Polymer Micronozzle Array for Multiple Electrosprays Produced by Templated Synthesis and Etching of Microstructured Fibers
Bibliographic record
Abstract
Microstructured fibers (MSFs) having raised polymer nozzles in each channel are custom designed, fabricated, and tested for use as multiple electrospray (MES) emitters for mass spectrometry (MS). There is strong motivation to develop electrospray emitters that operate at practical flow rates but give the much greater ionization efficiency associated with lower (nano) flow rates. This can be accomplished by splitting the flow into many lower-volume electrosprays, an approach known as MES. To couple with most modern mass spectrometers, the MES emitter must have a small diameter to allow efficient ion collection into the MS. In this work, a MSF, defined as a fiber having many empty channels running along its length, was designed to have 9 channels, 9 μm each, >100 μm apart arranged in a radial pattern, all in a fiber having a compatible diameter with both front-end LC equipment and typical MS inlets. This design seeks to promote independent electrospray from each channel while maintaining electric field homogeneity. While the MSFs themselves do not support MES, the formation of polymer nozzles protruding from each channel at the tip face enables independent electrospray from each nozzle. Microscope imaging, electrospray current measurement, and ESI-MS detection of a model analyte all confirm the MES behavior of the 9-nozzle emitter, showing significant signal enhancement relative to a single-nozzle emitter at the same total flow rate. LC/MS data from a protein digest obtained at an independent laboratory demonstrates the applicability and robustness of the emitter for real scientific challenges using modern LC/MS equipment.
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".