Numerical Analysis of the Effect of Different Channel Geometries and Electrode Materials on the Performance of Microfluidic Fuel Cells
Bibliographic record
Abstract
A typical microfluidic fuel cell is comprised of a Y- or T-shaped microchannel. The fuel and the oxidant streams are introduced from the two different inlets. The anodic and cathodic flows meet each other at the beginning of the main channel and start to travel together along the channel. Due to the fact that the viscous forces dominate the inertia forces in microchannels, the oxidant and the fuel streams establish a side-by-side co-laminar flow which makes the anolyte and catholyte flow together without turbulent mixing. Laminar flow in microfluidic fuel cells plays the role of the membrane in proton exchange membrane (PEM) fuel cells by maintaining the separation of the fuel and oxidant. This eliminates the need for the membrane and overcomes the membrane-related issues such as the ohmic overpotential and water management which are relevant to PEM fuel cells. In addition to the above advantage, the high surface-to-volume ratio of these micron-scale devices contributes to their high power density. This advantage is due to the fact that the electrochemical reactions in fuel cells are surface-based. The electrodes on which the electrochemical reactions are occurring are installed appropriately on the walls of the channel in a way that reacting flows are restricted to the proper electrodes. Since the flow is laminar the performance of the microfluidic fuel cell significantly depends on the device geometry. In this paper, different channel geometries and different electrode configurations are modeled and their performances are compared through the polarization curves. It has been found that the high aspect ratio provides the largest power density. In this work, the performance of the flow-through porous electrode was also modeled and compared against the conventional non-porous electrode microfluidic fuel cells. The flow-through porous electrode design is based on cross-flow of aqueous vanadium redox species through the electrodes into an exit channel, where the waste solutions meet and establish a co-laminar flow. This co-laminar flow of reacted species facilitates ionic charge transfer in a membraneless configuration. It has been found that the flow-through porous architecture provides an increased active surface area which contributes to a higher power density as opposed to the fuel cells with non-porous electrodes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".