1887
banner image
No data available.
Please log in to see this content.
You have no subscription access to this content.
No metrics data to plot.
The attempt to load metrics for this article has failed.
The attempt to plot a graph for these metrics has failed.
oa
One-dimensional embedded cluster approach to modeling CdS nanowires
Rent:
Rent this article for
Access full text Article
/content/aip/journal/jcp/139/12/10.1063/1.4820415
1.
1. Y.-L. Lee and Y.-S. Lo, Adv. Funct. Mater. 19, 604 (2009).
http://dx.doi.org/10.1002/adfm.200800940
2.
2. S. Rühle, M. Shalom, and A. Zaban, ChemPhysChem 11, 2290 (2010).
http://dx.doi.org/10.1002/cphc.201000069
3.
3. M. Seol, H. Kim, Y. Tak, and K. Yong, Chem. Commun. 46, 5521 (2010).
http://dx.doi.org/10.1039/c0cc00542h
4.
4. S.-M. Liu, F.-Q. Liu, H.-Q. Guo, Z.-H. Zhang, and Z.-G. Wang, Solid State Commun. 115, 615 (2000).
http://dx.doi.org/10.1016/S0038-1098(00)00254-4
5.
5. H. Zhao, E. P. Douglas, B. S. Harrison, and K. S. Schanze, Langmuir 17, 8428 (2001).
http://dx.doi.org/10.1021/la011348q
6.
6. W.-S. Chae, J.-H. Ko, I.-W. Hwang, and Y.-R. Kim, Chem. Phys. Lett. 365, 49 (2002).
http://dx.doi.org/10.1016/S0009-2614(02)01418-5
7.
7. J. Zhang, L. Sun, C. Liao, and C. Yan, Solid State Commun. 124, 45 (2002).
http://dx.doi.org/10.1016/S0038-1098(02)00448-9
8.
8. J. G. Díaz, J. Planelles, G. W. Bryant, and J. Aizpurua, J. Phys. Chem. B 108, 17800 (2004).
http://dx.doi.org/10.1021/jp047658+
9.
9. A. Vorokh and A. Rempel, Phys. Solid State 49, 148 (2007).
http://dx.doi.org/10.1134/S1063783407010246
10.
10. H. Cao, G. Wang, S. Zhang, X. Zhang, and D. Rabinovich, Inorg. Chem. 45, 5103 (2006).
http://dx.doi.org/10.1021/ic060440c
11.
11. H. M. Fan, X. F. Fan, Z. H. Ni, Z. X. Shen, Y. P. Feng, and B. S. Zou, J. Phys. Chem. C 112, 1865 (2008).
http://dx.doi.org/10.1021/jp7096839
12.
12. H. Li, X. Wang, J. Xu, Q. Zhang, Y. Bando, D. Golberg, Y. Ma, and T. Zhai, Adv. Mater. 25, 3017 (2013).
http://dx.doi.org/10.1002/adma.201300244
13.
13. Y. Ye, Y. Dai, L. Dai, Z. Shi, N. Liu, F. Wang, L. Fu, R. Peng, X. Wen, Z. Chen, Z. Liu, and G. Qin, ACS Appl. Mater. Interfaces 2, 3406 (2010).
http://dx.doi.org/10.1021/am1007672
14.
14. C. J. Barrelet, A. B. Greytak, and C. M. Lieber, Nano Lett. 4, 1981 (2004).
http://dx.doi.org/10.1021/nl048739k
15.
15. G. Dai, B. Zou, and Z. Wang, J. Am. Chem. Soc. 132, 12174 (2010).
http://dx.doi.org/10.1021/ja1037963
16.
16. Y. Huang, X. Duan, and C. Lieber, Small 1, 142 (2005).
http://dx.doi.org/10.1002/smll.200400030
17.
17. R.-M. Ma, L. Dai, H.-B. Huo, W.-J. Xu, and G. G. Qin, Nano Lett. 7, 3300 (2007).
http://dx.doi.org/10.1021/nl0715286
18.
18. X. Duan, Y. Huang, R. Agarwal, and C. M. Lieber, Nature (London) 421, 241 (2003).
http://dx.doi.org/10.1038/nature01353
19.
19. A. Pan, R. Liu, Q. Yang, Y. Zhu, G. Yang, B. Zou, and K. Chen, J. Phys. Chem. B 109, 24268 (2005).
http://dx.doi.org/10.1021/jp055164m
20.
20. H. Pan, G. Xing, Z. Ni, W. Ji, Y. P. Feng, Z. Tang, D. H. C. Chua, J. Lin, and Z. Shen, Appl. Phys. Lett. 91, 193105 (2007).
http://dx.doi.org/10.1063/1.2807840
21.
21. T. Zhai, X. Fang, Y. Bando, B. Dierre, B. Liu, H. Zeng, X. Xu, Y. Huang, X. Yuan, T. Sekiguchi, and D. Golberg, Adv. Funct. Mater. 19, 2423 (2009).
http://dx.doi.org/10.1002/adfm.200900295
22.
22. T. Zhai, X. Fang, Y. Bando, Q. Liao, X. Xu, H. Zeng, Y. Ma, J. Yao, and D. Golberg, ACS Nano 3, 949 (2009).
http://dx.doi.org/10.1021/nn800895k
23.
23. L. Li, P. Wu, X. Fang, T. Zhai, L. Dai, M. Liao, Y. Koide, H. Wang, Y. Bando, and D. Golberg, Adv. Mater. 22, 3161 (2010).
http://dx.doi.org/10.1002/adma.201000144
24.
24. J. S. Jie, W. J. Zhang, Y. Jiang, and S. T. Lee, Appl. Phys. Lett. 89, 223117 (2006).
http://dx.doi.org/10.1063/1.2398891
25.
25. J. Zhai, L. Wang, D. Wang, H. Li, Y. Zhang, D. Q. He, and T. Xie, ACS Appl. Mater. Interfaces 3, 2253 (2011).
http://dx.doi.org/10.1021/am200008y
26.
26. X. Duan, C. Niu, V. Sahi, J. Chen, J. W. Parce, S. Empedocles, and J. L. Goldman, Nature (London) 425, 274 (2003).
http://dx.doi.org/10.1038/nature01996
27.
27. Y. Tak, S. J. Hong, J. S. Lee, and K. Yong, J. Mater. Chem. 19, 5945 (2009).
http://dx.doi.org/10.1039/b904993b
28.
28. J. Li and L.-W. Wang, Phys. Rev. B 72, 125325 (2005).
http://dx.doi.org/10.1103/PhysRevB.72.125325
29.
29. S.-P. Huang, W.-D. Cheng, D.-S. Wu, J.-M. Hu, J. Shen, Z. Xie, H. Zhang, and Y.-J. Gong, Appl. Phys. Lett. 90, 031904 (2007).
http://dx.doi.org/10.1063/1.2432170
30.
30. H. Pan and Y. P. Feng, ACS Nano 2, 2410 (2008).
http://dx.doi.org/10.1021/nn8004872
31.
31. W. Sangthong, J. Limtrakul, F. Illas, and S. T. Bromley, Nanoscale 2, 72 (2010).
http://dx.doi.org/10.1039/b9nr00282k
32.
32. S. Karthikeyan, E. Deepika, and P. Murugan, J. Phys. Chem. C 116, 5981 (2012).
http://dx.doi.org/10.1021/jp2042729
33.
33. C. J. Barrelet, Y. Wu, D. C. Bell, and C. M. Lieber, J. Am. Chem. Soc. 125, 11498 (2003).
http://dx.doi.org/10.1021/ja036990g
34.
34. F. Mertins, Ann. Phys. (Leipzig) 8, 261 (1999).
http://dx.doi.org/10.1002/(SICI)1521-3889(199904)8:4<261::AID-ANDP261>3.0.CO;2-J
35.
35. P. Minary, J. A. Morrone, D. A. Yarne, M. E. Tuckerman, and G. J. Martyna, J. Chem. Phys. 121, 11949 (2004).
http://dx.doi.org/10.1063/1.1806403
36.
36. C. A. Rozzi, D. Varsano, A. Marini, E. K. U. Gross, and A. Rubio, Phys. Rev. B 73, 205119 (2006).
http://dx.doi.org/10.1103/PhysRevB.73.205119
37.
37. A. Warshel and M. Levitt, J. Mol. Biol. 103, 227 (1976).
http://dx.doi.org/10.1016/0022-2836(76)90311-9
38.
38. J. H. Harding, A. H. Harker, P. B. Keegstra, R. Pandey, J. M. Vail, and C. Woodward, Physica B 131, 151 (1985).
http://dx.doi.org/10.1016/0378-4363(85)90150-0
39.
39. L. Seijo and Z. Barandiaran, J. Math. Chem. 10, 41 (1992).
http://dx.doi.org/10.1007/BF01169170
40.
40. M. Sierka and J. Sauer, Faraday Discuss. 106, 41 (1997).
http://dx.doi.org/10.1039/a701492i
41.
41. L. N. Kantorovich, Int. J. Quantum Chem. 76, 511 (2000).
http://dx.doi.org/10.1002/(SICI)1097-461X(2000)76:4<511::AID-QUA3>3.0.CO;2-2
42.
42. L. N. Kantorovich, Int. J. Quantum Chem. 78, 306 (2000).
http://dx.doi.org/10.1002/(SICI)1097-461X(2000)78:5<306::AID-QUA3>3.0.CO;2-M
43.
43. T. A. Wesolowski and A. Warshel, J. Phys. Chem. 97, 8050 (1993).
http://dx.doi.org/10.1021/j100132a040
44.
44.ChemShell, a computational chemistry shell, 1999, see http://www.chemshell.org.
45.
45. P. Sherwood, A. H. de Vries, M. F. Guest, G. Schreckenbach, C. R. A. Catlow, S. A. French, A. A. Sokol, S. T. Bromley, W. Thiel, A. J. Turner, S. Billeter, F. Terstegen, S. Thiel, J. Kendrick, S. C. Rogers, J. Casci, M. Watson, F. King, E. Karlsen, M. Sjøvoll, A. Fahmi, A. Schäfer, and C. Lennartz, J. Mol. Struct.: THEOCHEM 632, 1 (2003).
http://dx.doi.org/10.1016/S0166-1280(03)00285-9
46.
46.A number of alternative approaches to the derivation of the embedding potential, which might improve its description, have been reported and include wave function or charge density based techniques as already mentioned (See Refs. 37–43).
47.
47. M. A. Nygren, L. G. M. Pettersson, Z. Barandiaran, and L. Seijo, J. Chem. Phys. 100, 2010 (1994).
http://dx.doi.org/10.1063/1.466553
48.
48. A. A. Sokol, S. T. Bromley, S. A. French, C. R. A. Catlow, and P. Sherwood, Int. J. Quantum Chem. 99, 695 (2004).
http://dx.doi.org/10.1002/qua.20032
49.
49. A. A. Sokol, S. A. French, S. T. Bromley, C. R. A. Catlow, H. J. J. van Dam, and P. Sherwood, Faraday Discuss. 134, 267 (2007).
http://dx.doi.org/10.1039/b607406e
50.
50. C. R. A. Catlow, A. A. Sokol, and A. Walsh, Chem. Commun. 47, 3386 (2011).
http://dx.doi.org/10.1039/c1cc10314h
51.
51. A. Walsh, C. R. A. Catlow, M. Miskufova, and A. A. Sokol, J. Phys.: Condens. Matter 23, 334217 (2011).
http://dx.doi.org/10.1088/0953-8984/23/33/334217
52.
52. G. Dutta, A. A. Sokol, C. R. A. Catlow, T. W. Keal, and P. Sherwood, ChemPhysChem 13, 3453 (2012).
http://dx.doi.org/10.1002/cphc.201200517
53.
53. D. O. Scanlon, C. W. Dunnill, J. Buckeridge, S. A. Shevlin, A. J. Logsdail, S. M. Woodley, C. R. A. Catlow, M. J. Powell, R. G. Palgrave, I. P. Parkin, G. W. Watson, T. W. Keal, P. Sherwood, A. Walsh, and A. A. Sokol, Nature Mater. 12, 798 (2013).
http://dx.doi.org/10.1038/nmat3697
54.
54. J. D. Gale and A. L. Rohl, Mol. Simul. 29, 291 (2003).
http://dx.doi.org/10.1080/0892702031000104887
55.
55.To automate the construction of CdS linear chain models with variable region sizes, we developed a shell script which reads in the total number of ions, the bond length, the size of the QM region, the size of the MM active and frozen regions, and the central ion type. A cluster is then cut with the regions divided into the appropriate sizes. In the MM region, due to the symmetry of the linear chain, atomic shells, which are used in modeling polarizable ions, are initially placed directly on cores (they may be displaced from the cores when a charged defect is present).
56.
56.We have developed a FORTRAN 90 program called TERMINATE to implement this procedure, which is available on request. The values of n, N, a0, and q are input, and Q and R are output.
57.
57. M. F. Guest, I. J. Bush, H. J. J. Van Dam, P. Sherwood, J. M. H. Thomas, J. H. Van Lenthe, R. W. A. Havenith, and J. Kendrick, Mol. Phys. 103, 719 (2005).
http://dx.doi.org/10.1080/00268970512331340592
58.
58. F. Weigend and R. Ahlrichs, Phys. Chem. Chem. Phys. 7, 3297 (2005).
http://dx.doi.org/10.1039/b508541a
59.
59. A. Bergner, M. Dolg, W. Küchle, H. Stoll, and H. Preuss, Mol. Phys. 80, 1431 (1993).
http://dx.doi.org/10.1080/00268979300103121
60.
60. K. A. Peterson, J. Chem. Phys. 119, 11099 (2003).
http://dx.doi.org/10.1063/1.1622923
61.
61. M. Ernzerhof and G. E. Scuseria, J. Chem. Phys. 110, 5029 (1999).
http://dx.doi.org/10.1063/1.478401
62.
62. C. Adamo and V. Barone, J. Chem. Phys. 110, 6158 (1999).
http://dx.doi.org/10.1063/1.478522
63.
63. J. P. Perdew, A. Ruzsinszky, G. I. Csonka, O. A. Vydrov, G. E. Scuseria, L. A. Constantin, X. Zhou, and K. Burke, Phys. Rev. Lett. 100, 136406 (2008).
http://dx.doi.org/10.1103/PhysRevLett.100.136406
64.
64. D. Andrae, U. Häussermann, M. Dolg, H. Stoll, and H. Preuss, Theor. Chim. Acta 77, 123 (1990).
http://dx.doi.org/10.1007/BF01114537
65.
65. G. Igel-Mann, Doktorabeit (Institut für Theoretische Chemie, Stuttgart, 1987).
66.
66. M. Dolg, U. Wedig, H. Stoll, and H. Preuss, J. Chem. Phys. 86, 866 (1987).
http://dx.doi.org/10.1063/1.452288
67.
67. B. Metz, H. Stoll, and M. Dolg, J. Chem. Phys. 113, 2563 (2000).
http://dx.doi.org/10.1063/1.1305880
68.
68. G. Kresse and J. Hafner, Phys. Rev. B 47, 558 (1993).
http://dx.doi.org/10.1103/PhysRevB.47.558
69.
69. G. Kresse and J. Hafner, Phys. Rev. B 49, 14251 (1994).
http://dx.doi.org/10.1103/PhysRevB.49.14251
70.
70. G. Kresse and J. Furthmüller, Comput. Mater. Sci. 6, 15 (1996).
http://dx.doi.org/10.1016/0927-0256(96)00008-0
71.
71. G. Kresse and J. Furthmüller, Phys. Rev. B 54, 11169 (1996).
http://dx.doi.org/10.1103/PhysRevB.54.11169
72.
72. P. E. Blöchl, Phys. Rev. B 50, 17953 (1994).
http://dx.doi.org/10.1103/PhysRevB.50.17953
73.
73. M. Born and K. Huang, Dynamical Theory of Crystal Lattices (Oxford University Press, Oxford, 1956).
74.
74. B. G. Dick and A. W. Overhauser, Phys. Rev. 112, 90 (1958).
http://dx.doi.org/10.1103/PhysRev.112.90
75.
75. A. W. Stevenson, M. Milanko, and Z. Barnea, Acta Crystallogr., Sect. B 40, 521 (1984).
http://dx.doi.org/10.1107/S0108768184002639
76.
76. Numerical Data and Functional Relationships in Science and Technology, edited by O. Madelung, M. Schölz, and H. Weiss (Springer, Berlin, 1982), Vol. 17.
77.
77. I. Kobiakov, Solid State Commun. 35, 305 (1980).
http://dx.doi.org/10.1016/0038-1098(80)90502-5
78.
78. S. Ninomiya and S. Adachi, J. Appl. Phys. 78, 1183 (1995).
http://dx.doi.org/10.1063/1.360355
79.
79.Alternatively, one can think in terms of the curvature of the orbitals, to which the kinetic energy is directly proportional. When the orbitals are out of phase, their curvature is greater in comparison to when they are in phase and overlap, meaning the kinetic energy is greater.
80.
80. T. Y. Lui, J. A. Zapien, H. Tang, D. D. D. Ma, Y. K. Liu, C. S. Lee, S. T. Lee, S. L. Shi, and S. J. Xu, Nanotechnology 17, 5935 (2006).
http://dx.doi.org/10.1088/0957-4484/17/24/006
81.
81. V. Ghiordanescu, M. Sima, I. Enculescu, M. N. Grecu, L. Mihut, M. Secu, and R. Neumann, Phys. Status Solidi A 202, 449 (2005).
http://dx.doi.org/10.1002/pssa.200406927
82.
82. K. S. Rathore, D. D. Patidar, N. S. Saxena, and K. B. Sharma, J. Ovonic Res. 5, 175 (2009).
83.
83. I. Shafiq and A. Sharif, J. Phys. D: Appl. Phys. 42, 035412 (2009).
http://dx.doi.org/10.1088/0022-3727/42/3/035412
84.
84. W. Zhou, D. Tang, and B. Zou, J. Alloys Compd. 551, 150 (2013).
http://dx.doi.org/10.1016/j.jallcom.2012.09.132
85.
85. E. Bertran, A. Lousa, M. Varela, M. V. García-Cuenca, and J. L. Morenza, Sol. Energy Mater. 17, 55 (1988).
http://dx.doi.org/10.1016/0165-1633(88)90037-8
86.
86. R. M. Ma, L. Dai, H. B. Huo, W. Q. Yang, G. G. Qin, P. H. Tan, C. H. Huang, and J. Zheng, Appl. Phys. Lett. 89, 203120 (2006).
http://dx.doi.org/10.1063/1.2387982
87.
87. R. K. Swank, Phys. Rev. 153, 844 (1967).
http://dx.doi.org/10.1103/PhysRev.153.844
88.
88. D. G. Seiler, D. Heiman, R. Feigenblatt, R. L. Aggarwal, and B. Lax, Phys. Rev. B 25, 7666 (1982).
http://dx.doi.org/10.1103/PhysRevB.25.7666
89.
89. S. Yan, L. Sun, P. Qu, N. Huang, Y. Song, and Z. Xiao, J. Solid State Chem. 182, 2941 (2009).
http://dx.doi.org/10.1016/j.jssc.2009.07.016
90.
90. J. P. Bosco, D. O. Scanlon, G. W. Watson, N. S. Lewis, and H. A. Atwater, J. Appl. Phys. 113, 203705 (2013).
http://dx.doi.org/10.1063/1.4807646
91.
91. J. Heyd, G. E. Scuseria, and M. Ernzerhof, J. Chem. Phys. 124, 219906 (2006).
http://dx.doi.org/10.1063/1.2204597
92.
92. D. M. Eigler and E. K. Schweizer, Nature (London) 344, 524 (1990).
http://dx.doi.org/10.1038/344524a0
93.
93. P. Caroff, K. A. Dick, J. Johansson, M. E. Messing, K. Deppert, and L. Samuelson, Nat. Nanotechnol. 4, 50 (2009).
http://dx.doi.org/10.1038/nnano.2008.359
94.
94. Computer Simulation of Solids, edited by C. R. A. Catlow and W. C. Mackrodt, 1st ed. (Springer, Berlin, 1982), Chap. 1.
95.
95. A. Walsh, J. Buckeridge, C. R. A. Catlow, A. J. Jackson, T. W. Keal, M. Miskufova, P. Sherwood, S. A. Shevlin, M. B. Watkins, S. M. Woodley, and A. A. Sokol, Chem. Mater. 22, 2924 (2013).
http://dx.doi.org/10.1021/cm402237s
http://aip.metastore.ingenta.com/content/aip/journal/jcp/139/12/10.1063/1.4820415
Loading

Figures

Image of FIG. 1.

Click to view

FIG. 1.

Schematic of the embedded cluster model of the CdS linear chain. Horizontal dotted lines represent repeated patterns of ions. The model is repeated in the negative direction.

Image of FIG. 2.

Click to view

FIG. 2.

The calculated DOS as a function of energy relative to the Fermi level for a series of embedded clusters with different QM cluster sizes.

Image of FIG. 3.

Click to view

FIG. 3.

Band dispersion for the pure CdS linear chain, determined using plane-wave DFT. Red and blue lines indicate conduction and valence bands respectively. The corresponding DOS is also shown.

Image of FIG. 4.

Click to view

FIG. 4.

The calculated DOS as a function of energy relative to the Fermi level for an embedded cluster with a 51 ion QM region (red line) and that of a linear chain calculated using a plane-wave DFT approach (black line).

Image of FIG. 5.

Click to view

FIG. 5.

The calculated spin DOS as a function of energy relative to the Fermi level for a pure CdS linear chain (black line), and that of a linear chain containing a single Cu defect (red and green lines). The DOS were determined using (a) a plane-wave DFT approach and (b) a hybrid QM/MM approach with a 51 atom QM region. Negative spin DOS indicates spin-down. Pure CdS is spin degenerate, therefore only spin-up DOS is shown.

Image of FIG. 6.

Click to view

FIG. 6.

The calculated spin DOS as a function of energy relative to the Fermi level for a pure CdS linear chain (black line), and that of a linear chain containing a single In defect (red and green lines). The DOS were determined using (a) a plane-wave DFT approach and (b) a hybrid QM/MM approach with a 51 atom QM region. Negative spin DOS indicates spin-down. Pure CdS is spin degenerate, therefore only spin-up DOS is shown.

Image of FIG. 7.

Click to view

FIG. 7.

The calculated spin density associated with (a) a defect and (b) an defect in a CdS linear chain, determined using a plane-wave DFT approach. Larger yellow spheres represent S ions, smaller blue spheres represent Cd ions, and medium (blue and purple) spheres at the center represent the impurity ions (Cu and In, respectively).

Image of FIG. 8.

Click to view

FIG. 8.

The calculated defect ionization energy levels associated with a Cu and In impurity substituting on a Cd site, in various relevant charge states, shown relative to the vacuum level. The conduction band (CB) is represented by red shading, while the valence band (VB) is represented by blue shading. Black levels represent electron ionizations and blue levels represent hole ionizations. Neutral defects are denoted by a cross, positive defects by a dot, and negative defects by a prime.

Tables

Generic image for table

Click to view

Table I.

Interatomic pair potential parameters for bulk CdS, including shell polarizations on Cd and S ions ( is the electronic charge).

Generic image for table

Click to view

Table II.

Material properties of wurtzite CdS determined experimentally and calculated using our interatomic potential model.

Loading

Article metrics loading...

/content/aip/journal/jcp/139/12/10.1063/1.4820415
2013-09-23
2014-04-20

Abstract

We present an embedded cluster model to treat one-dimensional nanostructures, using a hybrid quantum mechanical/molecular mechanical (QM/MM) approach. A segment of the nanowire (circa 50 atoms) is treated at a QM level of theory, using density functional theory (DFT) with a hybrid exchange-correlation functional. This segment is then embedded in a further length of wire, treated at an MM level of theory. The interaction between the QM and MM regions is provided by an embedding potential located at the interface. Point charges are placed beyond the ends of the wire segment in order to reproduce the Madelung potential of the infinite system. We test our model on the ideal system of a CdS linear chain, benchmarking our results against calculations performed on a periodic system using a plane-wave DFT approach, with electron exchange and correlation treated at the same level of approximation in both methods. We perform our tests on pure CdS and, importantly, the system containing a single In or Cu impurity. We find excellent agreement in the determined electronic structure using the two approaches, validating our embedded cluster model. As the hybrid QM/MM model avoids spurious interactions between charged defects, it will be of benefit to the analysis of the role of defects in nanowire materials, which is currently a major challenge using a plane-wave DFT approach. Other advantages of the hybrid QM/MM approach over plane-wave DFT include the ability to calculate ionization energies with an absolute reference and access to high levels of theory for the QM region which are not incorporated in most plane-wave codes. Our results concur with available experimental data.

Loading

Full text loading...

/deliver/fulltext/aip/journal/jcp/139/12/1.4820415.html;jsessionid=1qkj68bot9bhd.x-aip-live-06?itemId=/content/aip/journal/jcp/139/12/10.1063/1.4820415&mimeType=html&fmt=ahah&containerItemId=content/aip/journal/jcp
true
true
This is a required field
Please enter a valid email address
752b84549af89a08dbdd7fdb8b9568b5 journal.articlezxybnytfddd
Scitation: One-dimensional embedded cluster approach to modeling CdS nanowires
http://aip.metastore.ingenta.com/content/aip/journal/jcp/139/12/10.1063/1.4820415
10.1063/1.4820415
SEARCH_EXPAND_ITEM