journal article·2025-07-09Open access

Computational Guide to Optimize Electric Conductance in MoS2 Films

Alireza Ghasemifard, Agnieszka B. Kuc, Thomas Heine

ACS Applied Materials & Interfaces· Volume 17· Issue 27

Abstract

High Resolution Image Download MS PowerPoint Slide Molybdenum disulfide (MoS 2 ) is a high-potential material for nanoelectronic applications, especially when thinned to a few layers. Liquid-phase exfoliation enables large-scale fabrication of thin films comprising single- and few-layer flakes of MoS 2 or other transition-metal dichalcogenides (TMDCs), exhibiting variations in the flake size, geometry, edge terminations, and overlapping areas. Electronic conductivity of such films is thus determined by two contributions: the intraflake conductivity, reflecting the value of each single layer, and charge transport across these overlapping flakes. Employing first-principles simulations, we investigate the influence of various edge terminations and the overlap between flakes on the charge transport in MoS 2 film models. We identify characteristic electronic edge states originating from the edge atoms and their chemical environment, which resemble donor and acceptor states of doped semiconductors. This makes either electrons or holes to majority carriers and enables selective control over the dominant charge carrier type (n-type or p-type). Compared to pristine nanosheets, overlapping flakes exhibit lower overall conductance. In the best-performing hexagonal flakes occurring in Mo-rich environments, the conductance is reduced by 18% compared to the pristine layer, while the drop by 46% and 58% is predicted for truncated triangular and triangular flakes, respectively, in S-rich environments. An overlap of 6.5 nm is sufficient to achieve the highest possible interflake conductance. These findings allow for rational optimization of experimental conditions for the preparation of MoS 2 and other TMDC semiconducting thin films.
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Research context

OpenAlex topics: 2D Materials and Applications · MXene and MAX Phase Materials · Machine Learning in Materials Science

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Theoretical Chemistry, TU Dresden, Bergstraße 66c, 01062 Dresden, Germany

Helmholtz-Zentrum Dresden-Rossendorf, HZDR, Bautzner Landstraße 400, 01328 Dresden, Germany

Center for Advanced Systems Understanding, CASUS, Conrad-Schiedt-Straße 20, 02826 Görlitz, Germany

IBS-CNM, Yonsei University, Seodaemun-gu, Seoul 120-749, Republic of Korea

Funding

Deutsche Forschungsgemeinschaft · Horizon 2020 Framework Programme

References (60)

Show source-provided references
1. 10.1126/science.aal406210.1126/science.aal4062 ↗
2. 10.1002/smll.20230473510.1002/smll.202304735 ↗
3. 10.1038/s41578-021-00386-w10.1038/s41578-021-00386-w ↗
4. 10.1002/adma.20221115710.1002/adma.202211157 ↗
5. 10.1039/D1NR05815K10.1039/D1NR05815K ↗
6. 10.1038/s41467-024-48614-510.1038/s41467-024-48614-5 ↗
7. 10.1063/1.498947710.1063/1.4989477 ↗
8. 10.1021/acsami.4c1314710.1021/acsami.4c13147 ↗
9. 10.1038/ncomms557610.1038/ncomms5576 ↗
10. 10.1021/acs.nanolett.9b0524710.1021/acs.nanolett.9b05247 ↗
11. 10.1038/nnano.2013.10010.1038/nnano.2013.100 ↗
12. 10.1038/nmat368710.1038/nmat3687 ↗
13. 10.1021/ar500277z10.1021/ar500277z ↗
14. 10.1103/PhysRevB.83.24521310.1103/PhysRevB.83.245213 ↗
15. 10.1002/aelm.20210097210.1002/aelm.202100972 ↗
16. 10.1103/PhysRevB.104.08543310.1103/PhysRevB.104.085433 ↗
17. 10.1103/PhysRevB.98.11540310.1103/PhysRevB.98.115403 ↗
18. 10.1109/TED.2022.323208210.1109/TED.2022.3232082 ↗
19. 10.1039/C5NJ01698C10.1039/C5NJ01698C ↗
20. 10.1021/jp505858h10.1021/jp505858h ↗
21. 10.1002/adma.20160239110.1002/adma.201602391 ↗
22. 10.1021/acs.inorgchem.3c0242010.1021/acs.inorgchem.3c02420 ↗
23. 10.1016/j.carbon.2023.11864210.1016/j.carbon.2023.118642 ↗
24. 10.1021/acsnano.5b0722810.1021/acsnano.5b07228 ↗
25. 10.1038/s41467-023-44450-110.1038/s41467-023-44450-1 ↗
26. 10.1021/acs.nanolett.7b0219210.1021/acs.nanolett.7b02192 ↗
27. 10.1006/jcat.1999.259810.1006/jcat.1999.2598 ↗
28. 10.1103/PhysRevB.67.08541010.1103/PhysRevB.67.085410 ↗
29. 10.1021/acsnano.2c0117010.1021/acsnano.2c01170 ↗
30. 10.1038/nnano.2006.17110.1038/nnano.2006.171 ↗
31. 10.1038/s41467-018-03388-510.1038/s41467-018-03388-5 ↗
32. 10.1038/s41598-019-52119-310.1038/s41598-019-52119-3 ↗
33. 10.1021/jp509771310.1021/jp5097713 ↗
34. 10.1021/cm502566210.1021/cm5025662 ↗
35. 10.1002/adfm.20160388710.1002/adfm.201603887 ↗
36. 10.1038/s41598-017-15622-z10.1038/s41598-017-15622-z ↗
37. 10.1039/C7CP03068A10.1039/C7CP03068A ↗
38. 10.1088/1361-6528/abbfd310.1088/1361-6528/abbfd3 ↗
39. 10.1016/j.jcat.2003.09.01510.1016/j.jcat.2003.09.015 ↗
40. Datta, S. Lessons from Nanoelectronics: A New Perspective on Transport Part A: Basic Concepts; World Scientific Publishing, 2016; pp 10–100.10.1142/10440-vol1 ↗
41. 10.1039/C9MH00500E10.1039/C9MH00500E ↗
42. 10.1038/s41699-024-00482-910.1038/s41699-024-00482-9 ↗
43. 10.1016/j.mattod.2024.01.01210.1016/j.mattod.2024.01.012 ↗
44. 10.1088/2053-1583/aceb7510.1088/2053-1583/aceb75 ↗
45. 10.1021/acs.nanolett.3c0030310.1021/acs.nanolett.3c00303 ↗
46. 10.1021/acs.jpcc.9b0168210.1021/acs.jpcc.9b01682 ↗
47. 10.1039/D2TA07821J10.1039/D2TA07821J ↗
48. 10.1006/jcph.1995.103910.1006/jcph.1995.1039 ↗
49. 10.1021/jp004368u10.1021/jp004368u ↗
50. 10.1021/jp709896w10.1021/jp709896w ↗
51. 10.1016/j.parco.2011.08.00510.1016/j.parco.2011.08.005 ↗
52. 10.1021/acs.jpclett.6b0290210.1021/acs.jpclett.6b02902 ↗
53. 10.1088/1361-648X/ab400710.1088/1361-648X/ab4007 ↗
54. 10.1103/PhysRevLett.77.386510.1103/PhysRevLett.77.3865 ↗
55. 10.1002/jcc.2175910.1002/jcc.21759 ↗
56. 10.1016/j.cpc.2018.01.01210.1016/j.cpc.2018.01.012 ↗
57. 10.1021/acs.nanolett.7b0251110.1021/acs.nanolett.7b02511 ↗
58. 10.1103/PhysRevB.49.1622310.1103/PhysRevB.49.16223 ↗
59. 10.1103/PhysRevB.31.620710.1103/PhysRevB.31.6207 ↗
60. Datta, S. Lessons from Nanoelectronics: A New Perspective on Transport Part B: Quantum Transport; World Scientific Publishing, 2018; pp 10–100.10.1142/10440-vol2 ↗