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1.X. Hong, J. Kim, S.-F. Shi, Y. Zhang, C. Jin, Y. Sun, S. Tongay, J. Wu, Y. Zhang, and F. Wang, Nat. Nanotechnol. 9, 682 (2014).
2.K. F. Mak, C. Lee, J. Hone, J. Shan, and T. F. Heinz, Phys. Rev. Lett. 105, 136805 (2010).
3.A. Splendiani, L. Sun, Y. B. Zhang, T. S. Li, J. Kim, C. Y. Chim, G. Galli, and F. Wang, Nano Lett. 10, 1271 (2010).
4.S. Ithurria, G. Bousquet, and B. Dubertret, J. Am. Chem. Soc. 133, 3070 (2011).
5.C. She, I. Fedin, D. S. Dolzhnikov, A. Demortrère, R. D. Schaller, M. Pelton, and D. V. Talapin, Nano Lett. 14, 2772 (2014).
6.L. Britnell, R. M. Ribeiro, A. Eckmann, R. Jalil, B. D. Belle, A. Mishchenko, Y.-J. Kim, R. V. Gorbachev, T. Georiou, S. Z. Morozov et al., Science 340, 1311 (2013).
7.F. Bonaccorso, L. Colombo, G. Yu, M. Stoller, V. Tozzini, A. C. Ferrari, R. S. Ruoff, and V. Pellegrini, Science 347, 41 (2015).
8.M. Bernardi, M. Palummo, and J. C. Grossman, Nano Lett. 13, 3664 (2013).
9.C. Gong, H. J. Zhang, W. H. Wang, L. Colombo, R. M. Wallace, and K. J. Cho, Appl. Phys. Lett. 103, 053513 (2013).
10.H. Komsa and A. Krasheninnikov, Phys. Rev. B 88, 085318 (2013).
11.J. Kang, S. Tongay, J. Zhou, J. Li, and J. Wu, Appl. Phys. Lett. 102, 012111 (2013).
12.H. Terrones, F. López-Urías, and M. Terrones, Sci. Rep. 3, 1549 (2013).
13.K. Kosmider and J. Fernandez-Rossier, Phys. Rev. B 87, 075451 (2013).
14.J. T. W. Wang, J. M. Ball, E. M. Barea, A. Abate, J. A. Alexander-Webber, J. Huang, M. Saliba, I. Mora-Sero, J. Bisquert, H. J. Snaith et al., Nano Lett. 14, 724 (2014).
15.S.-S. Li, K.-H. Tu, C.-C. Lin, C.-W. Chen, and M. Chhowalla, ACS Nano 4, 3169 (2010).
16.Y.-J. Jeon, J.-M. Yun, D.-Y. Kim, S.-I. Na, and S.-S. Kim, Sol. Energy Mater. Sol. Cells 105, 96 (2012).
17.A. K. Geim and K. S. Novoselov, Nat. Mater. 6, 183 (2007).
18.I. Moreels, Nat. Mater. 14, 464 (2015).
19.S. Halivni, A. Sitt, I. Hadar, and U. Banin, ACS Nano 6, 2758 (2012).
20.P. L. Hérnandez-Martínez, A. O. Govorov, and H. V. Demir, J. Phys. Chem. C 117, 10203 (2013).
21.A. Sitt, N. Even-Dar, S. Halivni, A. Faust, L. Yedidya, and U. Banin, J. Phys. Chem. C 117, 22186 (2013).
22.C. E. Rowland, I. Fedin, H. Zhang, S. K. Gray, A. O. Govorov, D. V. Talapin, and R. D. Schaller, Nat. Mater. 14, 484 (2015).
23.J. S. Cao and R. J. Silbey, J. Phys. Chem. A 113, 13825 (2009).
24.P. Rebentrost, M. Mohseni, I. Kassal, S. Lloyd, and A. Aspuru-Guzik, New J. Phys. 11, 033003 (2009).
25.M. B. Plenio and S. F. Huelga, New J. Phys. 10, 113019 (2008).
26.K. M. Pelzer, A. F. Fidler, G. B. Griffin, S. K. Gray, and G. S. Engel, New J. Phys. 15, 095019 (2013).
27.S. Ithurria, M. D. Tessier, B. Mahler, R. P. S. M. Lobo, B. Dubertret, and A. L. Efros, Nat. Mater. 10, 936 (2011).
28.C. Chuang, J. Knoester, and J. Cao, J. Phys. Chem. B 118, 7827 (2014).
29.R. A. Shah, N. F. Scherer, M. Pelton, and S. K. Gray, Phys. Rev. B 88, 075411 (2013).
30.H. Haken and G. Strobl, Z. Phys. 262, 135 (1973).
31.J. Wu, F. Liu, Y. Shen, J. Cao, and R. J. Silbey, New J. Phys. 12, 105012 (2010).
32.J. B. Miller, N. Dandu, K. A. Velizhanin, R. J. Anthony, U. R. Kortshagen, D. M. Kroll, S. Kilina, and E. K. Hobbie, ACS Nano 9, 9772 (2015).
33.D. S. Kilin, K. Tsemekhman, O. V. Prezhdo, E. I. Zenkevich, and C. von Borczyskowski, J. Photochem. Photobiol., A 190, 342 (2007).
34.H. Tamura, J.-M. Mallet, M. Oheim, and I. Burghardt, J. Phys. Chem. C 113, 7548 (2009).
35.S. Lloyd, M. Mohseni, A. Shabani, and H. Rabitz, “The quantum Goldilocks effect: On the convergence of timescales in quantum transport,” e-print arXiv:1111.4982 (2011).
36.M. Mohseni, A. Shabani, S. Lloyd, and H. Rabitz, J. Chem. Phys. 140, 035102 (2014).
37.P. G. Wolynes, Proc. Natl. Acad. Sci. U. S. A. 106, 17247 (2009).
38.W. M. Itano, D. J. Heinzen, J. J. Bollinger, and D. J. Wineland, Phys. Rev. A 41, 2295 (1990).
39.C. B. Chiu, E. C. G. Sudarshan, and B. Misra, Phys. Rev. D 16, 520 (1977).
40.See supplementary material at for the following information. Fig. S1 presents comparisons of oscillating coherences for exciton sizes of 1.5 nm, 2.5 nm, and 5 nm, for the case of lowγintra-plate. This is the equivalent of Fig. 4 in the main text, which shows the same information for intermediate γintra-plate. Fig. S2 presents the same information as Figs. 4 and S1, but for high γintra-plate. The next section is titled “Efficiencies as measured by ΔDacct for varying values of ΔDacc,” which begins with a brief discussion of the ΔDacct metric of efficiency and is followed by all efficiency data that were not shown in the main text. Table I shows efficiencies ΔDacct for ΔDacc = 0.1, 0.2, 0.3, 0.4, and 0.5 for the case of parallel transition dipoles. Table II shows the same information for the case of randomly oriented transition dipoles, with data averaged over the five sets of randomly oriented transition dipoles. Table III shows this efficiency data for the case of randomly oriented transition dipoles, presenting the complete set of data from all runs without any averaging.[Supplementary Material]

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Two-dimensional nanoplatelets (NPLs) are an exciting class of materials with promising optical and energytransport properties. The possibility of efficient energytransport between nanoplatelets raises questions regarding the nature of energy transfer in these thin, laterally extended systems. A challenge in understanding excitontransport is the uncertainty regarding the size of the exciton. Depending on the material and defects in the nanoplatelet, an exciton could plausibly extend over an entire plate or localize to a small region. The variation in possible exciton sizes raises the question how exciton size impacts the efficiency of transport between nanoplatelet structures. Here, we explore this issue using a quantum master equation approach. This method goes beyond the assumptions of Förster theory to allow for quantum mechanical effects that could increase energy transfer efficiency. The model is extremely flexible in describing different systems, allowing us to test the effect of varying the spatial extent of the exciton. We first discuss qualitative aspects of the relationship between exciton size and transport and then conduct simulations of excitontransport between NPLs for a range of exciton sizes and environmental conditions. Our results reveal that exciton size has a strong effect on energy transfer efficiency and suggest that manipulation of exciton size may be useful in designing NPLs for energytransport.


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