Porous polymer monolith assisted electrospray from a glass microdevice
Bibliographic record
Abstract
The coupling of a lab-on-a-chip microfluidic device to a nanoelectrospray ionization mass spectrometer has the potential to automate many routine analytical procedures and produce a powerful analytical tool. However, past coupling strategies have relied on complex manufacturing steps including drilling and etching the device to attach a capillary or building a nanospray emitter directly into the device. This study shows that a nanospray emitter can be easily fabricated using a porous polymer monolith (PPM) at the end of a glass microdevice. These devices are able to obtain a stable electrospray at a variety of flow rates (50-500 nL/min) but optimal results are obtained at lower flow rates (50-100 nL/min) compatible with electroosmotic flow processes. The PPM is photo-patterned so that it can be placed in any position within the channel of the device with no dead volume. The porous character and the hydrophobic nature of the PPM both aid in development of a stable electrospray process. Total ion current traces for the constant infusion of leucine-enkephalin and PPG show relative standard errors as low as 4%, and produce mass spectra with good signal-to-noise (S/N 43) from only 2 fmol of material. In addition, multiple experiments in a given day show good repeatability with variability as low as 13%, and the multiple flow paths inherent in the PPM limit sprayer clogging.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".