Application of a solid phase extraction-liquid chromatography method to quantify phenolic compounds in woodwaste leachate
Bibliographic record
Abstract
Woodwaste produces large volumes of leachate, which often contains high concentrations of phenolic compounds. These compounds are environmental contaminants whose proper management and treatment are mandated to reduce associated environmental impacts. Quality diagnostic and treatment efficiency assessments necessitate the development of rapid, accurate, and reproducible methods of detection and analysis to accurately quantify phenolic compounds. Liquid chromatography (LC) analysis with ultraviolet (UV) detection and solid-phase extraction (SPE) sample preparation on Oasis HLB cartridges were performed and adapted to quantify eight priority phenolic compounds in woodwaste leachate. The method was validated on a synthetic solution simulating the woodwaste leachate, on spiked real woodwaste leachate to 1 μg mL−1, and applied to quantify phenolic compounds in the real woodwaste leachate. Calibration curves were linear for all compounds in the range of 1–30 μg mL−1, and high recoveries varying between 93.5% for 2-chlorophenol and 112.8% for 4-nitrophenol were obtained. Detection limits ranged from 0.06 μg L−1 for 2-chlorophenol to 0.129 μg L−1 for phenol. The proposed method reduced interference, background noise, analysis time, amount of organic solvents and is less costly when compared with other methods.
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How this classification was reachedexpand
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 | 0.008 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".