A new open-access resource containing over 4.5 terabytes of experimental battery data has launched to help researchers find, analyze, and reuse advanced battery imaging datasets, according to recent technical reports. Developed to support computational modeling and image analysis, the Battery Imaging Library brings together experimental results from 48 scientists across 18 institutions worldwide.
What Is the Battery Imaging Library?
The Battery Imaging Library, known as BIL, compiles raw data and reconstructed images utilizing 13 different imaging modalities to examine batteries at multiple length scales.
The collection addresses a major hurdle in materials science: managing and searching through massive terabyte-scale datasets. The platform lets users browse various imaging modalities, granting access to both raw experimental files and processed results.
High-Resolution X-Ray Imaging at Diamond Light Source
Scientists scanned commercial cylindrical lithium-ion, LiFeS2, and alkaline batteries.
These scans reveal the internal morphological structures of the cells. Furthermore, dynamic measurements capture structural changes happening inside operating batteries over time, giving researchers a clearer picture of degradation mechanisms and internal stress points.
Pro Tip: Researchers and students can use the open-access datasets in BIL to test computational models and evaluate image analysis software using realistic, industrially relevant samples.
Complementary Neutron Imaging from ISIS
Neutron imaging is less commonly available within the broader battery research community, making this inclusion particularly valuable.
Because neutrons interact strongly with lighter elements, the technique excels at examining components that X-rays often miss. Researchers can use these datasets to identify lithium-based electrolytes and track the precise location of lithium species inside a cell, providing a multi-faceted view of battery chemistry.
Did You Know? The Battery Imaging Library includes cone-beam X-ray micro-CT reconstructions covering various commercial form factors and chemistries, including sodium-ion (Na-ion), nickel-metal hydride (NiMH), and zinc-manganese (Zn-Mn) cells.
Applications in AI and Materials Research
Beyond traditional academic study, the massive scale and diversity of the BIL repository offer ideal material for training artificial intelligence models. The accompanying project publication highlights how open experimental datasets can accelerate machine learning applications in materials science.
Frequently Asked Questions
What is the Battery Imaging Library?
It is an open-access resource containing over 4.5 terabytes of experimental battery imaging data collected by 48 scientists across 18 institutions.
What types of imaging techniques are included?
The library features 13 different imaging modalities, including high-energy X-ray micro-computed tomography from Diamond Light Source and neutron computed tomography from the ISIS Neutron and Muon Source.
Who created the platform’s search interface?
Can the data be used for commercial or educational purposes?
Yes, the resource is designed to support computational modeling, image analysis, research training, and the development of new characterization methods.
What are your thoughts on open-access datasets in materials science? Let us know in the comments below, or subscribe to our newsletter for more updates on battery technology.
Related reading