Identifying Charge Transfer Mechanisms across Semiconductor Heterostructures via Surface Dipole Modulation and Multiscale Modeling
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Published on: September 11, 2018
Ryan T Pekarek1, Kara Kearney2,3, Benjamin M Simon1
1Department of Chemistry , The University of Texas at Austin , Austin , Texas 78712 , United States.
Abstract:
The design and fabrication of stable and efficient photoelectrochemical devices requires the use of multifunctional structures with complex heterojunctions composed of semiconducting, protecting, and catalytic layers. Understanding charge transport across such devices is challenging due to the interplay of bulk and interfacial properties. In this work, we analyze hole transfer across n-Si(111)- R|TiO2 photoanodes where - R is a series of mixed aryl/methyl monolayers containing an increasing number of methoxy units (mono, di, and tri). In the dimethoxy case, triethylene glycol units were also appended to substantially enhance the dipolar character of the surface. We find that hole transport is limited at the n-Si(111)- R|TiO2 interface and occurs by two processes- thermionic emission and/or intraband tunneling-where the interplay between them is regulated by the interfacial molecular dipole. This was determined by characterizing the photoanode experimentally (X-ray photoelectron spectroscopy, voltammetry, impedance) with increasingly thick TiO2 films and complementing the characterization with a multiscale computational approach (first-principles density functional theory (DFT) and finite-element device modeling). The tested theoretical model that successfully distinguished thermionic emission and intraband tunneling was then used to predict the effect of solution potential on charge transport. This prediction was then experimentally validated using a series of nonaqueous redox couples (ferrocence derivatives spanning 800 mV). As a result, this work provides a fundamental understanding of charge transport across TiO2-protected electrodes, a widely used semiconductor passivation scheme, and demonstrates the predictive capability of the combined DFT/device-modeling approach.
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