Development of high‐throughput wet‐chemical synthesis techniques for material research

Abstract

Abstract Combining material big data with artificial intelligence constitutes the fourth paradigm of material research. However, the sluggish development of high‐throughput (HT) experimentation has resulted in a lack of experimentally verified and validated material data, which has become the bottleneck of data‐driven material research. Wet‐chemical synthesis has the benefits of low equipment cost and scalability, but traditional wet‐chemical techniques are time‐consuming and ineffective at disclosing the interrelationships between synthesis, compositions, structures, and performance. Constructing a HT workflow in wet‐chemical synthesis is crucial to achieving the preparation of multidimensional materials and establishing the composition–structure–synthesis–performance relationships of functional materials for diverse applications. In this review, the most recent development in HT wet‐chemical synthesis techniques for material research are analyzed in depth. Additionally, the application of HT wet‐chemical synthesis in the fabrication of advanced hydrogels and catalysts is demonstrated through illustrative instances. Finally, this review suggests possible paths for enhancing the efficiency of HT experimentation and data acquisition in order to facilitate more effective material discovery.

Keywords

• artificial intelligence
• high‐throughput
• material genome
• wet‐chemical synthesis