Compound-Specific Carbon, Hydrogen, and Nitrogen Isotope Analysis of Nitro- and Amino-Substituted Chlorobenzenes in Complex Aqueous Matrices
Bibliographic record
Abstract
Compound-specific isotope analysis (CSIA) is an established tool to study the fate of legacy groundwater contaminants but is only emerging for nonconventional contaminants, e.g., nitro- and amino-substituted chlorobenzenes that are widely used as industrial feedstock and the target of this work. To date, CSIA of the target compound groups used special combustion interfaces and the potential matrix interferences in environmental samples has not been assessed. We validated CSIA methods for δ 13 C, δ 2 H, and δ 15 N of four analytes from each chemical group and developed a solid-phase extraction (SPE) method to minimize matrix interferences during preconcentration of complex aqueous samples. The SPE recovery was >80% and the method quantification limits of SPE-CSIA for δ 13 C, δ 2 H, and δ 15 N were 0.03–0.57, 1.3–2.7, and 3.4–10.2 μM aqueous-phase concentrations, respectively, using 2 L of spiked MQ water. The SPE-CSIA procedure showed negligible isotope fractionation for δ 13 C (≤0.5‰), δ 15 N (≤0.5‰), and δ 2 H (≤5‰ for nitroaromatics and ≤10‰ for aminoaromatics). In addition, solvent evaporation, water sample storage up to 7 months, and SPE extract storage for 1.5 years did not change analytes’ δ 13 C signatures beyond ±0.5‰. However, to avoid significant δ 2 H and δ 15 N fractionation of aminoaromatics, cartridge breakthrough should be avoided and SPE preconcentration must be conducted at pH > p K a + 2. Application of the method at a contaminated site showed excellent precision, at ≤0.3‰ for C and N, and ≤1.5‰ for H. The methods validated here now allow the use of multielement CSIA to track 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) | 0.000 | 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".