Operando monitoring of ion activities in aqueous batteries with plasmonic fiber-optic sensors
Bibliographic record
Abstract
Understanding ion transport kinetics and electrolyte-electrode interactions at electrode surfaces of batteries in operation is essential to determine their performance and state of health. However, it remains a challenging task to capture in real time the details of surface-localized and rapid ion transport at the microscale. To address this, a promising approach based on an optical fiber plasmonic sensor capable of being inserted near the electrode surface of a working battery to monitor its electrochemical kinetics without disturbing its operation is demonstrated using aqueous Zn-ion batteries as an example. The miniature and chemically inert sensor detects perturbations of surface plasmon waves propagating on its surface to rapidly screen localized electrochemical events on a sub-μm-scale thickness adjacent to the electrode interface. A stable and reproducible correlation between the real-time ion insertions over charge-discharge cycles and the optical plasmon response has been observed and quantified. This new operando measurement tool will provide crucial additional capabilities to battery monitoring methods and help guide the design of better batteries with improved electro-chemistries.
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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 | 0.001 |
| 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".