Exploring Alkyl Chains in Benzobisthiazole-Naphthobisthiadiazole Polymers: Impact on Solar-Cell Performance, Crystalline Structures, and Optoelectronics

Chirality-enhanced 2D conductive polymer for flexible electronics and chiral sensing applications

Chiral two-dimensional (2D) conductive polymers, encompassing chiral, 2D, flexible, and conductive properties, constitute a novel class of material that remains largely unexplored. The infusion of chirality into 2D conductive polymers taps into the unique characteristics associated with chirality, presenting opportunities to enhance or tailor the electronic, optical, and structural properties of materials for specific technological applications. In this study, we synthesized a chiral 2D PEDOT:PMo11V nanofilm through interfacial polymerization, effectively integrating a chiral monolayer, conductive polymer, and inorganic cluster. The inclusion of inorganic cluster serves to enhance the conductivity of the resulting chiral nanofilm. Furthermore, we demonstrated the chiral nanofilm as a capable electrochemical sensor for detecting drug enantiomers. The inherent flexibility of the chiral nanofilm also lays the groundwork for the development of chiral flexible/wearable devices.

Machine learning of atomic force microscopy images of organic solar cells

The bulk heterojunction structures of organic photovoltaics (OPVs) have been overlooked in their machine learning (ML) approach despite their presumably significant impact on power conversion efficiency (PCE). In this study, we examined the use of atomic force microscopy (AFM) images to construct an ML model for predicting the PCE of polymer : non-fullerene molecular acceptor OPVs. We manually collected experimentally observed AFM images from the literature, applied data curing and performed image analyses (fast Fourier transform, FFT; gray-level co-occurrence matrix, GLCM; histogram analysis, HA) and ML linear regression. The accuracy of the model did not considerably improve even by including AFM data in addition to the chemical structure fingerprints, material properties and process parameters. However, we found that a specific spatial wavelength of FFT (40-65 nm) significantly affects PCE. The GLCM and HA methods, such as homogeneity, correlation and skewness expand the scope of image analysis and artificial intelligence in materials science research fields.

Photochemical Synthesis and Electrochemical and Photophysical Properties of 2,7-Diarylbenzo[1,2-d:4,3-d']bis(thiazoles)

This article focuses on the development of practical approaches to the preparation of benzo[1,2-d:4,3-d']bis(thiazoles) using blue light-induced photochemical cyclization of N,N'-(1,4-aryl)dithioamides in the presence of p-chloranil as a mild oxidant. The proposed method allows to obtain benzo[1,2-d:4,3-d']bis(thiazoles) containing donor substituents in the conjugated chain. Photophysical and (spectro)electrochemical properties of 2,6-di([2,2'-bithiophen]-5-yl)benzo[1,2-d:4,3-d']bis(thiazole) and -benzo[1,2-d:4,5-d']bis(thiazole) are studied in detail. The properties of the synthesized compounds suggest their potential applications for organic electronics.

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

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

Pronounced Backbone Coplanarization by π-Extension in a Sterically Hindered Conjugated Polymer System Leads to Higher Photovoltaic Performance in Non-Fullerene Solar Cells

Achieving both the backbone order and solubility of π-conjugated polymers, which are often in a trade-off relationship, is imperative for maximizing the performance of organic solar cells. Here, we studied three different π-conjugated polymers based on thiazolothiazole (PSTz1 and POTz1) and benzobisthiazole (PNBTz1) that were combined with a benzodithiophene unit in the backbone, where PNBTz1 was newly synthesized. Because of the steric hindrance between the side chains located on neighboring heteroaromatic rings, POTz1 had a much less coplanar backbone than PSTz1 in which such a steric hindrance is absent. However, POTz1 showed higher photovoltaic performance in solar cells that used Y6 as the acceptor material. This was likely due to the significantly higher solubility of POTz1 than PSTz1, resulting in a better morphology. Interestingly, PNBTz1 was found to have markedly higher backbone coplanarity than POTz1, despite having similar steric hindrance between the side chains, most likely owing to the more extended π-electron system, whereas PNBTz1 had good solubility comparable to POTz1. As a result, PNBTz1 exhibited higher photovoltaic performance than POTz1 in the Y6-based cells: specifically, the fill factor was significantly enhanced. Our results indicate that the backbone order and solubility can be achieved by the careful molecular design, which indeed leads to higher photovoltaic performance.

Evaluation-oriented exploration of photo energy conversion systems: from fundamental optoelectronics and material screening to the combination with data science

Light is a form of energy that can be converted to electric and chemical energies. Thus, organic photovoltaics (OPVs), perovskite solar cells (PSCs), photocatalysts, and photodetectors have evolved as scientific and commercial enterprises. However, the complex photochemical reactions and multicomponent materials involved in these systems have hampered rapid progress in their fundamental understanding and material design. This review showcases the evaluation-oriented exploration of photo energy conversion materials by using electrodeless time-resolved microwave conductivity (TRMC) and materials informatics (MI). TRMC with its unique options (excitation sources, environmental control, frequency modulation, etc.) provides not only accelerated experimental screening of OPV and PSC materials but also a versatile route toward shedding light on their charge carrier dynamics. Furthermore, MI powered by machine learning is shown to allow extremely high-throughput exploration in the large molecular space, which is compatible with experimental screening and combinatorial synthesis.

Crucial Role of Fluorine in Fully Alkylated Ladder-Type Carbazole-Based Nonfullerene Organic Solar Cells

Two fused ladder-type nonfullerene acceptors, DTCCIC and DTCCIC-4F, based on an electron-donating alkylated dithienocyclopentacarbazole core flanked by electron-withdrawing nonfluorinated or fluorinated 1,1-dicyanomethylene-3-indanone (IC or IC-4F), are prepared and utilized in organic solar cells (OSCs). The two new molecules reveal planar structures and strong aggregation behavior, and fluorination is shown to red-shift the optical band gap and downshift energy levels. OSCs based on DTCCIC-4F exhibit a power conversion efficiency of 12.6%, much higher than that of DTCCIC-based devices (6.2%). Microstructural studies reveal that while both acceptors are highly crystalline, bulk heterojunction blends based on the nonfluorinated DTCCIC result in overly coarse domains, while blends based on the fluorinated DTCCIC-4F exhibit a more optimal nanoscale morphology. These results highlight the importance of end group fluorination in controlling molecular aggregation and miscibility.

A Simple Phenyl Group Introduced at the Tail of Alkyl Side Chains of Small Molecular Acceptors: New Strategy to Balance the Crystallinity of Acceptors and Miscibility of Bulk Heterojunction Enabling Highly Efficient Organic Solar Cells

Research on fused-ring small-molecular-acceptors (SMAs) has deeply advanced the development of organic solar cells (OSCs). Compared to fruitful studies of ladder-type cores and end-caps of SMAs, the exploration of side chains is monotonous. The widely utilized alkyl and aryl side chains usually produce a conflicting association between SMAs' crystallinity and miscibility. Herein, a fresh idea about the modification of side chains is reported to explore the subtle balance between the crystallinity and miscibility. Specifically, a phenyl is introduced to the tail of the alkyl side chain whereby a new acceptor IDIC-C4Ph is reported. Moderately weakened crystallinity is observed, while maintaining preferred absorption profiles and face-on orientations. Concurrently, remarkably improved heterojunction morphologies and stacking orientations are detected. PBDB-T:IDIC-C4Ph devices exhibit greater efficiency of 11.50% than devices from alky and aryl modified acceptors. Notably, the as-cast OSCs of PBDB-TF:IDIC-C4Ph reveal outstanding FF over 76% with the best efficiency up to 13.23%. The annealed devices reveal further increased efficiency exceeding 14% with the state of the art FF of 78.32%. Overall, an effective but easily navigable approach is demonstrated to modulate the crystallinity of SMAs toward synergistically improved morphologies and molecular orientations of bulk heterojunction enabling highly efficient OSCs.

Computer-Aided Screening of Conjugated Polymers for Organic Solar Cell: Classification by Random Forest

Owing to the diverse chemical structures, organic photovoltaic (OPV) applications with a bulk heterojunction framework have greatly evolved over the last two decades, which has produced numerous organic semiconductors exhibiting improved power conversion efficiencies (PCEs). Despite the recent fast progress in materials informatics and data science, data-driven molecular design of OPV materials remains challenging. We report a screening of conjugated molecules for polymer-fullerene OPV applications by supervised learning methods (artificial neural network (ANN) and random forest (RF)). Approximately 1000 experimental parameters including PCE, molecular weight, and electronic properties are manually collected from the literature and subjected to machine learning with digitized chemical structures. Contrary to the low correlation coefficient in ANN, RF yields an acceptable accuracy, which is twice that of random classification. We demonstrate the application of RF screening for the design, synthesis, and characterization of a conjugated polymer, which facilitates a rapid development of optoelectronic materials.