Composition Gradient High-Throughput Polymer Libraries Enabled by Passive Mixing and Elevated Temperature Operability

Abstract

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 under 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 under 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 data sets required to enable data-driven materials science.

From Solution to Thin Film: Molecular Assembly of π-Conjugated Systems and Impact on (Opto)electronic Properties

“…To tackle the labor-intensiveness of material discovery and device optimization process, high-throughput experimentation approaches have been developed . With the advantage of solution processing that enables parametric gradients in several processing conditions, researchers could leverage parameters such as thickness, , precursor solution formulation, ,− and temperature , to optimize the device performance. Researchers have also implemented self-driven laboratories based on autonomous robotic experimental platform and combined with machine learning (ML) or artificial intelligence (AI) to further minimize human resources and increase the data yield and reproducibility.…”

Bayesian optimization of radical polymerization reactions in a flow synthesis system

“…As examples, machine learning techniques have been employed in composition searches, [9][10][11][12][13] crystal phase and microstructure classifications 14,15 and physical property predictions. 16,17 Within the field of polymer chemistry, informatics is increasingly being applied to the design of polymers [18][19][20][21] and the prediction of properties [22][23][24] based on the use of molecular descriptors. Bayesian optimization (BO), in particular, has emerged as a powerful tool in many scientific disciplines, including materials science.…”

Closed-Loop Transfer Enables AI to Yield Chemical Knowledge

“…But the paradigm of rational design and discovery has increasingly been supplanted by theory-driven exploration and combinatorial, highthroughput experimentation. [8][9][10] Recently, artificial intelligence (AI)-guided closed-loop platforms, where predictions, experiments, and analysis are automated and connected in a positive feedback loop, have shown great potential to accelerate scientific discovery in intractably large search spaces. [11][12][13][14][15][16][17][18][19][20][21][22][23][24] Despite these recent successes, it is not yet possible to broadly leverage such a paradigm to drive knowledge-based discovery of molecular functions.…”