Scanning Micro X-ray Fluorescence (μXRF) as an Effective Tool in Quantifying Fe Dissolution in LiFePO<sub>4</sub> Cells: Towards a Mechanistic Understanding of Fe Dissolution
Bibliographic record
Abstract
Lithium iron phosphate (LiFePO 4 , or LFP) is a widely used cathode material in Li-ion cells due to its improved safety and low cost relative to other materials such as LiNi x Mn y Co z O 2 (x + y + z = 1, NMC). To improve the calendar life of LFP cells, an investigation of their failure mechanisms is necessary. Herein, we use scanning micro X-ray fluorescence ( μ XRF) to study Fe dissolution from LFP and deposition on the graphite electrode, which is thought to be a contributor to capacity fade. The impacts of the vinylene carbonate (VC) electrolyte additive, cycling conditions, and water content in the positive electrode on Fe dissolution were studied. There was no significant correlation between Fe dissolution and capacity fade found. Furthermore, we proposed that gas generation concomitant with Fe dissolution might be due to the reduction of the organic species coordinating Fe 2+ when they reach the negative electrode. Localized regions of increased Fe loading on the anode surface were found, which corresponded to regions with slight non-uniformities in stack pressure or current density. This work demonstrates the effectiveness of μ XRF in quantifying transition metal (TM) dissolution in Li-ion cells without any sample treatments that might mask valuable information such as element spatial distribution.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".