Time Weighted Average Concentration Monitoring Based on Thin Film Solid Phase Microextraction
Bibliographic record
Abstract
Time weighted average (TWA) passive sampling with thin film solid phase microextraction (TF-SPME) and liquid chromatography tandem mass spectrometry (LC-MS/MS) was used for collection, identification, and quantification of benzophenone-1, benzophenone-2, benzophenone-3, benzophenone-4, 2-phenylbenzimidazole-5-sulfonic acid, octocrylene, octylmethoxycinnamate, butylmethoxydibenzoylmethane, triclocarban and triclosan in the aquatic environment. Two types of TF-SPME passive samplers, including a retracted thin film device using a hydrophilic lipophilic balance (HLB) coating, and an open bed configuration with an octadecyl silica-based (C18)coating, were evaluated in an aqueous standard generation system. Laboratory calibration results indicated that the thin film retracted device using HLB coating is suitable to determine TWA concentrations of polar analytes in water, with an uptake that was linear up to 70 days. In open bed form, a one-calibrant kinetic calibration technique was accomplished by loading benzophenone3-d 5 as calibrant on the C18 coating to quantify all nonpolar compounds. The experimental results showed that the one-calibrant kinetic calibration technique can be used for determination of classes of compounds in cases where deuterated counterparts are either not available or expensive. The developed passive samplers were deployed in wastewater-dominated reaches of the Grand River (Kitchener, ON) to verify their feasibility for determination of TWA concentrations in on-site applications. Field trials results indicated that these devices are suitable for long-term and short-term monitoring of compounds varying in polarity, such as UV blockers and biocide compounds in water, and the data were in good agreement with literature data.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| 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".