Fluorescence Detection of Perfluorooctanoic Acid by High Performance Liquid Chromatography with Pre-Column Derivatization
Bibliographic record
Abstract
Abstract The extensive use of perfluorooctanoic acid (PFOA) has raised significant concerns about its environmental pollution. However, existing PFOA detection methods are often applied with high equipment and operation costs. This study developed a high-performance liquid chromatography-fluorescence detection method with pre-column derivatization for PFOA determination. This method was applied to the detection of PFOA in the effluent from disk-tubular reverse osmosis (DTRO) membrane treatment of leachate at a waste incineration plant. The chromatographic and derivatization conditions of the method were systematically optimized. PFOA concentrations were indirectly measured through the fluorescence peak area of its derivative with 3-bromoacetyl coumarin. The optimized method operates under the following conditions: a C18 column, a mobile phase consisting of acetonitrile and water (60 : 40, v/v) at 30°C, and fluorescence detection at an excitation wavelength of 305 nm and an emission wavelength of 420 nm. Derivatization was conducted at 70°C using 5.00 mg/mL 3-bromoacetyl coumarin in acetone, with 50 mg of tetrabutylammonium bromide as a catalyst for 150 min. The method has linear ranges of 0.01–0.10 and 0.10–1.00 mg/L, with limits of detection and quantification of 0.0021 and 0.0063 mg/L, respectively. Recoveries ranged from 86.21 to 95.81%, with a relative standard deviation (RSD) below 5%. Additionally, a spiked PFOA concentration of 0.01 mg/L in DTRO-treated leachate effluent following solid-phase extraction was successfully detected with an RSD below 5% using this method.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 0.001 |
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".