Mass Spectrometry Based Approach for Organic Synthesis Monitoring
Bibliographic record
Abstract
Current mass spectrometry-based methodologies for synthetic organic reaction monitoring largely use electrospray ionization (ESI), or other related atmospheric pressure ionization-based approaches. Monitoring of complex, heterogeneous systems may be problematic because of sampling hardware limitations, and many relevant analytes (neutrals) exhibit poor ESI performance. An alternative monitoring strategy addressing this significant impasse is condensed phase membrane introduction mass spectrometry using liquid electron ionization (CP-MIMS-LEI). In CP-MIMS, a semipermeable silicone membrane selects hydrophobic neutral analytes, rejecting particulates and charged chemical components. Analytes partition through the membrane, and are then transported to the LEI interface for sequential nebulization, vaporization, and ionization. CP-MIMS and LEI are both ideal for continuous monitoring applications of hydrophobic neutral molecules. We demonstrate quantitative reaction monitoring of harsh, complex reaction mixtures (alkaline, acidic, heterogeneous) in protic and aprotic organic solvents. Also presented are solvent-membrane compatibility investigations and, in situ quantitative monitoring of catalytic oxidation and alkylation reactions.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.010 | 0.006 |
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".