Simulation of ion migration in perovskite solar cells using a kinetic Monte Carlo/drift diffusion numerical model and analysis of the impact on device performance
Alessio Gagliardi a, Ajay Singh a, Waldemar Kaiser a
a Technische Universitaet Muenchen, Karlstrasse 45, Munich, 80333, Germany
International Conference on Hybrid and Organic Photovoltaics
Proceedings of International Conference on Hybrid and Organic Photovoltaics (HOPV18)
Benidorm, Spain, 2018 May 28th - 31st
Organizers: Emilio Palomares and Rene Janssen
Oral, Waldemar Kaiser, presentation 099
DOI: https://doi.org/10.29363/nanoge.hopv.2018.099
Publication date: 21st February 2018

Perovskite solar cells have gathered a large interest in the last years as a very compelling and promising photovoltaic technology thanks to many interesting properties such as a wide spectrum of deposition techniques, a simple integration with both organic and inorganic materials and, most important of all, a high light power conversion efficiency.

Perovskite materials have also challenged the scientific community due to the many different physical processes that concur to set the optical and electrical properties: from ferroelectricity [1], to ion migration [2], defects and different recombination processes [3]. An important aspect of perovskite films is the presence of grain and grain boundaries. Although many progresses have been obtained in the quality of the film, still grain boundaries within the perovskite film in fabricated devices are present.

The effect of these grain boundaries have been investigated by many groups, we refer here to just one reference [4], but the effect of these grain boundaries to free charges and ion migration is still under debate. In  [4] it has been demonstrated as the change in average size of grains have an important impact on cell performance and ionic diffusion.

In the present work we theoretically investigate the effect of ion migration with the presence of grain boundaries. We make a multiscale simulation tool based on kinetic Monte Carlo (for ion dynamics) [5] and drift diffusion based on finite elements (for the electrical part) [6] to understand how the different ion diffusion mechanisms can impact the final cell performance.

 

References

[1] A. Pecchia et al., Nano Lett., 16, 988 (2016)

[2]  J. M. Azpiroz et al., Energy & Environmental Science, 8,  2118-2127 (2015)

[3] L. M. Herz, Annual Rev. Phys. Chemistry, 67, 65-89 (2016)

[4] B. Roose et al., Nano Energy, 39, 24-29 (2017)

[5] T. Albes, A. Gagliardi, Physical Chemistry Chemical Physics,  19 (31), 20974-20983 (2017)

[6] A. Gagliardi and A. Abate, ACS Energy Lett., 3, 163 (2018)

 

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