A team of researchers from the National Institute of Standards and Technology, Gaithersburg, MD, along with other institutions, has developed a toolset to allow them to explore the interior of microscopic, multi-layered batteries. This allows them insight into the batteries’ performance without destroying them—resulting in both a useful probe for scientists and a potential power source for micromachines.

A “nanoforest” of nanowire lithium-ion batteries (Credit: Oleshko/NIST)

The tiny lithium-ion batteries are created by taking a silicon wire a few micrometers long and covering it in successive layers of different materials, like a tree. The batteries are attached by their “roots” to silicon wafers and clustered together by the million into “nanoforests,” as the team dubs them.

The layers enable the batteries to store and discharge electricity, and even be recharged, which could make them valuable for powering autonomous MEMS, or microelectromechanical machines, which have potential applications in many fields.

With so many layers that can vary in thickness, morphology and other parameters, it’s crucial to know the best way to build each layer to enhance the battery’s performance, as the team found in previous research. They developed a new technique to view the details using multimode scanning transmission electron microscopy (STEM) imaging. With STEM, electrons illuminate the battery, which scatters them at a wide range of angles. To see as much detail as possible, the team used a set of electron detectors to collect electrons in a wide range of scattering angles, an arrangement that gave them plenty of structural information to assemble a clear picture of the battery’s interior, down to the nanoscale level.

MEMS manufacturers could make use of the batteries themselves, a million of which can be fabricated on a square centimeter of a silicon wafer. But the same manufacturers also could benefit from the team’s analytical toolset, they said.

The field of additive manufacturing, which builds up component materials layer by layer, often needs to analyze its creations in a noninvasive way. This approach may be just the ticket.

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