The pursuit of high metal utilization in heterogeneous catalysis has triggered the burgeoning interest of various atomically dispersed catalysts. Our aim in this review is to assess key recent findings in the synthesis, characterization, structure-property relationship and computational studies of dual-atom catalysts (DACs), which cover the full spectrum of applications in thermocatalysis, electrocatalysis and photocatalysis. In particular, combination of qualitative and quantitative characterization with cooperation with DFT insights, synergies and superiorities of DACs compare to counterparts, high-throughput catalyst exploration and screening with machine-learning algorithms are highlighted. Undoubtably, it would be wise to expect more fascinating developments in the field of DACs as tunable catalysts.
Keywords: Characterization; Dual-Atom Catalysts; Electrocatalysis; Heterogeneous Catalysis; Machine-Learning.
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