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

涂层 材料科学 聚合物 吞吐量 混合(物理) 沉积(地质) 工艺工程 纳米技术 表征(材料科学) 化学工程 计算机科学 复合材料 电信 生物 物理 工程类 沉积物 古生物学 量子力学 无线
作者
Aaron L. Liu,Ezgi M. Dogan-Guner,Michael McBride,Rahul Venkatesh,Miguel A. González,Elsa Reichmanis,Martha A. Grover,J. Carson Meredith
出处
期刊:Chemistry of Materials [American Chemical Society]
卷期号:34 (15): 6659-6670 被引量:6
标识
DOI:10.1021/acs.chemmater.2c01500
摘要

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.

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