Abstract
There are two main methods for modelling the electronic properties of perovskite devices. Drift-diffusion modelling [1,2,3] works by solving differential equations, while equivalent circuit modelling [4,5] works by treating different parts of the device as electrical components. This work focuses on drift-diffusion modelling.
Most drift-diffusion models of perovskite use finite-difference methods [1,3], which solve the differential equations at equally-spaced grid points. These models have achieved a valuable qualitative description of the influence of ion motion on the electrical characteristics of perovskite solar cells. However, in order to relate the model predictions to microscopic material properties, a more quantitative model is required.
We use a hybrid model [2] where an asymptotic approximation is used to model the accumulation of ionic charge at the interfaces between materials. The charge accumulation, which is a function of time, is then input into a second model, in which the electron and hole concentrations across the film are expressed as a sum of Chebyshev polynomials using the MATLAB add-on module Chebfun. [6]
In this work, the ability of different drift-diffusion modelling methods to reproduce experimental current-voltage measurements quantitatively is compared.
References
[1] S. van Reenen, M. Kemerink and H. J. Snaith, J. Phys. Chem. Lett. (2015), Vol. 6, 1511-1515
[2] G. Richardson et al, Energy Environ. Sci. (2016), Vol 9, 1476-1485
[3] P. Calado et al., arXiv (2016), https://arxiv.org/abs/1606.00818
[4] A. Pockett et al., J. Phys. Chem. C. (2015), Vol. 119, 3456-3465
[5] L. Cojocaru et al., Chem. Lett. (2015), Vol. 44, 1750-1752
[6] T. A. Driscoll, N. Hale and L. N. Trefethen, Chebfun Guide (2015)
Most drift-diffusion models of perovskite use finite-difference methods [1,3], which solve the differential equations at equally-spaced grid points. These models have achieved a valuable qualitative description of the influence of ion motion on the electrical characteristics of perovskite solar cells. However, in order to relate the model predictions to microscopic material properties, a more quantitative model is required.
We use a hybrid model [2] where an asymptotic approximation is used to model the accumulation of ionic charge at the interfaces between materials. The charge accumulation, which is a function of time, is then input into a second model, in which the electron and hole concentrations across the film are expressed as a sum of Chebyshev polynomials using the MATLAB add-on module Chebfun. [6]
In this work, the ability of different drift-diffusion modelling methods to reproduce experimental current-voltage measurements quantitatively is compared.
References
[1] S. van Reenen, M. Kemerink and H. J. Snaith, J. Phys. Chem. Lett. (2015), Vol. 6, 1511-1515
[2] G. Richardson et al, Energy Environ. Sci. (2016), Vol 9, 1476-1485
[3] P. Calado et al., arXiv (2016), https://arxiv.org/abs/1606.00818
[4] A. Pockett et al., J. Phys. Chem. C. (2015), Vol. 119, 3456-3465
[5] L. Cojocaru et al., Chem. Lett. (2015), Vol. 44, 1750-1752
[6] T. A. Driscoll, N. Hale and L. N. Trefethen, Chebfun Guide (2015)
| Original language | English |
|---|---|
| Publication status | Published - 26 Sept 2016 |
| Event | 2nd International Conference on Perovskite Solar Cells and Optoelectronics - Palazzo Ducale, Genova, Italy Duration: 26 Sept 2016 → 28 Sept 2016 http://www.psco-conference.org/ |
Conference
| Conference | 2nd International Conference on Perovskite Solar Cells and Optoelectronics |
|---|---|
| Abbreviated title | PSCO 2016 |
| Country/Territory | Italy |
| City | Genova |
| Period | 26/09/16 → 28/09/16 |
| Internet address |