Rapid Screening of Carboxylic Acids from Waste and Surface Waters by ESI-MS/MS Using Barium Ion Chemistry and On-Line Membrane Sampling
Bibliographic record
Abstract
Negative ion tandem mass spectrometric analysis of aliphatic carboxylic acids often yields only non-diagnostic ([M - H](-)) ions with limited selective fragmentation. However, carboxylates cationized with Ba(2+) have demonstrated efficient dissociation in positive ion mode, providing structurally diagnostic product ions. We report the application of barium adducts followed by collision induced dissociation (CID), to improve selectivity for rapid screening of carboxylic acids in complex aqueous samples. The quantitative MS/MS method presented utilizes common product ions of [M - H + Ba](+) precursor ions. The mechanism of product ion formation is investigated using isotopically labeled standards and a series of structurally related carboxylic acids. The results suggest that hydrogen atoms in the β and γ positions yield common product ions ([BaH](+) and [BaOH](+)). Furthermore, the diagnostic product ion at m/z 196 serves as a qualifying ion for carboxylate species. This methodology has been successfully used in conjunction with condensed phase membrane introduction mass spectrometry (CP-MIMS), with barium acetate added directly to the methanol acceptor phase. The combination enables rapid screening of carboxylic acids directly from acidified water samples (wastewater effluent, spiked natural waters) using a capillary hollow fiber PDMS membrane immersion probe. We have applied this technique for the direct analysis of complex naphthenic acid mixtures spiked into natural surface waters using CP-MIMS. Selectivity at the ionization and tandem mass spectrometry level eliminate isobaric interferences from hydroxylated species present within the samples, which have been observed in negative electrospray ionization.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".