Porous Polymer Monolith Assisted Electrospray
Bibliographic record
Abstract
Coupling low-flow analytical separation instrumentation such as capillary electrophoresis, capillary electrochromatography, nano-HPLC, and microfluidic-based devices with electrospray ionization mass spectrometry has yielded powerful analytical tools. However, conventional coupling methodologies such as nanospray suffer from limitations including poor conductive coating robustness, constant clogging, complicated fabrication processes, and incompatibility with large flow rate regimes. This study demonstrates that robust nanospray emitters can be fabricated through the formation and utilization of a porous polymer monolith (PPM) at the end of a fused-silica capillary. Stable electrosprays can be produced from capillaries (75-100-microm i.d.) at a variety of flow rates (50-1000 nL/min) without the need to taper the capillaries by etching or pulling. The PPM is photopatterned to be present only near the capillary exit aperture using conditions that generate pore sizes similar to those seen with nanospray tips. The porous nature of the PPM aids in developing a stable electrospray generating a single clearly visible Taylor cone at relatively high flow rates while at low flow rates (<100 nL/min) a mist, presumably from multiple small Taylor cones, develops. The hydrophobic nature of the PPM should limit problems with band broadening associated with droplet spreading at the capillary exit, while the multiple flow paths inherent in the PPM minimize clogging problems associated with conventional nanospray emitters. Total ion current traces for a constant infusion of standard PPG and cytochrome c solutions are very stable with deviations ranging from only 3 to 8%. The PPM-assisted electrospray produces mass spectra with excellent signal-to-noise ratios from only a few femtomoles of material.
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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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".