X-ray nanotomography for battery characterization - a benchmarking approach

As the transition towards sustainable energy accelerates, lithium-ion batteries play a critical role in reducing reliance on fossil fuels [1]. Achieving this transition requires significant advancements in energy storage technologies, including the development of battery systems with higher energy densities, enhanced safety, and prolonged lifespans. These systems, often composed of advanced, multi-layered, and poly-phase materials, may incorporate diverse chemicals, including low- and light-element compositions or complex hierarchical microstructures.

Understanding the morphological properties of these materials is essential for elucidating the relationship between electrode microstructure and electrochemical performance, particularly in terms of cycling stability and degradation mechanisms. While recent progress in non-destructive 3D imaging techniques has facilitated the study of these systems [2], challenges remain in capturing their heterogeneities across multiple length scales – from individual particles and electrodes to full cells. Moreover, achieving in situ and operando insights is imperative to comprehensively understand the dynamic microstructural evolution of battery electrodes during operation, particularly at the nanoscale, where representative data remain scarce. 

This study addressed these challenges by leveraging X-ray phase-contrast nanotomography (nano-CT) at beamline ID16B [3]. A critical analysis of the workflow was conducted, evaluating the influence of each procedural step – from sample preparation to data acquisition and reconstruction – on the reliability of nano-CT analysis for battery materials (Figure 1).

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Fig. 1: X-ray phase-contrast nanotomography workflow for standardized and reliable morphological characterization of battery materials from 3D to 4D. The process consists of four key steps: sample preparation, data acquisition, image reconstruction, and image processing/analysis. The accuracy and reproducibility of the final characterization depend on standardized procedures and systematic quality control at each stage, ensuring reliable and quantifiable data. Each step is optimized based on the material’s interaction with X-rays and its microstructural properties. A fifth step is introduced for electrochemical preparation, enabling time-resolved 3D characterization of battery materials at the nanoscale for in situ and operando studies.

Standardization methods and acquisition protocols were introduced to optimize imaging parameters, ensuring reproducible and accurate characterization. The approach was demonstrated through the comparative analysis of three anode materials (graphite, silicon, and Li₄Ti₅O₁₂ (LTO)) and three cathode materials (LiFePO₄ (LFP), LiMn₂O₄ (LMO), and LiNi_0.33Mn_0.33Co0_.33O₂ (NMC)). The methodology was further extended to the design and development of a cell setup that addresses the constraints of nano-CT, enabling in situ and operando monitoring of electrochemical devices. Strategies were proposed to ensure reliable cell operation and mitigate X-ray-induced beam damage (Figure 2).

Click figure to enlarge

Fig. 1: X-ray phase-contrast nanotomography workflow for standardized and reliable morphological characterization of battery materials from 3D to 4D. The process consists of four key steps: sample preparation, data acquisition, image reconstruction, and image processing/analysis. The accuracy and reproducibility of the final characterization depend on standardized procedures and systematic quality control at each stage, ensuring reliable and quantifiable data. Each step is optimized based on the material’s interaction with X-rays and its microstructural properties. A fifth step is introduced for electrochemical preparation, enabling time-resolved 3D characterization of battery materials at the nanoscale for in situ and operando studies.

Through a systematic evaluation of parameters influencing the nano-CT workflow, this study provides solutions for minimizing experimental bias in image reconstruction and subsequent image-processing-based quantification. Optimizing sample size (based on material chemistry), preparation techniques, and acquisition energy enhances spatial resolution and maximizes imaging capabilities, enabling precise differentiation of electrode microstructure phases. Furthermore, an empirical law based on microstructural heterogeneity and particle size distribution was developed to determine the minimum representative elementary volume required for imaging. This analysis indicates that volumes of 102 × 102 × 102 µm³ (50 nm voxel size) are sufficiently representative for the six benchmark materials studied, effectively bridging the electrode and particle length scales. 

This work establishes guidelines for in situ and operando nano-CT measurements, presenting a validated in-house cell setup that preserves imaging resolution as well as electrochemical fidelity. A detailed investigation of X-ray beam interactions is provided, examining the relationship between radiation dose, and electrolyte/material integrity, as well as reliability of electrochemical and tomographic measurements.

This study demonstrates the significant potential of X-ray nano-CT as a powerful and versatile technique for high-resolution 3D morphological characterization of battery materials, effectively bridging the electrode and particle scales. The proposed methodology serves as a broadly applicable framework for systematic sensitivity analysis in other material systems and tomography techniques. Moreover, it paves the way for advanced in situ and operando investigations of next-generation battery materials, including all-solid-state, lithium-metal anode-free, and sodium-ion technologies. This work is expected to have a substantial impact on both the battery research community and the broader field of nanoscale characterization.
 

Principal publication and authors
Comparative Study of the Quantitative Analysis of Battery Materials with X-ray Nano-tomography: From Ex Situ toward Operando Measurements, V. Vanpeene (a,b), O. Stamati (a), C. Guilloud (a), R. Tucoulou (a), B. Holliger (a), M. Chandesris (b), S. Lyonnard (c), J. Villanova (a), ACS Nano 19, 9994-10012 (2025); https://doi.org/10.1021/acsnano.4c16419
(a) ESRF
(b) Université Grenoble Alpes, CEA, Liten, DEHT, Grenoble (France) 
(c) Université Grenoble Alpes, CEA, CNRS, Grenoble INP, IRIG, SyMMES, Grenoble (France)

References
[1] D. Larcher & J.M. Tarascon, Nat. Chem. 7, 19-29 (2015).
[2] J. Scharf et al., Nat. Nanotechnol. 17, 446-459 (2022).
[3] G. Martinez-Criado et al., J. Synchrotron Radiat. 23, 344-352 (2016).