This experimental setup was used by the team to measure the electrical output of a sample of solar cell material, under controlled conditions of varying temperature and illumination. The data from those tests was then used as the basis for computer modeling using statistical methods to predict the overall performance of the material in real-world operating conditions.
The worldwide quest by researchers to find better, more efficient materials for tomorrow's solar panels is usually slow and painstaking. Researchers typically must produce lab samples - which are often composed of multiple layers of different materials bonded together - for extensive testing. Now, a team at MIT and other institutions has come up with a way to bypass such expensive and time-consuming fabrication and testing, allowing for a rapid screening of far more variations than would be practical through the traditional approach. The new process could not only speed up the search for new formulations, but also do a more accurate job of predicting their performance, explains Rachel Kurchin, an MIT graduate student and co-author of a paper describing the new process that appears this week in the journal Joule . Traditional methods "often require you to make a specialized sample, but that differs from an actual cell and may not be fully representative" of a real solar cell's performance, she says. For example, typical testing methods show the behavior of the "majority carriers," the predominant particles or vacancies whose movement produces an electric current through a material. But in the case of photovoltaic (PV) materials, Kurchin explains, it is actually the minority carriers - those that are far less abundant in the material - that are the limiting factor in a device's overall efficiency, and those are much more difficult to measure.
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