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Tian H, Cui K, Chen X, Liu J, Zhang Q. Size-matched hierarchical porous carbon materials anchoring single-atom Fe-N 4 sites for PMS activation: An in-depth study of key active species and catalytic mechanisms. JOURNAL OF HAZARDOUS MATERIALS 2024; 461:132647. [PMID: 37788555 DOI: 10.1016/j.jhazmat.2023.132647] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Revised: 09/20/2023] [Accepted: 09/25/2023] [Indexed: 10/05/2023]
Abstract
Single-atom catalysts are considered to be one of the most promising catalysts for AOPs. However, how to design and synthesize cost-effective and highly loaded single-atom catalysts is the bottleneck limiting its development and application. In this study, we report a highly loaded single-atom iron catalyst (Fe-SAC-BC) using waste biomass as a carbon carrier to anchor Fe-N4 sites. The catalyst showed excellent catalytic performance and stability in wastewater treatment. Unlike conventional radical oxidation, the non-radical degradation process of Fe-N4 as the active site and high-valent iron-oxygen intermediates as the key active species identified by burst and probe experiments. DFT calculations and molecular dynamics simulations were applied to the catalytic mechanism of Fe-SAC-BC, in which Fe (III)-N4 is the most likely active site and Fe (IV)-OH is the most dominant active species. This study provides new strategies and understanding for the design of novel single-atom catalysts and the mechanistic probing of the non-radical pathways of AOPs.
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Affiliation(s)
- Haoran Tian
- School of Resource and Environmental Engineering, Hefei University of Technology, Hefei 230009, China
| | - Kangping Cui
- School of Resource and Environmental Engineering, Hefei University of Technology, Hefei 230009, China.
| | - Xing Chen
- School of Resource and Environmental Engineering, Hefei University of Technology, Hefei 230009, China; Key Lab of Aerospace Structural Parts Forming Technology and Equipment of Anhui Province, Institute of Industry and Equipment Technology, Hefei University of Technology, Hefei 230009, China
| | - Jun Liu
- Anhui Zhonghuan Environmental Protection Technology Co., Ltd, Hefei 230000, China
| | - Qiang Zhang
- Anhui Environmental Science and Technology Group Co. Ltd, Hefei 230088, China
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Wang P, Bu L, Zhou S, Wu Y, Deng L, Shi Z. Predictive models for the aqueous phase reactivity of inorganic radicals with organic micropollutants. CHEMOSPHERE 2023; 332:138793. [PMID: 37119929 DOI: 10.1016/j.chemosphere.2023.138793] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/05/2023] [Revised: 04/12/2023] [Accepted: 04/25/2023] [Indexed: 05/10/2023]
Abstract
Single-electron transfer (SET) is one of the most common reaction mechanisms for degrading organic micropollutants (OMPs) in advanced oxidation processes. We collected 300 SET reactions (CO3•-, SO4•-, Cl2•-, and Br2•--mediated) and calculated three key parameters for understanding the SET mechanism: aqueous phase free energies of activation (ΔG‡), free energies of reactions (ΔG), and orbital energy gaps of reactants (EOMPsHOMO-ERadiLUMO). Then, we classified the OMPs according to their structure, developed and evaluated linear energy relationships of the second-order rate constants (k) with ΔG‡, ΔG, or EOMPsHOMO-ERadiLUMO in each class. Considering that a single descriptor cannot capture all the chemical diversity, we combined ΔG‡, ΔG, and EOMPsHOMO-ERadiLUMO as inputs to develop multiple linear regression (MLR) models. Chemical classification is critical to the linear model described above. However, OMPs usually have multiple functional groups, making the classification challenging and uncertain. Therefore, we tried machine learning algorithms to predict k values without chemical classification. We found that decision trees (R2 = 0.88-0.95) and random forest (R2 = 0.90-0.94) algorithms show better performance on the prediction of the k values, whereas boosted tree algorithm cannot make an accurate prediction (R2 = 0.19-0.36). Overall, our study provides a powerful tool to predict the aqueous phase reactivity of OMP to certain radicals without the need for chemical classification.
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Affiliation(s)
- Pin Wang
- Hunan Engineering Research Center of Water Security Technology and Application, College of Civil Engineering, Hunan University, Changsha 410082, PR China; Key Laboratory of Building Safety and Energy Efficiency, Ministry of Education, Hunan University, Changsha, 410082, PR China
| | - Lingjun Bu
- Hunan Engineering Research Center of Water Security Technology and Application, College of Civil Engineering, Hunan University, Changsha 410082, PR China; Key Laboratory of Building Safety and Energy Efficiency, Ministry of Education, Hunan University, Changsha, 410082, PR China.
| | - Shiqing Zhou
- Hunan Engineering Research Center of Water Security Technology and Application, College of Civil Engineering, Hunan University, Changsha 410082, PR China; Key Laboratory of Building Safety and Energy Efficiency, Ministry of Education, Hunan University, Changsha, 410082, PR China
| | - Yangtao Wu
- Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, PR China
| | - Lin Deng
- Hunan Engineering Research Center of Water Security Technology and Application, College of Civil Engineering, Hunan University, Changsha 410082, PR China; Key Laboratory of Building Safety and Energy Efficiency, Ministry of Education, Hunan University, Changsha, 410082, PR China
| | - Zhou Shi
- Hunan Engineering Research Center of Water Security Technology and Application, College of Civil Engineering, Hunan University, Changsha 410082, PR China; Key Laboratory of Building Safety and Energy Efficiency, Ministry of Education, Hunan University, Changsha, 410082, PR China
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DeNOx of Nano-Catalyst of Selective Catalytic Reduction Using Active Carbon Loading MnOx-Cu at Low Temperature. Catalysts 2020. [DOI: 10.3390/catal10010135] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023] Open
Abstract
With the improvement of environmental protection standards, selective catalytic reduction (SCR) has become the mainstream technology of flue gas deNOx. Especially, the low-temperature SCR nano-catalyst has attracted more and more attention at home and abroad because of its potential performance and economy in industrial applications. In this paper, low-temperature SCR catalysts were prepared using the activated carbon loading MnOx-Cu. Then, the catalysts were packed into the fiedbed stainless steel micro-reactor to evaluate the selective catalytic reduction of NO performance. The influence of reaction conditions was investigated on the catalytic reaction, including the MnOx-Cu loading amount, calcination and reaction temperature, etc. The experimental results indicate that SCR catalysts show the highest catalytic activity for NO conversion when the calcination temperature is 350 °C, MnOx loading amount is 5%, Cu loading amount is 3%, and reaction temperature is 200 °C. Under such conditions, the NO conversion arrives at 96.82% and the selectivity to N2 is almost 99%. It is of great significance to investigate the influence of reaction conditions in order to provide references for industrial application.
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