Dataset Information

Revealing composition and structure dependent deep-level defect in antimony trisulfide photovoltaics.

ABSTRACT: Antimony trisulfide (Sb₂S₃) is a kind of emerging light-harvesting material with excellent stability and abundant elemental storage. Due to the quasi-one-dimensional symmetry, theoretical investigations have pointed out that there exist complicated defect properties. However, there is no experimental verification on the defect property. Here, we conduct optical deep-level transient spectroscopy to investigate defect properties in Sb₂S₃ and show that there are maximum three kinds of deep-level defects observed, depending on the composition of Sb₂S₃. We also find that the Sb-interstitial (Sb_i) defect does not show critical influence on the carrier lifetime, indicating the high tolerance of the one-dimensional crystal structure where the space of (Sb₄S₆)_n ribbons is able to accommodate impurities to certain extent. This study provides basic understanding on the defect properties of quasi-one-dimensional materials and a guidance for the efficiency improvement of Sb₂S₃ solar cells.

SUBMITTER: Lian W

PROVIDER: S-EPMC8166839 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Revealing composition and structure dependent deep-level defect in antimony trisulfide photovoltaics.

Lian Weitao W Jiang Chenhui C Yin Yiwei Y Tang Rongfeng R Li Gang G Zhang Lijian L Che Bo B Chen Tao T

Nature communications 20210531 1

Antimony trisulfide (Sb2S3) is a kind of emerging light-harvesting material with excellent stability and abundant elemental storage. Due to the quasi-one-dimensional symmetry, theoretical investigations have pointed out that there exist complicated defect properties. However, there is no experimental verification on the defect property. Here, we conduct optical deep-level transient spectroscopy to investigate defect properties in Sb2S3 and show that th ...[more]

PMID: 34059672

Similar Datasets

Project description:BackgroundAnalysis of data from multiple sources has the potential to enhance knowledge discovery by capturing underlying structures, which are, otherwise, difficult to extract. Fusing data from multiple sources has already proved useful in many applications in social network analysis, signal processing and bioinformatics. However, data fusion is challenging since data from multiple sources are often (i) heterogeneous (i.e., in the form of higher-order tensors and matrices), (ii) incomplete, and (iii) have both shared and unshared components. In order to address these challenges, in this paper, we introduce a novel unsupervised data fusion model based on joint factorization of matrices and higher-order tensors.ResultsWhile the traditional formulation of coupled matrix and tensor factorizations modeling only shared factors fails to capture the underlying structures in the presence of both shared and unshared factors, the proposed data fusion model has the potential to automatically reveal shared and unshared components through modeling constraints. Using numerical experiments, we demonstrate the effectiveness of the proposed approach in terms of identifying shared and unshared components. Furthermore, we measure a set of mixtures with known chemical composition using both LC-MS (Liquid Chromatography - Mass Spectrometry) and NMR (Nuclear Magnetic Resonance) and demonstrate that the structure-revealing data fusion model can (i) successfully capture the chemicals in the mixtures and extract the relative concentrations of the chemicals accurately, (ii) provide promising results in terms of identifying shared and unshared chemicals, and (iii) reveal the relevant patterns in LC-MS by coupling with the diffusion NMR data.ConclusionsWe have proposed a structure-revealing data fusion model that can jointly analyze heterogeneous, incomplete data sets with shared and unshared components and demonstrated its promising performance as well as potential limitations on both simulated and real data.

Dataset Information

Revealing composition and structure dependent deep-level defect in antimony trisulfide photovoltaics.

Publications

Revealing composition and structure dependent deep-level defect in antimony trisulfide photovoltaics.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets