Surfactant‐mediated matrix‐assisted laser desorption/ionization time‐of‐flight mass spectrometry of small molecules
Bibliographic record
Abstract
A variety of surfactants have been tested as matrix-ion suppressors for the analysis of small molecules by matrix-assisted laser desorption/ionization time-of flight mass spectrometry. Their addition to the common matrix alpha-cyano-4-hydroxycinnamic acid (CHCA) greatly reduces the presence of matrix-related ions when added at the appropriate mole ratio of CHCA/surfactant, while still allowing the analyte signal to be observed. A range of cationic quaternary ammonium surfactants, as well as a neutral and anionic surfactant, was tested for the analysis of phenolics, phenolic acids, peptides and caffeine. It was found that the cationic surfactants, particularly cetyltrimethylammonium bromide (CTAB), were suitable for the analysis of acidic analytes. The anionic surfactant, sodium dodecyl sulfate, showed promise for peptide analysis. For trialanine, the detection limit was observed to be in the 100 femtomole range. The final matrix/surfactant mole ratio was a critical parameter for matrix ion suppression and resulting intensity of analyte signal. It was also found that the mass resolution of analytes was improved by 25-75%. Depth profiling of sample spots, by varying the number of laser shots, revealed that the surfactants tend to migrate toward the top of the droplet during crystallization, and that it is likely that the analyte is also enriched in this surface region. Here, higher analyte/surfactant concentration would reduce matrix-matrix interactions (known to be a source of matrix-derived ions).
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) | 0.001 | 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 | 0.000 |
| Research integrity | 0.001 | 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".