<i>Operando</i> Tracking of Solution-Phase Concentration Profiles in Li-Ion Battery Positive Electrodes Using X-ray Fluorescence
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide The trade-off between energy density and power capabilities is a challenge for Li-ion battery design as it highly depends on the complex porous structures that holds the liquid electrolyte. Specifically, mass-transport limitations lead to large concentration gradients in the solution-phase and subsequently to crippling overpotentials. The direct study of these solution-phase concentration profiles in Li-ion battery positive electrodes has been elusive, in part because they are shielded by an opaque and paramagnetic matrix. Herein we present a new methodology employing synchrotron hard X-ray fluorescence to observe the concentration gradient formation within Li-ion battery electrodes in operando . This methodology is substantiated with data collected on a model LiFePO 4 /Li cell using a 1 M LiAsF 6 in 1:1 ethylene carbonate/dimethyl carbonate (EC/DMC) electrolyte under galvanostatic and intermittent charge profiles. As such, the technique holds great promise for optimization of new composite electrodes and for numerical model validation.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".