Rapid Characterization of Naphthenic Acids Using Differential Mobility Spectrometry and Mass Spectrometry
Bibliographic record
Abstract
To analyze the naphthenic acid content of environmental waters quickly and efficiently, we have developed a method that employs differential mobility spectrometry (DMS) coupled to mass spectrometry (MS). This technique combines the benefits of infusion-based MS experiments (parallel, on-demand access to individual components) with DMS's ability to provide liquid chromatography-like separations of isobaric and isomeric compounds in a fraction of the time. In this study, we have applied a DMS-MS workflow to the rapid gas-phase separation of naphthenic acids (NAs) within a technical standard and a real-world oil sands process-affected water (OSPW) extract. Among the findings provided by this workflow are the rapid characterization of isomeric NAs (i.e., same molecular formulas) in a complex OSPW sample, the ability to use DMS to isolate individual NA components (including isomeric NAs) for in-depth structural analyses, and a method by which NA analytes, background ions, and dimer species can be characterized by their distinct behaviors in DMS. Overall, the profiles of the NA content of the technical and OSPW samples were consistent with published values for similar samples, such that the benefits of DMS technology do not detract from the workflow's accuracy or quality.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".