Dec 5, 2024
8:00pm - 10:00pm
Hynes, Level 1, Hall A
Tod Grusenmeyer1,Christopher McCleese1,2,Steven Wolf1,2,Ecklin Crenshaw1,2,Yi Xie3,Michael Brennan1,2,David Turner1,2,Raul Castenada4,Hendrik Heinz5,Seth Marder5,David Mitzi3
Air Force Research Laboratory1,Azimuth Corporation2,Duke University3,New Mexico Highlands University4,University of Colorado Boulder5
Tod Grusenmeyer1,Christopher McCleese1,2,Steven Wolf1,2,Ecklin Crenshaw1,2,Yi Xie3,Michael Brennan1,2,David Turner1,2,Raul Castenada4,Hendrik Heinz5,Seth Marder5,David Mitzi3
Air Force Research Laboratory1,Azimuth Corporation2,Duke University3,New Mexico Highlands University4,University of Colorado Boulder5
Hybrid perovskites offer the opportunity to produce new materials based on the wide range of possible chemical substitutions that can be made and crystal dimensionalities that can be formed. However, the rate limiting step in discovering these new materials is the time it takes to grow and characterize the crystals. For solvothermal methods, typically a single hot plate is used and it can take days to grow crystals. Therefore, automation is a powerful tool to expedite the materials discovery process. In this talk, I will introduce our Hamilton Microlab Star Plus liquid handling robot and its current capabilities. For our first trial, the robot was used to explore solvothermal growth conditions for three dimensional MAPbBr<sub>3</sub> single crystals. Solvothermally grown crystals were characterized by programming the robot to measure the transmission spectrum of the crystals using an in-line UV-visible spectrophotometer. We have also utilized the robot to grow low dimensional chiral perovskite crystals. In this work we investigated how the ratio of lead to chiral cation affects the crystallization and the yield of crystal growth. Furthermore, we show how the robot allows for high-throughput crystal growth by scaling up to 24 different growths on single hot plate. This growth and characterization methodology can be used to rapidly produce new perovskites and the resulting data could potentially be used to build a materials database to help guide the growth of perovskites with desired properties.