Monitoring Microalgal Biofilm Growth and Phenol Degradation with Fiber-Optic Sensors
Bibliographic record
Abstract
Simple D-type plastic optical fiber (POF) probes (i.e., sensor, reference, and photochemical probes) were created to accurately monitor the progression and phenol degradation of a Chlorella vulgaris biofilm. The sensor and reference probes were used to monitor the biofilm growth (thickness). The sensor probe, which consisted of a D-shaped POF and Canada balsam doped with GeO 2 (CBG) coating, was developed to monitor the biofilm growth and change in the liquid-phase composition and its concentration inside the biofilm. The reference probe, which comprised a D-shaped POF, CBG coating, and glass fiber membrane (to separate the liquids from Chlorella vulgaris ), was used to measure the response to changes in the liquid phase. A model was developed to demonstrate the accurate measurement of the biofilm thickness. The photochemical POF probe was coupled with a high-permselectivity phenol polymer membrane to monitor the phenol concentration and analyze the degradation time of 50 mg/L phenol with microalgal biofilms. A fixed relationship was obtained between the biofilm sensor output information and biofilm thickness for a biofilm thickness range of 0–290 μm with a periodic supply of 50 mg/L phenol solution. The highest phenol degradation rate occurred at a biofilm thickness of 191–222 μm. The proposed system can be used to investigate microalgal biomass and can provide a promising avenue for research on renewable resources and pollutant degradation.
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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 | 0.000 |
| 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".