Studying the Chemistry of Cationized Triacylglycerols Using Electrospray Ionization Mass Spectrometry and Density Functional Theory Computations
Bibliographic record
Abstract
Analysis of triacylglycerols (TAGs), found as complex mixtures in living organisms, is typically accomplished using liquid chromatography, often coupled to mass spectrometry. TAGs, weak bases not protonated using electrospray ionization, are usually ionized by adduct formation with a cation, including those present in the solvent (e.g., Na(+)). There are relatively few reports on the binding of TAGs with cations or on the mechanisms by which cationized TAGs fragment. This work examines binding efficiencies, determined by mass spectrometry and computations, for the complexation of TAGs to a range of cations (Na(+), Li(+), K(+), Ag(+), NH4(+)). While most cations bind to oxygen, Ag(+) binding to unsaturation in the acid side chains is significant. The importance of dimer formation, [2TAG + M](+) was demonstrated using several different types of mass spectrometers. From breakdown curves, it became apparent that two or three acid side chains must be attached to glycerol for strong cationization. Possible mechanisms for fragmentation of lithiated TAGs were modeled by computations on tripropionylglycerol. Viable pathways were found for losses of neutral acids and lithium salts of acids from different positions on the glycerol moiety. Novel lactone structures were proposed for the loss of a neutral acid from one position of the glycerol moiety. These were studied further using triple-stage mass spectrometry (MS(3)). These lactones can account for all the major product ions in the MS(3) spectra in both this work and the literature, which should allow for new insights into the challenging analytical methods needed for naturally occurring TAGs.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".