Experimental Research of an Active Solution for Modeling In Situ Activating Selective Catalytic Reduction Catalyst

Size-matched hierarchical porous carbon materials anchoring single-atom Fe-N4 sites for PMS activation: An in-depth study of key active species and catalytic mechanisms

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.

Predictive models for the aqueous phase reactivity of inorganic radicals with organic micropollutants

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 (E_OMPsHOMO-E_RadiLUMO). 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 E_OMPsHOMO-E_RadiLUMO in each class. Considering that a single descriptor cannot capture all the chemical diversity, we combined ΔG^‡, ΔG, and E_OMPsHOMO-E_RadiLUMO 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.

DeNOx of Nano-Catalyst of Selective Catalytic Reduction Using Active Carbon Loading MnOx-Cu at Low Temperature

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.

Selective Catalytic Reduction of NOx

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