April 23, 2024

Solar panels must convert sunlight into electricity efficiently to move society towards renewable energy. The theoretical maximum efficiency of some state-of-the-art solar cells is close to what they can produce. However, physicists at the University of Utah (and Helmholtz Zentrum Berlin) have found a way to improve their performance.

Cross-correlation noise spectroscopy is a method that allows scientists to measure the fluctuations in electrical current between materials within silicon solar cells. Physicists in a new study used this technique. Researchers identified critical electrical noise signals completely inaccessible to traditional noise-measuring methods. The researchers also pinpointed the probable physical processes that cause the noise. This can often lead to a loss in energy and lower efficiency.

It is easy to measure noise on objects. It is easy to find devices that can do this. The problem is that these devices can also make noise,” stated Kevin Davenport, associate professor of physics at U and the lead author of this paper. Cross-correlation allows us to measure noise from the device and remove noise from our detector, allowing us to see smaller noise signals.

This technique was published in Scientific Reports on June 24, 2021. It is an essential tool for improving material interfaces to make solar cells more efficient and analyze complex device inefficiencies.

It’s incredible how crucial minor improvements in efficiency can be for the industry. A fraction of a percentage improvement can translate into billions of dollars because of the scale production,” stated Klaus Lips, co-author, and professor of physics at Freie Universitat Berlin. He is also the department head at Helmholtz-Zentrum Berlin, where the solar cells were designed and manufactured.

We have used cross-correlation to study relatively simple light-emitting diodes for research purposes. Still, the benefits of this technique came into focus in this study,” stated Andrey Rogachev (a professor of physics at U) and co-author. It goes far beyond the solar industry. Each interface between materials in a device can reduce efficiency in any way. This is why you need to be discreet to understand what’s happening and where it’s coming from. This technique allows us to do precisely that.

Complex devices can’t be understood with a single method. C.T. solar cell simulations have greatly helped in the interpretation of noise data. Trinh, a co-author of this study and a postdoctoral researcher at Helmholtz-Zentrum Berlin, was also a co-author. Mark Hayward is the final co-author. He was an undergraduate researcher at the University and is now a graduate student at the University of California, Irvine.

Analyzing noise

The study examined silicon heterojunction solar cells (HSCs), a high-end, single-material type of solar cell. 26.7% of the light that strikes the cell is converted to electricity. The efficiency of solar panels for residential houses is between 15% and 20%.

An HSC is a process that generates electricity from individual photons of light. These photons absorb the photoactive layer of crystalline silicon. This creates pairs of negatively charged electrons and positively charged holes, which are charges initiated by missing electrons. Two contacts, made from hydrogenated amorphous silicon modified with impurities, create an electric field that pulls electrons and holes in opposite directions. This creates current, which we use to power our electricity. The problem is that the photoactive silicon and selective electrodes must match perfectly. This makes defects that trap electrons. These defects are eliminated in research-grade solar cells like those in the study. The scientists placed between them a thin layer of pure amorphous silica. The five layers are then sandwiched between transparent conducting material (ITO) and two layers of gold electrodes.

The interconnection of layers affects the efficiency of HSCs. A mismatch in the layers between two layers can cause electrons to have difficulty getting to their destination. This will create a noise signal.

“This problem is hidden within these interfaces, and detecting any signal is tough. Davenport said that the noise technique we use is sensitive to very, very small individual movements. He said it’s similar to listening to different instruments play notes. While a C-note is the same on a violin as on a cello, they sound very different. If you analyze the message, you can find information about the instrument that produced it—for example, the material or length of the strings.

“We do something similar. This wide range of noise signals is visible and at different frequencies. Davenport said, “OK, this note we see, we can attribute it to this physical process, and that part is another physical process.” But the device is complete with these noise-generating processes, and it’s tough to separate them. It’s like trying to pull out one voice from a 200-member chorus. This technique can remove much of the unwanted signal.

Mapping inefficiencies

Although silicon HSCs work well as they do, there are still limitations. New techniques developed by the research team identified areas where specific physical processes produce electrical signals within the device. These stages can be adjusted to improve efficiency and make these cells more efficient. The physicists then ran simulations to determine the physical processes that were taking place at the signal’s location.

Tandem cells, the next generation of solar cells, comprise multiple photovoltaic materials. Each material is sensitive to different parts of the sun’s rays, allowing them to produce more energy. The perovskite material is one of the most popular device layers.

By working together, lips stated that the new solar cells could surpass the 30% efficiency limit.

Small losses are essential at this efficiency point. Material scientists have observed one such failure. The deposition of transparent ITO modifies the silicon layers underneath, creating defects that reduce the device’s efficiency. This interface is where the charges are trapped and then released. Another signal was when holes could pass through the barrier on the back of the device.

Davenport stated, “The ability to recognize these signals means we can understand their source and mitigate them.”

 

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