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Gas diffusion electrodes (GDEs) mediate the transport of reactants, products and electrons for the electrocatalytic CO2 reduction reaction (CO2RR) in membrane electrode assemblies. The random distribution of ionomer, added by the traditional physical mixing method, in the catalyst layer of GDEs affects the transport of ions and CO2. Such a phenomenon results in elevated cell voltage and decaying selectivity at high current densities. This paper describes a pre-confinement method to construct GDEs with homogeneously distributed ionomer, which enhances mass transfer locally at the active centers. The optimized GDE exhibited comparatively low cell voltages and high CO Faradaic efficiencies (FE > 90%) at a wide range of current densities. It can also operate stably for over 220 h with the cell voltage staying almost unchanged. This good performance can be preserved even with diluted CO2 feeds, which is essential for pursuing a high single-pass conversion rate. This study provides a new approach to building efficient mass transfer pathways for ions and reactants in GDEs to promote the electrocatalytic CO2RR for practical applications. [ABSTRACT FROM AUTHOR]

In recent decades, there has been an increasing interest in the relation between lexical features and texts of psychological states. Previous studies demonstrated that some lexical features varied significantly among the texts of psychological states. However, the lexical features at the textual level have received little attention. This paper extends this work by examining the performance of quantitative linguistic indices in classifying texts of psychological issues. A large dataset of forum posts including texts of anxiety, depression, suicide ideation, and normal states were experimented with Machine Learning algorithms. The results revealed that the quantitative linguistic indices with Machine Learning algorithms achieved a high level of success in identifying psychological states. Meanwhile, some quantitative linguistic indices, namely, ALT and Writer's view, may extract adequate lexical features for classifying texts of different psychological states. The study is probably the first attempt that uses quantitative linguistic indices as lexical features to detect texts of psychological states, and the findings may contribute to our understanding of how accuracy may be enhanced in the identification of various psychological states. Finally, the implications of these findings are discussed. [ABSTRACT FROM AUTHOR]

Experiments on the titanium alloy Ti-1023（ Ti-10V-2Fe-3Al） work hardening and work hardening layer depth was studied by down or up milling,first milling or quadratic milling in a new tool or the wear tool. The results show that titanium alloy Ti-1023 is milled in carbide cutting tools,down milling in work hardening and work hardening layer depth is smaller than up milling,new tool in work hardening is smaller than wear tool,the work hardening layer depth is less than 25 μm in new tool; the work hardening layer depth is up to 81 μm and quadratic milling causes the machined surface softening in case of tool flank wear VB = 0. 3 mm.