Where there is a valley, there is a peak: Study of ion size and image effects on nanoelectroosmotic pumping
Bibliographic record
Abstract
With the advent of nanotechnology, nano-electro-osmosis flow (nano-EOF) has shown great promise in the next generation of lab-on-a-chip systems such as attoliter or picoliter syringes or pipettors. In order to optimize the design of such systems and to precisely control analysis processes, it is important to obtain better fundamental understanding of EOF at nanoscales. Therefore, a more comprehensive electric double layer (EDL) theory is in need to improve upon the conventional EDL theory based on the Poisson-Boltzmann (PB) equation. In this paper, the modified PB theory is utilized to investigate the flow behavior of EOF at micro- and nanoscales. The effect of ion size and the image effect are particularly emphasized. Both effects remarkably influence nano-EOF. More importantly, this study predicts a new phenomenon: two "peaks" may appear in the velocity profile of nano-EOF for some specific solid-electrolyte systems.
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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) | 0.000 | 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".