Journal of Energy Storage, Год журнала: 2025, Номер 113, С. 115641 - 115641
Опубликована: Фев. 6, 2025
Язык: Английский
Journal of Energy Storage, Год журнала: 2025, Номер 113, С. 115641 - 115641
Опубликована: Фев. 6, 2025
Язык: Английский
Chemical Physics Reviews, Год журнала: 2025, Номер 6(1)
Опубликована: Март 1, 2025
Adverse climate change, global warming, and energy security have emerged as challenges, demanding advancements in high-performance battery technologies to drive sustainability. In this scenario, developing electrolytes has gained significant momentum among various innovations, given their critical role determining safety performance. However, the conventional trial-and-error approach electrolyte discovery is costly, complex, time-consuming, often inefficient. Recent artificial intelligence (AI) over past decade catalyzed innovations across diverse fields, ranging from nanotechnology space explorations, are now emerging a powerful tool for materials discovery. Numerous studies demonstrated effectiveness of AI screening characterizing next-generation electrolytes. This review offers comprehensive outlook on transformative designing novel Examination key parameters that influence electrochemical performance batteries conducted. The challenges opportunities using design with tailored properties explored. Furthermore, futuristic vision integrating science-driven AI-based approaches existing experimental theoretical methods accelerate presented. By offering such understanding, aims provide researchers, industries, policymakers insights into how can be leveraged electrolytes, paving way toward progress technology.
Язык: Английский
Процитировано
1Journal of Alloys and Compounds, Год журнала: 2024, Номер 1002, С. 175199 - 175199
Опубликована: Июнь 15, 2024
Язык: Английский
Процитировано
6Next Energy, Год журнала: 2024, Номер 5, С. 100159 - 100159
Опубликована: Июнь 27, 2024
Язык: Английский
Процитировано
6Energy & Fuels, Год журнала: 2024, Номер 38(15), С. 13722 - 13736
Опубликована: Июль 18, 2024
Язык: Английский
Процитировано
6Journal of Alloys and Compounds, Год журнала: 2024, Номер 1005, С. 176023 - 176023
Опубликована: Авг. 14, 2024
Язык: Английский
Процитировано
5Polymers, Год журнала: 2024, Номер 16(23), С. 3368 - 3368
Опубликована: Ноя. 29, 2024
The integration of machine learning (ML) into material manufacturing has driven advancements in optimizing biopolymer production processes. ML techniques, applied across various stages production, enable the analysis complex data generated throughout identifying patterns and insights not easily observed through traditional methods. As sustainable alternatives to petrochemical-based plastics, biopolymers present unique challenges due their reliance on variable bio-based feedstocks processing conditions. This review systematically summarizes current applications techniques aiming provide a comprehensive reference for future research while highlighting potential enhance efficiency, reduce costs, improve product quality. also shows role algorithms, including supervised, unsupervised, deep
Язык: Английский
Процитировано
5Chemical Engineering Journal, Год журнала: 2024, Номер 494, С. 153215 - 153215
Опубликована: Июнь 17, 2024
Язык: Английский
Процитировано
4Inorganic Chemistry Frontiers, Год журнала: 2024, Номер 11(12), С. 3596 - 3606
Опубликована: Янв. 1, 2024
Pt is efficiently deposited onto a self-supported MoO 2 electrode with low loading, resulting in excellent performance the HER process.
Язык: Английский
Процитировано
3International Journal of Hydrogen Energy, Год журнала: 2024, Номер 82, С. 636 - 645
Опубликована: Авг. 3, 2024
Язык: Английский
Процитировано
3Rare Metals, Год журнала: 2024, Номер unknown
Опубликована: Авг. 16, 2024
Язык: Английский
Процитировано
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