Post-column hydrogen–deuterium exchange technique to assist in the identification of small organic molecules by mass spectrometry
Bibliographic record
Abstract
To improve the certainty that a specific small organic molecule has been detected in a given sample by high-resolution mass spectrometry, other techniques that give conclusive evidence about the chemical structure of a compound like nuclear magnetic resonance or complementary information on its composition such as hydrogen–deuterium exchange (HDX) are often necessary. This study presents a systematic investigation that aims to improve the applicability of post-column HDX for those purposes. Key parameters like mobile-phase flow rates, volume percentage of H 2 O in the mobile phase, and D 2 O addition flow rates were optimized to provide an isotopic pattern that allows the accurate determination of the number of exchangeable hydrogen atoms in small organic molecules. A loop injection setup was used to emulate chromatographic conditions in the optimization process, and trimethoprim, a widely used anti-infective, was used as test compounds for the experiments. As expected, results showed that deuteration percentage decreased with a higher mobile-phase flow rate and increased with higher D 2 O flow rate. The post-column HDX technique was then validated with extracts of samples of river water and plants separated by liquid chromatography in hydrophilic interaction or reversed-phase modes. Mass spectra showed a completely visible isotopic pattern that allowed assessing correctly and unambiguously the number of exchangeable hydrogens in the compounds of interest. This study shows that post-column HDX can be used as a complementary technique to identify unknown small organic molecules in complex matrices. The current paper proposes an efficient, cost-effective, versatile technique of HDX that is helpful to assign a unique structure to a given high-resolution mass spectrometry signal.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".