Activating TiO2 through the Phase Transition-Mediated Hydrogen Spillover to Outperform Pt for Electrocatalytic pH-Universal Hydrogen Evolution.

Endowing conventional materials with specific functions that are hardly available is invariably of significant importance but greatly challenging. TiO2 is proven to be highly active for the photocatalytic hydrogen evolution while intrinsically inert for electrocatalytic hydrogen evolution reaction (HER) due to its poor electrical conductivity and unfavorable hydrogen adsorption/desorption behavior. Herein, the first activation of inert TiO2 for electrocatalytic HER is demonstrated by synergistically modulating the positions of d-band center and triggering hydrogen spillover through the dual doping-induced partial phase transition. The N, F co-doping-induced partial phase transition from anatase to rutile phase in TiO2 (AR-TiO2|(N,F)) exhibits extraordinary HER performance with overpotentials of 74, 80, and 142 mV at a current density of 10 mA cm-2 in 1.0 M KOH, 0.5 M H2SO4, and 1.0 M phosphate-buffered saline electrolytes, respectively, which are substantially better than pure TiO2, and even superior to the benchmark Pt/C catalysts. These findings may open a new avenue for the development of low-cost alternative to noble metal catalysts for electrocatalytic hydrogen production.

Green finance, energy consumption, urbanization, and economic growth: Quantile based evidence from China.

This study investigates the relationships between energy consumption, urbanization, green finance, and economic growth in China. By utilizing the quantile ARDL model and considering labor and capital as input factors, we analyze the period from 1999 to 2022. Our findings reveal that green finance and urbanization have negative effects on economic growth across different quantiles, in both the short and long run. Conversely, energy consumption exhibits a significantly positive impact on growth in various quantiles. Policymakers are encouraged to implement sustainable energy measures, promote eco-friendly urban planning, and embrace green technology to achieve both economic growth and environmental sustainability.

A Neural Network Model for Digitizing Enterprise Carbon Assets Based on Multimodal Knowledge Mapping.

In this paper, a multimodal knowledge mapping approach is used to digitize enterprise carbon assets, and a corresponding neural network model is designed for use in the practical process. Rich textual entity labels associated with images are obtained using an entity annotation system. A topology-based data fusion method is also designed based on the hierarchical relationship between WordNet and DBpedia to fuse the knowledge obtained from image visualization and text description mining. Existing neural network-based entity linking methods ignore the semantic gap between the context of sequential entity denotative items and the context of graph-structured entities, thus affecting the accuracy of entity linking. It is observed that the importance of words in the context of entity denotative items is different, and the importance of content in the entity context is also different. To solve the above problems, this paper proposes an entity linking method that combines a common attention mechanism with a graph convolutional neural network. Secondly, based on the basic theory of value assessment, the characteristics of classical asset valuation methods and their inapplicability to the valuation of carbon assets are analyzed, and thus the real option valuation method and its two classical models are introduced; after demonstrating the real option characteristics of carbon assets of power enterprise projects, a real option model-based carbon asset valuation model for power enterprise projects is constructed and its applicability is verified with case studies. Through analyzing the current situation and problems of carbon asset valuation work in power enterprises, targeted practical suggestions are put forward to further strengthen and enhance the carbon asset valuation work in power enterprises in the future.