Highly selective photocatalytic reduction of CO_(2) to CH_(4) on electron-rich Fe species cocatalyst under visible light irradiation

Efficient photocatalytic reduction of CO_(2) to high-calorific-value CH4,an ideal target product,is a blueprint for C_(1)industry relevance and carbon neutrality,but it also faces great challenges.Herein,we demonstrate unprecedented hybrid SiC photocatalysts modified by Fe-based cocatalyst,which are prepared via a facile impregnation-reduction method,featuring an optimized local electronic structure.It exhibits a superior photocatalytic carbon-based products yield of 30.0μmol g^(−1) h^(−1) and achieves a record CH_(4) selectivity of up to 94.3%,which highlights the effectiveness of electron-rich Fe cocatalyst for boosting photocatalytic performance and selectivity.Specifically,the synergistic effects of directional migration of photogenerated electrons and strongπ-back bonding on low-valence Fe effectively strengthen the adsorption and activation of reactants and intermediates in the CO_(2)→CH_(4) pathway.This study inspires an effective strategy for enhancing the multielectron reduction capacity of semiconductor photocatalysts with low-cost Fe instead of noble metals as cocatalysts.

Study of Synthesis Identification in Cutting Process with Fuzzy Neural Network

With the development of industrial production modernization, FMS and CIMS will become more and more popularized. For its control system is increasingly modeled, intellectualized and automatized, in order to raise the reliability and stability in the manufacturing process, the comprehensive monitoring and diagnosis aimed at cutting tool wear and chatter become more and more important and get rapid development. The paper tried to discuss of the intellectual status identification method based on acoustics-vibra characteristics of machining process, and propose that the working conditions may be taken as a core, complex fuzzy inference neural network model based on artificial neural network theory, and by using various kinds of modernized signal processing method to abstract enough characteristics parameters which will reflect overall processing status from machining acoustics-vibra signal as information source, to identify different working condition, and provide guarantee for automation and intelligence in machining process. The complex network is composed of NNw and NNs, Each of them is composed of BP model network, NNw is weight network at rule condition, NNs is decision-making network of each status. Y out is final inference result which is to take subordinate degree as weight from NNw, to weight reflecting result from NNs and obtain status inference of monitoring system. In the process of machining, the acoustics-vibor signal were gotten by the acoustimeter and the acceleration piezoelectricity detector, the date is analysed by the signal processing software in time and frequency domain, then form multi feature parameter vector of criterion pattern samples for the different stage of cutting chatter and acoustics-vibra multi feature parameter vector. The vector can give a accurate and comprehensive description for the cutting process, and have the characteristic which are speediness of time domain and veracity of frequency domain. The research works have been practically applied in identification of tool wear, cutting chatter, experiment results showed that it is practicable to identify the cutting chatter based on fuzzy neural network, and the new method based on fuzzy neural network can be applied to other state identification in machining process.

A platform of digital brain using crowd power

A powerful platform of digital brain is proposed using crowd wisdom for brain research,based on the computational artificial intelligence model of synthesis reasoning and multi-source analogical generating.The design of the platform aims to make it a comprehensive brain database,a brain phantom generator,a brain knowledge base,and an intelligent assistant for research on neurological and psychiatric diseases and brain development.Using big data,crowd wisdom,and high performance computers may significantly enhance the capability of the platform.Preliminary achievements along this track are reported.