Semiquantitative Nontargeted Screening of Organophosphorus Plastic Additives in Consumer Products Using <sup>31</sup>P NMR
Bibliographic record
Abstract
Mass spectrometry (MS) is typically employed for the nontargeted identification of unknown organophosphorus compounds (OPCs). However, quantitative analysis remains a major challenge due to the lack of authentic standards for most of the OPCs. A phosphorus NMR ( 31 P NMR) method for semiquantitative nontargeted analysis was developed and applied to 37 different plastic products, where OPCs were detected in nine at high concentrations (86.9–7206 nmol/g). Three classes of compounds were detected with clearly separated chemical shifts, including organophosphites (δ = 125–140 ppm) and organophosphates (δ = (−20)–5 ppm), as well as an unexpected “unannotated” class (δ = 5–20 ppm). The “unannotated” class was subsequently identified as organophosphite diesters and confirmed through in-lab hydrolysis trials. In this work, we demonstrate how a 31 P NMR method may serve as a powerful analytical tool when combined with mass spectrometry for the nontargeted analysis of OPCs. We highlight organophosphite diesters as a novel class of compounds which represent a potential source of organophosphate diesters in the environment.
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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 | 0.001 |
| 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".