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Yang S, Byun WJ, Zhao F, Chen D, Mao J, Zhang W, Peng J, Liu C, Pan Y, Hu J, Zhu J, Zheng X, Fu H, Yuan M, Chen H, Li R, Zhou M, Che W, Baek JB, Lee JS, Xu J. CO 2 Enrichment Boosts Highly Selective Infrared-Light-Driven CO 2 Conversion to CH 4 by UiO-66/Co 9S 8 Photocatalyst. Adv Mater 2024; 36:e2312616. [PMID: 38190551 DOI: 10.1002/adma.202312616] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Indexed: 01/10/2024]
Abstract
Photocatalytic CO2 reduction to high-value chemicals is an attractive approach to mitigate climate change, but it remains a great challenge to produce a specific product selectively by IR light. Hence, UiO-66/Co9S8 composite is designed to couple the advantages of metallic photocatalysts and porous CO2 adsorbers for IR-light-driven CO2-to-CH4 conversion. The metallic nature of Co9S8 endows UiO-66/Co9S8 with exceptional IR light absorption, while UiO-66 dramatically enhances its local CO2 concentration, revealed by finite-element method simulations. As a result, Co9S8 or UiO-66 alone does not show observable IR-light photocatalytic activity, whereas UiO-66/Co9S8 exhibits exceptional activity. The CH4 evolution rate over UiO-66/Co9S8 reaches 25.7 µmol g-1 h-1 with ca.100% selectivity under IR light irradiation, outperforming most reported catalysts under similar reaction conditions. The X-ray absorption fine structure spectroscopy spectra verify the presence of two distinct Co sites and confirm the existence of metallic Co─Co bond in Co9S8. Energy diagrams analysis and transient absorption spectra manifest that CO2 reduction mainly occurs on Co9S8 for UiO-66/Co9S8, while density functional theory calculations demonstrate that high-electron-density Co1 sites are the key active sites, possessing lower energy barriers for further protonation of *CO, leading to the ultra-high selectivity toward CH4.
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Affiliation(s)
- Siheng Yang
- Key Laboratory of Green Chemistry & Technology, Ministry of Education, College of Chemistry, Sichuan University, Chengdu, Sichuan, 610064, P. R. China
| | - Woo Jin Byun
- School of Energy and Chemical Engineering, Ulsan National lnstitute of Science and Technology (UNIST), 50 UNIST-gil, Ulsan, 44919, Republic of Korea
| | - Fangming Zhao
- Hefei National Research Center for Physical Science at Microscale, National Synchrotron Radiation Laboratory, University of Science and Technology of China, Hefei, Anhui, 230026, P. R. China
| | - Dingwen Chen
- Key Laboratory of Green Chemistry & Technology, Ministry of Education, College of Chemistry, Sichuan University, Chengdu, Sichuan, 610064, P. R. China
| | - Jiawei Mao
- Sichuan Institute of Product Quality Supervision and Inspection, Chengdu, Sichuan, 610100, P. R. China
| | - Wei Zhang
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, 610064, P. R. China
| | - Jing Peng
- Faculty of Materials Science and Energy Engineering/Institute of Technology for Carbon Neutrality, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Chengyuan Liu
- Hefei National Research Center for Physical Science at Microscale, National Synchrotron Radiation Laboratory, University of Science and Technology of China, Hefei, Anhui, 230026, P. R. China
| | - Yang Pan
- Hefei National Research Center for Physical Science at Microscale, National Synchrotron Radiation Laboratory, University of Science and Technology of China, Hefei, Anhui, 230026, P. R. China
| | - Jun Hu
- Hefei National Research Center for Physical Science at Microscale, National Synchrotron Radiation Laboratory, University of Science and Technology of China, Hefei, Anhui, 230026, P. R. China
| | - Junfa Zhu
- Hefei National Research Center for Physical Science at Microscale, National Synchrotron Radiation Laboratory, University of Science and Technology of China, Hefei, Anhui, 230026, P. R. China
| | - Xueli Zheng
- Key Laboratory of Green Chemistry & Technology, Ministry of Education, College of Chemistry, Sichuan University, Chengdu, Sichuan, 610064, P. R. China
| | - Haiyan Fu
- Key Laboratory of Green Chemistry & Technology, Ministry of Education, College of Chemistry, Sichuan University, Chengdu, Sichuan, 610064, P. R. China
| | - Maolin Yuan
- Key Laboratory of Green Chemistry & Technology, Ministry of Education, College of Chemistry, Sichuan University, Chengdu, Sichuan, 610064, P. R. China
| | - Hua Chen
- Key Laboratory of Green Chemistry & Technology, Ministry of Education, College of Chemistry, Sichuan University, Chengdu, Sichuan, 610064, P. R. China
| | - Ruixiang Li
- Key Laboratory of Green Chemistry & Technology, Ministry of Education, College of Chemistry, Sichuan University, Chengdu, Sichuan, 610064, P. R. China
| | - Meng Zhou
- Hefei National Research Center for Physical Science at Microscale, National Synchrotron Radiation Laboratory, University of Science and Technology of China, Hefei, Anhui, 230026, P. R. China
| | - Wei Che
- School of Energy and Chemical Engineering, Ulsan National lnstitute of Science and Technology (UNIST), 50 UNIST-gil, Ulsan, 44919, Republic of Korea
| | - Jong-Beom Baek
- School of Energy and Chemical Engineering, Ulsan National lnstitute of Science and Technology (UNIST), 50 UNIST-gil, Ulsan, 44919, Republic of Korea
| | - Jae Sung Lee
- School of Energy and Chemical Engineering, Ulsan National lnstitute of Science and Technology (UNIST), 50 UNIST-gil, Ulsan, 44919, Republic of Korea
| | - Jiaqi Xu
- Key Laboratory of Green Chemistry & Technology, Ministry of Education, College of Chemistry, Sichuan University, Chengdu, Sichuan, 610064, P. R. China
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Li D, Yang J, Pan Z, Li N. Traction Machine State Recognition Method Based on DPCA Algorithm and Convolution Neural Network. Sensors (Basel) 2023; 23:6646. [PMID: 37514939 PMCID: PMC10383604 DOI: 10.3390/s23146646] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Revised: 07/14/2023] [Accepted: 07/21/2023] [Indexed: 07/30/2023]
Abstract
It is important to improve the identification accuracy of the operating status of elevator traction machines. The distribution difference of the time-frequency signals utilized to identify operating circumstances is modest, making it difficult to extract features from the vibration signals of traction machines under various operating conditions, leading to low recognition accuracy. A novel method for identifying the operating status of traction machines based on signal demodulation method and convolutional neural network (CNN) is proposed. The original vibration time-frequency signals are demodulated by the demodulation method based on time-frequency analysis and principal component analysis (DPCA). Firstly, the signal demodulation method based on principal component analysis is used to extract the modulation features of the experimentally measured vibration signals. Then, The CNN is used for feature vector extraction, and the training model is obtained through multiple iterations to achieve automatic recognition of the running state. The experimental results show that the proposed method can effectively extract feature parameters under different states. The diagnostic accuracy is up to 96.94%, which is about 16.61% higher than conventional methods. It provides a feasible solution for identifying the operating status of elevator traction machines.
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Affiliation(s)
- Dongyang Li
- College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310013, China
- Hangzhou Special Equipment Inspection and Research Institute, Hangzhou 310051, China
| | - Jianyi Yang
- College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310013, China
| | - Zaisheng Pan
- Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou 310027, China
| | - Nanyang Li
- Zhejiang Promotion Association for Intelligent Technology Standards Innovation, Hangzhou 311121, China
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