Multiple electrosprays generated from a single polycarbonate microstructured fibre
Bibliographic record
Abstract
Electrospray ionization (ESI) has been invaluable to the mass spectrometric detection of biomolecules, due largely to the sensitivity afforded by the ionization technique. Lower flow rates, e.g. in the nanoelectrospray regime, result in smaller initial electrosprayed droplets, leading to higher ionization efficiency and greater signal. One approach to improving sensitivity without lowering flow rate is to generate multiple electrosprays (MESs) from the same sample, essentially splitting one larger flow into smaller flows in the nanoESI regime. Presented here is a series of novel MES emitters in the form of polycarbonate fibres. Based on microstructured fibre (MSF) technology whereby a set of homogeneous parallel channels are formed in a heat-drawn fibre intended to conduct light, a custom design was fabricated in which 3, 6, 9 and 12 holes were arranged in a radial pattern to prevent inhomogeneities in the electric field. The MSFs have dimensions that are compatible with current standards in nanoESI equipment, and the tip is more compatible with standard MS orifices than other larger multielectrospray emitters. By measuring the spray current provided by the various emitters under the same solvent/voltage/total flow rate conditions, a plot was obtained clearly demonstrating the expected dependence on the square root of the number of holes, i.e. the number of independent electrosprays. With this firm proof of principle using this design/format, further effort is justified in developing similar emitters in alternative materials that better prevent surface wetting and allow greater hole density, ultimately leading to greater signal enhancement.
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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".