Organic photovoltaic cells (OPVCs), having received significant attention over the last decade, are yet to be established as viable alternatives to conventional solar cells due to their low power conversion efficiency (PCE). Complex interactions of several phenomena coupled with the lack of understanding regarding the influence of fabrication conditions and nanostructure morphology have been major barriers to realizing higher PCE. To this end, we propose a computational microstructure design framework for designing the active layer of P3HT:PCBM based OPVCs conforming to the bulk heterojunction (BHJ) architecture. The framework pivots around the spectral density function (SDF), a frequency space microstructure characterization, and reconstruction methodology, for microstructure design representation. We validate the applicability of SDF for representing the active layer morphology in OPVCs using images of the nanostructure obtained by cross-sectional scanning tunneling microscopy and spectroscopy (XSTM/S). SDF enables a low-dimensional microstructural representation that is crucial in formulating a parametric-based microstructure optimization scheme. A level-cut Gaussian random field (GRF, governed by SDF) technique is used to generate reconstructions that serve as representative volume elements (RVEs) for structure–performance simulations. A novel structure–performance (SP) simulation approach is developed using a physics-based performance metric, incident photon to converted electron (IPCE) ratio, to account for the impact of microstructural features on OPVC performance. Finally, a SDF-based computational IPCE optimization study incorporating only three design variables results in 36.75% increase in IPCE, underlining the efficacy of the proposed design framework.

References

References
1.
Gleiter
,
H.
,
2000
, “
Nanostructured Materials: Basic Concepts and Microstructure
,”
Acta Mater.
,
48
(
1
), pp.
1
29
.
2.
Lee
,
W. K.
,
Yu
,
S.
,
Engel
,
C. J.
,
Reese
,
T.
,
Rhee
,
D.
, Chen, W., and
Odom
,
T. W.
,
2017
, “
Concurrent Design of Quasi-Random Photonic Nanostructures
,”
Proc. Natl. Acad. Sci. U.S.A.
,
114
(
33
), pp.
8734
8739
.
3.
Wang
,
C.
,
Yu
,
S.
,
Chen
,
W.
, and
Sun
,
C.
,
2013
, “
Highly Efficient Light-Trapping Structure Design Inspired by Natural Evolution
,”
Sci. Rep.
,
3
(
1
), p.
1025
.
4.
Yu
,
S.
,
Wang
,
C.
,
Sun
,
C.
, and
Chen
,
W.
,
2014
, “
Topology Optimization for Light-Trapping Structure in Solar Cells
,”
Struct. Multidiscip. Optim.
,
50
(
3
), pp.
367
382
.
5.
Yu
,
S.
,
Wang
,
C.
,
Zhang
,
Y.
,
Dong
,
B.
,
Jiang
,
Z.
,
Chen
,
X.
, Chen, W., and
Sun
,
C.
,
2017
, “
Design of Non-Deterministic Quasi-Random Nanophotonic Structures Using Fourier Space Representations
,”
Sci. Rep.
,
7
(
1
), p.
3752
.
6.
Yu
,
S.
,
Zhang
,
Y.
,
Wang
,
C.
,
Lee
,
W. K.
,
Dong
,
B.
,
Odom
,
T. W.
,
Sun
,
C.
, and
Chen
,
W.
,
2017
, “
Characterization and Design of Functional Quasi-Random Nanostructured Materials Using Spectral Density Function
,”
ASME J. Mech. Des.
,
139
(
7
), p.
071401
.
7.
Sanchis
,
L.
,
Håkansson
,
A.
,
López-Zanón
,
D.
,
Bravo-Abad
,
J.
, and
Sánchez-Dehesa
,
J.
,
2004
, “
Integrated Optical Devices Design by Genetic Algorithm
,”
Appl. Phys. Lett.
,
84
(
22
), pp.
4460
4462
.
8.
Gondarenko
,
A.
,
Preble
,
S.
,
Robinson
,
J.
,
Chen
,
L.
,
Lipson
,
H.
, and
Lipson
,
M.
,
2006
, “
Spontaneous Emergence of Periodic Patterns in a Biologically Inspired Simulation of Photonic Structures
,”
Phys. Rev. Lett.
,
96
(
14
), p.
143904
.
9.
Jensen
,
J. S.
, and
Sigmund
,
O.
,
2011
, “
Topology Optimization for Nano-Photonics
,”
Laser Photonics Rev.
,
5
(
2
), pp.
308
321
.
10.
Imboden
,
M.
, and
Bishop
,
D.
,
2014
, “
Top-down Nanomanufacturing
,”
Phys. Today
,
67
(
12
), pp.
45
50
.
11.
Kinoshita
,
S.
,
Yoshioka
,
S.
, and
Miyazaki
,
J.
,
2008
, “
Physics of Structural Colors
,”
Rep. Prog. Phys.
,
71
(
7
), p.
076401
.
12.
Vukusic
,
P.
, and
Sambles
,
J. R.
,
2003
, “
Photonic Structures in Biology
,”
Nature
,
424
(
6950
), p.
852
.
13.
Dufresne
,
E. R.
,
Noh
,
H.
,
Saranathan
,
V.
,
Mochrie
,
S. G.
,
Cao
,
H.
, and
Prum
,
R. O.
,
2009
, “
Self-Assembly of Amorphous Biophotonic Nanostructures by Phase Separation
,”
Soft Matter
,
5
(
9
), pp.
1792
1795
.
14.
Dong
,
B. Q.
,
Zhan
,
T. R.
,
Liu
,
X. H.
,
Jiang
,
L. P.
,
Liu
,
F.
,
Hu
,
X. H.
, and
Zi
,
J.
,
2011
, “
Optical Response of a Disordered Bicontinuous Macroporous Structure in the Longhorn Beetle Sphingnotus Mirabilis
,”
Phys. Rev. E
,
84
(
1
), p.
011915
.
15.
Walker
,
B.
,
Tamayo
,
A. B.
,
Dang
,
X. D.
,
Zalar
,
P.
,
Seo
,
J. H.
,
Garcia
,
A.
,
Tantiwiwat
,
M.
, and
Nguyen
,
T. Q.
,
2009
, “
Nanoscale Phase Separation and High Photovoltaic Efficiency in Solution-Processed, Small-Molecule Bulk Heterojunction Solar Cells
,”
Adv. Funct. Mater.
,
19
(
19
), pp.
3063
3069
.
16.
Peet
,
J.
,
Heeger
,
A. J.
, and
Bazan
,
G. C.
,
2009
, “
Plastic Solar Cells: Self-Assembly of Bulk Heterojunction Nanomaterials by Spontaneous Phase Separation
,”
Acc. Chem. Res.
,
42
(
11
), pp.
1700
1708
.
17.
Lee
,
W.-K.
,
Jung
,
W.-B.
,
Nagel
,
S. R.
, and
Odom
,
T. W.
,
2016
, “
Stretchable Superhydrophobicity From Monolithic, Three-Dimensional Hierarchical Wrinkles
,”
Nano Lett.
,
16
(
6
), pp.
3774
3779
.
18.
Zhang
,
Y.
,
Dong
,
B.
,
Chen
,
A.
,
Liu
,
X.
,
Shi
,
L.
, and
Zi
,
J.
,
2015
, “
Using Cuttlefish Ink as an Additive to Produce Non-Iridescent Structural Colors of High Color Visibility
,”
Adv. Mater.
,
27
(
32
), pp.
4719
4724
.
19.
Biswas
,
A.
,
Bayer
,
I. S.
,
Biris
,
A. S.
,
Wang
,
T.
,
Dervishi
,
E.
, and
Faupel
,
F.
,
2012
, “
Advances in Top–down and Bottom–Up Surface Nanofabrication: Techniques, Applications and Future Prospects
,”
Adv. Colloid Interface Sci.
,
170
(
1–2
), pp.
2
27
.
20.
Brabec
,
C.
,
Scherf
,
U.
, and
Dyakonov
,
V.
,
2011
,
Organic Photovoltaics: Materials, Device Physics, and Manufacturing Technologies
,
Wiley
, Hoboken, NJ.
21.
Brabec
,
C. J.
,
2004
, “
Organic Photovoltaics: Technology and Market
,”
Sol. Energy Mater. Sol. Cells
,
83
(
2–3
), pp.
273
292
.
22.
Kippelen
,
B.
, and
Brédas
,
J.-L.
,
2009
, “
Organic Photovoltaics
,”
Energy Environ. Sci.
,
2
(
3
), pp.
251
261
.
23.
Brabec
,
C. J.
,
Dyakonov
,
V.
,
Parisi
,
J.
, and
Sariciftci
,
N. S.
,
2013
,
Organic Photovoltaics: Concepts and Realization
,
Springer Science & Business Media
, New York.
24.
Heeger
,
A. J.
,
2001
, “
Nobel Lecture: Semiconducting and Metallic Polymers: The Fourth Generation of Polymeric Materials
,”
Rev. Mod. Phys.
,
73
(
3
), p.
681
.
25.
Berger
,
P.
, and
Kim
,
M.
,
2018
, “
Polymer Solar Cells: P3HT:PCBM and Beyond
,”
J. Renewable Sustainable Energy
,
10
(
1
), p.
013508
.
26.
Grancini
,
G.
,
Polli
,
D.
,
Fazzi
,
D.
,
Cabanillas-Gonzalez
,
J.
,
Cerullo
,
G.
, and
Lanzani
,
G.
,
2011
, “
Transient Absorption Imaging of P3HT:PCBM Photovoltaic Blend: Evidence for Interfacial Charge Transfer State
,”
J. Phys. Chem. Lett.
,
2
(
9
), pp.
1099
1105
.
27.
Dang
,
M. T.
,
Hirsch
,
L.
, and
Wantz
,
G.
,
2011
, “
P3HT:PCBM, Best Seller in Polymer Photovoltaic Research
,”
Adv. Mater.
,
23
(
31
), pp.
3597
3602
.
28.
McDowell
,
D. L.
,
Panchal
,
J.
,
Choi
,
H.-J.
,
Seepersad
,
C.
,
Allen
,
J.
, and
Mistree
,
F.
,
2009
,
Integrated Design of Multiscale, Multifunctional Materials and Products
,
Butterworth-Heinemann
, Oxford, UK.
29.
Mistree
,
F.
,
2002
, “
Robust Concept Exploration Methods in Materials Design
,”
Ninth AIAA/ISSMO Symposium on Multidisciplinary Analysis and Optimization
, p.
5568
.
30.
Olson
,
G. B.
,
1997
, “
Computational Design of Hierarchically Structured Materials
,”
Science
,
277
(
5330
), pp.
1237
1242
.
31.
Matthews
,
J.
,
Klatt
,
T.
,
Morris
,
C.
,
Seepersad
,
C. C.
,
Haberman
,
M.
, and
Shahan
,
D.
,
2016
, “
Hierarchical Design of Negative Stiffness Metamaterials Using a Bayesian Network Classifier
,”
ASME J. Mech. Des.
,
138
(
4
), p.
041404
.
32.
Liu
,
K.
,
Detwiler
,
D.
, and
Tovar
,
A.
,
2017
, “
Optimal Design of Nonlinear Multimaterial Structures for Crashworthiness Using Cluster Analysis
,”
ASME J. Mech. Des.
,
139
(
10
), p.
101401
.
33.
Seepersad
,
C. C.
,
Allen
,
J. K.
,
McDowell
,
D. L.
, and
Mistree
,
F.
,
2006
, “
Robust Design of Cellular Materials With Topological and Dimensional Imperfections
,”
ASME J. Mech. Des.
,
128
(
6
), pp.
1285
1297
.
34.
McDowell
,
D. L.
, and
Olson
,
G.
,
2008
, “
Concurrent Design of Hierarchical Materials and Structures
,”
Sci. Model. Simul.
,
Springer
,
15
(1), pp. 207–240.
35.
Fullwood
,
D. T.
,
Niezgoda
,
S. R.
,
Adams
,
B. L.
, and
Kalidindi
,
S. R.
,
2010
, “
Microstructure Sensitive Design for Performance Optimization
,”
Prog. Mater. Sci.
,
55
(
6
), pp.
477
562
.
36.
Şopu
,
D.
,
Soyarslan
,
C.
,
Sarac
,
B.
,
Bargmann
,
S.
,
Stoica
,
M.
, and
Eckert
,
J.
,
2016
, “
Structure-Property Relationships in Nanoporous Metallic Glasses
,”
Acta Mater.
,
106
, pp.
199
207
.
37.
Gupta
,
A.
,
Cecen
,
A.
,
Goyal
,
S.
,
Singh
,
A. K.
, and
Kalidindi
,
S. R.
,
2015
, “
Structure–Property Linkages Using a Data Science Approach: Application to a Non-Metallic Inclusion/Steel Composite System
,”
Acta Mater.
,
91
, pp.
239
254
.
38.
Çeçen
,
A.
,
Fast
,
T.
,
Kumbur
,
E.
, and
Kalidindi
,
S.
,
2014
, “
A Data-Driven Approach to Establishing Microstructure–Property Relationships in Porous Transport Layers of Polymer Electrolyte Fuel Cells
,”
J. Power Sources
,
245
, pp.
144
153
.
39.
Cecen
,
A.
,
Dai
,
H.
,
Yabansu
,
Y. C.
,
Kalidindi
,
S. R.
, and
Song
,
L.
,
2018
, “
Material Structure-Property Linkages Using Three-Dimensional Convolutional Neural Networks
,”
Acta Mater.
,
146
, pp.
76
84
.
40.
Xu
,
H.
,
Li
,
Y.
,
Brinson
,
C.
, and
Chen
,
W.
,
2014
, “
A Descriptor-Based Design Methodology for Developing Heterogeneous Microstructural Materials System
,”
ASME J. Mech. Des.
,
136
(
5
), p.
051007
.
41.
Zhang
,
Y.
,
Zhao
,
H.
,
Hassinger
,
I.
,
Brinson
,
L. C.
,
Schadler
,
L. S.
, and
Chen
,
W.
,
2015
, “
Microstructure Reconstruction and Structural Equation Modeling for Computational Design of Nanodielectrics
,”
Integrating Mater. Manuf. Innovation
,
4
(
1
), p.
14
.
42.
Bostanabad
,
R.
,
Zhang
,
Y.
,
Li
,
X.
,
Kearney
,
T.
,
Brinson
,
L. C.
,
Apley
,
D. W.
,
Wing
,
K. L.
, and
Chen
,
W.
,
2018
, “
Computational Microstructure Characterization and Reconstruction: Review of the State-of-the-Art Techniques
,”
Prog. Mater. Sci.
,
95
, pp.
1
41
.
43.
Liu
,
Y.
,
Greene
,
M. S.
,
Chen
,
W.
,
Dikin
,
D. A.
, and
Liu
,
W. K.
,
2013
, “
Computational Microstructure Characterization and Reconstruction for Stochastic Multiscale Material Design
,”
Comput. Aided Des.
,
45
(
1
), pp.
65
76
.
44.
Yeong
,
C.
, and
Torquato
,
S.
,
1998
, “
Reconstructing Random Media—II: Three-Dimensional Media From Two-Dimensional Cuts
,”
Phys. Rev. E
,
58
(
1
), p.
224
.
45.
Yeong
,
C.
, and
Torquato
,
S.
,
1998
, “
Reconstructing Random Media
,”
Phys. Rev. E
,
57
(
1
), p.
495
.
46.
Xu
,
H.
,
Dikin
,
D. A.
,
Burkhart
,
C.
, and
Chen
,
W.
,
2014
, “
Descriptor-Based Methodology for Statistical Characterization and 3D Reconstruction of Microstructural Materials
,”
Comput. Mater. Sci.
,
85
, pp.
206
216
.
47.
Bostanabad
,
R.
,
Bui
,
A. T.
,
Xie
,
W.
,
Apley
,
D. W.
, and
Chen
,
W.
,
2016
, “
Stochastic Microstructure Characterization and Reconstruction Via Supervised Learning
,”
Acta Mater.
,
103
, pp.
89
102
.
48.
Sundararaghavan
,
V.
, and
Zabaras
,
N.
,
2005
, “
Classification and Reconstruction of Three-Dimensional Microstructures Using Support Vector Machines
,”
Comput. Mater. Sci.
,
32
(
2
), pp.
223
239
.
49.
Cang
,
R.
,
Xu
,
Y.
,
Chen
,
S.
,
Liu
,
Y.
,
Jiao
,
Y.
, and
Ren
,
M. Y.
,
2017
, “
Microstructure Representation and Reconstruction of Heterogeneous Materials Via Deep Belief Network for Computational Material Design
,”
ASME J. Mech. Des.
,
139
(
7
), p.
071404
.
50.
Breneman
,
C. M.
,
Brinson
,
L. C.
,
Schadler
,
L. S.
,
Natarajan
,
B.
,
Krein
,
M.
,
Wu
,
K.
,
Morkowchuk
,
L.
,
Li
,
Y.
,
Deng
,
H.
, and
Xu
,
H.
,
2013
, “
Stalking the Materials Genome: A Data-Driven Approach to the Virtual Design of Nanostructured Polymers
,”
Adv. Funct. Mater.
,
23
(
46
), pp.
5746
5752
.
51.
Hassinger
,
I.
,
Li
,
X.
,
Zhao
,
H.
,
Xu
,
H.
,
Huang
,
Y.
,
Prasad
,
A.
,
Schadler
,
L.
,
Chen
,
W.
, and
Brinson
,
L. C.
,
2016
, “
Toward the Development of a Quantitative Tool for Predicting Dispersion of Nanocomposites Under Non-Equilibrium Processing Conditions
,”
J. Mater. Science
,
51
(
9
), pp.
4238
4249
.
52.
Xu
,
H.
,
Liu
,
R.
,
Choudhary
,
A.
, and
Chen
,
W.
,
2015
, “
A Machine Learning-Based Design Representation Method for Designing Heterogeneous Microstructures
,”
ASME J. Mech. Des.
,
137
(
5
), p.
051403
.
53.
van Lare
,
M.-C.
, and
Polman
,
A.
,
2015
, “
Optimized Scattering Power Spectral Density of Photovoltaic Light-Trapping Patterns
,”
ACS Photonics
,
2
(
7
), pp.
822
831
.
54.
Jin
,
R.
,
Chen
,
W.
, and
Sudjianto
,
A.
,
2005
, “
An Efficient Algorithm for Constructing Optimal Design of Computer Experiments
,”
J. Stat. Plann. Inference
,
134
(
1
), pp.
268
287
.
55.
Kleijnen
,
J. P.
,
2009
, “
Kriging Metamodeling in Simulation: A Review
,”
Eur. J. Oper. Res.
,
192
(
3
), pp.
707
716
.
56.
Zhang
,
X. Y.
,
Trame
,
M.
,
Lesko
,
L.
, and
Schmidt
,
S.
,
2015
, “
Sobol Sensitivity Analysis: A Tool to Guide the Development and Evaluation of Systems Pharmacology Models
,”
CPT: Pharmacometrics Syst. Pharmacol.
,
4
(
2
), pp.
69
79
.
57.
Brigham
,
E. O.
,
1988
,
The Fast Fourier Transform and Its Applications
,
Prentice Hall
,
Englewood Cliffs, NJ
.
58.
Chatfield
,
C.
,
1996
,
The Analysis of Time Series: An Introduction
,
5th ed.
,
Chapman and Hall
,
London; New York
.
59.
Panchal
,
J. H.
,
Kalidindi
,
S. R.
, and
McDowell
,
D. L.
,
2013
, “
Key Computational Modeling Issues in Integrated Computational Materials Engineering
,”
Comput. Aided Des.
,
45
(
1
), pp.
4
25
.
60.
Fullwood
,
D. T.
,
Niezgoda
,
S. R.
, and
Kalidindi
,
S. R.
,
2008
, “
Microstructure Reconstructions From 2-Point Statistics Using Phase-Recovery Algorithms
,”
Acta Mater.
,
56
(
5
), pp.
942
948
.
61.
Kadem
,
B.
,
Cranton
,
W.
, and
Hassan
,
A.
,
2015
, “
Metal Salt Modified PEDOT: PSS as Anode Buffer Layer and Its Effect on Power Conversion Efficiency of Organic Solar Cells
,”
Org. Electron.
,
24
, pp.
73
79
.
62.
Zhao
,
W.
,
Li
,
S.
,
Yao
,
H.
,
Zhang
,
S.
,
Zhang
,
Y.
,
Yang
,
B.
, and
Hou
,
J.
,
2017
, “
Molecular Optimization Enables Over 13% Efficiency in Organic Solar Cells
,”
J. Am. Chem. Soc.
,
139
(
21
), pp.
7148
7151
.
63.
Li
,
M.
,
Gao
,
K.
,
Wan
,
X.
,
Zhang
,
Q.
,
Kan
,
B.
,
Xia
,
R.
,
Liu
,
F.
,
Yang
,
X.
,
Feng
,
H.
,
Ni
,
W.
, and
Wang
,
Y.
,
2017
, “
Solution-Processed Organic Tandem Solar Cells With Power Conversion Efficiencies >12%
,”
Nat. Photonics
,
11
(
2
), p.
85
.
64.
Wang
,
A.
, and
Chien
,
T.
,
2018
, “
Perspectives of Cross-Sectional Scanning Tunneling Microscopy and Spectroscopy for Complex Oxide Physics
,”
Phys. Lett. A
,
382
(
11
), pp.
739
748
.
65.
Shih
,
M. C.
,
Huang
,
B. C.
,
Lin
,
C. C.
,
Li
,
S. S.
,
Chen
,
H. A.
,
Chiu
,
Y. P.
, and
Chen
,
C. W.
,
2013
, “
Atomic-Scale Interfacial Band Mapping Across Vertically Phased-Separated Polymer/Fullerene Hybrid Solar Cells
,”
Nano Lett.
,
13
(
6
), pp.
2387
2392
.
66.
Yost
,
A. J.
,
Pimachev
,
A.
,
Ho
,
C. C.
,
Darling
,
S. B.
,
Wang
,
L.
,
Su
,
W. F.
,
Dahnovsky
,
Y.
, and
Chien
,
T.
,
2016
, “
Coexistence of Two Electronic Nano-Phases on a CH3NH3PbI3–x Cl x Surface Observed in STM Measurements
,”
ACS Appl. Mater. Interfaces
,
8
(
42
), pp.
29110
29116
.
67.
Tumbleston
,
J. R.
,
Ko
,
D.-H.
,
Samulski
,
E. T.
, and
Lopez
,
R.
,
2010
, “
Nonideal Parasitic Resistance Effects in Bulk Heterojunction Organic Solar Cells
,”
J. Appl. Phys.
,
108
(
8
), p.
084514
.
68.
Mikhnenko
,
O. V.
,
Azimi
,
H.
,
Scharber
,
M.
,
Morana
,
M.
,
Blom
,
P. W. M.
, and
Loi
,
M. A.
,
2012
, “
Exciton Diffusion Length in Narrow Bandgap Polymers
,”
Energy Environ. Sci.
,
5
(
5
), p.
6960
.
69.
Mihailetchi
,
V. D.
,
Xie
,
H. X.
,
De Boer
,
B.
,
Koster
,
L. J. A.
, and
Blom
,
P. W. M.
,
2006
, “
Charge Transport and Photocurrent Generation in Poly(3-Hexylthiophene): Methanofullerene Bulk-Heterojunction Solar Cells
,”
Adv. Funct. Mater.
,
16
(
5
), pp.
699
708
.
70.
Park
,
S. H.
,
Roy
,
A.
, Beaupré, S.,
Cho
,
S.
,
Coates
,
N.
,
Moon
,
J. S.
,
Moses
,
D.
,
Leclerc
,
M.
,
Lee
,
K.
, and
Heeger
,
A. J.
,
2009
, “
Bulk Heterojunction Solar Cells With Internal Quantum Efficiency Approaching 100%
,”
Nat. Photonics
,
3
(
5
), p.
297
.
71.
Reyes-Reyes
,
M.
,
Kim
,
K.
, and
Carroll
,
D. L.
,
2005
, “
High-Efficiency Photovoltaic Devices Based on Annealed Poly(3-Hexylthiophene) and 1-(3-Methoxycarbonyl)-Propyl-1-Phenyl-(6,6)C61 Blends
,”
Appl. Phys. Lett.
,
87
(
8
), p.
083506
.
72.
Park
,
J.-S.
,
1994
, “
Optimal Latin-Hypercube Designs for Computer Experiments
,”
J. Stat. Plann. Inference
,
39
(
1
), pp.
95
111
.
73.
Jin
,
R.
,
Chen
,
W.
, and
Simpson
,
T. W.
,
2001
, “
Comparative Studies of Metamodelling Techniques Under Multiple Modelling Criteria," Computer-Aided Optimal Design of Stressed Solids
,”
Struct. Multidiscip. Syst.
,
23
(
1
), pp.
1
13
.
74.
Sun
,
Y.
,
Han
,
Y.
, and
Liu
,
J.
,
2013
, “
Controlling PCBM Aggregation in P3HT/PCBM Film by a Selective Solvent Vapor Annealing
,”
Chin. Sci. Bull.
,
58
(
22
), pp.
2767
2774
.
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