Fabrication of a polymer nozzle array in a microstructured fibre as a nanoelectrospray emitter for mass spectrometry
Bibliographic record
Abstract
We report a modified silica microstructured fibre (MSF) as a multiple electrospray (MES) emitter, with dimensional compatibility with conventional liquid chromatography and mass spectrometry equipment, to generate stable electrospray from a wide range of applied potentials and flow rates. An array of polymer nozzles is fabricated in the MSF by a procedure involving templated polymerization of microtubes and wet chemical etching of the silica at the tip. The structure of the emitting end of the MSF was optimized with respect to the etching process, and the morphology of the polymer nozzles was optimized with respect to polymerization conditions. The mechanisms of the etching and of the templated polymerization of the microtubes were explored. Optimization experiments were performed using commercially available MSF having 126 tubular air channels arranged in a hexagonal pattern with channel diameter of ∼5.6 μm. However, the flexibility and versatility in the pattern, shape, and size of channels in MSFs allowed a custom-designed MSF to be fabricated and tested for MES. In the new design, six channels were evenly spaced in a radial pattern, and when polymer nozzles were made, six stable electrosprays were observed over a wide range of electrospray conditions. Using these MES emitters, the spray current is enhanced by a factor related to the number of nozzles.
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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 | 0.000 | 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".