Analysis of rubber-derived contaminants in surface water and snow by liquid chromatography coupled with a hybrid linear ion-trap-Orbitrap high-resolution mass spectrometry
Bibliographic record
Abstract
Analytical methods were developed to quantify eight rubber-derived organic contaminants, including four p-phenylenediamine-derived quinones (PPDQs), 1,3-diphenylguanidine (DPG), 1,3-diphenylurea (DPU), N,N-dibutylbutan-1-amine (TBA), and 1,3-dicyclohexylurea (DCU) in the dissolved phase and suspended particulate matter (SPM) of surface water and melted snow. Dissolved-phase samples were prepared using solid-phase extraction with hydrophilic-lipophilic balance cartridges and a 3/7 (v/v) mixture of acetonitrile and dichloromethane as elution solvents. SPM samples underwent ultrasonic-assisted extraction using the same solvent mixture. Reduced glutathione was added before extraction to improve recovery and reduce variability of target compounds. Quantification was performed using ultra-high-performance liquid chromatography coupled with a hybrid linear ion-trap-Orbitrap high-resolution mass spectrometry (UHPLC/ESI-LTQ-Orbitrap) in positive electrospray ionization and full-scan mode (m/z 100-500). Method detection limits for surface water ranged from 0.2 to 7.0 ng/L in the dissolved phase and 0.1-2.6 ng/L in SPM. For melted snow, detection limits ranged from 0.2 to 5.0 ng/L in the dissolved phase and from 0.1 to 5.4 ng/L in SPM. Sample storage tests (4°C and -20°C) indicate that the dissolved phase should be extracted on the sample collection day, while SPM samples (filters stored at -20°C) should be extracted within seven days. Three of the eight target compounds, including DPG, DCU, and 2-((4-methylpentan-2-yl)amino)-5-(phenylamino)cyclohexa-2,5-diene-1,4-dione (6PPDQ), were detected in environmental surface water and snowmelt samples from Quebec, Canada. This is the first reported method using UHPLC/ESI-LTQ-Orbitrap to simultaneously determine these eight contaminants in the dissolved and SPM phases of surface water and melted snow.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".