Comparative studies of optoelectrical properties of prominent PV materials: Halide perovskite, CdTe, and GaAs

Condensed Matter - Materials Science 3104 Condensed Matter Physics 2500 Materials Science 2210 Mechanical Engineering Materials Science (cond-mat.mtrl-sci) FOS: Physical sciences Photoluminescence efficiency PV materials Physics - Applied Physics 02 engineering and technology Applied Physics (physics.app-ph) 530 7. Clean energy 01 natural sciences 0104 chemical sciences Passivation Organic–inorganic hybrid 2211 Mechanics of Materials SRH recombination Carrier diffusion 0210 nano-technology
DOI: 10.1016/j.mattod.2020.01.001 Publication Date: 2020-02-07T13:12:41Z
ABSTRACT
We compare three representative high performance PV materials: halide perovskite MAPbI3, CdTe, and GaAs, in terms of photoluminescence (PL) efficiency, PL lineshape, carrier diffusion, and surface recombination, over multiple orders of photo-excitation density. An analytic model is used to describe the excitation density dependence of PL intensity and extract the internal PL efficiency and multiple pertinent recombination parameters. A PL imaging technique is used to obtain carrier diffusion length without using a PL quencher, thus, free of unintended influence beyond pure diffusion. Our results show that perovskite samples tend to exhibit lower Shockley-Read-Hall (SRH) recombination rate in both bulk and surface, thus higher PL efficiency than the inorganic counterparts, particularly under low excitation density, even with no or preliminary surface passivation. PL lineshape and diffusion analysis indicate that there is considerable structural disordering in the perovskite materials, and thus photo-generated carriers are not in global thermal equilibrium, which in turn suppresses the nonradiative recombination. This study suggests that relatively low point-defect density, less detrimental surface recombination, and moderate structural disordering contribute to the high PV efficiency in the perovskite. This comparative photovoltaics study provides more insights into the fundamental material science and the search for optimal device designs by learning from different technologies.
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