Accelerated Optimization and Manufacturing of Lead-Free Perovskite Nanocrystals with a Self-Driving Fluidic Lab

Metal halide perovskite (MHP) nanocrystals (NCs) are cutting-edge colloidal materials widely used in photonic and energy devices due to their superior characteristics, such as high photoluminescence quantum yield (PLQY) and adjustable optical properties. Their integration into printed technologies, however, is profoundly restricted by the lead toxicity and their instability against light, moisture, and heat.¹ Recently, cesium copper halide perovskite NCs have been of great interest as a promising lead-free analogue, offering intriguing composition-tunable emissions.^1–3 A specific class of copper-based MHP NCs is cesium copper iodide (Cs₃Cu₂I₅) with an orthorhombic crystal structure that exhibits a high stability in ambient environment.¹ In addition, incorporating various metal halide additives into the halide source precursor has been shown to enhance the Cs₃Cu₂I₅ NCs PLQY.^4–6 Despite the successful synthesis of cation-doped Cs₃Cu₂I₅ NCs, significant challenges remain for their fundamental studies due to the batch-to-batch variations caused by these NCs fast formation kinetics. Herein, a machine learning (ML)-guided microfluidic platform was developed to enable autonomous manufacturing of metal cation-doped Cs₃Cu₂I₅ NCs in flow, achieving enhanced optoelectronic properties. Utilizing the developed self-driving fluidic lab (SDFL), we first autonomously studied the effect of various experimental parameters (e.g., dopant/copper ratio, precursors injection ratio, reaction time, and temperature) on the optical properties of the in-flow synthesized doped Cs₃Cu₂I₅ NCs. We then employed the closed-loop autonomous experimentation platform to accelerate mapping the parameter space and optimize the synthetic route to achieve the highest-performing cation-doped Cs₃Cu₂I₅ NCs at minimal experimental cost.

References:
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