A Novel Y-Channel Design for Measuring the Zeta Potential Using the Current Monitoring Technique
Bibliographic record
Abstract
A Y-channel design is proposed to find the zeta potential for PDMS and PDMS/glass microchannels with the current monitoring technique. The major advantages of this design are four fold: i) the displacement process can be quickly repeated many times increasing the amount of measurements for a single fluid; ii) start and end times are well defined since there is minimal diffusion at the liquid/liquid interface; iii) overall preparation and operation are simplified; and iv) errors caused by undesirable pressure driven flow (Laplace and head differences) and changes in pH due to electrolysis are suppressed. The zeta potential found from current monitoring experiments with the new Y-channel design gave similar results when compared to results found in literature using a conventional straight channel design. Modifications on the slope calculation method were proposed and results agreed with the total length calculation method within 6.8% variation of the zeta potential values averaged for all experiments compared to the total length method. Using the new design, the zeta potential of a number of commonly used buffer solutions for protein and DNA application were tested in PDMS and PDMS/glass hybrid channels.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".