[HTML][HTML] Unlocking the potential: machine learning applications in electrocatalyst design for electrochemical hydrogen energy transformation
Machine learning (ML) is rapidly emerging as a pivotal tool in the hydrogen energy industry
for the creation and optimization of electrocatalysts, which enhance key electrochemical …
for the creation and optimization of electrocatalysts, which enhance key electrochemical …
Layered double hydroxide-based nanomaterials for supercapacitors and batteries: Strategies and mechanisms
C Jing, S Tao, B Fu, L Yao, F Ling, X Hu… - Progress in Materials …, 2024 - Elsevier
Supercapacitors and batteries play crucial roles in sustainable energy storage devices.
Layered double hydroxide (LDH) exhibits outstanding adaptability to various …
Layered double hydroxide (LDH) exhibits outstanding adaptability to various …
Advances in the application of machine learning to boiling heat transfer: A review
H Chu, T Ji, X Yu, Z Liu, Z Rui, N Xu - … Journal of Heat and Fluid Flow, 2024 - Elsevier
Boiling heat transfer, one of the most common and effective heat dissipation methods, is
prevalent in industries and crucial for cooling electronic components such as chips. The key …
prevalent in industries and crucial for cooling electronic components such as chips. The key …
Using Machine Learning to Forecast the Conductive Substrate-Supported Heteroatom-Doped Metal Compound Electrocatalysts for Hydrogen Evolution Reaction
N Zhou, Y Zhao, Q Lv, Y Chen - The Journal of Physical Chemistry …, 2024 - ACS Publications
The heteroatom-doped metallic compounds supported on conductive substrates are
excellent catalysts for the hydrogen evolution reaction (HER) thanks to their tunable …
excellent catalysts for the hydrogen evolution reaction (HER) thanks to their tunable …