Modelling Hysteresis in Perovskite Solar Cells

Cave, James M.; Walker, Alison B.

doi:10.1002/9781119364214.ch10

Cited by 1 publication

(2 citation statements)

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“…This prediction lies within experimentally obtained activation energies of 0.36-0.43 eV [67]. Cave et al [21] predicted 0.41 eV for E a in a PSC with HTL, ETL of spiro, TiO 2 respectively and the DFT prediction of 0.37eV (0.51eV) for tetragonal (cubic) MAPbI 3 , allowing for a reaction enthalpy of 0.07 eV [68], and noting that the cubic-to-tetragonal phase transition in MAPI occurs around 54 • C, 327 K [69]. However, individual device characterisation, and this method, assume that mobile ion vacancy parameters vary between devices as a result of fabrication and device history and DFT predictions for E a are approximate.…”

Section: Resultssupporting

confidence: 81%

See 1 more Smart Citation

Bayesian parameter estimation for characterising mobile ion vacancies in perovskite solar cells

McCallum,

Nicholls,

Jensen

et al. 2023

J. Phys. Energy

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To overcome the challenges associated with poor temporal stability of perovskite solar cells, methods are required that allow for fast iteration of fabrication and characterisation, such that optimal device performance and stability may be actively pursued. Currently, establishing the causes of underperformance is both complex and time-consuming, and optimisation of device fabrication is thus inherently slow. Here, we present a means of computational device characterisation of mobile halide ion parameters from room temperature current–voltage ( J − V ) measurements only, requiring ∼2 h of computation on basic computing resources. With our approach, the physical parameters of the device may be reverse-modelled from experimental J − V measurements. In a drift-diffusion (DD) model, the set of coupled DD partial differential equations cannot be inverted explicitly, so a method for inverting the DD simulation is required. We show how Bayesian Parameter Estimation coupled with a DD perovskite solar cell model can determine the extent to which device parameters affect performance measured by J − V characteristics. Our method is demonstrated by investigating the extent to which device performance is influenced by mobile halide ions for a specific fabricated device. The ion vacancy density N 0 and diffusion coefficient DI were found to be precisely characterised for both simulated and fabricated devices. This result opens up the possibility of pinpointing origins of degradation by finding which parameters most influence device J − V curves as the cell degrades.

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Section: Resultssupporting

confidence: 81%

“…We would like to thank James Cave for permission to use the data of the J − V measurements from his PhD thesis [68] reported in figure 4 of this paper.…”

Section: Acknowledgmentsmentioning

confidence: 99%