Abstract

Accelerated Development of Quantum Dots by Autonomous Robotic Experimentation in Flow

Milad Abolhasani, Assistant Professor, North Carolina State University

In this talk, I will present the Artificial Chemist technology, that is, a modular flow chemistry platform operated by a machine learning-guided decision-making algorithm for accelerated development of energy-relevant colloidal nanomaterials. I will discuss the unique advantages of reconfigurable flow reactors for autonomous multi-step synthesis, optimization, and continuous manufacturing of colloidal quantum dots (QDs) for direct utilization in next-generation photonic devices. The Artificial Chemist can rapidly and efficiently (i) explore and learn the synthesis and processing universe of colloidal QDs, (ii) identify the composition and relevant synthesis and processing route(s) of QDs to achieve specific optical or optoelectronic properties, and (iii) continuously manufacture the rapidly optimized QDs at a fraction of time/cost of currently utilized batch techniques. The developed autonomous robotic experimentation strategy can be readily adapted for accelerated development and end-to-end manufacturing of other solution-processed nanomaterials.