Compound specific carbon, hydrogen, and nitrogen isotope analysis of nitro- and amino-substituted chlorobenzenes using solid phase extraction
Bibliographic record
Abstract
Compound specific isotope analysis (CSIA) is an established tool to study the fate of traditional groundwater contaminants but is only emerging for non-conventional heteroatom-bearing chemicals. We validated an accurate and precise CSIA technique for carbon, hydrogen, and nitrogen isotope analysis of nitro- and amino-substituted chlorobenzenes. The compounds are widely used as feedstock chemicals for numerous industrial applications and found as transformation products of larger molecules, e.g., pesticides and explosives. We developed a solid phase extraction preconcentration and separation method to apply CSIA to complex aqueous samples. The SPE-CSIA procedure showed negligible isotope fractionation for most compounds. Accurate and precise carbon, hydrogen, and nitrogen isotope analyses were obtained down to aqueous-phase concentrations of 0.03-0.48, 1.3-2.7, and 3.4-10.2 mM, respectively using 2 L water. However, only the amino-substituted chlorobenzenes showed significant deviations of δ2H (>10‰) and δ15N (>0.5‰) from the reference values during SPE if the extraction was conducted at pH below pKa+2 due to the protonation of NH2- functional group. Using a systematic approach, we showed that solvent evaporation, water sample storage for up to 7 18 months, and SPE extract storage for up to 1.5 years did not change analytes’ original isotope signatures. Finally, the method was applied at a contaminated site and the obtained δ13C, δ2H, and δ15N values showed excellent precision. The methods validated in this study are the first necessary step toward the application of SPE-CSIA to understand the environmental fate of nitro-and amino-substituted chlorobenzenes in complex aqueous samples.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 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".