SnS has emerged as an attractive catalyst for the electrochemical CO_(2)reduction reaction(CO_(2)RR)to formate,while its long-term operational stability is hindered by the self-reduction of Sn^(2+) and sulfur dissolut...SnS has emerged as an attractive catalyst for the electrochemical CO_(2)reduction reaction(CO_(2)RR)to formate,while its long-term operational stability is hindered by the self-reduction of Sn^(2+) and sulfur dissolution.Thus,maintaining high current efficiency across a wide negative potential range to achieve high production rates of formate remains a significant challenge.In this study,we present a heterostructure constructed with SnS and CuS for efficient CO_(2)RR to formate.The SnS-CuS(30)exhibits a remarkable formate Faradaic efficiency(FE_(f))of 93.94%at−1 V vs.reversible hydrogen electrode(RHE)and demonstrates long-term stability for 7.5 h,maintaining high activity(with an average FE_(f)of 85.6%)across a wide negative potential range(from-0.8 to-1.2 V(vs.RHE)).The results reveal that the heterogeneous interface between SnS and CuS mitigates the self-reduction issue of SnS by sacrificing Cu^(2+),highlighting that the true active species is SnS,which effectively resists structural changes during the electrolysis process under the protection of CuS.The synergistic interaction within the CuS and SnS heterostructure,combined with the tendency for electron self-conduction,enables the catalyst to maintain high formate activity and selectivity across a wide potential range.Furthermore,theoretical results further indicate that the incorporation of CuS enhances CO_(2)adsorption and lowers the energy barrier for the formation of formate intermediates.This study inspires the concept of applying protective layers to active species,promoting high selectivity in Sn-based electrocatalysts.展开更多
CO_(2) injection not only effectively enhances oil recovery(EOR)but also facilitates CO_(2) utilization and storage.Rapid screening and optimization of CO_(2)-EOR operations is urgently needed for unconventional reser...CO_(2) injection not only effectively enhances oil recovery(EOR)but also facilitates CO_(2) utilization and storage.Rapid screening and optimization of CO_(2)-EOR operations is urgently needed for unconventional reservoirs.However,it remains challenging due to a limited understanding of fluid flow in multiscale porous media and the problem complexity invoked by numerous factors.This work developed a new interpretable machine learning(ML)framework to specifically address this issue.Three different methods,namely random forest(RF),support vector regression(SVR),and artificial neural network(ANN),were used to establish proxy models using the data from a specific unconventional reservoir,and the RF model demonstrated a preferable performance.To enhance the interpretability of the established models,the multiway feature importance analysis and Shapley Additive Explanations(SHAP)were proposed to quantify the contribution of individual features to the model output.Based on the results of model interpretability,the genetic algorithm(GA)was coupled with RF(RF-GA model)to optimize the CO_(2)-EOR process.The proposed framework was validated by comparing the GA-RF predictions with simulation results under different reservoir conditions,which yielded a minimum relative error of 0.34%and an average relative error of 5.3%.The developed interpretable ML method was capable of rapidly screening suitable CO_(2)-EOR strategies based on reservoir conditions and provided a practical example for field applications.展开更多
基金supported by the National Key Research and Development Program of China(No.2018YFB1501405)the National Natural Science Foundation of China(No.52476185).
文摘SnS has emerged as an attractive catalyst for the electrochemical CO_(2)reduction reaction(CO_(2)RR)to formate,while its long-term operational stability is hindered by the self-reduction of Sn^(2+) and sulfur dissolution.Thus,maintaining high current efficiency across a wide negative potential range to achieve high production rates of formate remains a significant challenge.In this study,we present a heterostructure constructed with SnS and CuS for efficient CO_(2)RR to formate.The SnS-CuS(30)exhibits a remarkable formate Faradaic efficiency(FE_(f))of 93.94%at−1 V vs.reversible hydrogen electrode(RHE)and demonstrates long-term stability for 7.5 h,maintaining high activity(with an average FE_(f)of 85.6%)across a wide negative potential range(from-0.8 to-1.2 V(vs.RHE)).The results reveal that the heterogeneous interface between SnS and CuS mitigates the self-reduction issue of SnS by sacrificing Cu^(2+),highlighting that the true active species is SnS,which effectively resists structural changes during the electrolysis process under the protection of CuS.The synergistic interaction within the CuS and SnS heterostructure,combined with the tendency for electron self-conduction,enables the catalyst to maintain high formate activity and selectivity across a wide potential range.Furthermore,theoretical results further indicate that the incorporation of CuS enhances CO_(2)adsorption and lowers the energy barrier for the formation of formate intermediates.This study inspires the concept of applying protective layers to active species,promoting high selectivity in Sn-based electrocatalysts.
基金support of National Key Research and Development Program of China(2023YFE0120700)National Natural Science Foundation of China(52274041 and 52304023)+2 种基金Distinguished Young Sichuan Science Scholars(2023NSFSC1954)Natural Science Foundation of Chongqing(CSTB2022NSCQ-MSX0403)Chongqing Municipal Support Program for Overseas Students Returning for Entrepreneurship and Innovation(2205012980950154).
文摘CO_(2) injection not only effectively enhances oil recovery(EOR)but also facilitates CO_(2) utilization and storage.Rapid screening and optimization of CO_(2)-EOR operations is urgently needed for unconventional reservoirs.However,it remains challenging due to a limited understanding of fluid flow in multiscale porous media and the problem complexity invoked by numerous factors.This work developed a new interpretable machine learning(ML)framework to specifically address this issue.Three different methods,namely random forest(RF),support vector regression(SVR),and artificial neural network(ANN),were used to establish proxy models using the data from a specific unconventional reservoir,and the RF model demonstrated a preferable performance.To enhance the interpretability of the established models,the multiway feature importance analysis and Shapley Additive Explanations(SHAP)were proposed to quantify the contribution of individual features to the model output.Based on the results of model interpretability,the genetic algorithm(GA)was coupled with RF(RF-GA model)to optimize the CO_(2)-EOR process.The proposed framework was validated by comparing the GA-RF predictions with simulation results under different reservoir conditions,which yielded a minimum relative error of 0.34%and an average relative error of 5.3%.The developed interpretable ML method was capable of rapidly screening suitable CO_(2)-EOR strategies based on reservoir conditions and provided a practical example for field applications.
