Proceedings of Online nanoGe Fall Meeting 20 (OnlineNFM20)
Publication date: 4th October 2020
Advances in automation and data analytics can aid exploration of the complex chemistry of nanoparticles. Lead halide perovskite colloidal nanocrystals provide an interesting proving ground: there are reports of many different phases and transformations, which has made it hard to form a coherent conceptual framework for their controlled formation through traditional methods. In this work, we systematically explore the portion of Cs–Pb–Br synthesis space in which many optically distinguishable species are formed using high-throughput robotic synthesis to understand their formation reactions. We deploy an automated method that allows us to determine the relative amount of absorbance that can be attributed to each species in order to create maps of the synthetic space. These in turn facilitate improved understanding of the interplay between kinetic and thermodynamic factors that underlie which combination of species are likely to be prevalent under a given set of conditions. Based on these maps, we test potential transformation routes between perovskite nanocrystals of different shapes and phases. We find that shape is determined kinetically, but many reactions between different phases show equilibrium behavior. We demonstrate a dynamic equilibrium between complexes, monolayers, and nanocrystals of lead bromide, with substantial impact on the reaction outcomes. This allows us to construct a chemical reaction network that qualitatively explains our results as well as previous reports and can serve as a guide for those seeking to prepare a particular composition and shape.
This work was supported by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, Materials Sciences and Engineering Division, under Contract No. DEAC02-05-CH11231 within the Physical Chemistry of Inorganic Nanostructures Program (KC3103). Work at the Molecular Foundry was supported by the Office of Science, Office of Basic Energy Sciences, of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231. J.C.D. acknowledges support by the National Science Foundation Graduate Research Fellowship under DGE 1752814 and by the Kavli NanoScience Institute, University of California, Berkeley through the Philomathia Graduate Student Fellowship. We thank Dr. Samuel Niblett, Dr. Justin Ondry and Dr. Brent Kosher for providing insightful review of the manuscript. We would also like to thank Prof. David Limmer, Dr. Anubhav Jain, Dr. Yehonadav Bekenstein, Prof. Jennifer Listgarten, Dr. Assaf Ben-Moshe, Dr. Ayelet Teitelboim, Dr. Zhi Li, Dr. Ivan Moreno-Hernandez, Dr. David Nenon, Dr. Joseph Swabeck, Dr. David Hanifi, Dr. Dmitry Baranov, Abdullah Abbas, Samuel Gleason, and QinQin Yu for insightful discussions concerning this project, as well as our group-based undergraduate research program students from the 2019 and 2020 robotics section for inspiration.
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