Electrochemical Surface Plasmon Resonance Fiber-Optic Sensor: <i>In Situ</i> Detection of Electroactive Biofilms
Bibliographic record
Abstract
Spectroelectrochemistry has been found to be an efficient technique for revealing extracellular electron transfer (EET) mechanism of electroactive biofilms (EABs). Herein, we propose a novel electrochemical surface plasmon resonance (EC-SPR) optical fiber sensor for monitoring EABs in situ. The sensor uses a tilted fiber Bragg grating (TFBG) imprinted in a commercial single-mode fiber and coated with nanoscale gold film for high-efficiency SPR excitation. The wavelength shift of the surface plasmon resonance (SPR) over the fiber surface clearly identifies the electrochemical activity of the surface localized (adjacent to the electrode interface) bacterial cells in EABs, which differs from the "bulk" detections of the conventional electrochemical measurements. A close relationship between the variations of redox state of the EABs and the changes of the SPR under potentiostatic conditions has been achieved, pointing to a new way to study the EET mechanism of the EABs. Benefiting from its compact size, high sensitivity, and ease of use, together with remote operation ability, the proposed sensor opens up a multitude of opportunities for monitoring EABs in various hard-to-reach environments.
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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) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".