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Identifying the Nano Interface through Phase

The quantum dots (QD) interface in solution can significantly influence electron transfer dynamics, impacting quantum-dot-sensitized solar cells as well as various biological, environmental, and industrial systems. Here, we propose a method to identify the contribution of quantum dots’ interface-cre...

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Bibliographic Details
Published in:Journal of physical chemistry. C 2024-09, Vol.128 (35), p.14700-14708
Main Author: Mukhopadhyay, Madhuri
Format: Article
Language:English
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Summary:The quantum dots (QD) interface in solution can significantly influence electron transfer dynamics, impacting quantum-dot-sensitized solar cells as well as various biological, environmental, and industrial systems. Here, we propose a method to identify the contribution of quantum dots’ interface-created static electric field to the nonlinear optical response (NLO) due to four-wave mixing (FWM), especially for the nanoparticles where surface contribution is high. We implement a way to disentangle the FWM response in QDs, originating from the three incoming oscillating laser fields (NLO oscillating ) and a contribution (NLO static ) arising from the three oscillating laser fields and the static electric field caused by the interface. Advanced two-dimensional electronic spectroscopy (2DES) employs phase-resolved heterodyne techniques where the FWM response is measured in a particular phase-matched direction and the response is distinctively phase sensitive. Theoretical analysis shows that alterations in the interface can introduce phase variation in the NLO static signal resulting in distinct changes in 2D spectra. Our studies establish a range of ionic strength (10–6 M < x < 10–3 M), which can be important to untwine, the usual NLO signal (NLO oscillating ) from the NLO (NLO static ) contributed by the interface of quantum dots. This analysis could open up possibilities for studying various dynamics occurring specifically at the interface and also paves the way for exploring different ion interactions through phase changes in 2D spectra. Additionally, it offers enormous scope for employing deep learning-assisted phase recognition.
ISSN:1932-7447
1932-7455
DOI:10.1021/acs.jpcc.4c01442