Chlorines Are Not Evenly Substituted in Chlorinated Paraffins: A Predicted NMR Pattern Matching Framework for Isomeric Discrimination in Complex Contaminant Mixtures
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Chlorinated paraffins (CPs) can be mixtures of nearly a half-million possible isomers. Despite the extensive use of CPs, their isomer composition and effects on the environment remain poorly understood. Here, we reveal the isomeric distributions of nine CP mixtures with single-chain lengths (C 14/15 ) and varying degrees of chlorination. The molar distribution of C n H 2 n+ 2 –m Cl m in each mixture was determined using high-resolution mass spectrometry (MS). Next, the mixtures were analyzed by applying both one-dimensional 1 H, 13 C and two-dimensional nuclear magnetic resonance (NMR) spectroscopy. Due to substantially overlapping signals in the experimental NMR spectra, direct assignment of individual isomers was not possible. As such, a new NMR spectral matching approach that used massive NMR databases predicted by a neural network algorithm to provide the top 100 most likely structural matches was developed. The top 100 isomers appear to be an adequate representation of the overall mixture. Their modeled physicochemical and toxicity parameters agree with previous experimental results. Chlorines are not evenly distributed in any of the CP mixtures and show a general preference at the third carbon. The approach described here can play a key role in understanding of complex isomeric mixtures such as CPs that cannot be resolved by MS alone.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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 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".