Rapid Synthesis of “Best-in-Class” Materials for Specific Applications

Years of dedicated laboratory work are required to determine how to create materials of the highest quality for photonic and electronic applications. Researchers have now created an autonomous system that can determine how to synthesize “best-in-class” materials for specific uses in hours or days.

Rapid Synthesis of “Best-in-Class” Materials for Specific Applications

Image Credit: North Carolina State University

The SmartDope system was created to solve a persistent problem when doping materials known as perovskite quantum dots to improve their qualities.

These doped quantum dots are semiconductor nanocrystals that you have introduced specific impurities to in a targeted way, which alters their optical and physicochemical properties.

Milad Abolhasani, Study Corresponding Author and Associate Professor, Chemical Engineering, North Carolina State University

He added, “These particular quantum dots are of interest because they hold promise for next generation photovoltaic devices and other photonic and optoelectronic devices. For 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.

While the potential of these materials is great, it has been difficult to create quantum dots of the best possible quality and enhance their ability to convert UV light into the correct wavelengths of light.

We had a simple question. What’s the best possible doped quantum dot for this application? But answering that question using conventional techniques could take 10 years. So, we developed an autonomous lab that allows us to answer that question in hours,” Abolhasani stated.

SmartDope is a “self-driving” laboratory. To begin, the researchers instruct SmartDope on which precursor chemicals to use and assign it a purpose. The purpose of this research was to locate the doped perovskite quantum dot with the best “quantum yield,” or the largest ratio of photons emitted (as infrared or visible wavelengths of light) to photons absorbed (by UV light).

SmartDope begins doing experiments on its own after receiving the initial information. The experiments are carried out in a continuous flow reactor, which uses extremely small amounts of chemicals to swiftly carry out quantum dot synthesis experiments while the precursors flow through the system and react with one other.

SmartDope modifies a number of factors for each experiment, including the relative amounts of each precursor material, the temperature at which the precursors are mixed, and the length of reaction time given whenever new precursors are added. SmartDope also automatically characterizes the optical properties of the quantum dots created by each experiment as they exit the flow reactor.

As 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. The 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.

Milad Abolhasani, Study Corresponding Author and Associate Professor, Chemical Engineering, North Carolina State University

So, how effective is SmartDope?

Abolhasani stated, “The previous record for quantum yield in this class of doped quantum dots was 130% – meaning the quantum dot emitted 1.3 photons for every photon it absorbed. Within 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.

He continued, “This work showcases the power of self-driving labs using flow reactors to rapidly find solutions in chemical and material sciences. We are currently working on some exciting ways to move this work forward and are also open to working with industry partners.

The research was published in the open-access journal Advanced Energy Materials. Fazel Bateni and Sina Sadeghi, Ph.D. students at NC State, are the paper's co-first authors. Negin Orouji and Michael Rosko, Ph.D. students at NC State; Jeffrey Bennett, a postdoctoral researcher at NC State; Venkat Punati, a master’s student at NC State; Christine Stark, an undergraduate at NC State; Felix Castellano, Goodnight Innovation Distinguished Chair in Chemistry at NC State; Junyu Wang and Ou Chen of Brown University; and Kristofer Reyes of the University at Buffalo, all contributed to the study.

The study was funded by the National Science Foundation (grant number 1940959), the UNC Research Opportunities Initiative, and the Dreyfus Program for Machine Learning in the Chemical Sciences and Engineering (award number ML-21-064).

Journal Reference:

Bateni, F., et al. (2023) Smart Dope: A Self-Driving Fluidic Lab for Accelerated Development of Doped Perovskite Quantum Dots. Advanced Energy Materials. doi:10.1002/aenm.202302303

Source: https://www.ncsu.edu/

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