Liquid electron ionization-mass spectrometry as a novel strategy for integrating normal-phase liquid chromatography with low and high-resolution mass spectrometry
Bibliographic record
Abstract
Normal-phase liquid chromatography (NPLC) plays a pivotal role in the rapid separation of non-polar compounds, facilitating isomer separation and finding applications in various crucial areas where aprotic solvents are necessary. Similar to reversed-phase liquid chromatography (RPLC), NPLC requires a robust and sensitive detector to unequivocally identify the analytes, such as a mass spectrometer. However, coupling NPLC with mass spectrometry (MS) poses challenges due to the incompatibility between the non-polar solvents used as the mobile phase and the primary ionization techniques employed in MS. Several analytical methods have been developed to combine NPLC with electrospray ionization (ESI), but these methods are restricted to the analysis of polar compounds. In most cases, atmospheric pressure chemical ionization (APCI) becomes necessary to expand the range of analysis applications. To overcome these limitations and fully realize the potential of NPLC-MS coupling, a technique termed liquid electron ionization-mass spectrometry (LEI-MS) can be used. LEI-MS offers a straightforward solution by enabling the effective coupling of NPLC with both low and high-resolution MS. LEI allows for the comprehensive analysis of non-polar compounds and provides a powerful tool for isomer separation and precise identification of analytes. Optimal separations, mass spectral qualities, and matches with the NIST library were obtained in both configurations, demonstrating the potential of the proposed approach.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".