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Polymer-based semiconductors and organic electronics encapsulate a significant research thrust for informatics-driven materials development. However, device measurements are described by a complex array of design and parameter choices, many of which are sparsely reported. For example, the mobility of a polymer-based organic field-effect transistor (OFET) may vary by several orders of magnitude for a given polymer as a plethora of parameters related to solution processing, interface design/surface treatment, thin-film deposition, postprocessing, and measurement settings have a profound effect on the value of the final measurement. Incomplete contextual, experimental details hamper the availability of reusable data applicable for data-driven optimization, modeling (e.g., machine learning), and analysis of new organic devices. To curate organic device databases that contain reproducible and findable, accessible, interoperable, and reusable (FAIR) experimental data records, data ontologies that fully describe sample provenance and process history are required. However, standards for generating such process ontologies are not widely adopted for experimental materials domains. In this work, we design and implement an object−relational database for storing experimental records of OFETs. A data structure is generated by drawing on an international standard for batch process control (ISA-88) to facilitate the design. We then mobilize these representative data records, curated from the literature and laboratory experiments, to enable data-driven learning of process−structure−property relationships. The work presented herein opens the door for the broader adoption of data management practices and design standards for both the organic electronics and the wider materials community. 

Linear and nonlinear optical lineshapes reveal details of excitonic structure in semiconductor polymers. We implement absorption, photoluminescence, and transient absorption spectroscopies in DPP-DTT, an electron push-pull copolymer, to explore the relationship between their spectral lineshapes 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 lineshape 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 dithienothiphene unit, which suggests a longer exciton coherence length along the polymer-chain backbone. Furthermore, we observe a change of lineshape in the photoinduced absorption component of the transient absorption spectrum. The derivative-like lineshape may originate from two possibilities: a new excited-state absorption, or from Stark effect, both of which are consistent with the emergence of 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 the 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. 

The development of high-throughput experimentation (HTE) methods to efficiently screen multiparameter spaces is key to accelerating the discovery of high-performance multicomponent materials (e.g., polymer blends, colloids, etc.) for sensors, separations, energy, coatings, and other thin-film applications relevant to society. Although the generation and characterization of gradient thin-film library samples is a common approach to enable materials HTE, the ability to study many systems is impeded by the need to overcome unfavorable solubilities and viscosities among other processing challenges at ambient conditions. In this protocol, a solution coating system capable of operating temperatures over 110 °C is designed and demonstrated for the deposition of composition gradient polymer libraries. The system is equipped with a custom, solvent-resistant passive mixer module suitable for high-temperature mixing of polymer solutions at ambient pressure. Residence time distribution modeling was employed to predict the coating conditions necessary to generate composition gradient films using a poly(3-hexylthiophene) and poly(styrene) model system. Poly(propylene) and poly(styrene) blends were selected as a first demonstration of high temperature gradient film coating: the blend represents a polymer system where gradient films are traditionally difficult to generate via existing coating approaches due to solubility constraints at ambient conditions. The methodology developed here is expected to widen the range of solution processed materials that can be explored via high-throughput laboratory sampling and provides an avenue for efficiently screening multiparameter materials spaces and/or populating the large datasets required to enable data-driven materials science.