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Three-dimensional operando optical imaging of particle and electrolyte heterogeneities inside Li-ion batteries

Nature Nanotechnology volume 18, pages 1185–1194 (2023)Cite this article

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Abstract

Understanding (de)lithiation heterogeneities in battery materials is key to ensure optimal electrochemical performance. However, this remains challenging due to the three-dimensional morphology of electrode particles, the involvement of both solid- and liquid-phase reactants and a range of relevant timescales (seconds to hours). Here we overcome this problem and demonstrate the use of confocal microscopy for the simultaneous three-dimensional operando measurement of lithium-ion dynamics in individual agglomerate particles, and the electrolyte in batteries. We examine two technologically important cathode materials: LixCoO2 and LixNi0.8Mn0.1Co0.1O2. The surface-to-core transport velocity of Li-phase fronts and volume changes are captured as a function of cycling rate. Additionally, we visualize heterogeneities in the bulk and at agglomerate surfaces during cycling, and image microscopic liquid electrolyte concentration gradients. We discover that surface-limited reactions and intra-agglomerate competing rates control (de)lithiation and structural heterogeneities in agglomerate-based electrodes. Importantly, the conditions under which optical imaging can be performed inside the complex environments of battery electrodes are outlined.

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Fig. 1: Tracking charge state with optical reflection microscopy.
Fig. 2: Three-dimensional imaging in polycrystalline battery electrodes.
Fig. 3: Tracking agglomerate particle volume changes during cycling.
Fig. 4: Measurement of phase-front velocities through agglomerate particles.
Fig. 5: Unwrapping of particle surfaces.
Fig. 6: 2PEF from battery electrolytes.

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Data availability

The data underlying the figures in the main text are publicly available from the University of Cambridge repository at https://doi.org/10.17863/CAM.96940.

Code availability

Code is available from the corresponding authors upon request.

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Acknowledgements

R.P. acknowledges financial support from Clare College, University of Cambridge, and thanks A. Ashoka (Cambridge) for insightful discussions on optical microscopy; S. Keene (Cambridge) for critical reading of the manuscript; C. Schnedermann and A. J. Merryweather (Cambridge) for advice on the preparation of operando cells; V. Meunier, R. Dugas and J. Louis (Collège de France) for assistance with experiments; and U. F. Keyser for loan of a potentiostat and spectrometer. L.V. acknowledges funding from the Swiss National Science Foundation (grant P400P2_199329). F.D. acknowledges the École Normale Supérieure Paris-Saclay for his PhD scholarship. T.G.P. acknowledges the ESPRC NanoDTC (EP/L015978/1). T.S.M. acknowledges support from the Faraday Institution (EP/S003053/1) LiSTAR project (FIRG014). K.M. acknowledges support from the UCL H. Walter Stern Scholarship. We thank N. Rouach (Collège de France) and S. Vignolini (Cambridge) for access and use of the experimental resources.

Author information

Author notes

  1. These authors contributed equally: Lorenzo Valzania, Florian Dorchies, Fei Xia.

Authors and Affiliations

  1. Laboratoire Kastler Brossel, ENS-Université PSL, CNRS, Sorbonne Université, Collège de France, Paris, France

    Raj Pandya, Lorenzo Valzania, Fei Xia, Sylvain Gigan & Hilton B. de Aguiar

  2. Department of Physics, Cavendish Laboratory, University of Cambridge, Cambridge, UK

    Raj Pandya & Angus Mathieson

  3. Chimie du Solide et de l’Energie, UMR 8260, Collège de France, Paris, France

    Florian Dorchies, Jean-Marie Tarascon & Alexis Grimaud

  4. Réseau sur le stockage Electrochimique de l’Energie (RS2E), Amiens, France

    Florian Dorchies, Jean-Marie Tarascon & Alexis Grimaud

  5. Neuroglial Interactions in Cerebral Physiology and Pathologies, Center for Interdisciplinary Research in Biology, Collège de France, CNRS, INSERM, Labex Memolife, Université PSL, Paris, France

    Jeffrey Mc Hugh

  6. Department of Engineering, University of Cambridge, Cambridge, UK

    Angus Mathieson, Hwee Jien Tan & Michael De Volder

  7. Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge, UK

    Thomas G. Parton

  8. Université Paris Cité, ITODYS, CNRS, Paris, France

    Louis Godeffroy & Frédéric Kanoufi

  9. Electrochemical Innovation Lab Department of Chemical Engineering, UCL, London, UK

    Katrina Mazloomian & Thomas S. Miller

  10. Department of Chemistry, Merkert Chemistry Center, Boston College, Chestnut Hill, MA, USA

    Alexis Grimaud

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Contributions

R.P. conceived the idea, performed the optical experiments, analysed and interpreted the data and wrote the manuscript. L.V. developed the ellipsoid projection code and along with F.X. developed, analysed and interpreted the optical tomography experiments. F.D. prepared the operando cells and interpreted the data. J.M. supervised all the confocal microscopy measurements. T.G.P. performed the ex situ reflection microscopy experiments. A.M., H.J.T. and M.D.V. provided the experimental advice and interpreted the data. J.-M.T. interpreted the data. L.G. performed the SEM and wavelength-resolved imaging experiments under the supervision of F.K. K.M. performed and analysed the X-ray computed tomography under the supervision of T.S.M. S.G. and H.B.d.A. supervised the project and interpreted the optical data. A.G. supervised the project, interpreted the data and wrote the manuscript. All authors contributed to the preparation of the manuscript.

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Correspondence to Raj Pandya, Sylvain Gigan, Hilton B. de Aguiar or Alexis Grimaud.

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Extended data

Extended Data Fig. 1 Refractive indices of battery electrodes.

a-b. Real (n) and imaginary (k) parts of the refractive index of LCO and NMC extracted from fitting model as detailed in supplementary information 2.

Extended Data Fig. 2 Spatial patterns created by focal shifts.

Reflection images of LCO agglomerate whilst scanning focus (white label). A wide field imaging modality is used by increasing the pinhole size to 2.0 A.U. Although at all focus positions the agglomerate appears to remain nominally in focus (i.e. sharp outline), the spatial reflected intensity pattern changes. Scale bar is 5 μm.

Extended Data Fig. 3 Agglomerate volume changes as a function of electrode loading.

a-c. Extracted volume changes of individual LCO agglomerates as a function of electrode loading (60 wt%, 80 wt% and 92 wt%). The volume of agglomerates increases to a maximum of ~4% on charging before shrinking once again, in-line with previous studies as discussed in the main text. There is a large degree of heterogeneity in the absolute expansion. Some agglomerates in the electrode are inactive and hence remain at a constant volume throughout. The standard deviations of volume changes are as follows: σ60wt% = 0.44 σ80wt% = 0.58 σ92wt% = 0.48. The regions 1 and 2 correspond to different areas (20 μm × 20 μm) of the electrode which were imaged. All data is extracted from volume changes in the third or fourth cycling of the electrode. Data is shown for 40 agglomerates in a, 55 agglomerates in b and 75 agglomerates in c. The cycling rate was 2 C. The uncertainty on each ΔV/V value is ~10% as derived from measurement errors. Error bars are not shown on the plot to avoid obscuring of the data.

Extended Data Fig. 4 Correlation between LSCRM and SEM.

a-d. LSCRM (top) and SEM images (bottom) of same LCO agglomerates of four different regions of the electrode before and after charging to 4.25 V. Scale bars: a – left panel 4 μm, right panel 5 μm; b – left panel 4 μm, right panel 5 μm; c – left panel 5 μm, right panel 4 μm; d – left panel 4 μm, right panel 4 μm. Colour scale is arbitrary in images.

Extended Data Fig. 5 Cartoon explaining extraction of planes from agglomerates for velocity estimation.

The agglomerate is first orientated and slices through the core of the agglomerate extracted. For each slice the mean reflection intensity is calculated (after appropriate attenuation correction). This is then repeated over time (during the cycling) such that a depth and time varying reflectivity profile can be obtained.

Extended Data Fig. 6 Extraction of reflectivity profiles through sub-particles of an agglomerate.

a. 2D bright-field image of NMC811 agglomerate. Scale bar is 5 μm. b. 3D reconstruction of NMC811 agglomerate and re-orientation along long axis (z) to show agglomerate sub-structure (faded red lines) labelled P1 – P4. Scale bar is 5 μm. c. Normalised change in reflectivity for P1 – P4 regions in agglomerate shown in (a) as a function of time at top, centre and bottom of agglomerate. d-e. Normalised change in reflectivity for two other agglomerate with 3 and 5 identifiable sub-particles labelled P1 – P3(5). Reflectivity change shown as a function of time and depth in agglomerate. There is a spread in the onset time of reflectivity changes between sub-particles. The uncertainty on the pixel intensity increases with depth but sits between 3% and 5% for all points.

Extended Data Fig. 7 Spatial propagation of (de)lithiation heterogeneities at the surface and core of NMC811 agglomerates.

Charge-discharge cycle of NMC811 at C/2 and 2 C showing projections from shells at exterior and centre of agglomerate at set points during the cycle (letters A to F). For NMC811 the semi-major axes of the surface ellipsoid are 3.5, 2.8 and 6.5 μm, for the core it is 1.2, 1 and 1.8 μm. As for LCO some movement of intensity from the edges of the projection to the centre on delithiation and a reverse on lithiation can be observed, however the exact nature of the motion is unclear. For NMC811 there are a range of domains with different degrees of lithiation at the start of the charge, further complicating the analysis. Qualitatively it appears the overall difference in degree of lithiation within and between domains decreases on charge and increases once again on discharge. However, further work is required to fully interpret these observations. Data across the two C-rates represent measurements of identical agglomerates. At a rate of C/2: A – 3.85 V, B – 4.00 V, C – 4.17 V, D – 3.85 V, E – 3.73 V F – 3.55 V. At a rate of 2 C: A – 3.90 V, B – 4.01 V, C – 4.17 V, D – 3.72 V, E – 3.64 V F – 3.45 V.

Extended Data Fig. 8 Two-photon excited fluorescence (2PEF) from LP30 electrolyte during a 1 C charge of LCO followed by relaxation to OCP.

On relaxation to OCP the gradient in 2PEF intensity around the agglomerates rapidly disappears, providing further evidence that the observed gradient does indeed arise from polarisation concentration.

Extended Data Fig. 9 Two-photon excited fluorescence (2PEF) from LP30 electrolyte during a 2 C top and C/2 cycle of NMC811.

In a similar manner to LCO there is brightening of the electrolyte 2PEF on charge and dimming on discharge. Around the agglomerates (solid and dashed black lines are guides to the eye), the 2PEF is initially homogeneously distributed (panels A and B) before a concentration/2PEF gradients build-up at higher voltages above 4.0 V. The 2PEF concentration gradient is somewhat inhomogeneously distributed around agglomerates above 4.0 V (panels C and D). At C/2 the onset of the 2PEF concentration gradient is at higher voltages as compared to 2 C. Scale bar is 4 μm. Data across the two C-rates represent measurements of identical agglomerates.

Supplementary information

Supplementary Information

Supplementary Notes 1–12, Figs. 1–45 and discussion.

Supplementary Video 1

Focus stack over the surface of the LCO electrode particles. 30 nm z steps and the image is 120 × 120 pixels. The video is at 1 fps.

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Pandya, R., Valzania, L., Dorchies, F. et al. Three-dimensional operando optical imaging of particle and electrolyte heterogeneities inside Li-ion batteries. Nat. Nanotechnol. 18, 1185–1194 (2023). https://doi.org/10.1038/s41565-023-01466-4

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