Failure Mode Analysis of Li-Ion Batteries Using In Situ Scanning Electron Microscopy
Bibliographic record
Abstract
High cycle life is a key component that any lithium-ion battery technology must achieve in order to obtain a commercial success. In this regard, multiple electrochemical (Impedance, ASI, floating current, etc.) and non-electrochemical techniques (XRD, FT-IR, SEM/TEM, etc.) are used and developed. The most complete techniques involved measurement while the battery is cycling (in situ experiment). Among them, in-situ SEM/TEM is able to focus on the same region/particle over the all range of oxidation/reduction. Hydro-Quebec has developed a complete expertise for in-situ techniques, especially in situ SEM. Lithium Polymer Battery (LPB), an all solid battery, is the preferred battery configuration for SEM in-situ experiment. We present in Figure 1 micrographs taken during in-situ experiment of a LPB (Li/PEO based SPE/Li1.2V3O8 based cathode) showing the variation in thickness for the Li, SPE and cathode layers with the cell voltage. Only the Li thickness shows an important variation during plating and unplating. The Li plating rate can also be measured. This experiment can clearly be used to better understand the Li plating mechanism. In figure 2 we present micrographs taken during an in-situ SEM cycling of a Li/PEO based SPE/SiO cell using backscattered electron signal. We clearly see an important decrease in the BSE intensity as a function of the cell voltage as a result of the Si phase transformation upon cycling (upon Li ‘insertion’, the BSE intensity is lower and the particle becomes darker). We also note that a region of the big particle remains white (i.e. the Li did not have sufficient time to ‘diffuse’ inside their core region). This result explains the rather low capacity observed with such particles and it gives evidence that the system is out of thermodynamic equilibrium. This in-situ experiment also shows no cracking of the relatively big SiO particles since the voltage was kept higher than 0.1V (stop at the Li22Si7 phase instead of Li22Si5) when compared to previous results [1]. We will also present in-situ SEM of nano-Si particles that did not suffer any cracking, but agglomerate (electrochemical sintering), which results in a more rigid and fractured electrodes [2] This presentation will also used in-situ SEM to obtain a better understanding of the failure mechanism of new electrode materials, including Li-S. References: [1] http://batt.lbl.gov/blog/research-tasks/in-situ-sem-seeing-battery-cycling-in-action/?utm_source=rss&utm_medium=rss&utm_campaign=in-situ-sem-seeing-battery-cycling-in-action [2] Hovington et al, (2014) ‘in situ Scanning electron microscope study and micriostructural evolution of nano silicon anode for high energy Li-ion batteries’, Journal of Power Sources 248, 457-464
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".