The Controllable Synthesis of High‐Quality Two‐Dimensional Iron Sulfide with Specific Phases
Source abstract
Abstract
Abstract 2D Fe‐chalcogenides have drawn significant attention due to their unique structural phases and distinct properties in exploring magnetism and superconductivity. However, it remains a significant challenge to synthesize 2D Fe‐chalcogenides with specific phases in a controllable manner since Fe‐chalcogenides have multiple phases. Herein, a molecular sieve‐assisted strategy is reported for synthesizing ultrathin 2D iron sulfide on substrates via the chemical vapor deposition method. Using a molecular sieve and tuning growth temperatures to control the partial pressures of precursor concentrations, hexagonal FeS, tetragonal FeS, and non‐stoichiometric Fe7S8 nanoflakes can be precisely synthesized. The 2D h‐FeS, t‐FeS, and Fe7S8 have high conductivities of 5.4 × 105 S m−1, 5.8 × 105 S m−1, and 1.9 × 106 S m−1. 2D tetragonal FeS shows a superconducting transition at 4 K. The spin reorientation at ≈30 K on the non‐stoichiometric Fe7S8 nanoflakes with ferrimagnetism up to room temperature has also been observed. The controllable synthesis of various phases of 2D iron sulfide may provide a route for synthesizing other 2D compounds with various phases.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: 2D Materials and Applications · Iron-based superconductors research · MXene and MAX Phase Materials
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
College of Chemistry Fuzhou University Fuzhou 350108 P. R. China
CAS Key Laboratory of Design and Assembly of Functional Nanostructures Fujian Provincial Key Laboratory of Nanomaterials Fujian Institute of Research on the Structure of Matter Chinese Academy of Sciences Fuzhou 350002 P. R. China
Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China Fuzhou Fujian 350108 P. R. China
Funding
National Natural Science Foundation of China
References (52)
Show source-provided references
1. 10.1126/science.110289610.1126/science.1102896 ↗
2. 10.1038/nnano.2010.17210.1038/nnano.2010.172 ↗
3. 10.1038/nchem.158910.1038/nchem.1589 ↗
4. 10.1038/nnano.2014.3510.1038/nnano.2014.35 ↗
5. 10.1038/s41586-018-0631-z10.1038/s41586-018-0631-z ↗
6. 10.1021/nl304777e10.1021/nl304777e ↗
7. 10.1038/nature1538710.1038/nature15387 ↗
8. 10.1109/LED.2016.261466310.1109/LED.2016.2614663 ↗
9. 10.1002/smll.20220535310.1002/smll.202205353 ↗
10. 10.1039/C6DT03486A10.1039/C6DT03486A ↗
11. 10.1021/cm901150c10.1021/cm901150c ↗
12. 10.1038/s41598-017-03502-510.1038/s41598-017-03502-5 ↗
13. 10.1103/PhysRevB.93.09452210.1103/PhysRevB.93.094522 ↗
14. 10.1039/C6SC05268A10.1039/C6SC05268A ↗
15. 10.1038/s41535-020-0227-310.1038/s41535-020-0227-3 ↗
16. 10.1038/s41467-020-17253-x10.1038/s41467-020-17253-x ↗
17. 10.1016/j.mattod.2020.11.02210.1016/j.mattod.2020.11.022 ↗
18. 10.1021/acs.nanolett.1c0440310.1021/acs.nanolett.1c04403 ↗
19. 10.1021/cr050365810.1021/cr0503658 ↗
20. 10.1016/j.apsusc.2018.12.25410.1016/j.apsusc.2018.12.254 ↗
21. 10.1029/2011JB00823210.1029/2011JB008232 ↗
22. 10.1021/jacs.5b0668710.1021/jacs.5b06687 ↗
23. 10.1021/nn204571410.1021/nn2045714 ↗
24. 10.1016/j.cplett.2019.13699310.1016/j.cplett.2019.136993 ↗
25. 10.1039/C8TC00584B10.1039/C8TC00584B ↗
26. 10.1038/s41563-022-01291-510.1038/s41563-022-01291-5 ↗
27. 10.1038/s44160-022-00165-710.1038/s44160-022-00165-7 ↗
28. 10.1002/adfm.20160349110.1002/adfm.201603491 ↗
29. 10.1002/smll.20200807810.1002/smll.202008078 ↗
30. 10.1021/acs.nanolett.7b0191410.1021/acs.nanolett.7b01914 ↗
31. 10.1002/smll.20210161610.1002/smll.202101616 ↗
32. 10.1007/s11051-015-3197-910.1007/s11051-015-3197-9 ↗
33. 10.1039/C9TA05664E10.1039/C9TA05664E ↗
34. 10.1016/j.cej.2019.12372210.1016/j.cej.2019.123722 ↗
35. 10.1021/acsnano.2c0226710.1021/acsnano.2c02267 ↗
36. 10.1063/1.172977410.1063/1.1729774 ↗
37. 10.1021/acsnano.1c0577710.1021/acsnano.1c05777 ↗
38. 10.1021/acs.nanolett.8b0058310.1021/acs.nanolett.8b00583 ↗
39. 10.1103/PhysRevB.79.22051210.1103/PhysRevB.79.220512 ↗
40. 10.1103/PhysRev.147.29510.1103/PhysRev.147.295 ↗
41. 10.1103/PhysRevB.86.03543910.1103/PhysRevB.86.035439 ↗
42. 10.1021/acsnano.1c0573810.1021/acsnano.1c05738 ↗
43. 10.1021/ja304660310.1021/ja3046603 ↗
44. 10.1002/adma.20170312310.1002/adma.201703123 ↗
45. 10.1088/1361-648X/ab8fd310.1088/1361-648X/ab8fd3 ↗
46. 10.1039/D0CP04445H10.1039/D0CP04445H ↗
47. Koulialias D. · J. Geophys. Res. · 2018 · 30
48. 10.1021/jp800195910.1021/jp8001959 ↗
49. Density functional theory calculations including Bandstructure DensityOfStates TotalEnergy MullikenPopulation were carried out in QuantumATK https://www.synopsys.com/silicon/quantumatk.html(accessed: November2022).
50. 10.1103/PhysRevLett.77.386510.1103/PhysRevLett.77.3865 ↗
51. 10.1016/0009-2614(80)80396-410.1016/0009-2614(80)80396-4 ↗
52. The limited memory Broyden‐Fletcher‐Goldfarb‐Shanno (BFGS) algorithm is applied to optimize the structure https://wiki.fysik.dtu.dk/ase/ase/optimize.html(accessed: November 2022).