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Clean Coal Technology

CSCD

ISTIC

PKU

ISSN：1006-6772

CN：11-3676/TD

Frequency：Monthly

Website：none

Email：jjmjs@263.net

Postcode：100013

Articles4054

Citations40338

Funded2197

Impact Factor：2.44

Articles

2024 No.12

Total 14 articles

Column for Carbon Neutrality

Design of efficient catalysts and research of catalytic mechanisms for CO2 hydrogenation to liquid productsXIN Yue, ZENG Jie

1-21

Special Topic on Frontiers and Challenging Technologies in Hydrogen Energy

Recent advances in full-spectrum solar photothermocatalytic hydrogen productionGUAN Jian, MA Rong, LI Donghui, YAN Xiaoqing, WEI Jinjia, SUN Jie

22-37

Research progress and challenges of photovoltaic-driven electrolysis water splitting for hydrogen productionCHEN Xin, REN Liping, CHEN Chunying, WANG Qi, LIU Keyan, FAN Jinpeng, WEI Jinjia, CHEN Jie

38-55

Photothermal synergistic catalytic water splitting for H2 production:Challenges and breakthroughs from the perspective of energy and mass transfer and conversionYAN Xueli, WANG Xinyi, ZENG Ziyu, ZHANG Shiyue, ZHANG Yongwang, ZHAO Xinyuan, ZHAO Shidong, WANG Biao, WANG Shujian, LIU Maochang

56-75

Study and application of six-component high-entropy two-dimensional materials in photothermal methanol reforming for hydrogen productionHUANG Senyan, LIU Xin, LEI Runan, DU Kai, YU Chenyang, LI Yaguang

76-82

Efficient conversion and carbon deposition inhibit strategy of photothermal-driven methane reforming on Ni@SrTiO3 catalystMA Xu, YANG Weiwei

83-94

Simulation analysis of an energy storage system for solar-driven chemical looping reforming of methane to produce hydrogen and methanolWANG Yanjuan, LONG Yunfei, XIN Yu, JIANG Qiongqiong, XU Chao, QU Wanjun, HONG Hui

95-104

Techno-economic and environmental assessment of a novel hydrogen production system based on complementary coal and biomass gasification technologyZHANG Zhong, LI Sheng

105-117

Research progress about liquid organic hydrogen carriers technologyLU Shuhan, GONG Xiang, WANG Bin, YANG Fusheng, FANG Tao

118-133

Comparative study of machine learning regression algorithms for predicting thermal energy storage performance of metal hydrides with high hydrogen densityYANG Yikun, WU Zhen, LIU Honghao, ZHANG Zaoxiao

134-146