Manipulating the crystal structure of a conjugated polymer for efficient sequentially processed organic solar cells

Regioisomeric thieno[3,4-d]thiazole-based A-Q-D-Q-A-type NIR acceptors for efficient non-fullerene organic solar cells

This study explores the potential of regioisomeric quinoidal-resonance π-spacers in designing near-infrared (NIR) non-fullerene acceptors (NFAs) for high-performance organic solar cell devices. Adopting thienothiazole as the π-spacer, two new isomeric A-Q-D-Q-A NFAs, TzN-S and TzS-S, are designed and synthesized. Both NFAs demonstrate a broad spectral response extended to the NIR region. However, they exhibit different photovoltaic properties when they were mixed with the PCE10 donor to fabricate respective solar cells. The optimal device of TzS-S achieves a PCE of 10.75%, much higher than that of TzN-S based ones (6.13%). The more favorable energetic offset and better molecular packing contribute to the better charge generation and transport, which explains the relative superiority of TzS-S NFA. This work sheds new light on the regioisomeric effect of component materials for optoelectronic applications.

Integration of Machine Learning and Coarse-Grained Molecular Simulations for Polymer Materials: Physical Understandings and Molecular Design

In recent years, the synthesis of monomer sequence-defined polymers has expanded into broad-spectrum applications in biomedical, chemical, and materials science fields. Pursuing the characterization and inverse design of these polymer systems requires our fundamental understanding not only at the individual monomer level, but also considering the chain scales, such as polymer configuration, self-assembly, and phase separation. However, our accessibility to this field is still rudimentary due to the limitations of traditional design approaches, the complexity of chemical space along with the burdened cost and time issues that prevent us from unveiling the underlying monomer sequence-structure-property relationships. Fortunately, thanks to the recent advancements in molecular dynamics simulations and machine learning (ML) algorithms, the bottlenecks in the tasks of establishing the structure-function correlation of the polymer chains can be overcome. In this review, we will discuss the applications of the integration between ML techniques and coarse-grained molecular dynamics (CGMD) simulations to solve the current issues in polymer science at the chain level. In particular, we focus on the case studies in three important topics—polymeric configuration characterization, feed-forward property prediction, and inverse design—in which CGMD simulations are leveraged to generate training datasets to develop ML-based surrogate models for specific polymer systems and designs. By doing so, this computational hybridization allows us to well establish the monomer sequence-functional behavior relationship of the polymers as well as guide us toward the best polymer chain candidates for the inverse design in undiscovered chemical space with reasonable computational cost and time. Even though there are still limitations and challenges ahead in this field, we finally conclude that this CGMD/ML integration is very promising, not only in the attempt of bridging the monomeric and macroscopic characterizations of polymer materials, but also enabling further tailored designs for sequence-specific polymers with superior properties in many practical applications.

Insights into the Structural and Morphological Properties of Layer-by-Layer Processed Organic Photovoltaics

Recently, with the development of figure-of-merit non-fullerene acceptor materials combined with a ternary strategy and layer-by-layer (LbL) processing, the efficiency of single-junction organic solar cells has exceeded 18%. However, the structural properties of LbL-processed films have not been sufficiently elucidated. Herein, we systematically investigate films fabricated via LbL processing of three different systems, including a ternary system. In particular, we focus on the structural and morphological transitions associated with the diffusion process controlled by thermal annealing and an additive solvent. Different diffusion and crystal formation mechanisms were clearly identified, which were observed to be dependent on the characteristics of the upper layer formed during the LbL process. Based on this insight, the photovoltaic properties associated with various LbL conditions are elucidated, and an ideal path toward a better device is suggested.

Layer-by-Layer Solution-Processed Organic Solar Cells with Perylene Diimides as Acceptors

Layer-by-layer (LBL) sequential solution processing of the active layer has been proven as an effective strategy to improve the performance of organic solar cells (OSCs), which could adjust vertical phase separation and improve device performance. Although perylene diimide (PDI) derivatives are typical acceptors with excellent photoelectric properties, there are few studies on PDI-based LBL OSCs. Herein, three PDI acceptors (TBDPDI-C₅, TBDPDI-C₁₁, and SdiPDI) were used to fabricate LBL and bulk heterojunction (BHJ) OSCs, respectively. A series of studies including device optimization, photoluminescence (PL) quenching, dependence of light intensity, carrier mobility, atomic force microscopy (AFM), transmission electron microscopy (TEM), grazing-incidence wide-angle X-ray scattering (GIWAXS), and depth analysis X-ray photoelectron spectroscopy (DXPS) were carried out to make clear the difference of the PDI-based LBL and BHJ OSCs. The results show that LBL OSCs possess better charge transport, higher and more balanced carrier mobility, less exciton recombination loss, more favorable film morphology, and proper vertical component distribution. Therefore, all the three PDI acceptor-based LBL OSCs exhibit higher performance than their BHJ counterparts. Among them, TBDPDI-C₅ performs best with a power conversion efficiency of 6.11% for LBL OSCs, higher than its BHJ OSC (5.14%). It is the first time for PDI small molecular acceptors to fabricate high-efficiency OSCs by using an LBL solution-processed method.