Imaging the Solution Phase Concentration Gradient in Li-Ion Battery Positive Electrodes with X-Ray Fluorescence
Bibliographic record
Abstract
With the current climate crisis having no end in sight, communities worldwide are depending on new battery technology to store energy from intermittent sources of energy such as wind and solar. Li-ion batteries (LIBs) have presented themselves as a worthy candidate for the task given their high capacity for charge as well as their durability in terms of cycle life. LIB models have been developed extensively for the purpose of understanding current behaviour and predicting future performance. A limiting factor in LIBs is the rate at which they can be (dis)charged which can be extensively hindered by the formation of a concentration gradient of Li+ within the positive and negative electrodes. Although the models that exist can predict what these gradients should be within the electrodes, there has, as of yet, been no experimental data representing the concentration gradient formation within the electrode pores. Using X-ray fluorescence (XRF) in conjunction with a synchrotron light source allows the spatially resolved observation of the Li+ concentration profile. This work can validate the predictive power of established P2D models in order to improve their accuracy in addition to serving as a screening technique for new composite positive electrodes. Figure 1
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".