Separating chiral isomers of amphetamine and methamphetamine using chemical derivatization and differential mobility spectrometry
Bibliographic record
Abstract
The separation and analysis of chiral compounds, especially enantiomers, presents a great challenge to modern analytical chemistry, particularly to mass spectrometry (MS). As a result, integrated orthogonal separations, such as chiral liquid chromatography (chiral LC), gas chromatography (GC), or capillary electrophoresis (CE), are often employed to separate enantiomers prior to MS analysis. Here, we combine chemical derivatization with differential mobility spectrometry (DMS) and MS to separate and quantitate the transformed enantiomeric pairs R- and S-amphetamine, as well as R- and S-methamphetamine. We also demonstrate separation of these drugs by using reverse-phase LC. However, while the LC method requires ∼5 min to provide separation, we have developed a flow-injection analysis (FIA) method using DMS as the exclusive mode of separation (FIA-DMS), requiring only ∼1.5 min with equivalent quantitative metrics (1-1000 ng/mL range) to the LC method. The DMS-based separation of each diastereomeric pair is driven by differences in binding energies between the analyte ions and the chemical modifier molecules (acetonitrile) added to the DMS environment.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".