Automated, High-Throughput Analysis of Tire-Derived <i>p</i>-Phenylenediamine Quinones (PPDQs) in Water by Online Membrane Sampling Coupled to MS/MS
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The tire-derived contaminant N -(1,3-dimethylbutyl)- N ′-phenyl- p -phenylenediamine quinone (6-PPDQ) was recently identified as a potent toxin to coho salmon ( Oncorhynchus kisutch ). Studies investigating 6-PPDQ have employed solid-phase extraction (SPE) or liquid–liquid extraction (LLE) with liquid chromatography–mass spectrometry (LC-MS), providing excellent sensitivity and selectivity. However, cleanup and pre-enrichment steps (SPE/LLE) followed by chromatographic separation can be time- and cost-intensive, limiting sample throughput. The ubiquitous distribution of 6-PPDQ necessitates numerous measurements to identify hotspots for targeted mitigation. We recently developed condensed phase membrane introduction mass spectrometry (CP-MIMS) for rapid 6-PPDQ analysis (2.5 min/sample), with a simple workflow and low limit of detection (8 ng/L). Here, we describe improved quantitation using isotopically labeled internal standards and inclusion of a suite of PPDQ analogues. A low-cost autosampler and data processing software were developed from a three-dimensional (3D) printer and Matlab to fully realize the high-throughput capabilities of CP-MIMS. Cross-validation with a commercial LC-MS method for 10 surface waters provides excellent agreement (slope: 1.01; R 2 = 0.992). We employ this analytical approach to probe fundamental questions regarding sample stability and sorption of 6-PPDQ under lab-controlled conditions. Further, the results for 192 surface water samples provide the first spatiotemporal characterization of PPDQs on Vancouver Island and the lower mainland of British Columbia.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it