Using Scanning Micro X-Ray Fluorescence (µXRF) to Visualize, Understand and Quantify Transition Metal Dissolution in Li-Ion Cells
Bibliographic record
Abstract
State-of-the-art Li-ion batteries (LIBs) typically consist of a graphite anode with a capacity of 372 mAh g -1 and a cathode material consisting of a layered transition metal oxide in the form of LiMO 2 , where M = Ni, Mn, Co or Al (NMC and NCA material), or olivine-type material in the form of LMPO 4 , where M = Fe, such as LiFePO 4 (LFP). Proposed degradation mechanisms for the cathode material in LIBs include transition metal dissolution (TMD), particle cracking and phase transformation, which are believed to contribute to significant capacity fade due to the loss of cathode active material 1 . Here, we report the use of scanning Micro X-Ray Fluorescence (µXRF) to investigate the effects of cell drying conditions and VC additive on the extent of TMD in LFP type cells and its subsequent deposition on the anode surface. For quantitative analysis, we prepared a calibration wedge made up of a pristine graphite anode with a known linear gradient of sputtered Fe. Figure 1a shows the µXRF images of the calibration sample and blank, from which we were able to correlate known TM concentrations to an observed signal intensity in the form of net count per area to facilitate accurate quantification, schematically shown in Fig. 1b . Unlike previous reports of XRF use in TMD analysis 2 , our approach allows us to quantify TM concentration deposited on our anodes without having to alter the surface by DMC washing, or through ball-milling, which preserves the nature of the sample and allows us to extract both quantitative and qualitative information (such as element distribution) from our aged cells using matrix-matched calibrants. In this work, we demonstrate how our scanning µXRF approach enabled us to visualize the distribution of TMs and other elements present on the anode surface and quantify TMD in LFP Li-ion cells. We show that Fe dissolution can be greatly reduced with rigorous cell drying and appropriate choice of additives. Additionally, we report the presence of locally high TM concentration spots on the anode which we believe are due to non-uniformities in cell stack pressure. Finally, we observe that the extent of TMD is normally not significant enough to be responsible for cell capacity fade due to active material loss, and propose how Fe dissolution might be contributing to a more complicated cell failure mechanism which will be important to investigate in future work to understand its impact on cycle life. References: Börner, M. et al. Degradation effects on the surface of commercial LiNi0.5Co0.2Mn0.3O2 electrodes. J. Power Sources 335 , 45–55 (2016). Evertz, M., Lürenbaum, C., Vortmann, B., Winter, M. & Nowak, S. Development of a method for direct elemental analysis of lithium ion battery degradation products by means of total reflection X-ray fluorescence. Spectrochim. Acta - Part B At. Spectrosc. 112 , 34–39 (2015). Figure 1
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".