In this paper, the gamma-graphyne nanoribbons (γ-GYNRs) incorporating diamond-shaped segment (DSSs) with excellent thermoelectric properties are systematically investigated by combining nonequilibrium Green’s functions with adaptive genetic algorithm. Our calculations show that the adaptive genetic algorithm is efficient and accurate in the process of identifying structures with excellent thermoelectric performance. In multiple rounds, an average of 476 candidates (only 2.88% of all 16512 candidate structures) are calculated to obtain the structures with extremely high thermoelectric conversion efficiency. The room temperature thermoelectric figure of merit (ZT) of the optimal γ-GYNR incorporating DSSs is 1.622, which is about 5.4 times higher than that of pristine γ-GYNR (length 23.693 nm and width 2.660 nm). The significant improvement of thermoelectric performance of the optimal γ-GYNR is mainly attributed to the maximum balance of inhibition of thermal conductance (proactive effect) and reduction of thermal power factor (side effect). Moreover, through exploration of the main variables affecting genetic algorithm, it is revealed that the efficiency of genetic algorithm can be improved by optimizing the initial population gene pool, selecting a higher individual retention rate and a lower mutation rate. The results presented in this pa-per validate the effectiveness of genetic algorithm in accelerating the exploration of γ-GYNRs with high thermoelectric conversion efficiency, and could provide a new development solution for carbon-based thermoelectric materials.
A large number of by-products will occur during the transformation of ethanol to C4 olefin, resulting in the selection of the destination products that are not enough to meet industrial needs. Therefore, the selection of catalyst combinations and environmental conditions are of great significance for the preparation of C4 olefin. First of all, this paper adopts a random forest model to screen the experimental characteristics and the Grid Search is used for optimizing hyperparameters to evaluate the importance of different catalyst types to ethanol conversion rate and C4 olefin selectivity. On this basis, with the multiple regression equations established among different catalyst types, temperature and C4 olefin income as the target function, under the constraint of temperature, Co loads, Co/SiO2 and HAP quality ratio, ethanol concentration, obtain the combination of catalysts with the highest income of C4 olefin. The proportion of C4 olefin in the target product was increased. At the same time, it provides reference for exploring suitable chemical process conditions of C4 olefin.
Educational ecology is a branch of pedagogy that uses ecosystem theory to study the law between education and the external ecological environment. In this paper, We use ecology research methods to evaluate the health and sustainability of the education system. Based on Educational stability, carrying capacity and teaching quality, we build the three-dimensional baseline cube model (called the Tri-E model) for comprehensive measurement. In terms of educational stability, we build educational pyramids according to different academic qualifications and calculate Gini coefficient according to geographical distribution and the Educational Stability Index (ESI) is summarized. For the carrying capacity and quality of education, the weight of each factor is determined by the AHP. The Education Carrying Capacity Index (ECCI) and Education Quality Index (EQI) are calculated according to the relevant indicators of the countries studied. These indicators will determine a vector in the 3D Baseline cube to obtain a measure of the health status of higher education Tri-E Index (TEI). We analyzed some representative countries (UK, India, and China) with TEI of 0.741, 0.521, and 0.665, respectively. Referencing China’s education reform, we put forward the current problems facing the Indian education system and how to reform it. After a quantitative analysis of the actual resistance index, the conclusion is that it is difficult for the reform to be completely successful.
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