Monitoring Microalgal Biofilm Growth and Phenol Degradation with Fiber-Optic Sensors
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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 GeO2 (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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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) | 0.001 | 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