Support vector regression （SVR） combined with particle swarm optimization for its parameter optimization is employed to establish a model for predicting the Henry constants of multi-walled carbon nanotubes （MWNTs）...Support vector regression （SVR） combined with particle swarm optimization for its parameter optimization is employed to establish a model for predicting the Henry constants of multi-walled carbon nanotubes （MWNTs） for adsorption of volatile organic compounds （VOCs）. The prediction performance of SVR is compared with those of the model of theoretical linear salvation energy relationship （TLSER）. By using leave-one-out cross validation of SVR test Henry constants for adsorption of 35 VOCs on MWNTs, the root mean square error is 0.080, the mean absolute percentage error is only 1.19~, and the correlation coefficient （R2） is as high as 0.997. Compared with the results of the TLSER model, it is shown that the estimated errors by SVR are ali smaller than those achieved by TLSER. It reveals that the generalization ability of SVR is superior to that of the TLSER model Meanwhile, multifactor analysis is adopted for investigation of the influences of each molecular structure descriptor on the Henry constants. According to the TLSER model, the adsorption mechanism of adsorption of carbon nanotubes of VOCs is mainly a result of van der Waals and interactions of hydrogen bonds. These can provide the theoretical support for the application of carbon nanotube adsorption of VOCs and can make up for the lack of experimental data.展开更多
Exploring efficient strategies to enhance the catalytic performance for the oxygen evolution reaction(OER)is crucial for the rapid development of green hydrogen production based on water electrolysis.Here,a simple and...Exploring efficient strategies to enhance the catalytic performance for the oxygen evolution reaction(OER)is crucial for the rapid development of green hydrogen production based on water electrolysis.Here,a simple and extensible in situ electrochemical reduction method is proposed to improve the OER catalytic performance.A carbon nanotube-supported iron–nickel organometallic compound(Fe–Ni@CNT)and the corresponding R-Fe–Ni@CNT with further electrochemical reduction modulation serve as the pre-catalysts to obtain O–Fe–Ni@CNT and RO–Fe–Ni@CNT catalysts during the OER process,respectively.The characterization results show that the electrochemical reduction modulation can adjust the redox properties of the active species and the in situ transformation process to induce the formation of a greater abundance of Ni3+(efficient OER active sites).Hence,the RO–Fe–Ni@CNT catalyst displays significantly enhanced OER catalytic activity and stability compared to the O–Fe–Ni@CNT catalyst.This work reveals the unique role of electrochemical reduction modulation in OER catalytic performance,providing more opportunities for the design of efficient catalysts.展开更多
基金Supported by the Innovative Talent Funds for Project 985 under Grant No WLYJSBJRCTD201102the Fundamental Research Funds for the Central Universities under Grant No CQDXWL-2013-014+1 种基金the Natural Science Foundation of Chongqing under Grant No CSTC2006BB5240the Program for New Century Excellent Talents in Universities of China under Grant No NCET-07-0903
文摘Support vector regression （SVR） combined with particle swarm optimization for its parameter optimization is employed to establish a model for predicting the Henry constants of multi-walled carbon nanotubes （MWNTs） for adsorption of volatile organic compounds （VOCs）. The prediction performance of SVR is compared with those of the model of theoretical linear salvation energy relationship （TLSER）. By using leave-one-out cross validation of SVR test Henry constants for adsorption of 35 VOCs on MWNTs, the root mean square error is 0.080, the mean absolute percentage error is only 1.19~, and the correlation coefficient （R2） is as high as 0.997. Compared with the results of the TLSER model, it is shown that the estimated errors by SVR are ali smaller than those achieved by TLSER. It reveals that the generalization ability of SVR is superior to that of the TLSER model Meanwhile, multifactor analysis is adopted for investigation of the influences of each molecular structure descriptor on the Henry constants. According to the TLSER model, the adsorption mechanism of adsorption of carbon nanotubes of VOCs is mainly a result of van der Waals and interactions of hydrogen bonds. These can provide the theoretical support for the application of carbon nanotube adsorption of VOCs and can make up for the lack of experimental data.
基金supported by the Natural Science Foundation of Sichuan Province(No.2024NSFSC0278)the Opening Project of Hubei Key Laboratory of Wudang Local Chinese Medicine Research(Hubei University of Medicine)(WDCM2023008).
文摘Exploring efficient strategies to enhance the catalytic performance for the oxygen evolution reaction(OER)is crucial for the rapid development of green hydrogen production based on water electrolysis.Here,a simple and extensible in situ electrochemical reduction method is proposed to improve the OER catalytic performance.A carbon nanotube-supported iron–nickel organometallic compound(Fe–Ni@CNT)and the corresponding R-Fe–Ni@CNT with further electrochemical reduction modulation serve as the pre-catalysts to obtain O–Fe–Ni@CNT and RO–Fe–Ni@CNT catalysts during the OER process,respectively.The characterization results show that the electrochemical reduction modulation can adjust the redox properties of the active species and the in situ transformation process to induce the formation of a greater abundance of Ni3+(efficient OER active sites).Hence,the RO–Fe–Ni@CNT catalyst displays significantly enhanced OER catalytic activity and stability compared to the O–Fe–Ni@CNT catalyst.This work reveals the unique role of electrochemical reduction modulation in OER catalytic performance,providing more opportunities for the design of efficient catalysts.
