Carbon black plays an essential role in the manufacture of high performance batteries. It’s important for this carbon black to be free of impurities, which can decrease battery performance and life.

Researchers at UConn, in collaboration with several partners, have developed a new way to use microscopes to locate and identify potential impurities.

“It’s a classic problem in microscopy: finding a needle in a haystack,” says UConn engineer and director of the Reverse Engineering Fabrication, Inspection and Non-destructive Analysis (REFINE) lab Sina Shahbazmohamadi. Although finding something microscopic with a microscope might seem easy, it’s actually very difficult. The finer your point of view, the vaster the territory.

“Carbon black is a low density powder. Five grams is a lot of material, volume-wise. And in five grams we are searching for particles that are five to ten micrograms across,” says engineering doctoral candidate Abhinav Poozhikunnath, who led the research.

Finding them wouldn’t be easy.

The classic method would be to burn the material to reduce the volume and then sift through it. But burning could alter the impurity and destroy any clues that could hint at how it was getting into the carbon black.

Instead, Poozhikunnath decided to use a series of microscopes to narrow down the location of the impurities and eventually find them directly. He worked with Shahbazmohamadi in the REFINE lab, which has state-of-the-art microscopes of each type and close connections with ZEISS Microscopy, which also collaborated on the work.

First, Poozhikunnath embedded the carbon black (which resembles black dust or soot) in epoxy to fix it in place so that it could be mapped. Then he scanned it with x-rays to create a 3D map, the same way a CT scan maps the human body. He knew the impurities were metals, and probably a lot heavier than the carbon. That meant the x-rays would bounce off them, making bright spots in the map. And that’s just what happened.

Poozhikunnath then used a pulsed laser to remove larger lumps of epoxy and carbon black, followed by a Focused Ion Beam (FIB) microscope to finely cut away at the lump of epoxy and carbon black until it exposed an impurity. The FIB shoots gallium ions at the sample. Each atom of gallium chips away a tiny bit of the epoxy or carbon black. It’s like a very, very fine chisel.

Once the impurity was exposed, Poozhikunath took a picture of it and its surroundings with a Scanning Electron Microscope (SEM). SEMs can take beautiful visual images of microscopic surfaces, and can also reveal other things about the properties of what’s being imaged, depending on how the electrons interact with the surface.

He then used Energy Dispersive X-ray Spectroscopy (EDS) to analyze the impurities and the carbon black particles surrounding them.

Using a computer program called Atlas, developed with the help of researchers at ZEISS, Poozhikunath and Shahbazmohamadi were able to have the different microscopes “talk” to each other. With Atlas, the X-ray CT scan could tell the FIB and SEM exactly where to look to find the contaminants with respect to a fixed point on the sample. This allowed the FIB and SEM to avoid instead scanning the entire lump (which would have been almost impossible, or at least take far too long.)

“This ability to communicate between different instruments is a key technological development that allowed us to succeed,” says Poozhikunath.

In the sample carbon black, some of the impurities turned out to be flecks of iron oxide. But the same technique could be used to solve many similar problems faced by all kinds of industries. It could also be automated and used for quality control purposes in the semiconductor industry, Sina says.

The researchers are currently working with ZEISS to see if they can completely automate the process, with the data from each microscope flowing into Atlas and helping to guide the next microscope’s investigation. They’re also exploring ways they might use this to train automated systems to find contaminants via machine learning algorithms.

Automating the process so microscopes can talk to each other and solve a real industrial problem is a valuable result, the researchers say.