Antimony selenide (Sb2Se3) has garnered significant attention with its extraordinary optical and optoelectronic properties for optical and optoelectronic devices such as broadband photodetectors. The trends emerge in the synthesis of Sb2Se3 over a semiconducting substrate for the direct formation of heterostructures, which facilitate a pn junction for self‐powered photodetector. 1D Sb2Se3 nanorods are preferred to boost the charge carrier transport along the longitudinal direction as well as the optical absorption by light trapping. Great challenges remain for the vertical growth of nanorods. In this work, the precisely vertical alignment of Sb2Se3 nanorod arrays has been achieved in an epitaxial growth manner by selecting a lattice‐matching substrate, namely, boron‐doped ZnO (110) surface. The directly grown boron‐doped ZnO/Sb2Se3 nanorod arrays heterostructure leads to a high‐performance broadband photodetector. Eventually, the device demonstrates extraordinary figure‐of‐merit parameters, i.e., responsivity, on/off ratio, and specific detectivity. Furthermore, device simulation predicts the theoretical limit of the photodetector performances on the condition of suppressed defect density of the Sb2Se3. These results may shed light on the investigation of controlled growth of low‐dimensional materials, self‐powered photodetectors, and related optic and optoelectronic devices.
The automatic identification of the topology of power networks is important for the data-driven and situation-aware operation of power grids. Traditional methods of topology identification lack a data-tolerant mechanism, and the accuracy of their performance in terms of identification is thus affected by the quality of data. Topology identification is related to the link prediction problem. The graph neural network can be used to predict the state of unlabeled nodes (lines) through training on features of labeled nodes (lines) with fault tolerance. Inspired by the characteristics of the graph neural network, we applied it to topology identification in this study. We propose a method to identify the topology of a power network based on a knowledge graph and the graph neural network. Traditional knowledge graphs can quickly mine possible connections between entities and generate graph structure data, but in the case of errors or informational conflicts in the data, they cannot accurately determine whether the relationships between the entities really exist. The graph neural network can use data mining to determine whether a connection obtained between entities is based on their eigenvalues, and has a fault tolerance mechanism to adapt to errors and informational conflicts in the graph data, but needs the graph data as database. The combination of the knowledge graph and the graph neural network can compensate for the deficiency of the single knowledge graph method. We tested the proposed method by using the IEEE 118-bus system and a provincial network system. The results showed that our approach is feasible and highly fault tolerant. It can accurately identify network topology even in the presence of conflicting and missing measurement-related information.
The integrity of data is an essential basis for analyzing power system operating status based on data. Improper handling of measurement sampling, information transmission, and data storage can lead to data loss, thus destroying the data integrity and hindering data mining. Traditional data imputation methods are suitable for low-latitude, low-missing-rate scenarios. In high-latitude, high-missing-rate scenarios, the applicability of traditional methods is in doubt. This paper proposes a reconstruction method for missing data in power system measurement based on LSGAN (Least Squares Generative Adversarial Networks). The method is designed to train in an unsupervized learning mode, enabling the neural network to automatically learn measurement data, power distribution patterns, and other complex correlations that are difficult to model explicitly. It then optimizes the generator parameters using the constraint relations implied by true sample data, enabling the trained Generator to generate highly accurate data to reconstruct the missing data. The proposed approach is entirely data-driven and does not involve mechanistic modeling. It can still reconstruct the missing data in the case of high latitude and high loss rate. We test the effectiveness of the proposed method by comparing three other GAN derivation methods in our experiments. The experimental results show that the proposed method is feasible and effective, and the accuracy of the reconstructed data is higher while taking into account the computational efficiency.
In this paper, the wx-AMPS simulation software is used to model and simulate the antimony selenide (Sb<sub>2</sub>Se<sub>3</sub>) thin film solar cells. Three different electron transport layer models (CdS, ZnO and SnO<sub>2</sub>) are applied to the Sb<sub>2</sub>Se<sub>3</sub> solar cells, and the conversion efficiencies of which are obtained to be 7.35%, 7.48% and 6.62% respectively. It can be seen that the application of CdS and ZnO can achieve a better device performance. Then, the electric affinity of the electron transport layer (<i>χ</i><sub>e-ETL</sub>) is adjusted from 3.8 eV to 4.8 eV to study the effect of the energy band structure change on the solar cell performance. The results show that the conversion efficiency of the Sb<sub>2</sub>Se<sub>3</sub> solar cell first increases and then decreases with the increase of the <i>χ</i><sub>e-ETL</sub>. The lower <i>χ</i><sub>e-ETL</sub> creates a barrier at the interface between the electron transport layer and the Sb<sub>2</sub>Se<sub>3</sub> layer, which can be considered as a high resistance layer, resulting in the increase of series resistance. On the other hand, when the <i>χ</i><sub>e-ETL</sub> is higher than 4.6 eV, the electric field of the electron transport layer can be reversed, leading to the accumulation of the photon-generated carriers at the interface between the transparent conductive film and the electron transport layer, which could also hinder the carrier transport and increase the series resistance. At the same time, the electric field of Sb<sub>2</sub>Se<sub>3</sub> layer becomes weak with the value of <i>χ</i><sub>e-ETL</sub> increasing according to the band structure of the Sb<sub>2</sub>Se<sub>3</sub> solar cell, leading to the increase of the carriers' recombination and the reduction of the cell parallel resistance. As a result, too high or too low <i>χ</i><sub>e-ETL</sub> can lower the FF value and cause the device performance to degrade. Thus, to maintain high device performance, from 4.0 eV to 4.4 eV is a suitable range for the <i>χ</i><sub>e-ETL</sub> of the Sb<sub>2</sub>Se<sub>3</sub> solar cell. Moreover, based on the optimization of the <i>χ</i><sub>e-ETL</sub>, the enhancement of the Sb<sub>2</sub>Se<sub>3</sub> layer material quality can further improve the solar cell performance. In the case of removing the defect states of the Sb<sub>2</sub>Se<sub>3</sub> layer, the conversion efficiency of the Sb<sub>2</sub>Se<sub>3</sub> solar cell with a thickness of 0.6 μm is significantly increased from 7.87% to 12.15%. Further increasing the thickness of the solar cell to 3 μm, the conversion efficiency can be as high as 16.55% (<i>J</i><sub>sc</sub>=34.88 mA/cm<sup>2</sup>, <i>V</i><sub>oc</sub>=0.59 V, <i>FF</i>=80.40%). The simulation results show that the Sb<sub>2</sub>Se<sub>3</sub> thin film solar cells can obtain excellent performance with simple device structure and have many potential applications.
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