Modelling and Harnessing the Physics of Perovskite Solar Cells

: (Alternative Format Thesis)

Abstract

Perovskite solar cells are a promising energy technology. The halide perovskite absorber used has a high concentration of mobile ionic defects, and many studies are dedicated to understanding the behaviour of these ions. This paradigm can be reversed, by using the understanding of mobile ionic behaviour built thus far to infer material and device properties. This thesis takes this approach by using drift-diffusion simulations of perovskite solar cells which incorporate mobile ions to develop new characterisation methods and analysis tools.

As a first example of this, simulations are used as a lens to study the properties of the current density–voltage (J–V) relationship with scan rate. It is shown that fitting asymmetric Shockley-Read-Hall pseudo lifetimes to devices that present negative hysteresis is physically realistic and a spatial dependence of hysteresis–scan rate functions is demonstrated. This analysis predicts that the presence of mobile ions in perovskite solar cells is associated with signatures in J–V data beyond a binary assay of existence.

Secondly, the presence of mobile ions is leveraged in a new methodology using stabilise-and-pulse voltage protocols, investigating how interlayers between the perovskite and transport layers alter the energy alignment at the interface. It is found that the method effectively separates the electronic and ionic timescales, and that energy alignment modifications only occur for interlayers that can provide ideal dipole alignment by binding to both the perovskite and transport layer.

Finally, the steady-state impact of mobile ions on a population of perovskite solar cells with diverse device parameters is investigated. Through a supervised machine learning approach predicting ionic impact, the effect of mobile ions is used to identify key parameters for creating performant and ion-resilient devices. Transport layer band positions are highlighted as vital, due to the relative magnitude of the maximum power-point to the built-in voltage being a strong indicator of how mobile ions affect device performance at a range of steady-state voltages.

Date of Award	26 Jun 2024
Original language	English
Awarding Institution	University of Bath
Supervisor	Petra Cameron (Supervisor), Alison Walker (Supervisor) & Matthew J. Wolf (Supervisor)