Effect of Blend Composition on Bulk Heterojunction Organic Solar Cells: A Review

Wright, Matthew; Lin, Rui; Tayebjee, Murad J. Y.; Conibeer, Gavin

doi:10.1002/solr.201700035

Cited by 33 publications

(21 citation statements)

References 226 publications

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“…[1][2][3][4][5][6] The power conversion efficiency (PCE) of OPVs has already been achieved around 13% [7][8][9] by virtue of the recent tremendous progress in designing new organic materials and optimizing the device architecture. [1][2][3][4][5][6] The power conversion efficiency (PCE) of OPVs has already been achieved around 13% [7][8][9] by virtue of the recent tremendous progress in designing new organic materials and optimizing the device architecture.…”

Section: Introductionmentioning

confidence: 99%

Toward Predicting Efficiency of Organic Solar Cells via Machine Learning and Improved Descriptors

Sahu

Rao

Troisi

et al. 2018

Advanced Energy Materials

177

220

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include the strong electron-electron interactions and strong electron-phonon couplings as well as the complicated donor/ acceptor (D/A) interface morphology, which are fundamentally different from inorganic semiconductors. [23,24] Therefore, the accurate simulating of OPVs requires high-level theoretical methods in quantum chemistry, quantum dynamics and statistical mechanics, and in recent years there have been substantial progress for the theoretical understanding of many microscopic processes such as charge transport, [25] exciton dissociation, [26,27] singlet fission, [28][29][30] etc. These methods are useful for few benchmark systems but cannot be used to explore the chemical space, i.e., screen a large number of candidate materials.The predictive power of a theoretical model by high-throughput virtual screening has been explored in the recent years. [31][32][33][34][35][36][37][38][39][40] For example, in the Harvard Clean Energy Project (CEP), [41] Aspuru-Guzik and co-workers have screened ≈2.3 million compounds by employing the Scharber model [42,43] to find out efficient new donor molecules for OPVs. Here, the computed energies of the highest occupied molecular orbital (HOMO)/the lowest unoccupied molecular orbital (LUMO) of organic molecules are calibrated to experimentally determined values, and then employing an averaging scheme, energies of HOMO/LUMO are estimated to use them as input in the Scharber model. However, the prediction of PCE of an OPV is much more challenging compared to the energies of orbitals. Very recently, the same research team [44] noticed that the Scharber model, widely used in these virtual screening works, is not accurate enough for the prediction of PCE, and calibration of PCE by considering molecular similarity, molecular weight and band gap energy leads to improvement in correlation between experimental (49 OPVs) and calculated PCE (Pearson's coefficient (r) is increased from 0.30 to 0.43). Further, the Scharber model is only optimized for the PC 61 BM acceptor [42] and a more general model is desired to take account of nonfullerene molecules.In an OPV, energy conversion is accomplished by four consecutive steps: i) absorption of photons and exciton formation, ii) exciton diffusion to D/A interface, iii) exciton dissociation, and iv) transport of holes/electrons to the respective electrode. Taking account of all these processes, the efficiency of a device depends on many microscopic properties of the organic material such as optical gap, charge-carrier mobility, ionization To design efficient materials for organic photovoltaics (OPVs), it is essential to identify the largest number of parameters that control their properties and build a model using these parameters (known as descriptors) for the prediction of the power conversion efficiency (PCE). By constructing a dataset for 280 small molecule OPV systems, it is found that for all high-performing devices, frontier molecular orbitals of donor molecules are nearly degenerated and in such cases, orbitals other than just high...

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Section: Introductionmentioning

confidence: 99%

Toward Predicting Efficiency of Organic Solar Cells via Machine Learning and Improved Descriptors

Sahu

Rao

Troisi

et al. 2018

Advanced Energy Materials

177

220

View full text Add to dashboard Cite

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“…Three BHJ systems, namely, PTB7:PC 71 BM, PTB7‐Th:ITIC, and PBDB‐T:ITIC are chosen to reveal the relationship between FFs and electric field dependent carrier mobilities for different BHJ compositions. For donor:acceptor (D:A) BHJ systems, the physical properties and OPV performances are highly relevant to D:A compositions . Figure shows the chemical structures of the polymer donors and the acceptors.…”

Section: Introductionmentioning

confidence: 99%

“…For donor:acceptor (D:A) BHJ systems, the physical properties and OPV performances are highly relevant to D:A compositions. [18,19] Figure 1 shows the chemical structures of the polymer donors and the acceptors. Table 1 compares the performances of the PCE-optimized OPV cells versus the FF-optimized cells from this work and the literatures.…”

Section: Introductionmentioning

confidence: 99%

Balanced Electric Field Dependent Mobilities: A Key to Access High Fill Factors in Organic Bulk Heterojunction Solar Cells

Yin

Cheung

et al. 2018

Solar RRL

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The compositions of most lab‐based bulk‐heterojunction (BHJ) solar cells are optimized by their power conversion efficiencies (PCEs). In this report, we suggest that the compositions should be optimized by their fill‐factors (FFs) instead. With the optimized‐FF approach, BHJ cells tend to have higher acceptor contents and possess better thermal and operational stabilities. Three model systems, namely, PTB7:PC71BM, PTB7‐Th:ITIC, and PBDB‐T:ITIC BHJs, are chosen as case studies. Charge carrier transport measurements are used to reveal the origin of the enhanced FFs of these BHJ solar cells. We demonstrate that these acceptor‐rich BHJs possess better balanced field‐dependent electron‐to‐hole mobility ratios due to improved electron mobilities near open‐circuit conditions. We introduce a new parameter, known as charge imbalance factor (Δ), to quantify the impact of field dependent mobilities on the FF of the OPV cells. The improved mobility ratio (reduced Δ) suppresses carrier recombinations (especially at the open‐circuit conditions). Despite having slightly reduced PCEs, the FF‐optimized cells enjoy a much better stability. Our results suggest that FF‐optimized BHJ cells with higher acceptor contents should be considered for practical applications, due to better thermal and operational stability.

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“…Before discussing organic dyes and our motivations, it must be understood that in most cases, pi‐conjugated materials for use in OSCs work in what is referred to as a bulk‐heterojunction (BHJ) where two materials, an electron‐donor and an electron‐acceptor, are required for free charge generation and charge transport to electrodes.…”

Section: Introductionmentioning

confidence: 99%

Development of Organic Dye‐Based Molecular Materials for Use in Fullerene‐Free Organic Solar Cells

McAfee

Welch

2018

The Chemical Record

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This personal account describes the pursuit of non‐fullerene acceptors designed from simple and accessible organic pi‐conjugated building blocks and assembled through efficient direct (hetero)arylation cross‐coupling protocols. Initial materials development focused on isoindigo and diketopyrrolopyrrole organic dyes flanked by imide‐based terminal acceptors. Efficiencies in solution‐processed organic solar cells were modest but highlighted the potential of the material design. Materials performance was improved through structural engineering to pair perylene diimide with these organic dyes. Optimization of active layer processing and solar cell device fabrication identified the perylene diimide flanked diketopyrrolopyrrole structure as the best framework, with fullerene‐free organic solar cells achieving power conversion efficiencies above 6 %. This material has met our criteria for a simple wide band gap fullerene alternative for pairing with a range of donor polymers.

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Effect of Blend Composition on Bulk Heterojunction Organic Solar Cells: A Review

Cited by 33 publications

References 226 publications

Toward Predicting Efficiency of Organic Solar Cells via Machine Learning and Improved Descriptors

Toward Predicting Efficiency of Organic Solar Cells via Machine Learning and Improved Descriptors

Balanced Electric Field Dependent Mobilities: A Key to Access High Fill Factors in Organic Bulk Heterojunction Solar Cells

Development of Organic Dye‐Based Molecular Materials for Use in Fullerene‐Free Organic Solar Cells

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