Label-Free Ultrasensitive and Environment-Friendly Immunosensor Based on a Silica Optical Fiber for the Determination of Ciprofloxacin in Wastewater Samples
Bibliographic record
Abstract
Abstract Emerging contaminants, including pharmaceutical compounds, are receiving research attention as a result of their widespread presence in effluents and wastewater treatment plants (WWTPs). The antibiotic ciprofloxacin (CIP) is extensively employed to treat infections in animal and human medicine. Both CIP and its metabolites are common contaminants found in WWTPs. In this study, a label-free ultrasensitive U-bent optical fiber-based immunosensor for the determination of CIP in wastewater samples was developed using the properties of the conducting polymer polyaniline (PANI). The anti-CIP immunoglobulin G (IgG) was deposited on a silica optical fiber surface previously functionalized with PANI. Scanning electron microscopy and micro-Raman spectroscopy were used to investigate the surface of the immunosensor. The analysis of CIP in wastewater was performed without the use of an organic solvent or sample preparation steps, with only the sample dilution in saline buffer (pH = 7.4). The linear range for CIP was from 0.01 to 10,000 ng L–1. The detection limit was 3.30 × 10–3 ng L–1 and the quantification limit was 0.01 ng L–1. The immunosensor provided a high average recovery of 91% after spiking wastewater samples with CIP at a concentration of 9,100 ng L–1. The method was applied in triplicate to wastewater samples from Quebec (Canada), obtaining concentrations of 549 and 267 ng L–1. A comparison with a reference method showed no significant difference (t-test at 95% confidence). The new technique developed is selective, allowing a quantitative analysis of CIP in wastewater.
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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 | 0.001 | 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".