Programmable Light-Driven Color Tuning of Perovskite Quantum Dots

Area of Science:

• Materials Science
• Chemical Engineering
• Nanotechnology

Background:

• Precise bandgap tuning of metal halide perovskite (MHP) nanocrystals (NCs) is crucial for optoelectronics and photocatalysis.
• Photoinduced anion exchange reactions (PIAERs) offer control but face challenges in mechanistic understanding and optimization due to a vast parameter space.

Purpose of the Study:

• To develop a material-efficient, autonomous system for optimizing PIAERs in MHPs.
• To enable precise, scalable, and sustainable bandgap tuning of MHP nanocrystals across the UV-visible spectrum.

Main Methods:

• Integration of a single-droplet microfluidic photoreactor, in situ spectroscopy, and Bayesian optimization within a fluidic self-driving laboratory (FSDL).
• Machine learning-guided exploration of a complex parameter landscape to identify optimal synthesis conditions.
• Surrogate modeling to derive mechanistic insights into anion exchange kinetics.

Main Results:

• The FSDL autonomously identified synthesis conditions maximizing photoluminescence quantum yield and minimizing emission line width for target wavelengths.
• Distinct kinetic regimes for Br^-→Cl^- and Br^-→I^- exchanges were revealed, enabling reaction-specific tuning.
• Protocols scaled from droplet (∼10 μL) to continuous-flow (∼50-250 mL·day^-1) without reoptimization, maintaining performance and demonstrating knowledge scalability across 4 orders of magnitude in throughput.

Conclusions:

• The FSDL provides a reproducible, mechanistically informed route for programmable, light-directed bandgap tuning in MHP NCs.
• This approach is industrially relevant, scalable, and energy-efficient, paving the way for advanced MHP applications.