Trace-level quantification of <i>N</i>-nitrosopiperazine in treated wastewater using supported liquid extraction and hydrophilic interaction chromatography mass spectrometry
Bibliographic record
Abstract
Regenerable amine-based solvents used for post-combustion CO2 capture, primarily monoethanolamine and piperazine, are known to undergo degradation and secondary reactions over time forming, amongst other species, N-nitrosamines. These carcinogenic species can eventually make their way from treated wastewater into environmental waters. The United States Environmental Protection Agency (US EPA) recommends that the concentration of N-nitrosamines in surface water not exceed 1.24 μg/L. We have developed a straightforward method to quantify N-nitrosopiperazine in treated wastewater by hydrophilic interaction liquid chromatography – mass spectrometry (HILIC–MS) after sample preparation by supported liquid extraction (SLE). To achieve the best extraction recovery and method limits of quantification (MLOQ), standards were prepared in a high-salt synthetic matrix to mimic the treated wastewater effluent. To further improve the MLOQ, the drying steps after extraction were optimized. HILIC separation of the highly polar analytes was achieved using an ethylene-bridged hybrid amide stationary phase. Detection was achieved using a triple quadrupole mass spectrometer operated in positive electrospray ionisation and multiple reaction monitoring mode, providing a final MLOQ of 0.25 μg/L for N-nitrosopiperazine. Validation of the method was carried out to ensure good confidence in the data obtained for a treated wastewater sample from a post-combustion CO2 capture facility. In addition, N-nitrosopiperazine was quantified with the developed SLE-HILIC–MS method in eight degraded carbon capture samples that had not yet undergone wastewater treatment.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".