AI-Driven Phase Identification from X-ray Hyperspectral Imaging of cycled Na-ion Cathode Materials

Abstract

Na-ion batteries have emerged as viable candidates for large-scale energy storage applica- tions due to resource abundance and cost advantages. The constraints imposed on their performance and durability, for instance, by complex phase transformations in positive electrode materials during electrochemical cycling, can be addressed and are thus not detrimental to their development. However, diffusion-limited Na-ion transport can drive spatially heterogeneous phase nucleation and propagation, leading to multiphase coexis- tence and locally non-uniform electrochemical activity, generating complex reaction path- ways that challenge both mechanistic understanding and predictive material optimization. These challenges can be addressed by investigating single-crystalline regions of materials, i.e. down to the scale of individual particles, although such analyses are often constrained by energetically and/or spatially sparse hyperspectral datasets. Here, we developed an AI-driven method to process hyperspectral data under sparse sampling conditions and generate multiphase maps with nanometer-scale resolution over a micrometer-scale field of view. We applied this processing on scanning transmission X-ray microscopy (STXM) data to determine the distribution and coexistence of phases in individual particles of NaxV2(PO4)2F3 cathode materials, at different states of charge. The methodology relies on a workflow which combines a Gaussian mixture variational autoencoder (GMVAE) algorithm with the Pearson corre- lation coefficient to identify the sodium content and map their spatial distribution. Our approach reveals nanoscale phase heterogeneity and evolution within individual particles, and improves the reliability of phase detection by identifying ambiguity zones, false assign- ments, and transition phases localized at grain boundaries.

Figures (6)

• Figure 1: Scanning transmission X-ray microscopy (STXM) experimental setup. (a) Schematic of the STXM setup (not to scale). (b) Illustration of a stack of images acquired with low spatial resolution (low-sampling version of the image shown in panel d). (c) Optical density (OD) spectrum of a NVPF particle acquired with high energy resolution; the dashed line indicates the energies selected for high spatial resolution acquisitions. (d) High spatial resolution image of a NVPF particle (same as in (b)). (e) Illustration of the optical density (OD) spectrum from a low spectral resolution acquisition, where the bars represent the energy levels and their heights correspond to the optical density values.
• Figure 2: Reference XANES spectra of NVPF for different levels of Na$^{+}$ extraction. Optical density (OD) of V-L$_{2,3}$ and O-K edge X-ray absorption spectra acquired in STXM measurements on individual NVPF particles at different states of charge.
• Figure 3: STXM phase mapping workflow. (a) Pearson correlation phase mapping: the Pearson correlation is computed between each spectrum vector $s$ and each reference vector $r_n$ ($n$=1,2,3,4,5). The resulting correlation map, $\rho_n$ ($n$=1,2,3,4,5), represents the correlation between $s$ and $r_n$ across all pixels. The initial phase map is obtained by comparing the correlation values pixel by pixel and assigning each pixel to the reference with the highest correlation. (b) Ambiguity map: pixels where $\arg\max(\rho_n(i,j) - \rho_m(i,j)) \leq 0.005$, with $n \ne m$, are shown in grey and labeled as ambiguous pixels $v_a$. The ambiguity is resolved by projecting $v_a$ into the latent space of the trained Gaussian mixture variational autoencoder (GMVAE) containing the overall phase distributions, measuring the Mahalanobis distances, and assigning the pixel to the phase corresponding to the nearest phase distribution.
• Figure 4: Phase mapping of a Na$_2$V$_2$(PO$_4$)$_2$F sample. (a) Phase map obtained using PCC (b) Map of ambiguous regions, where the correlation coefficient between two phases is close ($\leq 0.005$), shown in grey. (c) Final phase map with ambiguities resolved using a GMVAE by projecting ambiguous spectra into the latent space and assigning them to the closest phase distribution. The scale bars correspond to $0.2\,\mu$m (d) Phase distributions in the GMVAE latent space corresponding to the global latent representation without the projection of ambiguous pixels. (e) Projection of ambiguous pixels (in gray). (f) Latent space after resolution of ambiguous pixels.
• Figure 5: Phase maps of four NVPF samples at different states of charge. Phase maps of Na$_3$VPF, Na$_2$VPF, Na$_1$VPF, and Na$_{1-y}$VPF, respectively, after ambiguity resolution. Each image corresponds to a sample halted at the state of charge indicated on the electrochemical curve of NVPF.