Accelerating net-zero carbon emissions by electrochemical reduction of carbon dioxide

Electroreduction of CO_(2)shows great potential for global CO_(2)utilization and uptake when collaborated with renewable electricity.Recent advances have been achieved in fundamental understanding and electrocatalyst development for CO_(2)electroreduction.We think this research area has progressed to the stage where significant efforts can focus on translating the obtained knowledge to the development of largescale electrolyzers,which have the potential to accelerate the transition of the current energy system into a sustainable and zero-carbon emission energy structure.In this perspective paper,we first critically evaluate the advancement of vapor-feed devices that use CO_(2)as reactants,from the point of view of industry applications.Then,by carefully comparing their performance to the state-of-the-art water electrolyzers which are well-established technology providing realistic performance targets,we looped back and discussed the remaining challenges including electrode catalysts,reaction conditions,mass transporting,membrane,device durability,operation mode,and so on.Finally,we provide perspectives on the challenges and suggest opportunities for generating fundamental knowledge and achieving technological progress toward the development of practical CO_(2)electrolyzers for the goal of building lowcarbon or/and net carbon-free economies.

Forecasting solar still performance from conventional weather data variation by machine learning method

Solar stills are considered an effective method to solve the scarcity of drinkable water.However,it is still missing a way to forecast its production.Herein,it is proposed that a convenient forecasting model which just needs to input the conventional weather forecasting data.The model is established by using machine learning methods of random forest and optimized by Bayesian algorithm.The required data to train the model are obtained from daily measurements lasting9 months.To validate the accuracy model,the determination coefficients of two types of solar stills are calculated as 0.935and 0.929,respectively,which are much higher than the value of both multiple linear regression(0.767)and the traditional models(0.829 and 0.847).Moreover,by applying the model,we predicted the freshwater production of four cities in China.The predicted production is approved to be reliable by a high value of correlation(0.868)between the predicted production and the solar insolation.With the help of the forecasting model,it would greatly promote the global application of solar stills.