Quantum Dot Color Emission Depends on Size 3D Rendering

3D Rendering of Quantum Dot Size-Dependent Color Emission (© atdigit - stock.adobe.com)

RALEIGH, N.C. — In what would have taken humans years to discover, a high-tech lab that functions autonomously was able to detect a best-in-class “quantum dot” in mere hours. Created by North Carolina State University researchers, SmartDope can drastically reduce the time required to develop high-quality materials for electronic and photonic devices. SmartDope has the potential to identify and synthesize top-quality materials for specific applications in just hours or days, a process that typically takes years of laborious labwork by humans.

The primary focus of SmartDope’s development was to tackle a persistent challenge related to enhancing the properties of materials known as perovskite quantum dots through a technique called “doping.” Doped quantum dots are tiny semiconductor crystals infused with specific impurities in a targeted manner, altering their optical and physicochemical properties.

“These particular quantum dots are of interest because they hold promise for next generation photovoltaic devices and other photonic and optoelectronic devices,” says study corresponding author Milad Abolhasani, an associate professor of chemical engineering at North Carolina State University, in a university release. “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.”

However, synthesizing high-quality doped quantum dots has been a significant challenge, limiting their potential.

“We had a simple question: What’s the best possible doped quantum dot for this application?” explains Abolhasani. “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.”

It can take years of focused laboratory work to determine how to make the highest quality materials for use in electronic and photonic devices
It can take years of focused laboratory work to determine how to make the highest quality materials for use in electronic and photonic devices. Researchers have now developed an autonomous system that can identify how to synthesize “best-in-class” materials for specific applications in hours or days. (CREDIT: Milad Abolhasani, NC State University)

SmartDope functions as a “self-driving” lab. Researchers provide it with information about the precursor chemicals to use and the specific goal, such as finding the doped perovskite quantum dot with the highest “quantum yield.” Quantum yield measures the ratio of emitted photons (infrared or visible light) to absorbed photons (UV light).

Once armed with this information, SmartDope autonomously conducts experiments in a continuous flow reactor, using minimal amounts of chemicals for rapid quantum dot synthesis. It manipulates variables like precursor material quantities, mixing temperatures, and reaction times, while automatically characterizing the optical properties of the produced quantum dots.

“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,” says Abolhasani. “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.”

Researchers say the results were astonishing.

“The previous record for quantum yield in this class of doped quantum dots was 130 percent — meaning the quantum dot emitted 1.3 photons for every photon it absorbed,” notes Abolhasani. “Within one day of running SmartDope, we identified a route for synthesizing doped quantum dots that produced a quantum yield of 158 percent. 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.”

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

The study is published in the journal Advanced Energy Materials.

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