Covalently Grafting Graphene onto Si Photocathode to Expedite Aqueous Photoelectrochemical CO2 Reduction.

Silicon semiconductor functionalized with molecular catalysts emerges as a promising cathode for photoelectrochemical (PEC) CO2 reduction reaction (CO2 RR). However, the limited kinetics and stabilities remains a major hurdle for the development of such composites. We herein report an assembling strategy of silicon photocathodes via chemically grafting a conductive graphene layer onto the surface of n+ -p Si followed by catalyst immobilization. The covalently-linked graphene layer effectively enhances the photogenerated carriers transfer between the cathode and the reduction catalyst, and improves the operating stability of the electrode. Strikingly, we demonstrate that altering the stacking configuration of the immobilized cobalt tetraphenylporphyrin (CoTPP) catalyst through calcination can further enhance the electron transfer rate and the PEC performance. At the end, the graphene-coated Si cathode immobilized with CoTPP catalyst managed to sustain a stable 1-Sun photocurrent of -1.65 mA cm-2 over 16 h for CO production in water at a near neutral potential of -0.1 V vs. reversible hydrogen electrode. This represents a remarkable improvement of PEC CO2 RR performance in contrast to the reported photocathodes functionalized with molecular catalysts.

Sulfidation-Induced Surface Local Electronic and Atomic Structures in a Silver Catalyst Enables Silicon Photocathode for Selective and Efficient Photoelectrochemical CO2 Reduction.

Converting CO2 to value-added chemicals through a photoelectrochemical (PEC) system is a creative approach toward renewable energy utilization and storage. However, the rational design of appropriate catalysts while being effectively integrated with semiconductor photoelectrodes remains a considerable challenge for achieving single-carbon products with high efficiency. Herein, we demonstrate a novel sulfidation-induced strategy for in situ grown sulfide-derived Ag nanowires on a Si photocathode (denoted as SD-Ag/Si) based on the standard crystalline Si solar cells. Such an exquisite design of the SD-Ag/Si photocathode not only provides a large electrochemically active surface area but also endows abundant active sites of Ag2S/Ag interfaces and high-index Ag facets for PEC CO production. The optimized SD-Ag/Si photocathode displays an ideal CO Faradic efficiency of 95.2% and an onset potential of +0.26 V versus the reversible hydrogen electrode, ascribed to the sulfidation-induced synergistic effect of the surface atomic arrangement and electronic structure in Ag catalysts that promote charge transfer, facilitate CO2 adsorption and activation, and suppress hydrogen evolution reaction. This sulfidation-induced strategy represents a scalable approach for designing high-performance catalysts for electrochemical and PEC devices with efficient CO2 utilization.

A bilateral employment situation prediction model for college students using GCN and LSTM.

Due to the prevailing trend of globalization, the competition for social employment has escalated significantly. Moreover, the job market has become exceedingly competitive for students, warranting immediate attention. In light of this, a novel prognostic model employing big data technology is proposed to facilitate a bilateral employment scenario for graduates, aiding college students in promptly gauging the prevailing social employment landscape and providing precise employment guidance. Initially, the focus lies in meticulously analyzing pivotal aspects of college students' employment by constructing a specialized employment platform. Subsequently, a classification model grounded in a graph convolution network (GCN) is built, leveraging big data technology to comprehensively comprehend graduates' strengths and weaknesses in the employment milieu. Furthermore, based on the outcomes derived from the comprehensive classification of college students' qualities, a college students' employment trend prediction method employing long and short term memory (LSTM) is proposed. This method supplements the analysis of graduates' employability and enables accurate forecasting of college students' employment trends. Empirical evidence substantiates that my proposed methodology effectively evaluates graduates' comprehensive qualities and successfully predicts their employment prospects. The achieved F-values, 82.45% and 69.89%, respectively, demonstrate the efficacy of anticipating the new paradigm in graduates' dual-line employment.