Renewed Prospects for Organic Photovoltaics

Chemical Reviews, Journal Year: 2022, Volume and Issue: 122(18), P. 14180 - 14274

Published: Aug. 5, 2022

Organic photovoltaics (OPVs) have progressed steadily through three stages of photoactive materials development: (i) use poly(3-hexylthiophene) and fullerene-based acceptors (FAs) for optimizing bulk heterojunctions; (ii) development new donors to better match with FAs; (iii) non-fullerene (NFAs). The application NFAs an A–D–A configuration (where A = acceptor D donor) has enabled devices efficient charge generation small energy losses (Eloss < 0.6 eV), resulting in substantially higher power conversion efficiencies (PCEs) than FA-based devices. discovery Y6-type (Y6 2,2′-((2Z,2′Z)-((12,13-bis(2-ethylhexyl)-3,9-diundecyl-12,13-dihydro-[1,2,5]-thiadiazolo[3,4-e]-thieno[2″,3″:4′,5′]thieno-[2′,3′:4,5]pyrrolo-[3,2-g]thieno-[2′,3′:4,5]thieno-[3,2-b]indole-2,10-diyl)bis(methanylylidene))bis(5,6-difluoro-3-oxo-2,3-dihydro-1H-indene-2,1-diylidene))dimalononitrile) A–DA′ D–A further propelled the PCEs go beyond 15% due smaller Eloss values (∼0.5 eV) external quantum efficiencies. Subsequently, Y6-series single-junction increased >19% may soon approach 20%. This review provides update recent progress OPV following aspects: developments novel donors, understanding structure–property relationships underlying mechanisms state-of-the-art OPVs, tasks underpinning commercialization such as device stability, module development, potential applications, high-throughput manufacturing. Finally, outlook prospects section summarizes remaining challenges technology.

Language: Английский

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A time and resource efficient machine learning assisted design of non-fullerene small molecule acceptors for P3HT-based organic solar cells and green solvent selection

Journal of Materials Chemistry A, Journal Year: 2021, Volume and Issue: 9(28), P. 15684 - 15695

Published: Jan. 1, 2021

A time and money efficient machine learning assisted design of non-fullerene small molecule acceptors for P3HT based organic solar cells is reported. Green solvents are also selected using predicted Hansen solubility parameters.

Language: Английский

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Machine learning advancements in organic synthesis: A focused exploration of artificial intelligence applications in chemistry

Artificial Intelligence Chemistry, Journal Year: 2024, Volume and Issue: 2(1), P. 100049 - 100049

Published: Jan. 19, 2024

Artificial intelligence (AI) is driving a revolution in chemistry, reshaping the landscape of molecular design. This review explores AI's pivotal roles field organic synthesis applications. AI accurately predicts reaction outcomes, controls chemical selectivity, simplifies planning, accelerates catalyst discovery, and fuels material innovation so on. It seamlessly integrates data-driven algorithms with intuition to redefine As chemistry advances, it promises accelerated research, sustainability, innovative solutions chemistry's pressing challenges. The fusion poised shape field's future profoundly, offering new horizons precision efficiency. encapsulates transformation marking moment where data converge revolutionize world molecules.

Language: Английский

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An integrated high-throughput robotic platform and active learning approach for accelerated discovery of optimal electrolyte formulations

Nature Communications, Journal Year: 2024, Volume and Issue: 15(1)

Published: March 29, 2024

Abstract Solubility of redox-active molecules is an important determining factor the energy density in redox flow batteries. However, advancement electrolyte materials discovery has been constrained by absence extensive experimental solubility datasets, which are crucial for leveraging data-driven methodologies. In this study, we design and investigate a highly automated workflow that synergizes high-throughput experimentation platform with state-of-the-art active learning algorithm to significantly enhance organic solvents. Our identifies multiple solvents achieve remarkable threshold exceeding 6.20 M archetype molecule, 2,1,3-benzothiadiazole, from comprehensive library more than 2000 potential Significantly, our integrated strategy necessitates assessments fewer 10% these candidates, underscoring efficiency approach. results also show binary solvent mixtures, particularly those incorporating 1,4-dioxane, instrumental boosting 2,1,3-benzothiadiazole. Beyond designing efficient developing high-performance batteries, machine learning-guided robotic presents robust general approach expedited functional materials.

Language: Английский

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Materials genome engineering accelerates the research and development of organic and perovskite photovoltaics

Materials Genome Engineering Advances, Journal Year: 2024, Volume and Issue: 2(1)

Published: March 1, 2024

Abstract The emerging photovoltaic (PV) technologies, such as organic and perovskite PVs, have the characteristics of complex compositions processing, resulting in a large multidimensional parameter space for development optimization technologies. Traditional manual methods are time‐consuming labor‐intensive screening optimizing material properties. Materials genome engineering (MGE) advances an innovative approach that combines efficient experimentation, big database artificial intelligence (AI) algorithms to accelerate materials research development. High‐throughput (HT) platforms perform experimental tasks rapidly, providing amount reliable consistent data creation databases. Therefore, novel combining HT AI can design application, which is beneficial establishing material‐processing‐property relationships overcoming bottlenecks PV This review introduces key technologies involved MGE overviews accelerating role field PVs.

Language: Английский

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Boosting the Performances of Semitransparent Organic Photovoltaics via Synergetic Near‐Infrared Light Management

Advanced Materials, Journal Year: 2024, Volume and Issue: 36(18)

Published: Jan. 25, 2024

Abstract Semitransparent organic photovoltaics (ST‐OPVs) offer promising prospects for application in building‐integrated photovoltaic systems and greenhouses, but further improvement of their performance faces a delicate trade‐off between the two competing indexes power conversion efficiency (PCE) average visible transmittance (AVT). Herein, authors take advantage coupling plasmonics with optical design ST‐OPVs to enhance near‐infrared absorption hence simultaneously improve transparency maximum extent. By integrating core–bishell PdCu@Au@SiO 2 nanotripods that act as optically isotropic Lambertian sources near‐infrared‐customized localized surface plasmon resonance an optimal ternary PM6:BTP‐eC9:L8‐BO‐based ST‐OPV, it is shown interplay multilayer layer, consisting ZnS(130 nm)/Na 3 AlF 6 (60 nm)/WO (100 nm)/LaF (50 nm) identified from high‐throughput screening, leads record‐high PCE 16.14% (certified 15.90%) along excellent AVT 33.02%. The strong enhancement light utilization by ≈50% compared counterpart device without engineering provides encouraging universal pathway promoting breakthroughs meticulous design.

Language: Английский

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Machine Learning Approaches in Polymer Science: Progress and Fundamental for a New Paradigm

SmartMat, Journal Year: 2025, Volume and Issue: 6(1)

Published: Jan. 9, 2025

ABSTRACT Machine learning (ML), material genome, and big data approaches are highly overlapped in their strategies, algorithms, models. They can target various definitions, distributions, correlations of concerned physical parameters given polymer systems, have expanding applications as a new paradigm indispensable to conventional ones. Their inherent advantages building quantitative multivariate largely enhanced the capability scientific understanding discoveries, thus facilitating mechanism exploration, prediction, high‐throughput screening, optimization, rational inverse designs. This article summarizes representative progress recent two decades focusing on design, preparation, application, sustainable development materials based exploration key composition–process–structure–property–performance relationship. The integration both data‐driven insights through ML deepen fundamental discover novel is categorically presented. Despite construction application robust models, strategies algorithms deal with variant tasks science still rapid growth. challenges prospects then We believe that innovation will thrive along approaches, from efficient design applications.

Language: Английский

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Accelerating organic solar cell material's discovery: high-throughput screening and big data

Energy & Environmental Science, Journal Year: 2021, Volume and Issue: 14(6), P. 3301 - 3322

Published: Jan. 1, 2021

This review article presents the state-of-the-art in high-throughput computational and experimental screening routines with application organic solar cells, including materials discovery, device optimization machine-learning algorithms.

Language: Английский

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Machine Learning-Assisted Development of Organic Solar Cell Materials: Issues, Analyses, and Outlooks

The Journal of Physical Chemistry Letters, Journal Year: 2021, Volume and Issue: 12(51), P. 12391 - 12401

Published: Dec. 23, 2021

Nonfullerene, a small molecular electron acceptor, has substantially improved the power conversion efficiency of organic photovoltaics (OPVs). However, large structural freedom π-conjugated polymers and molecules makes it difficult to explore with limited resources. Machine learning, which is based on rapidly growing artificial intelligence technology, high-throughput method accelerate speed material design process optimization; however, suffers from limitations in terms prediction accuracy, interpretability, data collection, available (particularly, experimental data). This recognition motivates present Perspective, focuses utilizing set for ML efficiently aid OPV research. Perspective discusses trends ML-OPV publications, NFA category, effects size explanatory variables (fingerprints or Mordred descriptors) accuracy explainability, broadens scope would be useful development next-generation solar cell materials.

Language: Английский

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Hexanary blends: a strategy towards thermally stable organic photovoltaics

Nature Communications, Journal Year: 2023, Volume and Issue: 14(1)

Published: Aug. 1, 2023

Non-fullerene based organic solar cells display a high initial power conversion efficiency but continue to suffer from poor thermal stability, especially in case of devices with thick active layers. Mixing five structurally similar acceptors electron affinities, and blending donor polymer is explored, yielding up 17.6%. The hexanary device performance unaffected by annealing the bulk-heterojunction layer for at least 23 days 130 °C dark an inert atmosphere. Moreover, blends offer degree stability thickness 390 nm, which advantageous high-throughput processing cells. Here, generic strategy on multi-component acceptor mixtures presented that permits considerably improve non-fullerene thus paves way large-area

Language: Английский

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