The reformation of catalyst:From a trial-and-error synthesis to rational design

The appropriate catalysts can accelerate the reaction rate and effectively boost the efficient conversion of various molecules,which is of great importance in the study of chemistry,chemical industry,energy,materials and environmental science.Therefore,efficient,environmentally friendly,and easy to operate synthesis methods have been used to prepare various types of catalysts.Although previous studies have reported the synthesis and characterization of the aforementioned catalysts,more still remain in trial and error methods,without in-depth consideration and improvement of traditional synthesis methods.Here,we comprehensively summarize and compare the preparation methods of the trial-and-error synthesis strategy,structure–activity relationships and density functional theory(DFT)guided catalysts rational design for nanomaterials and atomically dispersed catalysts.We also discuss in detail the utilization of the nanomaterials and single atom catalysts for converting small molecules(H_(2)O,O_(2),CO_(2),N_(2),etc.)into value-added products driven by electrocatalysis,photocatalysis,and thermocatalysis.Finally,the challenges and outlooks of mass preparation and production of efficient and green catalysts through conventional trial and error synthesis and DFT theory are featured in accordance with its current development.

Coordination Control of Greenhouse Environmental Factors

Optimal control of greenhouse climate is one of the key techniques in digital agriculture.Greenhouse climate,a nonlinear and uncertain system,consists of several major environmental factors such as temperature,humidity,light intensity,and CO 2 concentration.Due to the complex coupled correlations,it is a challenge to achieve coordination control of greenhouse environmental factors.This paper proposes a model-free coordination control approach for greenhouse environmental factors based on Q-learning.Coordination control policy is found through systematic interaction with the dynamic environment to achieve optimal control for greenhouse climate with the control cost constraints.In order to decrease systematic trial-and-error risk and reduce the computational complexity in Q-learning algorithm,case-based reasoning （CBR） is seamlessly incorporated into the Q-learning process.The experimental results demonstrate that this approach is practical,highly effective and efficient.