Researchers all over the world have been pursuing a mission to find better, more efficient materials for tomorrow’s solar panels. This has been a slow and painstaking process. Researchers usually have to produce lab samples, which are typically composed of multiple layers of different materials bonded together with extensive testing.
A team at MIT has come up with a way to bypass this expensive and time-consuming fabrication and testing. This has allowed for rapid screening of way more variations than would be practical through the previous traditional approach.
The new process could speed up the search for new formulations and do a more accurate job of predicting their performance. Traditional methods usually require researchers to make a specialized sample. But this usually differs from an actual cell and may not be representative of a real solar cell.
Typical testing methods show the behavior of the “majority carriers” which are the predominant particles or vacancies whose movement produces an electric current through a material. But in the case of photovoltaic (PV) materials, it is the minority carriers, which are less abundant in the material, that are the limiting factor in a device’s overall efficiency. These are way more difficult to measure than the majority carriers. Along with this, typical procedures only measure the flow of current in one set of directions, while it’s its up-down flow that is actually harnessed in a working cell. The flow can be radically different in many materials, which makes it critical to understand so they can properly characterize the material.
"Historically, the rate of new materials development is slow — typically 10 to 25 years," says Tonio Buonassisi, an associate professor of mechanical engineering at MIT and senior author of the paper. "One of the things that make the process slow is the long time it takes to troubleshoot early-stage prototype devices," he says. "Performing characterization takes time — sometimes weeks or months — and the measurements do not always have the necessary sensitivity to determine the root cause of any problems."
According to Buonassisi, “The bottom line is, if we want to accelerate the pace of new materials development, it is imperative that we figure out faster and more accurate ways to troubleshoot our early-stage materials and prototype devices."
This is what the team has been able to accomplish. They have developed a set of tools that can be used to make accurate, rapid assessments of proposed materials with a series of simple lab tests that are combined with computer modeling of the physical properties of the material itself. This is along with additional modeling that is based on Bayesian interference, a statistical method.
The system starts by making a simple test device, measuring the current output under different levels of illumination and different voltages to quantify how the performance varies in changing conditions. The values are used to refine the statistical model.
"After we acquire many current-voltage measurements [of the sample] at different temperatures and illumination intensities, we need to figure out what combination of materials and interface variables make the best fit with our set of measurements," Buonassisi explains. "Representing each parameter as a probability distribution allows us to account for experimental uncertainty, and it also allows us to suss out which parameters are covarying."
The Bayesian interference process allows the estimates of each parameter to be updated on the new measurement, gradually refining the estimates and getting closer to the exact answer.
Through seeking out a combination of materials for a particular kind of application, according to Rachel Kurchin, an MIT graduate student and co-author of the paper describing the new process, “we put in all these materials properties and interface properties, and it will tell you what the output will look like."
The system is pretty simple, even for the materials that have been less well-characterized in the lab. Kurchin says, “we're still able to run this without tremendous computer overhead. Making use of the computational tools to screen possible materials will be increasingly useful because lab equipment has gotten more expensive, and computers have gotten cheaper. This method allows you to minimize your use of complicated lab equipment.”
The basic methodology could be applied to a lot of different materials evaluations outside of the solar cell. It may apply to any system that uses a computer model for the output of an experimental measurement.
Buonassisi says, "For example, this approach excels in figuring out which material or interface property might be limiting performance, even for complex stacks of materials like batteries, thermoelectric devices, or composites used in tennis shoes or airplane wings. It is especially useful for early-stage research, where many things might be going wrong at once."
From here on out, Buonassisi says, “our vision is to link up this fast characterization method with the faster materials and device synthesis methods we've developed in our lab." Ultimately, he says, "I'm very hopeful the combination of high-throughput computing, automation, and machine learning will help us accelerate the rate of novel materials development by more than a factor of five. This could be transformative, bringing the timelines for new materials-science discoveries down from 20 years to about three to five years."
The paper on this research was published in Joule.