Layer- and temperature-dependent work function modulation in CrSBr
Two-dimensional (2D) magnetic semiconductors have emerged as promising materials for next-generation nanoelectronic and spintronic devices, owing to their unique coupling of electronic and magnetic properties. CrSBr, a quasi-2D magnetic semiconductor, offers a versatile platform for exploring coupled electronic and magnetic phenomena in low-dimensional systems. Here, we systematically investigate the surface work function of CrSBr nanosheets using scanning Kelvin probe microscopy (SKPM), revealing a nonlinear dependence on thickness and a thermally tunable, reversible behavior. Furthermore, a CrSBr-based 2D field-effect transistor (FET) demonstrates that such thermal modulation of the work function effectively alters the Schottky barrier height, directly impacting charge injection. Density functional theory (DFT) calculations reveal the layer- and magnetism-dependent nature of the work function in few-layer CrSBr. This work highlights the importance of work function engineering in CrSBr and provides a foundation for its application in magnetoelectric coupling, spintronic devices, and van der Waals electronics.
Authors
- Yizhang Wu (ORCID: https://orcid.org/0000-0001-6244-8458)
- Yongjie Xu (ORCID: https://orcid.org/0000-0002-2660-6575)
- Yong Wang (ORCID: https://orcid.org/0000-0001-5085-5312)
- Yang Liu (ORCID: https://orcid.org/0000-0002-6276-1468)
- Lu Zhang (ORCID: https://orcid.org/0000-0001-5531-075X)
- Miao Wang (ORCID: https://orcid.org/0000-0003-4892-6139)
- Yu Zhang (ORCID: https://orcid.org/0000-0002-8681-3867)
- Tingting Wang (ORCID: https://orcid.org/0000-0002-1774-1783)
- Yichen Liu (ORCID: https://orcid.org/0000-0003-0950-3248)
- Shaopeng Wang (ORCID: https://orcid.org/0000-0002-2680-0503)
- Yongmei Wang (ORCID: https://orcid.org/0000-0002-6009-3267)
- Wei Xu (ORCID: https://orcid.org/0000-0002-5826-3730)
- Yue Hao (ORCID: https://orcid.org/0000-0002-8081-2919)
- Dingyi Yang
Institutions
- University of North Carolina at Chapel Hill (US)
- Xidian University (CN)
- Ningxia University (CN)
- Applied Physical Sciences (United States) (US)
- Shaanxi University of Science and Technology (CN)
- Nanjing Medical University (CN)
- Hong Kong Metropolitan University (HK)
- Nanjing University (CN)
Publication Details
- Journal
- CHAIN
- Published
- 2025-01-01
- DOI
- https://doi.org/10.23919/chain.2025.000013
- Primary Topic
- Machine Learning in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 0.00