Electrokinetic Energy Conversion by Microchannel Array: Electrical Analogy, Experiments, and Electrode Polarization
Bibliographic record
Abstract
This paper takes a system-wide perspective of electrokinetic energy conversion devices based on an array of microchannels to help understanding their operation. The approach taken was a combination of developing an electrical analogy and conducting experiments. The electrical analogy included current sources for the convection current, resistors for the conduction current, a capacitor for accumulating the partitioned ions, resistors for ion transport in the reservoirs, diodes and capacitors for the electrochemistry and polarization at the electrodes, and a simple external resistive load. The number of parallel channels profoundly affected the summative resistive and capacitive characteristics of the array, and highlights the differences between a single channel and an array of channels, especially in the transient responses and the role of the electrodes. The electrical analogy was solved by Laplace Transforms to demonstrate a rich and varied response that such a system exhibits to a step change in flow in relation to relative magnitudes of the various resistors and capacitors. Experiments were conducted on a structured glass microchannel array with approximately three million channels (10 μm diameter pore size) with aqueous KCl as the working fluid and tested a variety of electrodes. Besides providing data for in situ resistances and capacitances, in particular for the electrodes, keys aspects of the experimental results were interpreted using the electrical analogy. Results include the potential challenges in interpretation of externally measured potentials and currents as streaming potentials and streaming currents, respectively, measuring the resistances and capacitances of electrodes by novel methodologies, and using the electrical analogy quantitatively to explore maximizing electrokinetic energy conversion in the steady state.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".