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Cited by 253

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A Charge-Orbital Balance Picture of Doping in Colloidal Quantum Dot Solids

Voznyy, Oleksandr, Zhitomirsky, David, Stadler, Philipp, Ning, Zhijun, Hoogland, Sjoerd, Sargent, Edward H.·2012·ACS Nano

Abstract

We present a framework--validated using both modeling and experiment--to predict doping in CQD films. In the ionic semiconductors widely deployed in CQD films, the framework reduces to a simple accounting of the contributions of the oxidation state of each constituent, including both inorganic species and organic ligands. We use density functional theory simulations to confirm that the type of doping can be reliably predicted based on the overall stoichiometry of the CQDs, largely independent of microscopic geometrical bonding configurations. Studies employing field-effect transistors constructed from CQDs that have undergone various chemical treatments, coupled with Rutherford backscattering and X-ray photoelectron spectroscopy to provide compositional analysis, allow us to test and confirm the proposed model in an experimental framework. We investigate both p- and n-type electronic doping spanning a wide range of carrier concentrations from 10(16) cm(-3) to over 10(18) cm(-3), and demonstrate reversible switching between p- and n-type doping by changing the CQD stoichiometry. We show that the summation of the contributions from all cations and anions within the film can be used to predict accurately the majority carrier type. The findings enable predictable control over majority carrier concentration via tuning of the overall stoichiometry.

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Authors
Voznyy, O., Zhitomirsky, D., Stadler, P., Ning, Z., Hoogland, S., & Sargent, E. H.
Venue
ACS Nano
Published
2012-09-07
Volume
6
Issue
9
Pages
8448-8455
ISSN
  • 1936-0851 (ISSN-L)
  • 1936-086X
OpenAlex
W2328953009
Cited by
253
References
0
FWCI
10.43
Type
journal article
Language
en
Open access
closed

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.