A rejuvenation method for poly(<b><i>N</i></b>,<b><i>N</i></b>‐dimethylacrylamide)‐coated glass microfluidic chips
Bibliographic record
Abstract
As microfluidic chips come to integrate the higher levels of functionality required for the implementation of advanced bioanalytical protocols, a crucial factor is that of cost. Although glass chips provide advantages in multilayer integrations, their cost is far higher than that of polymer chips. However, a simple and effective rejuvenation protocol for glass microchips may enable higher levels of integration and functionality on glass microchips. Here we present a method to rejuvenate glass microchips that had been used for capillary electrophoresis to the extent that their performance was degraded. This degradation was due to one of the two mechanisms: (i) a deterioration of the polymer coating on the inner surface of the microchannel or (ii) an aging of the glass substrate. Using the method presented here, we have rejuvenated more than 50 such "aged" microchips. The performance of these microchips was fully restored after the rejuvenation and lasted for hundreds of DNA separation runs. Our experiments indicate that the loss of resolution in microchip separations was not associated with glass aging, but was due to the degradation of the polymer coating on the inner surface of microchannels. This suggests that it is possible to extend the microchip lifetime "forever" using the rejuvenation protocol and that the exploration of higher levels of integration and functionality on glass microchips (or of hybrid structures involving materials capable of withstanding the reagents and elevated temperatures used) is feasible.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".