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Organic Photovoltaics: Processing Dr...
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Munshi, Joydeep.
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Organic Photovoltaics: Processing Driven Morphology and Properties of Bulk Heterojunction Thin Films.
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Organic Photovoltaics: Processing Driven Morphology and Properties of Bulk Heterojunction Thin Films./
作者:
Munshi, Joydeep.
出版者:
Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:
202 p.
附註:
Source: Dissertations Abstracts International, Volume: 82-04, Section: B.
Contained By:
Dissertations Abstracts International82-04B.
標題:
Mechanical engineering. -
電子資源:
https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28150961
ISBN:
9798684656675
Organic Photovoltaics: Processing Driven Morphology and Properties of Bulk Heterojunction Thin Films.
Munshi, Joydeep.
Organic Photovoltaics: Processing Driven Morphology and Properties of Bulk Heterojunction Thin Films.
- Ann Arbor : ProQuest Dissertations & Theses, 2021 - 202 p.
Source: Dissertations Abstracts International, Volume: 82-04, Section: B.
Thesis (Ph.D.)--Lehigh University, 2021.
This item must not be sold to any third party vendors.
Organic photovoltaics (OPV) have been into the limelight of the solar energy research for last few decades. Significant research have been devoted since the development of first organic solar cell (OSC) in 1986. The working principle of solar energy conversion using organic semiconductor material is complex due to the underlying novel physics of exciton generation and dissociation inside the photo-active layer. Experimental characterization of the 3-Dimensional (3D) morphology of the bulk heterojunction (BHJ) photoactive layer is always challenging, and the poor contrast of the reconstructed morphology due to weak electron scattering of organic materials is a major limitation of the microstructural imaging by electron microscopy. In an aim to supplement experimental efforts, atomistically informed investigation based on all-atom (AA) and coarse-grained (CG) molecular dynamics, on the other hand, are leveraged as a promising tool to get insight into the morphological characterizations from the theoretical standpoint.In this thesis, I employ coarse-grained molecular dynamics (CGMD) simulation framework to mimic solvent evaporation and thermal annealing of typical BHJ active layer consisting of semiconducting electron-donor polymer and electron-acceptor fullerene materials. Dependence of conversion efficiency of OPV device as well as the thermo-mechanical stability of the blend morphology on the different solution processing parameters are extensively explored. Based on the identified processing parameters correlation between the dominant design variables and the microstructure is obtained. Composition of constituent donor and acceptor materials, molecular weight and polydispersity of donor polymer chains and thermal annealing temperature are observed to significantly affect the morphology as well as the thermo-mechanical stability of the BHJ blend.The CGMD framework to investigate morphology evolution and dynamics is further expanded to a design optimization problem using metaheursitic evolutionary algorithm in conjunction with the existing atomistic framework. The machine learned cuckoo search algorithm (MOCS) reveals a Pareto frontier consisting of the dominant solutions in an aim to simultaneously optimize exciton generation to charge transport probability (performance metric) and ultimate tensile strength (stability metric) of the blend morphology. While a mixture of regioregular Poly-(3-hexyl-thiophene) (P3HT) as donor and Phenyl-C61-butyricacid methyl ester (PCBM) as acceptor are utilized due to their easy accessibility, the strategy, discussed in this dissertation, can be easily transferred to new class of materials to optimize the design variables towards targeted properties. In the final effort to develop a self-sustaining high-throughput design framework, a generative model based on recurrent neural network (RNN) is implemented to predict new molecular fingerprints of donor polymer materials. The data-enabled generative modeling strategy along with the atomistically informed simulation framework can significantly augment the experimental efforts to accelerate the discovery of novel organic materials with the potential to integrate fundamental molecular-level physics and its application in harnessing inexhaustible source of energy.
ISBN: 9798684656675Subjects--Topical Terms:
649730
Mechanical engineering.
Subjects--Index Terms:
Computational Materials Science
Organic Photovoltaics: Processing Driven Morphology and Properties of Bulk Heterojunction Thin Films.
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Organic photovoltaics (OPV) have been into the limelight of the solar energy research for last few decades. Significant research have been devoted since the development of first organic solar cell (OSC) in 1986. The working principle of solar energy conversion using organic semiconductor material is complex due to the underlying novel physics of exciton generation and dissociation inside the photo-active layer. Experimental characterization of the 3-Dimensional (3D) morphology of the bulk heterojunction (BHJ) photoactive layer is always challenging, and the poor contrast of the reconstructed morphology due to weak electron scattering of organic materials is a major limitation of the microstructural imaging by electron microscopy. In an aim to supplement experimental efforts, atomistically informed investigation based on all-atom (AA) and coarse-grained (CG) molecular dynamics, on the other hand, are leveraged as a promising tool to get insight into the morphological characterizations from the theoretical standpoint.In this thesis, I employ coarse-grained molecular dynamics (CGMD) simulation framework to mimic solvent evaporation and thermal annealing of typical BHJ active layer consisting of semiconducting electron-donor polymer and electron-acceptor fullerene materials. Dependence of conversion efficiency of OPV device as well as the thermo-mechanical stability of the blend morphology on the different solution processing parameters are extensively explored. Based on the identified processing parameters correlation between the dominant design variables and the microstructure is obtained. Composition of constituent donor and acceptor materials, molecular weight and polydispersity of donor polymer chains and thermal annealing temperature are observed to significantly affect the morphology as well as the thermo-mechanical stability of the BHJ blend.The CGMD framework to investigate morphology evolution and dynamics is further expanded to a design optimization problem using metaheursitic evolutionary algorithm in conjunction with the existing atomistic framework. The machine learned cuckoo search algorithm (MOCS) reveals a Pareto frontier consisting of the dominant solutions in an aim to simultaneously optimize exciton generation to charge transport probability (performance metric) and ultimate tensile strength (stability metric) of the blend morphology. While a mixture of regioregular Poly-(3-hexyl-thiophene) (P3HT) as donor and Phenyl-C61-butyricacid methyl ester (PCBM) as acceptor are utilized due to their easy accessibility, the strategy, discussed in this dissertation, can be easily transferred to new class of materials to optimize the design variables towards targeted properties. In the final effort to develop a self-sustaining high-throughput design framework, a generative model based on recurrent neural network (RNN) is implemented to predict new molecular fingerprints of donor polymer materials. The data-enabled generative modeling strategy along with the atomistically informed simulation framework can significantly augment the experimental efforts to accelerate the discovery of novel organic materials with the potential to integrate fundamental molecular-level physics and its application in harnessing inexhaustible source of energy.
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