Folded emitters for nanoelectrospray ionization mass spectrometry
Bibliographic record
Abstract
Electrospray ionization (ESI) has revolutionized mass spectrometry (MS), providing a facile method for the ionization of macromolecules for analysis by mass. The development of nanoESI-MS has further extended the utility of ESI-MS, permitting the analysis of small-volume samples with enhanced sensitivity over conventional ESI-MS. Traditional nanoESI-MS experiments use pulled-glass capillary emitters, which are expensive to purchase and require specialized instruments and training to fabricate in-house. Furthermore, these emitters suffer from problems including clogging, sample contamination, and irreproducible spray stability. Here, we report a new emitter for nanoESI-MS, made by folding small pieces of polyimide tape. In comparison with conventional pulled-glass capillary emitters, the new emitters are inexpensive and simple to make. Their low cost makes them disposable after a single use, such that sample contamination or clogging is never a problem. Emitter performance has been evaluated for diverse analytes encompassing a large mass range, including small molecules, peptides, proteins, and synthetic polymers. In all cases, the performance is similar to that of pulled-glass capillary emitters, with the advantages of low cost, ease of use, and disposability.
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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 | 0.000 | 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".