Development and Calibration of an Organic-Diffusive Gradients in Thin Films Aquatic Passive Sampler for a Diverse Suite of Polar Organic Contaminants
Bibliographic record
Abstract
A unique configuration of the diffusive gradients in thin films sampler for polar organics (o-DGT) without a poly(ether sulfone) membrane was developed, calibrated, and field-evaluated. Diffusion coefficients ( D ) through agarose diffusive gels ranged from (1.02 to 4.74) × 10 –6 cm 2 /s for 34 pharmaceuticals and pesticides at 5, 13, and 23 °C. Analyte-specific diffusion–temperature plots produced linear ( r 2 > 0.85) empirical relationships whereby D could be estimated at any environmentally relevant temperature (i.e., matched to in situ water conditions). Linear uptake for all analytes was observed in a static renewal calibration experiment over 25 days except for three macrolide antibiotics, which reached saturation at 300 ng (≈15 d). Experimental sampling rates ranged from 8.8 to 16.1 mL/d and were successfully estimated with measured and modeled D within 19% and 30% average relative error, respectively. Under slow flowing (2.4 cm/s) and static conditions, the in situ diffusive boundary layer (DBL) thickness ranged from 0.023 to 0.075 cm, resulting in a maximum contribution to mass transfer of <45%. Estimated water concentrations by o-DGT at a wastewater treatment plant agreed well with grab samples and appeared to be less influenced by the boundary layer compared to that of polar organic chemical integrative samplers (POCIS) deployed simultaneously. The o-DGT sampler is a promising monitoring tool that is largely insensitive to the DBL under typical flow conditions, facilitating the application of measured/modeled diffusion-based sampling rates. This significantly reduces the need for sampler calibration, making o-DGT more widely applicable, reliable, and cost-effective compared to current polar passive samplers.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".