The rapid development of Internet technology has formed a huge virtual information space. In the information space, information flow has become a link of communication between objects. Information flow is an alternative or supplement to the traditional physical flow for the study of the spatial interaction of geographical entities. The research uses toponym co-occurrence and search index as information flow data, verifies the geographical laws hidden in the information space by spatial autocorrelation analysis and gravity model fitting, and analyzes the spatial interaction patterns of provinces in China in the information space by complex network analysis methods. The results show that: (1) information flow in the information space obeys Tobler’s first law of geography and Goodchild’s second law of geography. The spatial interaction represented by information flow has a distance decay effect. The best distance decay coefficients for toponym co-occurrence and the search index are 0.189 and 0.186, respectively. (2) The inter-provincial spatial interaction network of China shows a hierarchical pattern of the triangular primary network and diamond secondary network, and the ranking of provinces in the centrality analysis is basically stable, but the network hierarchy is deepening. The gravity center of spatial interaction is located in the east-central region of China. (3) The information flow-based interaction network is of higher asymmetry than the population mobility network, and its spatial structure is also obvious. This research provides a new idea for studying the spatial interaction of geographical entities in the physical world from the perspective of information flow.
Dual-atom catalysts (DACs) have emerged as potential catalysts for effective electroreduction of CO2 due to their high atom utilization efficiency and multiple active sites. However, the screening of DACs remains a challenge due to the large number of possible combinations, making exhaustive experimental or computational screening a daunting task. In this study, a density functional theory (DFT)-based machine learning (ML)-accelerated (DFT-ML) hybrid approach was developed to test a set of 406 dual transition metal catalysts on N-doped graphene (NG) for the electroreduction of CO2 to HCOOH. The results showed that the ML algorithms can successfully capture the relationship between the descriptors of the DACs (inputs) and the limiting potential for HCOOH generation (output). Of the four ML algorithms studied in this work, the feedforward neural network model achieved the highest prediction accuracy (the highest correlation coefficient (R2) of 0.960 and the lowest root mean square error (RMSE) of 0.319 eV on the test set) and the predicted results were verified by DFT calculations with an average absolute error of 0.14 eV. The DFT-ML approach identified Co-Co-NG and Ir-Fe-NG as the most active and stable electrocatalysts for the electrochemical reduction of CO2 to HCOOH. The DFT-ML hybrid approach exhibits exceptional prediction accuracy while enabling a significant reduction in screening time by an impressive 64% compared to conventional DFT-only calculations. These results demonstrate the immense potential of using ML methods to accelerate the screening and rational design of efficient catalysts for various energy and environmental applications.
Abstract The investigation of the thermal stability and evolution of Pd-Au core-shell nanostructures across various temperatures proceeded with in situ heating transmission electron microscopy (TEM) along with spectral analysis techniques. In situ heating transmission electron microscopy (TEM) was utilized to examine the morphology and elemental distribution of core-shell nanostructures composed of Pd 0.5 -Au 0.5 and Pd 0.9 -Au 0.1 , which were synthesized via the seed growth technique. Compared with the Pd 0.5 -Au 0.5 core-shell nanostructures, the Pd 0.9 -Au 0.1 core-shell nanostructures have better thermal stability.
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