Modeling of plasmonic properties of nanostructures for next generation solar cells and beyond

Advances in Physics: X

Sergei Manzhos¹  Giacomo Giorgi²  Johann Lüder³  Manabu Ihara⁴ 

[1] Centre Énergie Matériaux Télécommunications, Institut National de la Recherche Scientifique - Université du Québec, Quebec, Canad;Department of Civil & Environmental Engineering, (DICA), The University of Perugia, Perugia, Ital;CNR-SCITEC, Perugia, Ital;Department of Materials and Optoelectronic Science, National Sun Yat-sen University, Kaohsiung, Taiwa;Center of Crystal Research, National Sun Yat-sen University, Kaohsiung, Taiwa;School of Materials and Chemical Technology, Tokyo Institute of Technology, Meguro-ku, Japa;

关键词: Surface plasmon;    plasmonic solar cell;    time-dependent density functional theory;    time-dependent density functional tight binding;    time-dependent orbital-free dft;   

DOI  :  10.1080/23746149.2021.1908848

来源: Taylor & Francis

【 摘 要 】

Plasmonic particles and nanostructures are widely used in photovoltaic and photonics. Surface plasmons were found to enhance different types of solar cells including plasmonic DSSCs, plasmonic solid semiconductor solar cells, plasmonic organic solar cells, and plasmonic perovskite solar cell. Size, composition, and shape of plasmonic nanoparticles as well as nanometer-distance control between particles are key design factors of plasmonic nanostructures. Modeling is rapidly gaining in importance for mechanistic understanding and rational design of plasmonic nanostructures. We review the modeling approaches used to model plasmon resonance features of nanostructures, from classical approaches that can routinely handle most particle sizes used in solar cells to approaches beyond classical electrodynamics such as ab initio approaches based on time-dependent density functional theory (TD-DFT). We highlight recently emerging approaches which have the potential to significantly enhance modeling capabilities in the coming years, in particular, by allowing atomistic (ab initio) modeling at realistic length scales, i.e. of particle sizes beyond 10 nm which are of most interest to plasmonic solar cells but remain problematic with traditional DFT-based techniques, such as density functional tight binding (DFTB) based approaches, time-dependent orbital-free DFT, and machine learning-based approaches, as well as many-body perturbation theory which is expected to gain usage with advances in computing power.

【 授权许可】

CC BY