Unveiling Readily Ionized and Robust Anionic Species: A Gateway to Enhanced Ionization Efficiency in ESI-MS Analysis
Bibliographic record
Abstract
Electrospray ionization mass spectrometry has long been a prevalent ionization method in the analysis of low volatility molecules with biological, environmental, and industrial relevance. To address analytical challenges associated with molecules suffering from low ionization efficiencies (IEs), chemical derivatization (CD) strategies have been developed and are frequently adopted into workflows. However, only a minute number of CD techniques have been developed for negative polarity. To address this disparity, we evaluated 27 anions based on three criteria: (1) IE relative to a sodium dodecyl sulfate ( 11 + Na + ) internal standard; (2) stability to collision induced dissociation; (3) diagnostic tandem mass spectrometry behavior. Highly fluorous ions exhibiting weakly coordinating and hydrophobic properties displayed enhanced IE. Trifluoromethanesulfonyl-containing ions proved to be unexpectedly labile, while tetrakis[3,5-bis(trifluoromethyl)phenyl]borate ( 23 ) and bis(nonafluoro-1-butane)sulfonimidate ( 25 ) were determined to be of optimal IE of 332% ± 25% and 939% ± 92%, respectively, and in tandem MS exhibited survival yields of 100% ± 0% and 72.6% ± 0.8% at −50 eV. 23 and 25 were also comparable in IE across several solvents, and combinations thereof, that are ubiquitous in liquid chromatography. Various salts of 25 were evaluated for cation effects, where the IE of 25 ranged from 939% ± 92% to 3195% ± 145% across K +, NH 4 +, Na +, and H + . Compared to tetra- n -butylammonium, tetra- n -butylphosphonium, and (4-methylphenyl)diphenylsulfonium cations, 25 displayed signal enhancements ranging from 136% ± 6% to 181% ± 14%, thereby making it an optimal candidate for CD development.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".