Machine learning techniques for prediction of capacitance and remaining useful life of supercapacitors: A comprehensive review
Supercapacitors are appealing energy storage devices for their promising features like high
power density, outstanding cycling stability, and a quick charge–discharge cycle. The …
power density, outstanding cycling stability, and a quick charge–discharge cycle. The …
Leveraging machine learning in porous media
The emergence of artificial intelligence (AI) and, more particularly, machine learning (ML),
has had a significant impact on engineering and the fundamental sciences, resulting in …
has had a significant impact on engineering and the fundamental sciences, resulting in …
Machine-learning-assisted material discovery of oxygen-rich highly porous carbon active materials for aqueous supercapacitors
Porous carbons are the active materials of choice for supercapacitor applications because of
their power capability, long-term cycle stability, and wide operating temperatures. However …
their power capability, long-term cycle stability, and wide operating temperatures. However …
Yield prediction and optimization of biomass-based products by multi-machine learning schemes: Neural, regression and function-based techniques
Pyrolysis, as a thermochemical conversion of biomass, is a superior biofuel production
procedure. The determining procedure for the optimal operational parameters, biomass …
procedure. The determining procedure for the optimal operational parameters, biomass …
Sandwich-like porous MXene/Ni3S4/CuS derived from MOFs as superior supercapacitor electrode
Metal-organic frameworks (MOFs) have ordered porous structure and intriguing properties
for supercapacitors, however, poor conductivity and cycle stability limits their application. To …
for supercapacitors, however, poor conductivity and cycle stability limits their application. To …
Data-driven machine learning approach for predicting the capacitance of graphene-based supercapacitor electrodes
AG Saad, A Emad-Eldeen, WZ Tawfik… - Journal of Energy …, 2022 - Elsevier
Graphene-based nanocomposites have shown strong potential as active components of
high-capacity supercapacitors electrodes in energy storage systems. Develo** an …
high-capacity supercapacitors electrodes in energy storage systems. Develo** an …
Methods, progresses, and opportunities of materials informatics
C Li, K Zheng - InfoMat, 2023 - Wiley Online Library
As an implementation tool of data intensive scientific research methods, machine learning
(ML) can effectively shorten the research and development (R&D) cycle of new materials by …
(ML) can effectively shorten the research and development (R&D) cycle of new materials by …
Core-shell carbon@ Ni2 (CO3)(OH) 2 particles as advanced cathode materials for hybrid supercapacitor: The key role of carbon for enhanced electrochemical …
Three-dimensional porous Ni 2 (CO 3)(OH) 2 compounds were grown on carbon
nanopowder using a facile hydrothermal method for the production of core-shell carbon@ Ni …
nanopowder using a facile hydrothermal method for the production of core-shell carbon@ Ni …
A multi-criteria decision-making (MCDM) approach to determine the synthesizing routes of biomass-based carbon electrode material in supercapacitors
The selection of desirable synthesis procedures to achieve the idea of physiochemical and
capacitive properties of activated carbons (ACs) can be carried out by the multi-criteria …
capacitive properties of activated carbons (ACs) can be carried out by the multi-criteria …
Modeling and classifying the in-operando effects of wear and metal contaminations of lubricating oil on diesel engine: A machine learning approach
The lubricating oil analysis may be used to verify an assessment of the engine's health and
operational conditions, as well as the need for oil changes. The wide sight of oil …
operational conditions, as well as the need for oil changes. The wide sight of oil …