Very recently, the local coordination environment of active sites has been found to strongly influence their performance in electrocatalytic CO_(2) reduction by tuning the intrinsic kinetics of CO_(2) activation and i...Very recently, the local coordination environment of active sites has been found to strongly influence their performance in electrocatalytic CO_(2) reduction by tuning the intrinsic kinetics of CO_(2) activation and intermediate stabilization. It is imperative to elucidate the mechanism for such an influence towards the rational design of efficient catalysts;however, the complex interactions between the multiple factors involved in the system make it challenging to establish a clear structure–performance relationship. In this work, we chose ion-intercalated silver(I)-based coordination networks(AgCNs) with a well-defined structure as a model platform, which enables us to understand the regulation mechanism of counterions as the counterions are the only tuning factor involved in such a system. We prepared two isostructural Ag CNs with different intercalation ions or counterions of BF_(4)^(-) and ClO_(4)^(-)(named as AgCNs-BF_(4) and AgCNs-ClO_(4)) and found that the former has a more competitive CO_(2) electroreduction performance than the latter. AgCNs-BF_(4) achieves the highest Faradaic efficiency for CO_(2) to CO of 87.1% at-1.0 V(vs. RHE) with a higher partial current density, while AgCNs-ClO_(4) exhibits only 77.2% at the same applied potential.Spectroscopic characterizations and theoretical calculation reveal that the presence of BF_(4)^(-)is more favorable for stabilizing the COOH^(*) intermediate by weakening hydrogen bonds, which accounts for the superior activity of Ag CNs-BF_(4).展开更多
Purpose-In order to solve the problem that the performance of the existing local feature descriptors in uncontrolled environment is greatly affected by illumination,background,occlusion and other factors,we propose a ...Purpose-In order to solve the problem that the performance of the existing local feature descriptors in uncontrolled environment is greatly affected by illumination,background,occlusion and other factors,we propose a novel face recognition algorithm in uncontrolled environment which combines the block central symmetry local binary pattern(CS-LBP)and deep residual network(DRN)model.Design/methodology/approach-The algorithm first extracts the block CSP-LBP features of the face image,then incorporates the extracted features into the DRN model,and gives the face recognition results by using a well-trained DRN model.The features obtained by the proposed algorithm have the characteristics of both local texture features and deep features that robust to illumination.Findings-Compared with the direct usage of the original image,the usage of local texture features of the image as the input of DRN model significantly improves the computation efficiency.Experimental results on the face datasets of FERET,YALE-B and CMU-PIE have shown that the recognition rate of the proposed algorithm is significantly higher than that of other compared algorithms.Originality/value-The proposed algorithm fundamentally solves the problem of face identity recognition in uncontrolled environment,and it is particularly robust to the change of illumination,which proves its superiority.展开更多
Artificial neural networks consist of the mechanisms of identifying the solution for the problem with the layers design and implementation, and the hidden layers will have rules to be done by the model. The model cons...Artificial neural networks consist of the mechanisms of identifying the solution for the problem with the layers design and implementation, and the hidden layers will have rules to be done by the model. The model consists of different approaches, and based on the priority and the requirement, we need to create a model, and this article is dealing with the prediction related to bank customers and their loan processing, etc. In this article, we have a global dataset collected related to 10000 customers of a single bank and their account and other details related to the customers of the bank. Here, we are implementing using tensor flow and Keras libraries to create an artificial neural network which will work on the model and hidden layers. The hidden layers are the most import part of the presentation, and the virtual environment in the field can be helpful for the better prediction of the related things. Machine learning implementations with the combination of deep learning artificial neural networks and also with tensor flow and Keras will be the most exciting and attractive portion of the research work in any field of science and technology. This architecture and application will help to predict future bank applications, and this can be helpful for the customers to understand their level of applications and products usage based on their account weight.展开更多
基金supported by financial support in part by NSFC (91961106, 51902253, 21725102)Anhui Provincial Natural Science Foundation (Grant 2108085MB46)+1 种基金Key Project of Youth Elite Support Plan in Universities of Anhui Province (Grant gxyqZD2021121)Shaanxi Provincial Natural Science Foundation (2020JQ-778)。
文摘Very recently, the local coordination environment of active sites has been found to strongly influence their performance in electrocatalytic CO_(2) reduction by tuning the intrinsic kinetics of CO_(2) activation and intermediate stabilization. It is imperative to elucidate the mechanism for such an influence towards the rational design of efficient catalysts;however, the complex interactions between the multiple factors involved in the system make it challenging to establish a clear structure–performance relationship. In this work, we chose ion-intercalated silver(I)-based coordination networks(AgCNs) with a well-defined structure as a model platform, which enables us to understand the regulation mechanism of counterions as the counterions are the only tuning factor involved in such a system. We prepared two isostructural Ag CNs with different intercalation ions or counterions of BF_(4)^(-) and ClO_(4)^(-)(named as AgCNs-BF_(4) and AgCNs-ClO_(4)) and found that the former has a more competitive CO_(2) electroreduction performance than the latter. AgCNs-BF_(4) achieves the highest Faradaic efficiency for CO_(2) to CO of 87.1% at-1.0 V(vs. RHE) with a higher partial current density, while AgCNs-ClO_(4) exhibits only 77.2% at the same applied potential.Spectroscopic characterizations and theoretical calculation reveal that the presence of BF_(4)^(-)is more favorable for stabilizing the COOH^(*) intermediate by weakening hydrogen bonds, which accounts for the superior activity of Ag CNs-BF_(4).
基金The education and scientific research project of young and middle-aged teachers of Fujian Provincial Department of education(No.JAT171070).
文摘Purpose-In order to solve the problem that the performance of the existing local feature descriptors in uncontrolled environment is greatly affected by illumination,background,occlusion and other factors,we propose a novel face recognition algorithm in uncontrolled environment which combines the block central symmetry local binary pattern(CS-LBP)and deep residual network(DRN)model.Design/methodology/approach-The algorithm first extracts the block CSP-LBP features of the face image,then incorporates the extracted features into the DRN model,and gives the face recognition results by using a well-trained DRN model.The features obtained by the proposed algorithm have the characteristics of both local texture features and deep features that robust to illumination.Findings-Compared with the direct usage of the original image,the usage of local texture features of the image as the input of DRN model significantly improves the computation efficiency.Experimental results on the face datasets of FERET,YALE-B and CMU-PIE have shown that the recognition rate of the proposed algorithm is significantly higher than that of other compared algorithms.Originality/value-The proposed algorithm fundamentally solves the problem of face identity recognition in uncontrolled environment,and it is particularly robust to the change of illumination,which proves its superiority.
文摘Artificial neural networks consist of the mechanisms of identifying the solution for the problem with the layers design and implementation, and the hidden layers will have rules to be done by the model. The model consists of different approaches, and based on the priority and the requirement, we need to create a model, and this article is dealing with the prediction related to bank customers and their loan processing, etc. In this article, we have a global dataset collected related to 10000 customers of a single bank and their account and other details related to the customers of the bank. Here, we are implementing using tensor flow and Keras libraries to create an artificial neural network which will work on the model and hidden layers. The hidden layers are the most import part of the presentation, and the virtual environment in the field can be helpful for the better prediction of the related things. Machine learning implementations with the combination of deep learning artificial neural networks and also with tensor flow and Keras will be the most exciting and attractive portion of the research work in any field of science and technology. This architecture and application will help to predict future bank applications, and this can be helpful for the customers to understand their level of applications and products usage based on their account weight.
