Chain Conformation and Exciton Delocalization in a Push-Pull Conjugated Polymer.

Linear and nonlinear optical line shapes reveal details of excitonic structure in polymer semiconductors. We implement absorption, photoluminescence, and transient absorption spectroscopies in DPP-DTT, an electron push-pull copolymer, to explore the relationship between their spectral line shapes and chain conformation, deduced from resonance Raman spectroscopy and from ab initio calculations. The viscosity of precursor polymer solutions before film casting displays a transition that suggests gel formation above a critical concentration. Upon crossing this viscosity deflection concentration, the line shape analysis of the absorption spectra within a photophysical aggregate model reveals a gradual increase in interchain excitonic coupling. We also observe a red-shifted and line-narrowed steady-state photoluminescence spectrum along with increasing resonance Raman intensity in the stretching and torsional modes of the dithienothiophene unit, which suggests a longer exciton coherence length along the polymer-chain backbone. Furthermore, we observe a change of line shape in the photoinduced absorption component of the transient absorption spectrum. The derivative-like line shape may originate from two possibilities: a new excited-state absorption or Stark effect, both of which are consistent with the emergence of a high-energy shoulder as seen in both photoluminescence and absorption spectra. Therefore, we conclude that the exciton is more dispersed along the polymer chain backbone with increasing concentrations, leading to the hypothesis that polymer chain order is enhanced when the push-pull polymers are processed at higher concentrations. Thus, tuning the microscopic chain conformation by concentration would be another factor of interest when considering the polymer assembly pathways for pursuing large-area and high-performance organic optoelectronic devices.

Computational Approaches for Organic Semiconductors: From Chemical and Physical Understanding to Predicting New Materials.

While a complete understanding of organic semiconductor (OSC) design principles remains elusive, computational methods─ranging from techniques based in classical and quantum mechanics to more recent data-enabled models─can complement experimental observations and provide deep physicochemical insights into OSC structure-processing-property relationships, offering new capabilities for in silico OSC discovery and design. In this Review, we trace the evolution of these computational methods and their application to OSCs, beginning with early quantum-chemical methods to investigate resonance in benzene and building to recent machine-learning (ML) techniques and their application to ever more sophisticated OSC scientific and engineering challenges. Along the way, we highlight the limitations of the methods and how sophisticated physical and mathematical frameworks have been created to overcome those limitations. We illustrate applications of these methods to a range of specific challenges in OSCs derived from π-conjugated polymers and molecules, including predicting charge-carrier transport, modeling chain conformations and bulk morphology, estimating thermomechanical properties, and describing phonons and thermal transport, to name a few. Through these examples, we demonstrate how advances in computational methods accelerate the deployment of OSCsin wide-ranging technologies, such as organic photovoltaics (OPVs), organic light-emitting diodes (OLEDs), organic thermoelectrics, organic batteries, and organic (bio)sensors. We conclude by providing an outlook for the future development of computational techniques to discover and assess the properties of high-performing OSCs with greater accuracy.

The Solution is the Solution: Data-Driven Elucidation of Solution-to-Device Feature Transfer for π-Conjugated Polymer Semiconductors.

The advent of data analytics techniques and materials informatics provides opportunities to accelerate the discovery and development of organic semiconductors for electronic devices. However, the development of engineering solutions is limited by the ability to control thin-film morphology in an immense parameter space. The combination of high-throughput experimentation (HTE) laboratory techniques and data analytics offers tremendous avenues to traverse the expansive domains of tunable variables offered by organic semiconductor thin films. This Perspective outlines the steps required to incorporate a comprehensive informatics methodology into the experimental development of polymer-based organic semiconductor technologies. The translation of solution processing and property metrics to thin-film behavior is crucial to inform efficient HTE for data collection and application of data-centric tools to construct new process-structure-property relationships. We argue that detailed investigation of the solution state prior to deposition in conjunction with thin-film characterization will yield a deeper understanding of the physicochemical mechanisms influencing performance in π-conjugated polymer electronics, with data-driven approaches offering predictive capabilities previously unattainable via traditional experimental means.

Effect of Block Length and Side Chain Length Ratios on Determining a Multicompartment Micelle Structure.

Previous work has identified the importance of the lipophilic-fluorophilic block length ratio Rl in predicting the morphology of linear lipophilic-hydrophilic-fluorophilic (hereafter referred to as BAC) micelle systems. Here, a generalized form R of this structural parameter is developed that makes no assumption of BAC triblock co-polymer linearity, while still providing accurate predictions of the micelle morphology. The morphologies of BAC micelles formed by triblock co-polymers with R≪1 or R≫1 have similar features, with the only notable difference being an inversion of the lipophilic and fluorophilic regions. A destabilization of the single-core micelle structure occurs as R approaches unity from either direction. Finally, the extent to which the micelle morphology depends on the polymer architecture instead of the composition alone is examined, with a decreased patchiness observed in BAC systems with very long block lengths. Through the modification of both the R -value and the polymer architecture, the micelle morphology can be effectively tuned for use in immobilized catalysis and nanoreactor applications.

Structural Tunability of Multicompartment Micelles as a Function of Lipophilic-Fluorophilic Block Length Ratio.

Structural variation in multicompartment micelles consisting of lipophilic-hydrophilic-fluorophilic (hereafter referred to as BAC) triblock copolymers is investigated using the dissipative particle dynamics simulation method. It is demonstrated from our results that the structure of BAC multicompartment micelles is effectively tuned as a function of the lipophilic-fluorophilic ratio parameter, here termed [Formula: see text], of the constituent linear triblock copolymers. In particular, a morphological deviation from onion-like ABC micelles arises in BAC micelle systems as [Formula: see text] increases. The morphologies of BAC micelles with [Formula: see text] or [Formula: see text] display striking similarities, with the only notable difference being an inversion of the lipophilic and fluorophilic regions. When [Formula: see text], segmented worm-like structures with multiple cores are favored in BAC micelle systems. Through this study, it is confirmed that the block length ratio is an effective control parameter to tune the structure of multicompartment micelles.

Molecular Modeling Approach to Determine the Flory-Huggins Interaction Parameter for Polymer Miscibility Analysis.

In this work, we present a thorough procedure for estimating the Flory-Huggins χ-parameter for use in atomistic and mesoscale molecular simulations in computational materials science. In particular, we propose improvements upon traditional Flory-Huggins theory by implementing a Connolly volume normalization (CVN). We apply this technique to several test systems, including a blend of poly (epichlorohydrin) and poly (methyl acrylate), a blend of polyethylene glycol and poly (methyl methacrylate), a blend of polystyrene and deuterated polystyrene, and three molecular-weight variants (monomer, dimer, and trimer) of a triblock copolymer for use in multicompartment micelle applications. Our results demonstrate that the newly developed procedure offers high accuracy and efficiency in predicting the Flory-Huggins χ-parameter for miscibility analysis compared to traditional experimental and computational methods. There are still several factors that cause the magnitude of the χ-parameter to vary between simulations performed on molecular species with the same identity but different degrees of polymerization; although we discuss possible explanations for these factors, this is nonetheless a primary focus for further exploration into this new methodology.