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dc.contributor.authorKosasih, Felix
dc.date.accessioned2022-02-04T02:49:27Z
dc.date.available2022-02-04T02:49:27Z
dc.date.issued2022-01-21
dc.date.submitted2021-09-26
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/333637
dc.description.abstractThe rapid ascent of perovskite photovoltaics over the past decade has enabled this technology to now stand on the cusp of commercialisation. However, a successful entry into the market will only be feasible if the power conversion efficiencies of perovskite solar modules can at least approach those of laboratory-scale cells. Achieving this feat requires spatially homogeneous depositions of device layers over a large area and high-quality interconnections between adjacent cells in a module. Since perovskite photovoltaic devices are nanostructured, materials characterisation with a nanometre spatial resolution can provide valuable insights to optimise the processes involved in scalable film deposition and interconnection fabrication. This thesis presents nanoscale electron microscopy investigations of perovskite photovoltaic devices made using scalable deposition methods and the cell interconnections within. A characterisation workflow consisting of cross-sectional specimen preparation, data acquisition, and multivariate statistical data analysis is developed and validated. Preparation of electron-transparent specimens is performed using focused ion beam milling, which is shown to have minimum impact on the perovskite specimen. Nanoscale compositional mapping is performed using energy-dispersive X-ray spectroscopy in a scanning transmission electron microscope, where the applied electron dose is minimised to suppress beam-induced specimen damage while still ensuring statistical significance in the data. Principal component analysis, a multivariate statistical analysis algorithm, is optimised and applied to improve the signal-to-noise ratio in the obtained datasets by an order of magnitude. This sequence allows acquisition of spatially resolved morphological and compositional data with minimum damage on the perovskite specimen, which are supported by complementary computational methods and other characterisation techniques. The optimised workflow is applied to study perovskite solar modules deposited by blade coating, where electron microscopy revealed how additives in the perovskite precursor solutions contribute towards a more homogeneous device stack and, ultimately, more efficient modules. Finally, the interconnections are studied as they are critical to ensure good electrical performance in solar modules. Compositional characterisation shows how laser pulses used in scribing the interconnection lines can decompose the perovskite layer next to those lines, and also how the decomposition is affected by the perovskite’s homogeneity. Furthermore, elemental mapping reveals diffusion of sodium from the glass substrate into the active layers through the interconnection lines, even before the devices are operated. Sodium diffusion results in passivated defect sites and stronger perovskite luminescence, but also carries an inherent risk of excessive diffusion throughout the device’s lifetime.
dc.description.sponsorshipJardine Foundation; Cambridge Trust
dc.rightsAll Rights Reserved
dc.rights.urihttps://www.rioxx.net/licenses/all-rights-reserved/
dc.subjectElectron microscopy
dc.subjectSolar energy
dc.subjectPhotovoltaics
dc.subjectPerovskite solar cells
dc.subjectMultivariate statistical analysis
dc.subjectMultidimensional data analysis
dc.titleLow Dose Analytical Electron Microscopy of Hybrid Perovskite Photovoltaic Devices
dc.typeThesis
dc.type.qualificationlevelDoctoral
dc.type.qualificationnameDoctor of Philosophy (PhD)
dc.publisher.institutionUniversity of Cambridge
dc.date.updated2022-01-30T14:18:18Z
dc.identifier.doi10.17863/CAM.81053
rioxxterms.licenseref.urihttps://www.rioxx.net/licenses/all-rights-reserved/
dc.contributor.orcidKosasih, Felix [0000-0003-1060-4003]
rioxxterms.typeThesis
dc.publisher.collegeDowning
cam.supervisorDucati, Caterina
cam.supervisor.orcidDucati, Caterina [0000-0003-3366-6442]
cam.depositDate2022-01-30
pubs.licence-identifierapollo-deposit-licence-2-1
pubs.licence-display-nameApollo Repository Deposit Licence Agreement


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