Autonomous MicroED data collection enables compositional analysis

Abstract

Abstract MicroED is an effective method for analyzing the structural properties of sub-micron crystals, which are frequently found in small-molecule powders. By developing and using an autonomous and high throughput approach to MicroED, we demonstrate the expansion of capabilities and the possibility of performing complete compositional analysis of complex samples. With the use of SerialEM for data collection of thousands of datasets from thousands of crystals and an automated processing pipeline, compositional analysis of complex mixtures of organic and inorganic compounds can be accurately executed. Quantitative analysis suitable for compounds having similar chemical properties can be made on the fly. These compounds can be distinguished by their crystal structure properties prior to structure solution. Additionally, with sufficient statistics from the autonomous approach, even small amounts of compounds in mixtures can be reliably detected. Finally, atomic structures can be determined from the thousands of data sets.