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

Abstract

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 highthroughput 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.

Not All Aggregates Are Made the Same: Distinct Structures of Solution Aggregates Drastically Modulate Assembly Pathways, Morphology, and Electronic Properties of Conjugated Polymers

“…[ 5,6 ] This multiscale morphology in the solid state is largely affected by solution‐state aggregates and their assembly pathways from solution to thin films. [ 7–9 ] In‐depth understanding of the solution aggregation and assembly pathways will lead to precise control of many electronic and optical properties, such as charge‐carrier generation and transport, [ 10–12 ] interfacial charge transfer, [ 13 ] signal transduction, [ 14 ] spin transport, [ 15 ] and optical absorption and emission [ 16,17 ] —properties of close relevance to a wide range of device applications including transistors, thermoelectrics, solar cells, bioelectronics, photocatalysts, and spintronics.…”

“…[5,6] This multiscale morphology in the solid state is largely affected by solution-state aggregates and their assembly pathways from solution to thin films. [7][8][9] In-depth understanding of the solution aggregation and assembly pathways will lead to precise control of many electronic and optical properties, Tuning structures of solution-state aggregation and aggregation-mediated assembly pathways of conjugated polymers is crucial for optimizing their solid-state morphology and charge-transport property. However, it remains challenging to unravel and control the exact structures of solution aggregates, let alone to modulate assembly pathways in a controlled fashion.…”

System-Level Network Analysis of a Catechol Component for Redox Bioelectronics

“…Our long-term goal is to extend this theory-guided analysis of mediated electrochemical probing (MEP) to more complex or ill-defined situations that do not lend themselves to a firstprinciples modeling approach (e.g., to use MEP for the reconstruction of an unknown biological redox reaction network). We envision that a first-principles theoretical framework can be abstracted in terms of readily measurable signal metrics 22 that can be broadly applied to reveal characteristic signatures of such a complex redox network.…”

“…Ultimately, we envision that this theory-guided framework can be coupled with data-driven analysis to characterize more complex systems (e.g., for network reconstruction of a redox interactome). 22,23 The specific experimental measurements to be analyzed in this study are obtained by electrodes coated with hydrogel containing redox-active catechol moieties. As illustrated in Scheme 1c, catechol-containing hydrogels have emerged as important signal-processing components in redox bioelectronics because they offer unique abilities to bridge redoxbased communication between biology and electronics.…”

“…To date, these design and analysis goals have been approached empirically on a case-by-case basis using chemical/biological intuition , or data-driven statistical analysis. , Here, we propose a theory-guided framework that can be broadly applied to explain existing results with comparatively simple experimental systems. Ultimately, we envision that this theory-guided framework can be coupled with data-driven analysis to characterize more complex systems (e.g., for network reconstruction of a redox interactome). , …”