Comparison of High-Field Asymmetric Waveform Ion Mobility Spectrometry with GC Methods in Analysis of Haloacetic Acids in Drinking Water
Bibliographic record
Abstract
Haloacetic acids (HAAs) are major byproducts of chlorination of drinking water. Electrospray ionization high-field asymmetric waveform ion mobility spectrometry mass spectrometry (ESI-FAIMS-MS) provides a tool for direct monitoring of these compounds. However, treated drinking water samples can be challenging to analyze due to the large number of chemicals present and due to matrix effects that can hinder quantitation of analytes. We developed a standard addition ESI-FAIMS-MS method that permits submicrogram per liter detection of haloacetic acids and overcomes matrix effects. An advantage of FAIMS is increased selectivity through a significant reduction in the chemical background from ESI. Moreover, detection limits with this method are much lower than with previously existing GC and GC/MS methods, and quantitation results compare favorably with other existing methods. This new method does not require sample preparation or chromatographic separation and provides a fast, simple, sensitive, and selective method for monitoring HAAs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".