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Abstract Over the past decade, lead halide perovskite (LHP) nanocrystals (NCs) have attracted significant attention due to their tunable optoelectronic properties for next‐generation printed photonic and electronic devices. High‐energy photons in the presence of haloalkanes provide a scalable and sustainable pathway for precise bandgap engineering of LHP NCs via photo‐induced anion exchange reaction (PIAER) facilitated by in situ generated halide anions. However, the mechanisms driving photo‐induced bandgap engineering in LHP NCs remain not fully understood. This study elucidates the underlying PIAER mechanisms of LHP NCs through an advanced microfluidic platform. Additionally, the first instance of a PIAER, transforming CsPbBr₃NCs into high‐performing CsPbI₃NCs, with the assistance of a thiol‐based additive is reported. Utilizing an intensified photo‐flow microreactor accelerates the anion exchange rate 3.5‐fold, reducing material consumption 100‐fold compared to conventional batch processes. It is demonstrated that CsPbBr₃NCs act as photocatalysts, driving oxidative bond cleavage in dichloromethane and promoting the photodissociation of 1‐iodopropane using high‐energy photons. Furthermore, it is demonstrated that a thiol‐based additive plays a dual role: surface passivation, which enhances the photoluminescence quantum yield, and facilitates the PIAER. These findings pave the way for the tailored design of perovskite‐based optoelectronic materials. 

Abstract With the rise of self-driving labs (SDLs) and automated experimentation across chemical and materials sciences, there is a considerable challenge in designing the best autonomous lab for a given problem based on published studies alone. Determining what digital and physical features are germane to a specific study is a critical aspect of SDL design that needs to be approached quantitatively. Even when controlling for features such as dimensionality, every experimental space has unique requirements and challenges that influence the design of the optimal physical platform and algorithm. Metrics such as optimization rate are therefore not necessarily indicative of the capabilities of an SDL across different studies. In this perspective, we highlight some of the critical metrics for quantifying performance in SDLs to better guide researchers in implementing the most suitable strategies. We then provide a brief review of the existing literature under the lens of quantified performance as well as heuristic recommendations for platform and experimental space pairings.