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author:

Lan, Fei (Lan, Fei.) [1] | Jiang, Minlin (Jiang, Minlin.) [2] | Wei, Fanan (Wei, Fanan.) [3] (Scholars:魏发南) | Tao, Quan (Tao, Quan.) [4] | Li, Guangyong (Li, Guangyong.) [5]

Indexed by:

CPCI-S

Abstract:

Nanoscale morphology of the active layer in organic solar cells (OSCs) plays a significant role in affecting the overall performance of OSCs. In this work, by using the domain size to describe the morphology change with different annealing temperatures, the influence of annealing to nanoscale morphology in OSCs is intensively studied. Also, machine learning is utilized in investigating the relation between domain size and the annealing temperature. The realization of mapping the domain sizes to their annealing temperatures is significant in bridging the simulation work of OSCs to the actual fabrication process, indicating that the optimized domain sizes obtained through simulation could now be converted to actual annealing temperature in device fabrication.

Keyword:

Annealing Machine learning Morphology Organic solar cell

Community:

  • [ 1 ] [Lan, Fei]Univ Pittsburgh, Dept Elect & Comp Engn, Pittsburgh, PA 15213 USA
  • [ 2 ] [Jiang, Minlin]Univ Pittsburgh, Dept Elect & Comp Engn, Pittsburgh, PA 15213 USA
  • [ 3 ] [Tao, Quan]Univ Pittsburgh, Dept Elect & Comp Engn, Pittsburgh, PA 15213 USA
  • [ 4 ] [Li, Guangyong]Univ Pittsburgh, Dept Elect & Comp Engn, Pittsburgh, PA 15213 USA
  • [ 5 ] [Wei, Fanan]Fuzhou Univ, Sch Machine Engn & Automat, Fuzhou 350108, Peoples R China

Reprint 's Address:

  • [Lan, Fei]Univ Pittsburgh, Dept Elect & Comp Engn, Pittsburgh, PA 15213 USA

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Source :

2016 IEEE 16TH INTERNATIONAL CONFERENCE ON NANOTECHNOLOGY (IEEE-NANO)

Year: 2016

Page: 329-332

Language: English

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 1

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