Efficient NO_x abatement by passive adsorption over a Pd-SAPO-34 catalyst prepared by solid-state ion exchange

Palladium-exchanged chabazite(Pd-CHA) zeolites as passive NO_x adsorbers(PNAs) enable efficient purification of nitrogen oxides(NO_x) in cold-start diesel exhausts. Their commercial application, however,is limited by the lack of facile preparation method. Here, high-performance CHA-type Pd-SAPO-34 zeolite was synthesized by a modified solid-state ion exchange(SSIE) method using PdO as Pd precursor,and demonstrated superior PNA performance as compared to Pd-SAPO-34 prepared by conventional wetchemistry strategies. Structural characterization using Raman spectroscopy and X-ray diffraction revealed that the SSIE method avoided water-induced damage to the zeolite framework during Pd loading. Mechanistic investigations on the SSIE process by in situ infrared spectroscopy and X-ray photoelectron spectroscopy disclosed that, while PdO precursor was mainly converted to Pd^(2+) cations coordinated to the zeolite framework by consuming the-OH groups of the zeolite, a portion of PdO could also undergo thermal decomposition to form highly dispersed Pd~0 clusters in the pore channels. This simplified and scalable SSIE method paves a new way for the cost-effective synthesis of defect-free high-performance Pd-SAPO-34 zeolites as PNA catalysts.

Modeling and simulation of temperature control system in plant factory using energy balance

Closed production systems,such as plant factories and vertical farms,have emerged to ensure a sustainable supply of fresh food,to cope with the increasing consumption of natural resource for the growing population.In a plant factory,a microclimate model is one of the direct control components of a whole system.In order to better realize the dynamic regulation for the microclimate model,energy-saving and consumption reduction,it is necessary to optimize the environmental parameters in the plant factory,and thereby to determine the influencing factors of atmosphere control systems.Therefore,this study aims to identify accurate microclimate models,and further to predict temperature change based on the experimental data,using the classification and regression trees(CART)algorithm.A random forest theory was used to represent the temperature control system.A mechanism model of the temperature control system was proposed to improve the performance of the plant factories.In terms of energy efficiency,the main influencing factors on temperature change in the plant factories were obtained,including the temperature and air volume flow of the temperature control device,as well as the internal relative humidity.The generalization error of the prediction model can reach 0.0907.The results demonstrated that the proposed model can present the quantitative relationship and prediction function.This study can provide a reference for the design of high-precision environmental control systems in plant factories.