The process of bringing new materials to solar panels can be full of repetitive tasks, evaluations, and risk. It requires a researcher to prepare a sample and then go through multiple steps to test each sample using different instruments — a process that is both time consuming and requires a lot of electricity. Researchers at North Carolina State University have created RoboMapper, a robot capable of conducting experiments more efficiently and sustainably to develop a range of new semiconductor materials with desirable attributes.
“The RoboMapper originated from the desire to accelerate energy materials research, which is essential to solving global societal problems like climate change,” said Aram Amassian, Professor of Materials Science and Engineering at North Carolina State University. “We wanted poor materials to fail fast and fail early. The RoboMapper streamlines the material-level tests in several significant ways and saves time by allowing researchers to skip coating optimization and device testing in early testing.”
The RoboMapper automates the main tasks of material-level preparation and testing of solution-processed semiconductors. It uses a computer-controlled formulation bot to prepare various perovskite alloys and a printing bot that then deposits the perovskites with different compositions at designated locations on a chip. The chips are loaded into different instruments and are scanned automatically to generate a data map of structure, properties, and stability with respect to the perovskite material’s composition.
“First, it creates a pre-coating and pre-device workflow that collects sufficiently meaningful information about the material, including whether it can achieve the perovskite phase when deposited on a substrate, what is its bandgap, and whether the material is light stable,” said Amassian. “Second, we use physics-based understanding of the light stability based on our recent study in Nature Materials, to predict which perovskite compounds at the same bandgap will achieve more long-term stability, more efficiency, and hysteresis-free device operation.”
This is achieved without the need to optimize thin film coatings or fabricate solar cells. According to Amassian, the robot collects data at least 10 times more efficiently than any human or existing robot, providing reduction in cost, time, and energy consumption.
By miniaturizing and placing dozens of materials on the same chip and then allowing all of these materials to be tested at once in a short time, the RoboMapper reduces the amount of material and electricity required to generate the equivalent dataset by preparing and testing samples one by one or in small groups.
This approach was particularly effective at reducing greenhouse gas emissions and environmental impact associated with electricity use by characterization methods employed for data generation, with materials consumption coming second.
Amassian teamed up with an environmental economist who conducted lifecycle assessment. They considered “data” as the product of research and evaluated the environmental impact of data in traditional materials research as well as existing automation and the RoboMapper workflows.
“I had initially assumed material waste reduction would be the winning category, but it turns out it came second to electricity savings. We could reduce the energy required per dataset from 8.9 kWh in traditional workflows to 0.34 kWh for the RoboMapper, with a commensurate cost reduction per dataset from $17.6 to $0.34 excluding labor,” he said.
Amassian believes this type of outcome will hopefully inspire others to consider evaluating and modifying their workflows to achieve sustainability in the context of big data. “Everyone would also benefit from free software that makes it easier to evaluate the environmental impact and carbon footprint of lab research to help us make more sensible decisions in the laboratory, especially when it comes to evaluating electricity and associated carbon footprint and environmental impacts.”
This article was written by Chitra Sethi, Director, Editorial and Digital Content, SAE Media Group. For more information visit here .