Examining nonlinear effects of socioecological drivers on urban solar energy development in China using machine learning and high-dimensional data.

In the context of carbon neutrality target, renewable energy sources have been transforming from "supplementary energy" to "main energy", which have promoted the green and low-carbon transition of global energy supply system. In-depth analyzing the spatial patterns and driving mechanisms of renewable energy expansion are of significance for optimizing the spatial layout of clean power, and avoiding the phenomenon of wind and solar power curtailment. In this paper, we proposed an ensemble learning model to examine the nonlinear effects of physical geography, resource endowment, and socio-economic factors on solar photovoltaic (PV) capacity at the prefecture-level city scale in China. Using the city-level multi-sources geospatial big data, we extensively collected a total of 175 related explanatory variables and cumulative installed capacity of solar PV power for 295 prefecture-level cities of China. The recursive feature elimination algorithm (SVM-REF) is firstly used to extract the optimal feature subset of urban PV capacity from multi-dimensional features variables. Furthermore, three advanced machine learning models (random forest, decision tree, extreme gradient boosting) are developed to identify the key influencing factors and nonlinear driving effect of urban solar PV power expansion in China. The results show that China's PV installation capacity is highly concentrated in Northern and Northwest parts of China, with the occupancy over 70% in 2019. Moreover, the XGBoost model has the best prediction accuracy (R2 = 0.97) among three methods. We also found that total amount of urban water resources, average solar radiation, and population density are the most important controlling factors for urban solar PV capacity expansion in China, with contribution of 35.6%, 17.7%, and 13.3%, respectively. We suggested that urban solar PV layout mode in China is recommended to gradually shift from resource orientation to the "resource-environment-demand" comprehensive orientation. The paper provides a replicable, scalable machine learning models for simulating solar PV power capacity at the prefecture-level city scale, and serves as a motivation for decision-making reference of the macro siting optimization and sustainable development of China's green power industry.

Mechanisms of 2π phase control in dielectric metasurface and transmission enhancement effect.

Metasurfaces, two-dimensional structures composed of nanoantennas in an array configuration, can be used to fully control electromagnetic waves, which requires a 2π phase shift. Herein, we apply the silicon metasurface as an example to interpret the mechanisms of full 2π phase coverage. It is found that the mechanism varies from Fabry-Pérot resonance to Mie resonance as the period increases for a metasurface with certain height. Particularly, there is a transition region between these two types of resonance. We present the corresponding periods and wavelength regions of the different mechanisms when considering the phase-gradient metasurface with at most three diffraction orders. Moreover, the transmission enhancement of metasurface is investigated. The transmission efficiency can be effectively improved when the nanoantenna is changed from a uniform structure to a gradient-index one. We expect that the results can simplify the design process and provide a reference for the future design of all-dielectric metasurface with 2π phase control.

Computational Study on N-N Homolytic Bond Dissociation Enthalpies of Hydrazine Derivatives.

The hydrazine derivatives have been regarded as the important building blocks in organic chemistry for the synthesis of organic N-containing compounds. It is important to understand the structure-activity relationship of the thermodynamics of N-N bonds, in particular, their strength as measured by using the homolytic bond dissociation enthalpies (BDEs). We calculated the N-N BDEs of 13 organonitrogen compounds by eight composite high-level ab initio methods including G3, G3B3, G4, G4MP2, CBS-QB3, ROCBS-QB3, CBS-Q, and CBS-APNO. Then 25 density functional theory (DFT) methods were selected for calculating the N-N BDEs of 58 organonitrogen compounds. The M05-2X method can provide the most accurate results with the smallest root-mean-square error (RMSE) of 8.9 kJ/mol. Subsequently, the N-N BDE predictions of different hydrazine derivatives including cycloalkylhydrazines, N-heterocyclic hydrazines, arylhydrazines, and hydrazides as well as the substituent effects were investigated in detail by using the M05-2X method. In addition, the analysis including the natural bond orbital (NBO) as well as the energies of frontier orbitals were performed in order to further understand the essence of the N-N BDE change patterns.

Facile Synthesis of Amorphous Ge Supported by Ni Nanopyramid Arrays as an Anode Material for Sodium-Ion Batteries.

In this work, we introduce Ni nanopyramid arrays (NPAs) supported amorphous Ge anode architecture and demonstrate its effective improvement in sodium storage properties. The Ni-Ge NPAs are prepared by facile electrodeposition and sputtering method, which eliminates the need for any binder or conductive additive when used as a Na-ion battery anode. The electrodes display stable cycling performance and enhanced rate capabilities in contrast with planar Ge electrodes, which can be owing to the rational design of the architectured electrodes and firm bonding between current collector and active material (i. e. Ni and Ge, respectively). To validate improvement of nanostructures on electrochemical performance, sodium insertion behavior of crystalline Ge derived from Mg2Ge precursor has been investigated, in which limited but effective enhancement of sodium storage properties are realized by introducing porous nanostructure in crystalline Ge. These results show that elaborately designed configuration of Ge electrodes may be a promising anode for Na-ion battery applications.

Construction and analysis of an intergeneric fusion from Pigmentiphaga sp. strain AAP-1 and Pseudomonas sp. CTN-4 for degrading acetamiprid and chlorothalonil.

Pseudomonas sp. CTN-4 degrades chlorothalonil (CTN) but not acetamiprid (AAP), and Pigmentiphaga sp. strain AAP-1 degrades AAP but not CTN. A functional strain, AC, was constructed through protoplast fusion of two parental strains (Pseudomonas sp. CTN-4 and Pigmentiphaga sp. strain AAP-1) in order to simultaneously improve the degradation efficiency of AAP and CTN. Fusant-AC with eight transfers on plates containing two antibiotics and CTN was obtained. For the purpose of identifying and confirming the genetic relationship between fusant-AC and its parents, randomly amplified polymorphic DNA (RAPD), scanning electron microscopy (SEM), and 16S ribosomal DNA (rDNA) analysis were performed. In toto, RAPD fingerprint analysis produced 194 clear bands with 9 primers, which not only had bands in common with strains CTN-4 and AAP-1, but also had its own novel fusant-specific bands. The genetic similarity indices between fusant-AC and parental strains CTN-4 and AAP-1 were 0.40 and 0.69, respectively. The result of SEM indicated that the cell morphology of fusant-AC differed from both its parents. The fusant strain AC possesses a strong capability for AAP and CTN degradation. At AAP concentration (50-300 mg L(-1)), the degradation was achieved within 5 h. At the initial dose of 50 and 100 mg L(-1) CTN, the percentages reached 96 and 91 % over a 36-h incubation period. The present study indicates that the protoplast-fusion technique may have possible applications in environmental pollution control.