Non-Fullerene Acceptor-Based Solar Cells: From Structural Design to Interface Charge Separation and Charge Transport

Wang, Qungui; Li, Yuanzuo; Song, Peng; Su, Runzhou; Ma, Fang; Yang, Yanhui

doi:10.3390/polym9120692

Cited by 32 publications

(41 citation statements)

References 91 publications

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“…The exciton binding and reorganization energies of NFAs calculated using B3LYP are comparable with reported previously. [35,36] In this work, molecular solvation effects are considered by combining the DFT calculations with the Polarizable Continuum Model. [37] The as a function of was studied by blending the acceptors with a variety of donors at an acceptor concentration of 2 ± 1 wt%, and with a wide energy gap poly(methyl methacrylate) (PMMA) or Bathophenanthroline (Bphen) matrix as a reference.…”

Section: Methodsmentioning

confidence: 99%

Energy Loss in Organic Photovoltaics: Nonfullerene Versus Fullerene Acceptors

Liu

Ding

et al. 2019

Phys. Rev. Applied

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The energy loss experienced by organic photovoltaics (OPVs) is the difference between the lowest photogenerated exciton energy of donor or acceptor and the open circuit energy. It sets a fundamental limit to the open circuit voltage and hence the efficiency of OPVs. This loss can be as large as 0.7 eV for fullerene acceptors, although non-fullerene acceptors (NFAs) reduce this to 0.6 eV. Here, we systematically quantify the relationship between charge transfer energy loss (Δ ), non-radiative recombination loss, exciton binding energy, and intra-and inter-molecular electron-phonon couplings. Density functional theory and comprehensive quantum mechanical modeling is used to associate molecular volume, effective conjugation length, and the nonbonding character of molecules to these several energy losses. Nonradiative recombination in donor/NFA heterojunctions is quantified by the charge transfer state emission quantum yield, and its Frank-Condon shift. Our analytical results are consistent with measurements where Δ is varied between 0 and 0.6 eV using a variety of fullerene derivatives and thiophene-based NFAs paired with donor molecules. Molecular design rules to decrease the energy loss in OPVs derived from our analysis are provided.at the donor-acceptor HJ is: [5] .(Heterojunctions employing fullerene derivatives usually suffer from a loss of > 0.7 eV.Alternatives to fullerene acceptors have therefore been sought to reduce while extending the absorption spectrum into the infrared. The development of nonfullerene acceptors (NFAs)with acceptor-donor-acceptor (a-d-a) or perylene diimide (PDI)-based molecular motifs give freedom to tune the molecular energetics, absorption spectra and thin film morphologies through

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Section: Methodsmentioning

confidence: 99%

Energy Loss in Organic Photovoltaics: Nonfullerene Versus Fullerene Acceptors

Liu

Ding

et al. 2019

Phys. Rev. Applied

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“…Organic solar cells (OSCs) have received wide attention due to their low cost and ease of preparation, but lower power conversion efficiency (PCE) limits its wide application . For polymer solar cells, one promising strategy is to use donor–acceptor (D–A) polymers, which allows for efficient and selective tuning of the highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) by varying the D and A units, respectively . Jørgensen et al trained a grammar variational autoencoder (GVA) model by a dataset of 3,989 monomers obtained from DFT calculations.…”

Section: Machine Learning Application In Energy Materialsmentioning

confidence: 99%

Simulation and design of energy materials accelerated by machine learning

Wang

2019

WIREs Comput Mol Sci

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In the light of mature mathematical algorithms and material database construction, a basic research framework of machine learning (ML) method integrated with computational chemistry toolkits exhibits great potentials and advantages in the field of material researches. In this review, we introduce a work flow of ML in energy materials and demonstrate its recent applications in accelerating the material exploration, especially significant progresses in designing novel catalysts, organic and inorganic battery materials and metal-organic framework materials. As a rising research direction, we also identify the prospects and challenges of ML. More automated and intelligent workflows will be widely used in energy material design with the development of ML. Our review provides a guideline to study and design energy materials in the framework of ML. DatabasesDescription URL AFLOWLIBStructure and property repository from high-throughput ab initio calculations of inorganic materials http://afowlib.org Materials Project Computed properties of known and hypothetical materials carried out using a standard calculation scheme https://materialsproject.org GDB Databases of hypothetical small organic molecules http://gdb.unibe.ch/downloads ZINC Commercially available organic molecules in 2D and 3D formats https://zinc15.docking.org NREL Materials Database Computed properties of materials for renewable energy applications https://materials.nrel.gov Harvard Clean Energy Project Computed properties of candidate organic solar absorber materials https://cepdb.molecularspace.org TEDesignLab Experimental and computed properties to aid the design of new thermoelectric materials

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“…Based on the optimized structures of Jy5-Jy10, we calculated their UV-Vis absorption spectra by employing the time-dependent density functional theory (TD-DFT) method [23] with CAM-B3LYP functional [24] at 6-31G(d) level associated with C-PCM model in dichloromethane solvent. DFT calculations were used to study the excited state features of dye-sensitized solar cells (DSSCs) [25], the photo physical properties of expanded bacteriochlorins [26] and the charge transport in solar cell [27,28]. To obtain the density of state (DOS) and partial density of states (PDOS) of six investigated molecules, the Multiwfn 3.4.1 package [29] was employed.…”

Section: Computational Detailsmentioning

confidence: 99%

“…For organic materials, the barrier of charge transfer can be estimated by ionization potentials (IPs) and electron affinities (EAs) [27,36]. In general, the lower IP means the easier hole transfer, and the higher EA is more favorable for electron transfer [37].…”

Section: Ionization Potentials Electron Affinities and Absolute Harnessmentioning

confidence: 99%

“…At room temperature, the charge transport type is viewed as the incoherent hopping type, that is to say, the carriers jump from one molecule (where they localized) to adjacent molecule [40,43,44]. And the Marcus theory is widely employed to estimate the rate of hole mobility (k h ) at room temperature, which can be expressed as [27,45]:…”

Section: Hole Mobility Rate and Hole Mobility Of Htmsmentioning

confidence: 99%

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Molecular Controlling the Transport Properties for Benzothiadiazole-Based Hole Transport Materials

et al. 2018

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Three experimental hole transport materials containing fluorine-substituted benzothiadiazole-based organic molecules (Jy5–Jy7) have been studied to explore the relationship between photoelectric performances and the core structures of hole transport materials (HTM). By employing density functional theory (DFT) and time-dependent density functional theory (TD-DFT), it was found that the substitution of the hydrogen atom by fluorine atom in the core structure can significantly boost the hole mobility; and the replacement of core structure from electron-withdrawing group to electron-donating group has strong influence on the increment of LUMO level energy, ability to preventing electron-backflow, molecular stability and oscillator strength of HTM molecules. We hope our investigation can provide theoretical guidance to reasonably optimize HTM molecules for perovskite solar cells.

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Non-Fullerene Acceptor-Based Solar Cells: From Structural Design to Interface Charge Separation and Charge Transport

Cited by 32 publications

References 91 publications

Energy Loss in Organic Photovoltaics: Nonfullerene Versus Fullerene Acceptors

Energy Loss in Organic Photovoltaics: Nonfullerene Versus Fullerene Acceptors

Simulation and design of energy materials accelerated by machine learning

Molecular Controlling the Transport Properties for Benzothiadiazole-Based Hole Transport Materials

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