Guangqian Luo

Huazhong University of Science and Technology

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Yuxian Wang

China University of Petroleum, Beijing

Hongyun Hu

Huazhong University of Science and Technology

Huan Liu

Huazhong University of Science and Technology

Zhenzhong Hu

Hebei University of Technology

Xian Li

Inner Mongolia Electric Power (China)

Hong Yao

Huazhong University of Science and Technology

Hong Yao

Huazhong University of Science and Technology

Jing Gu

Guangzhou Institute of Energy Conversion

Wuyang Xiao

Xinjiang University

Xian Li

Huazhong University of Science and Technology

Cooperative Institutions

Huazhong University of Science and Technology

Chinese Academy of Sciences

Wuhan University of Science and Technology

Xinjiang University

University of Science and Technology of China

Guangzhou Institute of Energy Conversion

Nanjing University

Harbin Institute of Technology

Lanzhou University of Technology

Hebei University of Technology

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Preparation of a heat storage material from Nano-SiC based composite carbonate with boosted heat storage density for high temperature thermal energy storage

Journal of Energy Storage (2024)

Yuxian Wang Zhenzhong Hu Yan Zhu Jing Gu Xian Li

10.1016/j.est.2024.111290

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Boosted Thermal Storage Performance of LiOH·H2O by Carbon Nanotubes Isolated Multilayered Graphene Oxide Frames

Advances in Materials Science and Engineering (2022)

Yuxian Wang Jing Gu Xian Li Yan Zhu Hongyun Hu

Cellulose-originated three-dimensional graphene oxide CNT-modified LiOH·H2O (3D-GO-CNTs-LiOH·H2O) was synthesized by the hydrothermal method. LiOH·H2O nanoparticles (5–50 nm) were homogeneously dispersed inside the 3D-GO-CNTs frames. The composite showed enhanced heat storage density, excellent thermal conductivity, and greatly improved hydration rate due to both the hydrophilic reaction interface of 3D-GO-CNTs frames and reduced size of LiOH·H2O nanoparticles. LiOH·H2O content ratio of 23% (3D-GO-CNTs-LiOH·H2O-1) results in best heat storage performance with activation energy of 23.8 kJ/mol, thermal conductivity of 3.06 W/m·K, and heat storage capacity of 2800 kJ/kg. 3D-GO-CNTs-LiOH·H2O shows 4.2 folders heat storage capacity than that of pristine LiOH·H2O after the same hydration reaction. Other composite materials also show good performance: 3D-GO-CNTs-LiOH·H2O-2 (activation energy: 28.5 kJ/mol, thermal conductivity: 2.33 W/m·K, and heat storage capacity: 2051 kJ/kg.); 3D-GO-CNTs-LiOH·H2O-3 (activation energy: 32.3 kJ/mol, thermal conductivity: 2.01 W/m·K, and heat storage capacity: 1983 kJ/kg.). The addition of cellulose originated 3D-GO-CNTs was proved to be an excellent strategy to boost the heat storage performance of LiOH·H2O.

10.1155/2022/3109178

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Adsorption and nucleation mechanism of molten carbonate on nano-SiC surface

Chemical Engineering Journal (2024)

Yuxian Wang Shi Liu Xian Li Xin Guo Zhenzhong Hu

10.1016/j.cej.2024.154904

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FeMn/Al2O3 as a high-efficient and low-cost catalyst for chlorobenzene removal in a nonthermal plasma-catalysis reactor

Process Safety and Environmental Protection (2022)

Linbo Qin Jia Song Wangsheng Chen Guangqian Luo Jun Han

Chlorobenzene

Coprecipitation

Nonthermal plasma

10.1016/j.psep.2022.05.028

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"Thermal-Dissolution Based Carbon Enrichment" Treatment of Biomass: Modeling and Kinetic Study Via Combined Lumped Reaction Model and Machine Learning Algorithm

SSRN Electronic Journal (2022)

Zhenzhong Hu Yue Yuan Xian Li Yuxian Wang Omar D. Dacres

The homogenizing pyrolytic method denoted as the “Thermal-dissolution based carbon enrichment (TDCE)” was effective in converting different biomasses into value-added carbon products. However, the product yields from the TDCE process varied greatly despite the biomasses undergoing similar reactions. Consequently, a generalized kinetic study is essential in determining the different kinetic parameters of each biomass treatment. In this study, the lumped reaction model was coupled with machine learning algorithm (ML-LRM) for the general acquisition of these kinetic parameters. The results showed that 304 yield data of cellulose were accurately predicted by the machine learning algorithm. In addition, the derived conversion pathways were quite reliable, as the one from predicted data was similar to that calculated from experimental data (R2 = 0.95) under experimental conditions, but with higher accuracy (R2 > 0.99). Moreover, the TDCE process was quantitatively divided into three stages, thus consistent with the pre-proposed reaction mechanism. The ML-LRM method was proven to be effective in elucidating the main reactions at any temperature throughout the TDCE treatment and can therefore provide guidance towards directed regulation and practical application of said products.

Carbon fibers

10.2139/ssrn.4050295

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