High-Throughput Printing of Combinatorial Materials from Aerosols

The development of new materials and their compositional and microstructural optimizations are essential to next-generation technologies such as clean energy and environmental sustainability. However, materials discovery and optimization have been a frustratingly slow process. The Edisonian trial-and-error process is time-consuming and resource-inefficient, particularly when contrasted to vast materials design spaces. While traditional combinatorial deposition methods can generate material libraries, it suffers from limited material options and inability to leverage immense breakthroughs in nanomaterials synthesis. Here we present a high-throughput combinatorial printing (HTCP) method capable of fabricating materials with compositional gradients with microscale spatial resolution. The <i>in situ</i> “mix and print” in the aerosol phase allows instantaneous tuning of the mixing ratio of a broad range of materials on the fly, which is an important feature unobtainable in conventional multi-materials printing using feedstocks in liquid/liquid or solid/solid phases. We demonstrate a variety of high-throughput printing strategies and applications in combinatorial materials discovery, functional grading, and chemical reaction, enabling materials explorations of doped chalcogenides and compositionally graded materials with gradient properties. The versatile aerosol based HTCP enables universal printing and integration of a broad range of materials including metals, semiconductors, dielectrics, as well as polymers and biomaterials, leading to facile fabrication of multifunctional and flexible/wearable devices for energy conversion/storage, sensing, and health monitoring. The ability to combine the top-down design freedom of additive manufacturing with bottom-up control over the local material compositions promises compositionally complex materials inaccessible via conventional manufacturing approaches. The fabrication freedom and data-rich nature of HTCP along with machine learning and artificial intelligence guided design strategies is expected to accelerate the discovery and development of a broad range of materials with intriguing and unprecedented properties for emerging applications.