Mechanism, performance enhancement, and economic feasibility of CO2 microbial electrosynthesis systems: A data-driven analysis of research topics and trends

Renewable and Sustainable Energy Reviews, Journal Year: 2024, Volume and Issue: 202, P. 114704 - 114704

Published: June 28, 2024

Language: Английский

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Advancements in Nanocomposites: An In-depth Exploration of Microstructural, Electrical, and Mechanical Dynamics

Published: Jan. 3, 2024

This research presents a comprehensive bibliometric and sentiment analysis of nanocomposite literature from 1990 to 2024. Employing cutting-edge computational methods, the study delves deep into progression microstructural characterization, electrical properties, mechanical behaviors nanocomposites. The utilizes advanced Boolean search strategies Scopus database, ensuring thorough extraction thematic content. results explore various themes insights, shedding light on trends evident in particularly prominence microstructure, attributes, performance. paper also offers textual analytics data, showcasing critical collaborative efforts influential studies. Significant discoveries encompass evolution language, shifts focus, global contributions, providing perspective current landscape its dynamic evolution. Moving forward, "State-of-the-Art Gaps Extracted Results Discussions" section most recent advancements research. It types nanocomposites, with particular emphasis their characteristics, dynamics, application films. identifies key themes, traces historical progress, highlights emerging while underscoring significance collaboration influence pivotal studies that have shaped field. Lastly, "Literature Review Guided by Artificial Intelligence" section, introduces revised approach for researching nanocomposites through AI-guided techniques. prioritizes articles published 2023 based citation frequency. Here, focus is exploring relationship between emphasizing fundamental interactions impact characteristics. Various systems are covered, highlighting composition, structure, functionality. Findings integrated provide overview state knowledge this area. Notably, analysis, anchored an average score 0.638771, underscores positive trajectory academic discourse, growing recognition potential exploration maps intellectual domain, crosslinking time attributes. While thorough, it acknowledges limitations advocates broader database inclusion future endeavors. work elucidates prevailing research, indispensable role tools comprehending vast wealth information.

Language: Английский

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Stabilized oily-wastewater separation based on superhydrophilic and underwater superoleophobic ceramic membranes: Integrated experimental design and standalone machine learning algorithms

Journal of the Taiwan Institute of Chemical Engineers, Journal Year: 2024, Volume and Issue: 164, P. 105704 - 105704

Published: Aug. 14, 2024

Language: Английский

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Attention-Based Interpretable Multiscale Graph Neural Network for MOFs

Journal of Chemical Theory and Computation, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 22, 2025

Metal–organic frameworks (MOFs) hold great potential in gas separation and storage. Graph neural networks (GNNs) have proven effective exploring structure–property relationships discovering new MOF structures. Unlike molecular graphs, crystal graphs must consider the periodicity patterns. MOFs' specific features at different scales, such as covalent bonds, functional groups, global structures, influenced by interatomic interactions, exert varying degrees of impact on adsorption or selectivity. Moreover, redundant interactions hinder training accuracy, leading to overfitting. This research introduces a construction method for multiscale which considers scales decomposing graph into multiple subgraphs based within distance ranges. Additionally, it takes account structure encoding periodic patterns unit cells. We propose MSAIGNN, atomic interaction network with self-attention-based pooling mechanism, incorporates three-body bond angle information, accounts structural minimizes interference from interactions. Compared traditional methods, MSAIGNN demonstrates higher prediction accuracy assessing single-component adsorption, separation, features. Visualization attention scores confirms learning highlighting MSAIGNN's interpretability. Overall, offers novel, efficient, multilayered, interpretable approach property complex porous structures like MOFs using deep learning.

Language: Английский

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Clearing the Air: Nanotechnology’s Role in Tackling Atmospheric Pollution

Published: Jan. 1, 2025

Language: Английский

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