journal article·2025-08-26

Structural Hole Traps in III–V Quantum Dots

Ezra Alexander, Alexandra Alexiu, Matthias Kick, Troy Van Voorhis

Chemistry of Materials· Volume 37· Issue 16
Publisher page

Abstract

Nontoxic III–V quantum dots (QDs) are plagued with a higher density of performance-limiting trap states than II–VI and IV–VI QDs. Such trap states are generally understood to arise from under-coordinated atoms on the QD surface. Here, we present computational evidence for, and an exploration of, trap states in InP and GaP QDs that arise from fully coordinated atoms with distorted geometries, denoted here as structural traps. In particular, we focus on the properties of anion-centered hole traps, which we show to be relatively insensitive to the choice of the (typically cation-coordinating) ligand. Through interpolation of trap center cutouts, we arrive at a simple molecular orbital (MO) argument for the existence of structural traps, finding two main modalities: bond stretches and angular distortion to a seesaw-like geometry. These structural trap states will be important for understanding the low performance of III–V QDs, as even core–shell passivation may not remove these defects unless they can rigidify the structure. Moreover, they may lead to interesting temperature-dependent dynamical properties as distorted structures could form transiently.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Semiconductor Quantum Structures and Devices · Quantum Dots Synthesis And Properties · ZnO doping and properties

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

Affiliations

Department of Chemistry

Massachusetts Institute of Technology

Funding

Division of Chemistry

References (89)

Show source-provided references
1. 10.1002/qute.20210011610.1002/qute.202100116 ↗
2. 10.1126/science.aao596510.1126/science.aao5965 ↗
3. 10.1016/j.cej.2023.14139910.1016/j.cej.2023.141399 ↗
4. 10.1039/D2TA09293J10.1039/D2TA09293J ↗
5. 10.1016/j.jddst.2020.10230810.1016/j.jddst.2020.102308 ↗
6. 10.3390/nano1210164510.3390/nano12101645 ↗
7. 10.1002/adom.20220203610.1002/adom.202202036 ↗
8. 10.1016/j.jphotochemrev.2023.10058810.1016/j.jphotochemrev.2023.100588 ↗
9. 10.1016/j.mser.2025.10094010.1016/j.mser.2025.100940 ↗
10. 10.1021/acsnano.4c0329010.1021/acsnano.4c03290 ↗
11. 10.1016/j.cej.2025.16072910.1016/j.cej.2025.160729 ↗
12. 10.1038/s41586-019-1771-510.1038/s41586-019-1771-5 ↗
13. 10.1021/jacs.8b1290810.1021/jacs.8b12908 ↗
14. 10.1021/jp953719f10.1021/jp953719f ↗
15. 10.1103/PhysRevB.56.149610.1103/PhysRevB.56.1496 ↗
16. 10.1021/acs.chemmater.8b0367110.1021/acs.chemmater.8b03671 ↗
17. 10.1021/acsnano.9b0702710.1021/acsnano.9b07027 ↗
18. 10.1002/smll.20210279210.1002/smll.202102792 ↗
19. 10.1021/acsnano.9b0796910.1021/acsnano.9b07969 ↗
20. 10.1021/jacs.8b0875310.1021/jacs.8b08753 ↗
21. 10.1021/acs.jpclett.7b0142610.1021/acs.jpclett.7b01426 ↗
22. 10.1021/acs.jpcc.7b0904010.1021/acs.jpcc.7b09040 ↗
23. 10.1002/adom.20210042710.1002/adom.202100427 ↗
24. 10.1021/acsami.1c2008810.1021/acsami.1c20088 ↗
25. 10.1021/jacs.8b0778310.1021/jacs.8b07783 ↗
26. 10.1021/acs.jpclett.6b0017710.1021/acs.jpclett.6b00177 ↗
27. 10.1021/acsanm.8b0194710.1021/acsanm.8b01947 ↗
28. 10.1021/acsnano.8b0669210.1021/acsnano.8b06692 ↗
29. 10.1021/acs.chemmater.1c0179510.1021/acs.chemmater.1c01795 ↗
30. 10.1021/acs.jpcc.2c0149910.1021/acs.jpcc.2c01499 ↗
31. 10.1021/acs.chemmater.4c0049210.1021/acs.chemmater.4c00492 ↗
32. 10.1002/adma.20240448010.1002/adma.202404480 ↗
33. 10.1021/acs.nanolett.4c0110710.1021/acs.nanolett.4c01107 ↗
34. 10.1002/adma.20230362110.1002/adma.202303621 ↗
35. 10.1021/acsnano.3c0702910.1021/acsnano.3c07029 ↗
36. 10.1021/acs.chemmater.2c0280010.1021/acs.chemmater.2c02800 ↗
37. 10.1021/acs.chemmater.6b0464810.1021/acs.chemmater.6b04648 ↗
38. 10.1038/s41467-021-21153-z10.1038/s41467-021-21153-z ↗
39. 10.1021/acs.jpclett.2c0331710.1021/acs.jpclett.2c03317 ↗
40. 10.1039/D2NR06785D10.1039/D2NR06785D ↗
41. 10.1021/ct400485s10.1021/ct400485s ↗
42. 10.1021/nn302371q10.1021/nn302371q ↗
43. 10.1002/adfm.20200059410.1002/adfm.202000594 ↗
44. 10.1021/jacs.8b0196610.1021/jacs.8b01966 ↗
45. 10.1021/acs.jpclett.5b0220210.1021/acs.jpclett.5b02202 ↗
46. 10.1021/acs.chemmater.6b0412610.1021/acs.chemmater.6b04126 ↗
47. 10.1021/jacs.8b1103510.1021/jacs.8b11035 ↗
48. 10.1021/jacs.2c0202710.1021/jacs.2c02027 ↗
49. 10.1016/j.matchemphys.2016.02.03410.1016/j.matchemphys.2016.02.034 ↗
50. 10.1016/j.jallcom.2011.08.04010.1016/j.jallcom.2011.08.040 ↗
51. 10.1021/acs.nanolett.7b0220310.1021/acs.nanolett.7b02203 ↗
52. 10.1038/s41467-020-14365-210.1038/s41467-020-14365-2 ↗
53. 10.1039/C5TA04978D10.1039/C5TA04978D ↗
54. 10.1038/nmat466110.1038/nmat4661 ↗
55. 10.1021/acs.nanolett.7b0224810.1021/acs.nanolett.7b02248 ↗
56. 10.1021/acsnano.8b0979410.1021/acsnano.8b09794 ↗
57. 10.1021/acsnano.8b0709210.1021/acsnano.8b07092 ↗
58. 10.1002/smtd.20210093210.1002/smtd.202100932 ↗
59. 10.1126/science.119275910.1126/science.1192759 ↗
60. 10.1038/s41467-021-22116-010.1038/s41467-021-22116-0 ↗
61. 10.1126/science.aax323310.1126/science.aax3233 ↗
62. 10.1126/sciadv.170148310.1126/sciadv.1701483 ↗
63. van der Maaten L. V. D. · J. Mach. Learn. Res. · 2008 · 9 · 2579
64. 10.1145/273338110.1145/2733381 ↗
65. 10.1214/aos/101320345110.1214/aos/1013203451 ↗
66. 10.1021/nn303364d10.1021/nn303364d ↗
67. 10.1063/5.000704510.1063/5.0007045 ↗
68. 10.1080/00268976.2014.95269610.1080/00268976.2014.952696 ↗
69. 10.1002/(SICI)1097-461X(1999)75:4/5<889::AID-QUA54>3.0.CO;2-810.1002/(SICI)1097-461X(1999)75:4/5<889::AID-QUA54>3.0.CO;2-8 ↗
70. 10.1021/ct400513s10.1021/ct400513s ↗
71. 10.1039/b508541a10.1039/b508541a ↗
72. 10.1103/RevModPhys.32.29610.1103/RevModPhys.32.296 ↗
73. 10.1021/ct400793q10.1021/ct400793q ↗
74. 10.1021/ed200565h10.1021/ed200565h ↗
75. 10.1063/1.45658810.1063/1.456588 ↗
76. 10.1063/1.174763210.1063/1.1747632 ↗
77. 10.1063/1.509030310.1063/1.5090303 ↗
78. Pedregosa F. · J. Mach. Learn. Res. · 2011 · 12 · 2825
79. McInnes L. · Arxiv · 2020
80. 10.7551/mitpress/1113.003.000810.7551/mitpress/1113.003.0008 ↗
81. 10.1023/A:101093340432410.1023/A:1010933404324 ↗
82. Hinton, G. E. In Machine Learning, Kodratoff, Y.; Michalski, R. S., Eds. Morgan Kaufmann: San Francisco (CA), 1990, pp. 555–610.
83. 10.1613/jair.95310.1613/jair.953 ↗
84. 10.1109/IJCNN.2008.463396910.1109/IJCNN.2008.4633969 ↗
85. 10.1023/A:100756330633110.1023/A:1007563306331 ↗
86. 10.1103/PhysRev.41.20810.1103/PhysRev.41.208 ↗
87. Alexander, E. QD ML. 2025 https://github.com/troyvvgroup/qd_ml Accessed: 25–June–2025.
88. 10.1116/6.000389910.1116/6.0003899 ↗
89. 10.1038/s41467-024-52368-510.1038/s41467-024-52368-5 ↗