Reactive oxygen species(ROSs)in Fenton process are of great importance in treating contaminants in wastewater.It is crucial to understand their chemical properties,formation,and reaction mechanisms with contaminants.T...Reactive oxygen species(ROSs)in Fenton process are of great importance in treating contaminants in wastewater.It is crucial to understand their chemical properties,formation,and reaction mechanisms with contaminants.This review summarizes the reactive oxygen species in Fenton process,including hydroxyl radical(·OH),superoxide radical(O_(2)·-),singlet oxygen(1O_(2)),hydroperoxyl radical(HO_(2)·),and high-valent iron.·OH shows a trend to react with chemistry groups with abundant electrons through H-atom abstraction,radical adduct formation and single electron transfer.Electron transfer is discovered to be an important pathway when1O_(2)degrades organic pollutants.Ring-opening andβ-scission are proposed to be the possible ways of1O_(2)to certain contaminants.Proton abstraction,nucleophilic substitution,and single electron transfer are proposed to explain how O_(2)·-degrade pollutants.As the conjugated acid of O_(2)·-,radical adduct formation and H-atom abstraction are reported for the reaction mechanisms of hydroperoxyl radical.High-valent iron in Fenton,namely Fe(IV),reacts with certain pollutants via single-or two-electron transfer.This review is important for researchers to understand the ROSs produced in Fenton and how they react with pollutants.展开更多
In this work,boron(B)was used to promote Fe^(3+)/peracetic acid(Fe^(3+)/PAA)for the degradation of sulfamethazine(SMT).An SMT degradation efficiency of 9.1%was observed in the Fe^(3+)/PAA system over 60 min,which was ...In this work,boron(B)was used to promote Fe^(3+)/peracetic acid(Fe^(3+)/PAA)for the degradation of sulfamethazine(SMT).An SMT degradation efficiency of 9.1%was observed in the Fe^(3+)/PAA system over 60 min,which was significantly increased to 99.3%in the B/Fe^(3+)/PAA system over 10 min.The B/Fe^(3+)/PAA process also exhibited superior resistance to natural substances,excellent adaptability to different harmful substances,and good removal of antibiotics in natural fresh water samples.The mechanism of action of boron for Fe^(3+)reduction was determined using scanning electron microscopy(SEM),X-ray photoelectron spectroscopy(XPS),Fourier transform infrared(FT-IR)spectroscopy,density functional theory(DFT)calculations,and electrochemical tests.The dominant role of^(·)OH was confirmed using quenching experiments,electron spin resonance(EPR)spectroscopy,and quantitative tests.Organic radicals(R-O^(·))and Fe(IV)also significantly contribute to the removal of SMT.DFT calculations on the reaction between Fe^(2+)and the PAA were conducted to further determine the contribution from ^(·)OH,R-O^(·),and Fe(IV)from the perspective of thermodynamics and the reaction pathways.Different boron dosages,Fe^(3+)dosages,and initial pH values were also investigated in the B/Fe^(3+)/PAA system to study their effect of SMT removal and the production of the reactive species.Fe(IV)production determined the k_(R-O·+Fe(IV))value suggesting that Fe(IV)may play a more important role than R-O^(·).A comparison of the results with other processes has also proved that the procedure described in this study(B/Fe^(3+)/PAA)is an effective method for the degradation of antibiotics.展开更多
The computational demands of 3D continuum models for proton exchange membrane fuel cells remain substantial. One prevalent approach is the hierarchical model combining a 2D/3D flow field with a 1D sub-model for the ca...The computational demands of 3D continuum models for proton exchange membrane fuel cells remain substantial. One prevalent approach is the hierarchical model combining a 2D/3D flow field with a 1D sub-model for the catalyst layers and membrane. However, existing studies often simplify the 1D domain to a linearized 0D lumped model, potentially resulting in significant errors at high loads. In this study, we present a computationally efficient neural network driven 3D+1D model for proton exchange membrane fuel cells. The 3D sub-model captures transport in the gas channels and gas diffusion layers and is coupled with a 1D electrochemical sub-model for microporous layers, membrane, and catalyst layers. To reduce computational intensity of the full 1D description, a neural network surrogates the 1D electrochemical sub-model for coupling with the 3D domain. Trained by model-generated large synthetic datasets, the neural network achieves root mean square errors of less than 0.2%. The model is validated against experimental results under various relative humidities. It is then employed to investigate the nonlinear distribution of internal states under different operating conditions. With the neural network operating at 0.5% of the computing cost of the 1D sub-model, the hybrid model preserves a detailed and nonlinear representation of the internal fuel cell states while maintaining computational costs comparable to conventional 3D+0D models. The presented hybrid data-driven and physical modeling framework offers high accuracy and computing speed across a broad spectrum of operating conditions, potentially aiding the rapid optimization of both the membrane electrode assembly and the gas channel geometry.展开更多
基金supported by the National Natural Science Foundation of China(Nos.22176102 and 21806081)Natural Science Foundation of Tianjin(No.19JCQNJC07900)+2 种基金Fundamental Research Funds for the Central UniversitiesNatural Science Foundation of Jiangsu Province in China(No.BK20230410)Natural Science Research of Jiangsu Higher Education Institution of China(No.23KJB610010)。
文摘Reactive oxygen species(ROSs)in Fenton process are of great importance in treating contaminants in wastewater.It is crucial to understand their chemical properties,formation,and reaction mechanisms with contaminants.This review summarizes the reactive oxygen species in Fenton process,including hydroxyl radical(·OH),superoxide radical(O_(2)·-),singlet oxygen(1O_(2)),hydroperoxyl radical(HO_(2)·),and high-valent iron.·OH shows a trend to react with chemistry groups with abundant electrons through H-atom abstraction,radical adduct formation and single electron transfer.Electron transfer is discovered to be an important pathway when1O_(2)degrades organic pollutants.Ring-opening andβ-scission are proposed to be the possible ways of1O_(2)to certain contaminants.Proton abstraction,nucleophilic substitution,and single electron transfer are proposed to explain how O_(2)·-degrade pollutants.As the conjugated acid of O_(2)·-,radical adduct formation and H-atom abstraction are reported for the reaction mechanisms of hydroperoxyl radical.High-valent iron in Fenton,namely Fe(IV),reacts with certain pollutants via single-or two-electron transfer.This review is important for researchers to understand the ROSs produced in Fenton and how they react with pollutants.
基金supported by the Natural Science Foundation of Jiangsu Province in China (No.BK20210952)the Jiangsu Provincial Key Laboratory of Environmental Engineering (No.ZX2022002)+2 种基金the National Natural Science Foundation of China (Nos.52200095,22176102,and 21806081)the China Postdoctoral Science Foundation Project (No.2020M681552)the Natural Science Foundation of Tianjin (No.19JCQNJC07900)。
文摘In this work,boron(B)was used to promote Fe^(3+)/peracetic acid(Fe^(3+)/PAA)for the degradation of sulfamethazine(SMT).An SMT degradation efficiency of 9.1%was observed in the Fe^(3+)/PAA system over 60 min,which was significantly increased to 99.3%in the B/Fe^(3+)/PAA system over 10 min.The B/Fe^(3+)/PAA process also exhibited superior resistance to natural substances,excellent adaptability to different harmful substances,and good removal of antibiotics in natural fresh water samples.The mechanism of action of boron for Fe^(3+)reduction was determined using scanning electron microscopy(SEM),X-ray photoelectron spectroscopy(XPS),Fourier transform infrared(FT-IR)spectroscopy,density functional theory(DFT)calculations,and electrochemical tests.The dominant role of^(·)OH was confirmed using quenching experiments,electron spin resonance(EPR)spectroscopy,and quantitative tests.Organic radicals(R-O^(·))and Fe(IV)also significantly contribute to the removal of SMT.DFT calculations on the reaction between Fe^(2+)and the PAA were conducted to further determine the contribution from ^(·)OH,R-O^(·),and Fe(IV)from the perspective of thermodynamics and the reaction pathways.Different boron dosages,Fe^(3+)dosages,and initial pH values were also investigated in the B/Fe^(3+)/PAA system to study their effect of SMT removal and the production of the reactive species.Fe(IV)production determined the k_(R-O·+Fe(IV))value suggesting that Fe(IV)may play a more important role than R-O^(·).A comparison of the results with other processes has also proved that the procedure described in this study(B/Fe^(3+)/PAA)is an effective method for the degradation of antibiotics.
基金support of the Henry Royce Institute for advanced materials through the Materials Challenge Accelerator Programme(MCAP)funded from a grant provided by the Engineering and Physical Sciences Research Council(EP/X527257/1).
文摘The computational demands of 3D continuum models for proton exchange membrane fuel cells remain substantial. One prevalent approach is the hierarchical model combining a 2D/3D flow field with a 1D sub-model for the catalyst layers and membrane. However, existing studies often simplify the 1D domain to a linearized 0D lumped model, potentially resulting in significant errors at high loads. In this study, we present a computationally efficient neural network driven 3D+1D model for proton exchange membrane fuel cells. The 3D sub-model captures transport in the gas channels and gas diffusion layers and is coupled with a 1D electrochemical sub-model for microporous layers, membrane, and catalyst layers. To reduce computational intensity of the full 1D description, a neural network surrogates the 1D electrochemical sub-model for coupling with the 3D domain. Trained by model-generated large synthetic datasets, the neural network achieves root mean square errors of less than 0.2%. The model is validated against experimental results under various relative humidities. It is then employed to investigate the nonlinear distribution of internal states under different operating conditions. With the neural network operating at 0.5% of the computing cost of the 1D sub-model, the hybrid model preserves a detailed and nonlinear representation of the internal fuel cell states while maintaining computational costs comparable to conventional 3D+0D models. The presented hybrid data-driven and physical modeling framework offers high accuracy and computing speed across a broad spectrum of operating conditions, potentially aiding the rapid optimization of both the membrane electrode assembly and the gas channel geometry.
