Battery (electricity)

Quantum mechanics

Mathematics education

Materials science

Power (physics)

Computer science

Nano-

Multiscale modeling

Computer network

Porosity

Tomography

Physics

Bridging (networking)

Nanotechnology

Mathematics

Optics

Characterization (materials science)

Synchrotron

Computational chemistry

Artificial intelligence

Composite material

Microscale chemistry

Tortuosity

Chemistry

Nanoscopic scale

Nature Nanotechnology

X-ray computed tomography (CT) is a non-destructive imaging technique in which contrast originates from the materials' absorption coefficient. The recent development of laboratory nanoscale CT (nano-CT) systems has pushed the spatial resolution for battery material imaging to voxel sizes of 50 nm, a limit previously achievable only with synchrotron facilities. Given the non-destructive nature of CT, in situ and operando studies have emerged as powerful methods to quantify morphological parameters, such as tortuosity factor, porosity, surface area and volume expansion, during battery operation or cycling. Combined with artificial intelligence and machine learning analysis techniques, nano-CT has enabled the development of predictive models to analyse the impact of the electrode microstructure on cell performances or the influence of material heterogeneities on electrochemical responses. In this Review, we discuss the role of X-ray CT and nano-CT experimentation in the battery field, discuss the incorporation of artificial intelligence and machine learning analyses and provide a perspective on how the combination of multiscale CT imaging techniques can expand the development of predictive multiscale battery behavioural models.

Bridging nano- and microscale X-ray tomography for battery research by leveraging artificial intelligence