SinoGreenTech Academic Portal
YC
Verified CAS / Academic Author2 Decoded Studies

Prof. Yana Chen

College of Environmental Science and Engineering, North China Electric Power University

Research Publications & English Decoded Briefs

Showing 2 publications
SCIENCE CHINA Materials2026DOI: 10.1007/s40843-025-3700-7

Accelerated oxygen activation over uranyl decorated covalent organic framework for universally promoted H2O2 photosynthesis

Photocatalytic synthesis has been considered a promising technology for solar-to-chemicals conversion. Here, a series of novel photocatalysts was synthesized by decorating uranyl sites on imine-based covalent organic frameworks (i-COF) and proved functioning for the uniformly boosted H2O2 production by 1.6–10.1 folds compared with the bare i-COFs in a wide pH range from 2 to 11. Typically, an optimal H2O2 production rate of 1435.9 μmol g−1 h−1, i.e., 28.72 mmol g(U)−1 h−1, was realized over uranyl decorated TTa-COFs under visible light. Systematic investigations reveal that the universally and remarkably promoted performance is attributed to the outstanding electron-transfer ability, accelerated activation of molecular oxygen and favored formation of ·O2− and *OOH as the key intermediate by virtue of the decorated uranyl ions; thus the two-step single-electron oxygen reduction reaction (ORR) for H2O2 photo-generation is significantly facilitated. This work paves a new way for the uranyl-decorated COFs as a novel photocatalyst and provides in-depth insight to the reaction mechanism for photocatalytic H2O2 production.

SCIENCE CHINA Materials2026DOI: 10.1007/s40843-025-4074-6

AI for Electrocatalytic Energy Conversion: From Atoms to Industry

Achieving carbon neutralization relies heavily on green hydrogen and electrochemical carbon-nitrogen cycles. However, the complexity of these systems and the cost of traditional Edisonian trial-and-error methods hinder rapid progress. Artificial intelligence (AI) has emerged as a transformative tool, enabling high-throughput data processing and dynamic adaptation. This review surveys the landscape of AI-driven electrochemistry, bridging the gap from atomic-scale design to industrial-scale implementation. Specifically, we focus on three areas: atomic structure-function decoding, fully automated “self-driving” laboratories, and macro-scale simulations for device durability. Furthermore, we elucidate the critical challenges in integrating AI with materials science. By mapping current trends and future directions, this work aims to unlock the full transformative potential of AI in next-generation energy storage and conversion.

Prof. Yana Chen | Publications & Academic Profile | SinoGreenTech | SinoGreenTech