Polymer electrolyte membrane water electrolysis (PEMWE) has been regarded as a promising technology for renewable hydrogen production. However, acidic oxygen evolution …
W Li, Z Yin, Z Gao, G Wang, Z Li, F Wei, X Wei, H Peng… - Nature Energy, 2022 - nature.com
Many CO2 electrolysers under development use liquid electrolytes (KOH solutions, for example), yet using solid-state polymer electrolytes can in principle improve efficiency and …
SP Zeng, H Shi, TY Dai, Y Liu, Z Wen, GF Han… - Nature …, 2023 - nature.com
Developing robust nonprecious-metal electrocatalysts with high activity towards sluggish oxygen-evolution reaction is paramount for large-scale hydrogen production via …
Y Zhang, Y Wang, B Yu, K Yin, Z Zhang - Advanced Materials, 2022 - Wiley Online Library
Plasmonic metals demonstrate significant potential for solar steam generation (SSG) because of their localized surface plasmon resonance effect. However, the inherently …
The unprecedented ability of computations to probe atomic-level details of catalytic systems holds immense promise for the fundamentals-based bottom-up design of novel …
K Wang, J Huang, H Chen, Y Wang, W Yan… - Electrochemical Energy …, 2022 - Springer
High entropy alloys (HEAs), which can incorporate five or more constituents into a single phase stably, have received considerable attention in recent years. The …
C Du, P Li, Z Zhuang, Z Fang, S He, L Feng… - Coordination Chemistry …, 2022 - Elsevier
Porous nanostructures have been widely studied as electrocatalysts since they are structurally advantageous for enhancing the mass transport and providing accessible active …
H Meng, Q Ran, TY Dai, H Shi, SP Zeng, YF Zhu… - Nano-micro letters, 2022 - Springer
Metallic zinc (Zn) is one of the most attractive multivalent-metal anode materials in post- lithium batteries because of its high abundance, low cost and high theoretical capacity …
R Mattey, S Ghosh - Computer Methods in Applied Mechanics and …, 2022 - Elsevier
A physics informed neural network (PINN) incorporates the physics of a system by satisfying its boundary value problem through a neural network's loss function. The PINN approach …