Automated, High-Throughput Analysis of Tire-Derived <i>p</i>-Phenylenediamine Quinones (PPDQs) in Water by Online Membrane Sampling Coupled to MS/MS
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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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".