TY - JOUR
T1 - Towards Improving the Efficiency of Organic Solar Cells by Coarse-Grained Atomistic Modeling of Processing Dependent Morphologies
AU - Balasubramanian, Ganesh
AU - Munshi, Joydeep
AU - Chen, Wei
AU - Chien, Te Yu
N1 - Publisher Copyright:
© 1999-2011 IEEE.
PY - 2021/5/1
Y1 - 2021/5/1
N2 - Solar energy conversion to electricity using organic semiconductor materials is a complex process due to the underlying transport physics of electrons and photons. Experimental characterizations of the 3-D morphology of bulk heterojunction organic photovoltaics are challenging; the poor contrast of the reconstructed morphology due to weak electronic scattering of organic molecules is a major impediment for microstructural imaging by electron microscopy. Thus, enhancing the power-conversion efficiency (PCE) of organic solar cells requires predictive design of both material and processing parameters. To this end, large-scale coarse-grained molecular simulations are needed to probe the morphology of the nanostructures, to develop process-structure-performance correlations that assist in fundamental understanding of the physical mechanisms, and to simultaneously aid in selection and optimization of design parameters. Here, we summarize the outcomes from high-performance coarse-grained molecular dynamics simulations that are employed to mimic solvent evaporation and thermal annealing of typical bulk heterojunction solar cell active layers. The latter consist of a blend of electron-donor and electron-acceptor materials. We extensively explore the dependence of PCE and the thermo-mechanical stability of the blend morphology on different solution processing conditions, and correlate the identified parameters with the dominant design variables and the microstructure. The simulations reveal that the composition of constituent donor and acceptor materials, respective molecular weights, polydispersity of donor polymer chains, and the thermal annealing temperature are the major parameters that significantly impact the morphology, thermo-mechanical stability, and subsequently the PCE of organic photovoltaics.
AB - Solar energy conversion to electricity using organic semiconductor materials is a complex process due to the underlying transport physics of electrons and photons. Experimental characterizations of the 3-D morphology of bulk heterojunction organic photovoltaics are challenging; the poor contrast of the reconstructed morphology due to weak electronic scattering of organic molecules is a major impediment for microstructural imaging by electron microscopy. Thus, enhancing the power-conversion efficiency (PCE) of organic solar cells requires predictive design of both material and processing parameters. To this end, large-scale coarse-grained molecular simulations are needed to probe the morphology of the nanostructures, to develop process-structure-performance correlations that assist in fundamental understanding of the physical mechanisms, and to simultaneously aid in selection and optimization of design parameters. Here, we summarize the outcomes from high-performance coarse-grained molecular dynamics simulations that are employed to mimic solvent evaporation and thermal annealing of typical bulk heterojunction solar cell active layers. The latter consist of a blend of electron-donor and electron-acceptor materials. We extensively explore the dependence of PCE and the thermo-mechanical stability of the blend morphology on different solution processing conditions, and correlate the identified parameters with the dominant design variables and the microstructure. The simulations reveal that the composition of constituent donor and acceptor materials, respective molecular weights, polydispersity of donor polymer chains, and the thermal annealing temperature are the major parameters that significantly impact the morphology, thermo-mechanical stability, and subsequently the PCE of organic photovoltaics.
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U2 - 10.1109/MCSE.2021.3072626
DO - 10.1109/MCSE.2021.3072626
M3 - Article
AN - SCOPUS:85104267043
SN - 1521-9615
VL - 23
SP - 48
EP - 55
JO - Computing in Science and Engineering
JF - Computing in Science and Engineering
IS - 3
M1 - 9403884
ER -