Moore, Gareth John (2022). Charge Dynamics in Organic Photovoltaics: Effects of Morphology on Formation, Separation and Recombination. (Thesis). Universität Bern, Bern
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22moore_gj.pdf - Thesis Available under License Creative Commons: Attribution-Noncommercial-No Derivative Works (CC-BY-NC-ND 4.0). Download (7MB) | Preview |
Abstract
Organic Photovoltaics offer a conversion of solar energy into electricity using materials that are light-weight, flexible and increasingly cheap to produce. However, the challenges of efficiency and stability remain. Even though relatively high efficiencies (up to 18%) can be achieved under laboratory conditions, the production of large scale, efficient, stable arrays remain difficult. To this end deeper understanding of the fundamental photo-physical processes alongside property optimisation are needed to bring this technology into widespread use. In this thesis the processes of charge formation, separation and recombination are closely observed using model systems and morphologies. Small-molecule TAPC:C₆₀ and α6T:C₆₀ blends are studied with transient absorption spectroscopy on ultra-short timescales, and with femtosecond resolution, such that each step in the charge generation process can be observed and measured. In order to understand the effects of interfacial and bulk morphology, blend ratios ranging from 5:95 to 1:1, along with bilayer configurations, are studied. This is in order to give a more nuanced view of the charge formation, separation and recombination processes in the complex bulk heterojunction systems. This work, focused on the fundamental understanding, is followed up by exploring the optimal properties of different organic semi-conducting polymers in terms of their ability to efficiently form and extract charges when blended with a small molecule acceptor. Here the promising non-fullerene acceptor mITIC is blended with donor polymers with different morphological properties and studied resulting in design rules for organic solar cellswith a specific view to large scale production where fine morphological control is not possible. Finally large modern molecular property datasets are leveraged to train a machine learning model in a way that a pre-synthetic prediction of the frontier energy levels can be made for semi-conducting polymers for use in organic solar cells. Here a convolutional neural network architecture is used to make predictions from molecular structure images. This results in a both a tool for molecular screening and demonstrates the potential for modern deep learning techniques to push the field of organic photovoltaics further.
| Item Type: | Thesis |
|---|---|
| Dissertation Type: | Cumulative |
| Date of Defense: | 10 February 2022 |
| Subjects: | 500 Science > 540 Chemistry |
| Institute / Center: | 08 Faculty of Science > Department of Chemistry, Biochemistry and Pharmaceutical Sciences (DCBP) |
| Depositing User: | Hammer Igor |
| Date Deposited: | 16 Jul 2024 13:19 |
| Last Modified: | 16 Jul 2024 22:25 |
| URI: | https://boristheses.unibe.ch/id/eprint/4896 |
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