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 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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".