters on MXene properties is discussed in detail. Characterisation techniques, ranging from spectroscopic methods to electron microscopy, are essential for elucidating MXenes' structure-property relationships. Research into energy storage leverages MXenes' high electrical conductivity, large surface area, and chemical tunability. This has led to significant progress in the field. This paper presents research efforts focused on optimising MXenes for both battery and supercapacitor applications. Additionally, the catalytic prowess of MXenes, particularly in electrocatalysis and photocatalysis, is explored, emphasising their role in green energy technologies and environmental remediation. MXenes' remarkable sensitivity and selectivity make them promising candidates for sensing various gases, biomolecules, and ions, offering exciting possibilities in healthcare and environmental monitoring. Importantly, this review underscores the need for continued optimisation of MXene synthesis protocols to achieve large-scale production, enhanced stability, and precise control over properties across various fields. MXenes ; Nanotechnology ; Materials science ; MAX phases ; Electrocatalyst ; Carbide ; Chemistry MXene and MAX Phase Materials ; Graphene and Nanomaterials Applications ; 2D Materials and Applications
Authors: Nacer Belkessa | Aymen Amine Assadi | Abdelkrim Bouzaza | Phuong Nguyen‐Tri | Abdetif Amrane | Lotfi Khezami
Source: Environmental Research (review, 2024-06-06)
Field: Materials Science — Materials Chemistry

A review of non-thermal plasma -catalysis: The mutual influence and sources of synergetic effect for boosting volatile organic compounds removal Catalysis ; Nonthermal plasma ; Chemistry ; Plasma ; Photocatalysis ; Heterogeneous catalysis ; Environmental chemistry ; Nanotechnology ; Chemical engineering ; Process engineering ; Organic chemistry ; Materials science ; Engineering Catalytic Processes in Materials Science ; Plasma Applications and Diagnostics ; Gas Sensing Nanomaterials and Sensors
Authors: Ximeng Liu | Dan Zhao | John Wang
Source: Nano-Micro Letters (review, 2024-03-21)
Field: Materials Science — Materials Chemistry

Challenges and Opportunities in Preserving Key Structural Features of 3D-Printed Metal/Covalent Organic Framework Metal-organic framework (MOF) and covalent organic framework (COF) are a huge group of advanced porous materials exhibiting attractive and tunable microstructural features, such as large surface area, tunable pore size, and functional surfaces, which have significant values in various application areas. The emerging 3D printing technology further provides MOF and COFs (M/COFs) with higher designability of their macrostructure and demonstrates large achievements in their performance by shaping them into advanced 3D monoliths. However, the currently available 3D printing M/COFs strategy faces a major challenge of severe destruction of M/COFs' microstructural features, both during and after 3D printing. It is envisioned that preserving the microstructure of M/COFs in the 3D-printed monolith will bring a great improvement to the related applications. In this overview, the 3D-printed M/COFs are categorized into M/COF-mixed monoliths and M/COF-covered monoliths. Their differences in the properties, applications, and current research states are discussed. The up-to-date advancements in paste/scaffold composition and printing/covering methods to preserve the superior M/COF microstructure during 3D printing are further discussed for the two types of 3D-printed M/COF. Throughout the analysis of the current states of 3D-printed M/COFs, the expected future research direction to achieve a highly preserved microstructure in the 3D monolith is proposed. 3d printed ; 3D printing ; Monolith ; Microstructure ; Materials science ; Nanotechnology ; Porosity ; Covalent organic framework ; Metal-organic framework ; Composite material ; Manufacturing engineering ; Chemistry ; Engineering ; Adsorption Covalent Organic Framework Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Membrane Separation and Gas Transport