Solid amine-based adsorbents were widely studied as an alternative to liquid amine for post-combustion CO_(2)capture(PCC).However,most of the amine adsorbents suffer from low thermal stability and poor cyclic regenera...Solid amine-based adsorbents were widely studied as an alternative to liquid amine for post-combustion CO_(2)capture(PCC).However,most of the amine adsorbents suffer from low thermal stability and poor cyclic regenerability at the temperature of hot flue gases.Here we present an amine loaded proton type Y zeolite(HY)where the amines namely monoethanolamine(MEA)and ethylenediamine(ED)are chemical immobilized via ionic bond to the zeolite framework to overcome the amine degradation problem.The MEA and ED of 5%,10%and 20%(mass)concentration-immobilized zeolites were characterized by X-ray diffraction,Fourier-transform infrared spectroscopy,and N_(2)-196℃ adsorption to confirm the structure integrity,amine functionalization,and surface area,respectively.The determination of the amine loading was given by C,H,N elemental analysis showing that ED has successfully grafted almost twice as many amino groups as MEA within the same solvent concentration.CO_(2)adsorption capacity and thermal stability of these samples were measured using thermogravimetric analyser.The adsorption performance was tested at the adsorption temperature of 30,60 and 90℃,respectively using pure CO_(2)while the desorption was carried out with pure N_(2)purge at the same temperature and then followed by elevated temperature at 150℃.It was found that all the amine@HY have a substantial high selectivity of CO_(2)over N_(2).The sample 20%ED@HY has the highest CO_(2)adsorption capacity of1.76 mmol·g^(-1)at 90℃ higher than the capacity on parent Na Y zeolite(1.45 mmol·g^(-1)only).The amine@HY samples presented superior performance in cyclic thermal stability in the condition of the adsorption temperature of 90℃ and the desorption temperature of 150℃.These findings will foster the design of better adsorbents for CO_(2)capture from flue gas in post-combustion power plants.展开更多
In the past decades,machine learning(ML)has impacted the field of electrocatalysis.Modern researchers have begun to take advantage of ML‐based data‐driven techniques to overcome the computational and experimental li...In the past decades,machine learning(ML)has impacted the field of electrocatalysis.Modern researchers have begun to take advantage of ML‐based data‐driven techniques to overcome the computational and experimental limitations to accelerate rational catalyst design.Hence,significant efforts have been made to perform ML to accelerate calculation and aid electrocatalyst design for CO_(2) reduction.This review discusses recent applications of ML to discover,design,and optimize novel electrocatalysts.First,insights into ML aided in accelerating calculation are presented.Then,ML aided in the rational design of the electrocatalyst is introduced,including establishing a data set/data source selection and validation of descriptor selection of ML algorithms validation and predictions of the model.Finally,the opportunities and future challenges are summarized for the future design of electrocatalyst for CO_(2) reduction with the assistance of ML.展开更多
文摘Solid amine-based adsorbents were widely studied as an alternative to liquid amine for post-combustion CO_(2)capture(PCC).However,most of the amine adsorbents suffer from low thermal stability and poor cyclic regenerability at the temperature of hot flue gases.Here we present an amine loaded proton type Y zeolite(HY)where the amines namely monoethanolamine(MEA)and ethylenediamine(ED)are chemical immobilized via ionic bond to the zeolite framework to overcome the amine degradation problem.The MEA and ED of 5%,10%and 20%(mass)concentration-immobilized zeolites were characterized by X-ray diffraction,Fourier-transform infrared spectroscopy,and N_(2)-196℃ adsorption to confirm the structure integrity,amine functionalization,and surface area,respectively.The determination of the amine loading was given by C,H,N elemental analysis showing that ED has successfully grafted almost twice as many amino groups as MEA within the same solvent concentration.CO_(2)adsorption capacity and thermal stability of these samples were measured using thermogravimetric analyser.The adsorption performance was tested at the adsorption temperature of 30,60 and 90℃,respectively using pure CO_(2)while the desorption was carried out with pure N_(2)purge at the same temperature and then followed by elevated temperature at 150℃.It was found that all the amine@HY have a substantial high selectivity of CO_(2)over N_(2).The sample 20%ED@HY has the highest CO_(2)adsorption capacity of1.76 mmol·g^(-1)at 90℃ higher than the capacity on parent Na Y zeolite(1.45 mmol·g^(-1)only).The amine@HY samples presented superior performance in cyclic thermal stability in the condition of the adsorption temperature of 90℃ and the desorption temperature of 150℃.These findings will foster the design of better adsorbents for CO_(2)capture from flue gas in post-combustion power plants.
基金ANU Futures Scheme,Grant/Award Number:Q4601024National Natural Science Foundation of China,Grant/Award Number:22078054+1 种基金Australian Research Council,Grant/Award Number:DP190100295China Scholarship Council(CSC)Program。
文摘In the past decades,machine learning(ML)has impacted the field of electrocatalysis.Modern researchers have begun to take advantage of ML‐based data‐driven techniques to overcome the computational and experimental limitations to accelerate rational catalyst design.Hence,significant efforts have been made to perform ML to accelerate calculation and aid electrocatalyst design for CO_(2) reduction.This review discusses recent applications of ML to discover,design,and optimize novel electrocatalysts.First,insights into ML aided in accelerating calculation are presented.Then,ML aided in the rational design of the electrocatalyst is introduced,including establishing a data set/data source selection and validation of descriptor selection of ML algorithms validation and predictions of the model.Finally,the opportunities and future challenges are summarized for the future design of electrocatalyst for CO_(2) reduction with the assistance of ML.
