Understanding and optimizing perovskite optoelectronic devices with multi-dimensional imaging techniques
type: CreativeWork
description: This thesis explores the application of multi-dimensional optical imaging techniques in understanding and optimizing perovskite-based optoelectronics. Chapters 1 and 2 give the motivation behind this work and background to perovskite optoelectronics and machine learning. Chapter 3 introduces the main experimental techniques. The debated passivation strategies on perovskite solar cells (PSCs) are studied in Chapter 4 through quantitative hyperspectral imaging. Specifically, alkali metal passivation imposes distinct effects on the optical and structural properties of the devices based on different transport layers. It is shown that the formation of secondary phases, either due to the additives in the perovskite precursor or in the transport layer, leads to an increase in nonradiative recombination and local open-circuit voltage loss. This provides important guidance to the development of passivation techniques toward efficient and stable PSCs. Chapter 5 expands the capability of the latest hyperspectral microscopy technique by developing a machine-learning-based image processing algorithm that is suitable for scientific research. The proposed algorithm achieves state-of-the-art denoising performances compared to recent ML models and conventional handcrafted algorithms. It is able to strengthen signals from unknown samples under low illumination conditions by exploiting spectral information and adopting a self-learning approach. This enables fast and low-dose measurements for emerging semiconductor materials with poor stability. Chapter 6 characterizes perovskite materials for a wide range of applications using the imaging platform developed in Chapter 5. The degradation of mixed halide perovskite light-emitting diodes (LEDs) is tracked through in-situ PL and in-operando electroluminescence mapping. We reveal that lateral ion migration under device operation leads to the growth of chloride-rich defective regions that emit poorly. This is the first time lateral halide m…
abstract: This thesis explores the application of multi-dimensional optical imaging techniques in understanding and optimizing perovskite-based optoelectronics. Chapters 1 and 2 give the motivation behind this work and background to perovskite optoelectronics and machine learning. Chapter 3 introduces the main experimental techniques. The debated passivation strategies on perovskite solar cells (PSCs) are studied in Chapter 4 through quantitative hyperspectral imaging. Specifically, alkali metal passivation imposes distinct effects on the optical and structural properties of the devices based on different transport layers. It is shown that the formation of secondary phases, either due to the additives in the perovskite precursor or in the transport layer, leads to an increase in nonradiative recombination and local open-circuit voltage loss. This provides important guidance to the development of passivation techniques toward efficient and stable PSCs. Chapter 5 expands the capability of the latest hyperspectral microscopy technique by developing a machine-learning-based image processing algorithm that is suitable for scientific research. The proposed algorithm achieves state-of-the-art denoising performances compared to recent ML models and conventional handcrafted algorithms. It is able to strengthen signals from unknown samples under low illumination conditions by exploiting spectral information and adopting a self-learning approach. This enables fast and low-dose measurements for emerging semiconductor materials with poor stability. Chapter 6 characterizes perovskite materials for a wide range of applications using the imaging platform developed in Chapter 5. The degradation of mixed halide perovskite light-emitting diodes (LEDs) is tracked through in-situ PL and in-operando electroluminescence mapping. We reveal that lateral ion migration under device operation leads to the growth of chloride-rich defective regions that emit poorly. This is the first time lateral halide m…
author: Ji, Kangyu
language: eng
keywords: Degradation mechanism, Imaging, Light-emitting diodes, Machine learning, Optoelectronics, Perovskite, Solar cells
license: https://apollo8-f-pro.lib.cam.ac.uk/bitstreams/3c4bbfa2-305b-480f-80c2-75974b3bd25b/download
license: https://creativecommons.org/licenses/by-nc-nd/4.0/
date published: 2022-04-01
date modified: 2026-07-22T22:24:11Z
provider: Cambridge University
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: University of Cambridge

PropertyValue
property ID: DOI
value: 10.17863/CAM.96612