journal article·2021Open access

Defect dynamics in two-dimensional black phosphorus under argon ion irradiation

Saransh Gupta, Prakash Periasamy, Badri Narayanan

Nanoscale· Volume 13· Issue 18

Abstract

Fundamental understanding of the atomic-scale mechanisms underlying production, accumulation, and temporal evolution of defects in phosphorene during noble-gas ion irradiation is crucial to design efficient defect engineering routes to fabricate next-generation materials for energy technologies. Here, we employed classical molecular dynamics (CMD) simulations using a reactive force field to unravel the effect of defect dynamics on the structural changes in a monolayer of phosphorene induced by argon-ion irradiation, and its subsequent relaxation during post-radiation annealing treatment. Analysis of our CMD trajectories using unsupervised machine learning methods showed that radiation fluence strongly influences the types of defect that form, their dynamics, and their relaxation mechanisms during subsequent annealing. Low ion fluences yielded a largely crystalline sheet featuring isolated small voids (up to 2 nm), Stone-Wales defects, and mono-/di-vacancies; while large nanopores (∼10 nm) can form beyond a critical fluence of ∼1014 ions per cm2. During post-radiation annealing, we found two distinct relaxation mechanisms, depending on the fluence level. The isolated small voids (1-2 nm) formed at low ion-fluences heal via local re-arrangement of rings, which is facilitated by a cooperative mechanism involving a series of atomic motions that include thermal rippling, bond formation, bond rotation, angle bending and dihedral twisting. On the other hand, damaged structures obtained at high fluences exhibit pronounced coalescence of nanopores mediated by 3D networks of P-centered tetrahedra. These findings provide new perspectives to use ion beams to precisely control the concentration and distribution of specific defect types in phosphorene for emerging applications in electronics, batteries, sensing, and neuromorphic computing.
Source: europepmc · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: 2D Materials and Applications · MXene and MAX Phase Materials · Graphene research and applications

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

Affiliations

Department of Mechanical Engineering

University of Louisville

Louisville

USA

Lam Research Corporation

4400 Cushing Parkway

Fremont

Funding

University of Louisville · U.S. Department of Energy · Lam Research Corporation

References (91)

Show source-provided references
1. Banhart · ACS Nano · 2011 · 5 · 2610.1021/nn102598m ↗
2. Kalbac · Adv. Mater. · 2013 · 25 · 100410.1002/adma.201203807 ↗
3. Krasheninnikov · Nat. Mater. · 2007 · 6 · 72310.1038/nmat1996 ↗
4. Lin · 2D Mater. · 2016 · 3 · 02200210.1088/2053-1583/3/2/022002 ↗
5. López-Polín · Nat. Phys. · 2015 · 11 · 2610.1038/nphys3183 ↗
6. Cheng · 2D Mater. · 2019 · 6 · 03400510.1088/2053-1583/ab1764 ↗
7. Susi · Nat. Rev. Phys. · 2019 · 1 · 39710.1038/s42254-019-0058-y ↗
8. Tomašević-Ilić · Appl. Surf. Sci. · 2018 · 458 · 44610.1016/j.apsusc.2018.07.111 ↗
9. Guo · Chem. Soc. Rev. · 2015 · 44 · 63710.1039/C4CS00302K ↗
10. Shim · Adv. Mater. · 2016 · 28 · 698510.1002/adma.201601002 ↗
11. Tosun · ACS Nano · 2016 · 10 · 685310.1021/acsnano.6b02521 ↗
12. Ghorbani-Asl · 2D Mater. · 2017 · 4 · 02507810.1088/2053-1583/aa6b17 ↗
13. Vierimaa · Nanoscale · 2016 · 8 · 794910.1039/C6NR00179C ↗
14. O'Hern · Nano Lett. · 2014 · 14 · 123410.1021/nl404118f ↗
15. Iberi · Sci. Rep. · 2016 · 6 · 110.1038/srep30481 ↗
16. Jhon · Adv. Funct. Mater. · 2016 · 26 · 755110.1002/adfm.201602647 ↗
17. Mathew · Appl. Phys. Lett. · 2012 · 101 · 10210310.1063/1.4750237 ↗
18. Ochedowski · J. Appl. Phys. · 2013 · 113 · 21430610.1063/1.4808460 ↗
19. Patra · ACS Nano · 2018 · 12 · 800610.1021/acsnano.8b02844 ↗
20. Stanford · Sci. Rep. · 2016 · 6 · 2727610.1038/srep27276 ↗
21. Grosso · Nat. Commun. · 2017 · 8 · 110.1038/s41467-017-00810-2 ↗
22. Tran · ACS Nano · 2016 · 10 · 733110.1021/acsnano.6b03602 ↗
23. Wei · ACS Nano · 2011 · 5 · 291610.1021/nn103548r ↗
24. Zhang · J. Appl. Phys. · 2020 · 128 · 10090210.1063/5.0021093 ↗
25. Kaewmaraya · J. Phys. Chem. C · 2018 · 122 · 2018610.1021/acs.jpcc.8b06803 ↗
26. Surwade · Nat. Nanotechnol. · 2015 · 10 · 45910.1038/nnano.2015.37 ↗
27. Nan · ACS Nano · 2014 · 8 · 573810.1021/nn500532f ↗
28. Li · Nat. Nanotechnol. · 2014 · 9 · 37210.1038/nnano.2014.35 ↗
29. Liu · ACS Nano · 2014 · 8 · 403310.1021/nn501226z ↗
30. Pang · Adv. Energy Mater. · 2018 · 8 · 170209310.1002/aenm.201702093 ↗
31. Qiao · Nat. Commun. · 2014 · 5 · 447510.1038/ncomms5475 ↗
32. Jiang · Nat. Commun. · 2014 · 5 · 472710.1038/ncomms5727 ↗
33. Kou · J. Phys. Chem. Lett. · 2014 · 5 · 267510.1021/jz501188k ↗
34. Xia · Nat. Commun. · 2014 · 5 · 445810.1038/ncomms5458 ↗
35. Buscema · Nat. Commun. · 2014 · 5 · 465110.1038/ncomms5651 ↗
36. Dai · J. Phys. Chem. Lett. · 2014 · 5 · 128910.1021/jz500409m ↗
37. Cai · Nanoscale · 2019 · 11 · 2098710.1039/C9NR06608J ↗
38. Cai · J. Am. Chem. Soc. · 2016 · 138 · 1019910.1021/jacs.6b04926 ↗
39. Yao · 2D Mater. · 2020 · 8 · 02500410.1088/2053-1583/abce09 ↗
40. Hu · J. Phys. Chem. C · 2015 · 119 · 2047410.1021/acs.jpcc.5b06077 ↗
41. Cai · Chem. Mater. · 2019 · 31 · 894810.1021/acs.chemmater.9b03031 ↗
42. Zhu · J. Am. Chem. Soc. · 2017 · 139 · 1323410.1021/jacs.7b08416 ↗
43. Atashzar · Nanoscale · 2020 · 12 · 2036410.1039/D0NR02735A ↗
44. Ahmed · Adv. Mater. · 2021 · 33 · 200420710.1002/adma.202004207 ↗
45. Ren · J. Mater. Chem. C · 2018 · 6 · 938310.1039/C8TC03089H ↗
46. Kiraly · Nano Lett. · 2017 · 17 · 360710.1021/acs.nanolett.7b00766 ↗
47. Chartier · Phys. Rev. Lett. · 2005 · 94 · 02550510.1103/PhysRevLett.94.025505 ↗
48. Dholabhai · J. Chem. Phys. · 2014 · 140 · 19470110.1063/1.4876225 ↗
49. Narayanan · J. Appl. Phys. · 2013 · 113 · 03350410.1063/1.4775838 ↗
50. Trachenko · J. Phys.: Condens. Matter · 2004 · 16 · R1491
51. Trachenko · Phys. Rev. B: Condens. Matter Mater. Phys. · 2006 · 73 · 17420710.1103/PhysRevB.73.174207 ↗
52. Becquart · Metall. Mater. Trans. A · 2011 · 42 · 85210.1007/s11661-010-0460-7 ↗
53. Lehtinen · Phys. Rev. B: Condens. Matter Mater. Phys. · 2010 · 81 · 15340110.1103/PhysRevB.81.153401 ↗
54. Ghaderzadeh · ACS Appl. Mater. Interfaces · 2020 · 12 · 3745410.1021/acsami.0c09255 ↗
55. Xiao · J. Phys. Chem. A · 2017 · 121 · 613510.1021/acs.jpca.7b05257 ↗
56. Plimpton · J. Comput. Phys. · 1995 · 117 · 110.1006/jcph.1995.1039 ↗
57. J. F. Ziegler and J. P.Biersack , The stopping and range of ions in matter , in Treatise on heavy-ion science , Springer , 1985 , pp. 93–129
58. Kretschmer · ACS Appl. Mater. Interfaces · 2018 · 10 · 3082710.1021/acsami.8b08471 ↗
59. Su · ACS Appl. Mater. Interfaces · 2021 · 13 · 1236610.1021/acsami.0c22288 ↗
60. Rumyantsev · Appl. Surf. Sci. · 2021 · 540 · 14827810.1016/j.apsusc.2020.148278 ↗
61. Yoon · ACS Nano · 2016 · 10 · 837610.1021/acsnano.6b03036 ↗
62. Senftle · npj Comput. Mater. · 2016 · 2 · 1501110.1038/npjcompumats.2015.11 ↗
63. van Duin · J. Phys. Chem. A · 2001 · 105 · 939610.1021/jp004368u ↗
64. van Duin · J. Phys. Chem. A · 2003 · 107 · 380310.1021/jp0276303 ↗
65. Narayanan · Modell. Simul. Mater. Sci. Eng. · 2011 · 20 · 01500210.1088/0965-0393/20/1/015002 ↗
66. Nordlund · Phys. Rev. B: Condens. Matter Mater. Phys. · 1998 · 57 · 755610.1103/PhysRevB.57.7556 ↗
67. Björkas · Nucl. Instrum. Methods Phys. Res., Sect. B · 2007 · 259 · 85310.1016/j.nimb.2007.03.076 ↗
68. Stoller · J. Chem. Theory Comput. · 2016 · 12 · 287110.1021/acs.jctc.5b01194 ↗
69. Grimme · J. Comput. Chem. · 2011 · 32 · 145610.1002/jcc.21759 ↗
70. Lehtinen · Phys. Rev. B: Condens. Matter Mater. Phys. · 2010 · 81 · 15340110.1103/PhysRevB.81.153401 ↗
71. Lehtinen · Nucl. Instrum. Methods Phys. Res., Sect. B · 2011 · 269 · 132710.1016/j.nimb.2010.11.027 ↗
72. S. Taioli , Enabling Materials By Dimensionality: From 0D to 3D Carbon-Based Nanostructures , in Theoretical Chemistry for Advanced Nanomaterials: Functional Analysis by Computation and Experiment , ed. T. Onishi , Springer Singapore , Singapore , 2020 , pp. 135–200
73. Qiao · Nat. Commun. · 2014 · 5 · 1
74. Yu · Phys. Chem. Chem. Phys. · 2015 · 17 · 1635110.1039/C5CP01732G ↗
75. Cherukara · J. Phys. Chem. Lett. · 2016 · 7 · 375210.1021/acs.jpclett.6b01562 ↗
76. Li · Appl. Phys. Rev. · 2017 · 4 · 01110310.1063/1.4977087 ↗
77. Chen · Phys. Chem. Chem. Phys. · 2018 · 20 · 2183210.1039/C8CP01334A ↗
78. Carvalho · Nat. Rev. Mater. · 2016 · 1 · 1606110.1038/natrevmats.2016.61 ↗
79. Osman · Inorg. Chem. · 1976 · 15 · 28710.1021/ic50156a009 ↗
80. Pauling · J. Chem. Phys. · 1952 · 20 · 2910.1063/1.1700191 ↗
81. Liu · J. Phys. Chem. C · 2015 · 119 · 2467410.1021/acs.jpcc.5b08593 ↗
82. M. Ester , H.-P.Kriegel , J.Sander and X.Xu , A density-based algorithm for discovering clusters in large spatial databases with noise , in Proceedings of the Second International Conference on Knowledge Discovery and Data Mining , AAAI Press , Portland, Oregon , 1996 , pp. 226–231
83. Liu · Nano Lett. · 2014 · 14 · 678210.1021/nl5021393 ↗
84. Hu · Nanotechnology · 2015 · 26 · 06570510.1088/0957-4484/26/6/065705 ↗
85. Gao · Nanoscale · 2013 · 5 · 978510.1039/c3nr02826g ↗
86. Gaberle · Nanoscale · 2018 · 10 · 1953610.1039/C8NR06640J ↗
87. Umar Farooq · Sci. Rep. · 2015 · 5 · 1248210.1038/srep12482 ↗
88. Srivastava · J. Phys. Chem. C · 2015 · 119 · 653010.1021/jp5110938 ↗
89. Cupo · ACS Nano · 2017 · 11 · 749410.1021/acsnano.7b04031 ↗
90. Narayanan · Nanoscale · 2017 · 9 · 1822910.1039/C7NR06038F ↗
91. Chan · npj Comput. Mater. · 2020 · 6 · 110.1038/s41524-019-0267-z ↗