In situ plasmonic optical fiber detection of the state of charge of supercapacitors for renewable energy storage
Why this work is in the frame
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Bibliographic record
Abstract
In situ and continuous monitoring of electrochemical activity is key to understanding and evaluating the operation mechanism and efficiency of energy storage devices. However, this task remains challenging. For example, the present methods are not capable of providing the real-time information about the state of charge (SOC) of the energy storage devices while in operation. To address this, a novel approach based on an electrochemical surface plasmon resonance (SPR) optical fiber sensor is proposed here. This approach offers the capability of in situ comprehensive monitoring of the electrochemical activity (the electrode potential and the SOC) of supercapacitors (used as an example). The sensor adopted is a tilted fiber Bragg grating imprinted in a commercial single-mode fiber and coated with a nanoscale gold film for high-efficiency SPR excitation. Unlike conventional "bulk" detection methods for electrode activity, our approach targets the "localized" (sub-μm-scale) charge state of the ions adjacent to the electrode interface of supercapacitors by monitoring the properties of the SPR wave on the fiber sensor surface located adjacent to the electrode. A stable and reproducible correlation between the real-time charge-discharge cycles of the supercapacitors and the optical transmission of the optical fiber has been found. Moreover, the method proposed is inherently immune to temperature cross-talk because of the presence of environmentally insensitive reference features in the optical transmission spectrum of the devices. Finally, this particular application is ideally suited to the fundamental qualities of optical fiber sensors, such as their compact size, flexible shape, and remote operation capability, thereby opening the way for other opportunities for electrochemical monitoring in various hard-to-reach spaces and remote environments.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it