Characterization of electroosmotic flow through nanoporous self‐assembled arrays
Bibliographic record
Abstract
Characterization of EOF mobility for Tris and TBE buffer solutions is performed in nanoporous arrays using the fluorescent marker method to examine the magnitude of EOFs through nanopores with mean diameters close to electric double layer thickness (Debye length). Structures made from solid silica nanospheres with effective pore sizes from 104 nm down to 8 nm are produced within the microchannel using an evaporation self-assembly method. EOF results in nanoporous matrices show higher EOF mobilities for stronger electrolyte solutions, which are drastically different compared to microchannel EOF. The effects of scaling are also examined by comparing the EOF mobility for varying ratios of pore diameters to the Debye length, which shows a surprising consistency across all particle sizes examined. This work demonstrates various factors which must be considered when designing nanofluidic devices, and discusses the causes of these small scale effects.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 teacher head, 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".