An electropneumatic‐heated nebulizer for enhancing spray ionization in PhotoSpray atmospheric pressure photoionization sources for liquid chromatography/mass spectrometry
Bibliographic record
Abstract
We introduce a novel electropneumatic-heated nebulizer (EPn-HN), incorporating an electrified internal pneumatic nebulizer, to enhance the yield of sprayed ions from PhotoSpray atmospheric pressure photoionization (APPI) sources for liquid chromatography/mass spectrometry (LC/MS). Spray ionization from the pneumatic-heated nebulizers used in APPI sources provides a supplemental, complementary ionization method to be used for involatile and thermally labile compounds, otherwise intractable to APPI. Details of the construction and operation of the EPn-HN device are provided. The performance of the EPn-HN is demonstrated using two model compounds: substance P, a peptide used as a standard in studies of ion fragmentation mechanisms, and aztreonam, a thermally labile antibiotic. At the optimum voltage for spray ionization, improvements in sensitivity of two orders of magnitude are obtained relative to when the sprayer is grounded, the conventional case. Since both substance P and aztreonam cannot be detected using the APPI method alone, the results demonstrate how spray ionization from the EPn-HN may be used to extend the range of compounds amenable to PhotoSpray sources.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".