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

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,...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Sergei Manzhos [verfasserIn] Giacomo Giorgi [verfasserIn] Johann Lüder [verfasserIn] Manabu Ihara [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	surface plasmon plasmonic solar cell time-dependent density functional theory time-dependent density functional tight binding time-dependent orbital-free dft

Übergeordnetes Werk:	In: Advances in Physics: X - Taylor & Francis Group, 2017, 6(2021), 1
Übergeordnetes Werk:	volume:6 ; year:2021 ; number:1

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1080/23746149.2021.1908848

Katalog-ID:	DOAJ072211997

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spelling	10.1080/23746149.2021.1908848 doi (DE-627)DOAJ072211997 (DE-599)DOAJ8b476a40a63a44389efeee147dcea6bf DE-627 ger DE-627 rakwb eng QC1-999 Sergei Manzhos verfasserin aut Modeling of plasmonic properties of nanostructures for next generation solar cells and beyond 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. surface plasmon plasmonic solar cell time-dependent density functional theory time-dependent density functional tight binding time-dependent orbital-free dft Physics Giacomo Giorgi verfasserin aut Johann Lüder verfasserin aut Manabu Ihara verfasserin aut In Advances in Physics: X Taylor & Francis Group, 2017 6(2021), 1 (DE-627)882820370 (DE-600)2889082-6 23746149 nnns volume:6 year:2021 number:1 https://doi.org/10.1080/23746149.2021.1908848 kostenfrei https://doaj.org/article/8b476a40a63a44389efeee147dcea6bf kostenfrei http://dx.doi.org/10.1080/23746149.2021.1908848 kostenfrei https://doaj.org/toc/2374-6149 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 6 2021 1
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callnumber-first	Q - Science
author	Sergei Manzhos
spellingShingle	Sergei Manzhos misc QC1-999 misc surface plasmon misc plasmonic solar cell misc time-dependent density functional theory misc time-dependent density functional tight binding misc time-dependent orbital-free dft misc Physics Modeling of plasmonic properties of nanostructures for next generation solar cells and beyond
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topic_title	QC1-999 Modeling of plasmonic properties of nanostructures for next generation solar cells and beyond surface plasmon plasmonic solar cell time-dependent density functional theory time-dependent density functional tight binding time-dependent orbital-free dft
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title	Modeling of plasmonic properties of nanostructures for next generation solar cells and beyond
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title_full	Modeling of plasmonic properties of nanostructures for next generation solar cells and beyond
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title_sort	modeling of plasmonic properties of nanostructures for next generation solar cells and beyond
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title_auth	Modeling of plasmonic properties of nanostructures for next generation solar cells and beyond
abstract	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.
abstractGer	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.
abstract_unstemmed	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.
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title_short	Modeling of plasmonic properties of nanostructures for next generation solar cells and beyond
url	https://doi.org/10.1080/23746149.2021.1908848 https://doaj.org/article/8b476a40a63a44389efeee147dcea6bf http://dx.doi.org/10.1080/23746149.2021.1908848 https://doaj.org/toc/2374-6149
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Modeling of plasmonic properties of nanostructures for next generation solar cells and beyond

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