An evaluation of the performance of porous polymer monolith assisted electrospray
Bibliographic record
Abstract
A novel electrospray interface, which has distinct advantages over conventional pulled silica emitters, has been developed. This novel interface can be easily fabricated by forming a porous polymer monolith (PPM) at the end of a fused-silica capillary that facilitates a stable electrospray over a wide range of flow rates with only a modest increase in back-pressure. A comparison was made between the PPM-assisted electrospray and a commercial nanosprayer in terms of sensitivity, stability and robustness. A PPM-filled electrospray tip produced a day-to-day signal variation of 23% relative standard deviation (RSD) over a 3-day period when spraying a 1.0 microM test peptide solution. Furthermore, three different capillaries fabricated by the same process produced a signal variation of 17% RSD, indicating that the fabrication process shows good reproducibility. The multiple flow paths of the PPM function to split the flow and reduce clogging. Even following the accumulation of debris after prolonged use, a stable spray could still be generated with the PPM-filled capillary while the commercial nanosprayer ceased to function properly. In terms of sensitivity, PPM-assisted electrospray showed an enhancement in sensitivity at infusion flow rates between 100 to 1000 nL/min while commercial nanosprayers performed slightly better at flow rates below 100 nL/min. A sample purification step can be combined with the PPM-assisted sprayer, using the PPM as a stationary phase to desalt and preconcentrate samples prior to mass spectrometric detection.
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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 | 0.002 |
| 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 | 0.001 |
| 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".