(294e) High-Throughput Optimization of Functional Materials Via Combinatorial Additive Manufacturing

The development and optimization of new materials are essential for the advancement of technologies in fields like renewable energy and environmental protection. The process of identifying and enhancing materials, however, has been traditionally slow and inefficient. Existing methods for combinatorial synthesis are capable of generating extensive libraries of materials, including chemical molecules and semiconductor materials, yet they are restricted by a narrow range of material options and fail to fully utilize the recent progress in colloidal nanomaterial synthesis. Here, we present a bottom-up combinatorial printing (CP) technique that facilitates the production of materials with gradient compositional variations for materials optimization. This technique allows for the on-the-fly adjustment of mixing ratios among a wide array of material choices. By altering the chemical composition of the inks and the dynamics of their deposition, we demonstrate a broad spectrum of printing techniques and high-throughput applications, including combinatorial doping, functional grading, and combinatorial reactions. As a proof of concept of CP, we explore the effects of doping levels on thermoelectric materials, efficiently determining the optimum doping level that leads to the synthesis of an n-type material with a superior power factor at room temperature. This method, blending the adaptability of additive manufacturing with precise control over the composition of precursor materials, presents a promising platform for expediting the synthesis and evaluation of a wide range of material systems.

References: Zeng, Minxiang, et al. "High-throughput printing of combinatorial materials from aerosols." Nature 617.7960 (2023): 292-298.