{"title":"探索钙钛矿包晶 BaZrS3 的缺陷景观和掺杂性","authors":"Rushik Desai, Shubhanshu Agarwal, Kiruba Catherine Vincent, Alejandro Strachan, Rakesh Agrawal, Arun Mannodi-Kanakkithodi","doi":"10.1021/acs.jpcc.5c01597","DOIUrl":null,"url":null,"abstract":"BaZrS<sub>3</sub> is a chalcogenide perovskite that has shown promise as a photovoltaic absorber, but its performance is limited because of defects and impurities, which have a direct influence on carrier concentrations. Functional dopants that show lower donor-type or acceptor-type formation energies than naturally occurring defects can help tune the optoelectronic properties of BaZrS<sub>3</sub>. In this work, we applied first-principles computations to comprehensively investigate the defect landscape of BaZrS<sub>3</sub>, including all intrinsic defects and a set of selected impurities and dopants. BaZrS<sub>3</sub> intrinsically exhibits n-type equilibrium conductivity under both S-poor and S-rich conditions, which remains largely unchanged in the presence of O and H impurities. La and Nb dopants created stable donor-type defects which make BaZrS<sub>3</sub> even more n-type, whereas As and P dopants formed amphoteric defects with relatively high formation energies. This work highlights the difficulty of creating p-type BaZrS<sub>3</sub> owing to the low formation energies of donor defects, both intrinsic and extrinsic. Defect formation energies were also used to compute expected defect concentrations and make comparisons with experimentally reported values. Our dataset of defects in BaZrS<sub>3</sub> paves the path for training machine learning models to subsequently perform larger-scale prediction and screening of defects and dopants across many chalcogenide perovskites, including cation-site or anion-site alloys.","PeriodicalId":61,"journal":{"name":"The Journal of Physical Chemistry C","volume":"218 1","pages":""},"PeriodicalIF":3.3000,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Exploring the Defect Landscape and Dopability of Chalcogenide Perovskite BaZrS3\",\"authors\":\"Rushik Desai, Shubhanshu Agarwal, Kiruba Catherine Vincent, Alejandro Strachan, Rakesh Agrawal, Arun Mannodi-Kanakkithodi\",\"doi\":\"10.1021/acs.jpcc.5c01597\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"BaZrS<sub>3</sub> is a chalcogenide perovskite that has shown promise as a photovoltaic absorber, but its performance is limited because of defects and impurities, which have a direct influence on carrier concentrations. Functional dopants that show lower donor-type or acceptor-type formation energies than naturally occurring defects can help tune the optoelectronic properties of BaZrS<sub>3</sub>. In this work, we applied first-principles computations to comprehensively investigate the defect landscape of BaZrS<sub>3</sub>, including all intrinsic defects and a set of selected impurities and dopants. BaZrS<sub>3</sub> intrinsically exhibits n-type equilibrium conductivity under both S-poor and S-rich conditions, which remains largely unchanged in the presence of O and H impurities. La and Nb dopants created stable donor-type defects which make BaZrS<sub>3</sub> even more n-type, whereas As and P dopants formed amphoteric defects with relatively high formation energies. This work highlights the difficulty of creating p-type BaZrS<sub>3</sub> owing to the low formation energies of donor defects, both intrinsic and extrinsic. Defect formation energies were also used to compute expected defect concentrations and make comparisons with experimentally reported values. Our dataset of defects in BaZrS<sub>3</sub> paves the path for training machine learning models to subsequently perform larger-scale prediction and screening of defects and dopants across many chalcogenide perovskites, including cation-site or anion-site alloys.\",\"PeriodicalId\":61,\"journal\":{\"name\":\"The Journal of Physical Chemistry C\",\"volume\":\"218 1\",\"pages\":\"\"},\"PeriodicalIF\":3.3000,\"publicationDate\":\"2025-04-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"The Journal of Physical Chemistry C\",\"FirstCategoryId\":\"1\",\"ListUrlMain\":\"https://doi.org/10.1021/acs.jpcc.5c01597\",\"RegionNum\":3,\"RegionCategory\":\"化学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"CHEMISTRY, PHYSICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"The Journal of Physical Chemistry C","FirstCategoryId":"1","ListUrlMain":"https://doi.org/10.1021/acs.jpcc.5c01597","RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"CHEMISTRY, PHYSICAL","Score":null,"Total":0}
Exploring the Defect Landscape and Dopability of Chalcogenide Perovskite BaZrS3
BaZrS3 is a chalcogenide perovskite that has shown promise as a photovoltaic absorber, but its performance is limited because of defects and impurities, which have a direct influence on carrier concentrations. Functional dopants that show lower donor-type or acceptor-type formation energies than naturally occurring defects can help tune the optoelectronic properties of BaZrS3. In this work, we applied first-principles computations to comprehensively investigate the defect landscape of BaZrS3, including all intrinsic defects and a set of selected impurities and dopants. BaZrS3 intrinsically exhibits n-type equilibrium conductivity under both S-poor and S-rich conditions, which remains largely unchanged in the presence of O and H impurities. La and Nb dopants created stable donor-type defects which make BaZrS3 even more n-type, whereas As and P dopants formed amphoteric defects with relatively high formation energies. This work highlights the difficulty of creating p-type BaZrS3 owing to the low formation energies of donor defects, both intrinsic and extrinsic. Defect formation energies were also used to compute expected defect concentrations and make comparisons with experimentally reported values. Our dataset of defects in BaZrS3 paves the path for training machine learning models to subsequently perform larger-scale prediction and screening of defects and dopants across many chalcogenide perovskites, including cation-site or anion-site alloys.
期刊介绍:
The Journal of Physical Chemistry A/B/C is devoted to reporting new and original experimental and theoretical basic research of interest to physical chemists, biophysical chemists, and chemical physicists.