While it can take years of focused laboratory work to determine how to make the highest quality materials for use in electronic and photonic device applications, researchers at North Carolina State University developed an autonomous system that can identify how to synthesize \u201cbest-in-class\u201d materials in hours or days.<\/p>\n
The new system, called SmartDope, was developed to address a longstanding challenge regarding enhancing properties of materials called perovskite quantum dots via doping.<\/p>\n
\u201cThese doped quantum dots are semiconductor nanocrystals that you have introduced specific impurities to in a targeted way, which alters their optical and physicochemical properties,\u201d explains Milad Abolhasani, an associate professor of chemical engineering at North Carolina State University and corresponding author of the paper published in the journal Advanced Energy Materials<\/em>.<\/p>\n \u201cThese particular quantum dots are of interest because they hold promise for next generation photovoltaic devices and other photonic and optoelectronic devices,\u201d Abolhasani says. \u201cFor example, they could be used to improve the efficiency of solar cells, because they can absorb wavelengths of UV light that solar cells don’t absorb efficiently and convert them into wavelengths of light that solar cells are very efficient at converting into electricity.\u201d<\/p>\n These materials are very promising, but there’s been a challenge in developing ways to synthesize quantum dots of the highest possible quality to maximize their efficiency of converting UV light into the desired wavelengths of light.<\/p>\n Answering the question of finding the best possible doped quantum dot for this application using conventional techniques could take 10 years. So, the team developed an autonomous lab that allows them to answer that question in hours.<\/p>\n The SmartDope system is a \u201cself-driving\u201d lab. The researchers first tell SmartDope which precursor chemicals to work with and give it a designated goal. In this study, the goal was to find the doped perovskite quantum dot with the highest \u201cquantum yield,\u201d or the highest ratio of photons the quantum dot emits (as infrared or visible wavelengths of light) relative to the photons it absorbs (via UV light).<\/p>\n Once it has received that initial information, SmartDope begins running experiments autonomously. The experiments are conducted in a continuous flow reactor that uses extremely small amounts of chemicals to conduct quantum dot synthesis experiments rapidly as the precursors flow through the system and react with each other.<\/p>\n For each experiment, SmartDope manipulates a suite of variables, such as: the relative amounts of each precursor material; the temperature at which it mixes those precursors; and the amount of reaction time given whenever new precursors are added. SmartDope also characterizes the optical properties of the quantum dots produced by each experiment automatically as they leave the flow reactor.<\/p>\n \u201cAs SmartDope collects data on each of its experiments, it uses machine learning to update its understanding of the doped quantum dot synthesis chemistry and inform which experiment to run next, with the goal of making the best quantum dot possible,\u201d Abolhasani says. \u201cThe process of automated quantum dot synthesis in a flow reactor, characterization, updating the machine learning model, and next-experiment selection is called closed-loop operation.\u201d<\/p>\n \u201cThe previous record for quantum yield in this class of doped quantum dots was 130%\u2014meaning the quantum dot emitted 1.3 photons for every photon it absorbed,\u201d Abolhasani says. \u201cWithin one day of running SmartDope, we identified a route for synthesizing doped quantum dots that produced a quantum yield of 158%. That’s a significant advance, which would take years to find using traditional experimental techniques. We found a best-in-class solution for this material in one day.<\/p>\n \u201cThis work showcases the power of self-driving labs using flow reactors to rapidly find solutions in chemical and material sciences,\u201d Abolhasani says. \u201cWe’re currently working on some exciting ways to move this work forward and are also open to working with industry partners.\u201d<\/p>\n <\/p>\n