Development of Embedded Fiber-Optic Evanescent Wave Sensors for Optical Characterization of Graphite Anodes in Lithium-Ion Batteries
Bibliographic record
Abstract
The development, fabrication, and embedment of fiber-optic evanescent wave sensors (FOEWSs) to monitor the state of charge (SOC) and the state of health (SOH) of lithium-ion batteries (LIBs) are presented. Etching of FOEWSs is performed using a solution of 40 wt % ammonium fluoride (NH 4 F) and 49 wt % hydrofluoric acid (HF) (6:1), which is found to be superior to an etching solution containing just 49 wt % HF. FOEWSs were characterized using glycerol and found to have the highest sensitivity in a lithium-ion battery when they lose 92% of their transmittance in the presence of glycerol on their sensing region. The physical effect that the FOEWS has on the graphite anode is also investigated and is found to be much more significant in Swagelok cells compared to that in in-house-fabricated pouch cells, mainly due to pressure variation. The FOEWS was found to be most sensitive to the changes in the LIB when it was completely embedded using a slurry of graphite anode material within a pouch cell. The optimized fabrication process of the embedded FOEWS demonstrates the potential of using such sensors commercially for real-time monitoring of the SOC and SOH of LIBs while in operation.
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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 | 0.000 |
| 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 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".