Energy harvesting from the Sun through solar cells is still inefficient and scientists are looking for new types of solar cells with higher efficiency. However, search for new materials that can make for more efficient solar cells is a pain staking one involving sampling and lab experiments. To do away with the initial practical hurdles of the screening process, scientists have developed a new technique that could bypass 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 has been developed by researchers at MIT who claim that not only can the process speed up the search for new formulations, but it can also do a more accurate job of predicting their performance.
The system involves making a simple test device, then measuring its current output under different levels of illumination and different voltages, to quantify exactly how the performance varies under these changing conditions. These values are then used to refine the statistical model.
The Bayesian inference process allows the estimates of each parameter to be updated based on each new measurement, gradually refining the estimates and homing in ever closer to the precise answer, scientists explain.
In seeking a combination of materials for a particular kind of application, all materials properties and interface properties are added in and it will tell you what the output will look like.
The system is simple enough that, even for materials that have been less well-characterized in the lab and doesn’t require a whole lot of computing power. 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.”