Bibliographic record
Abstract
Microfluidic fuel cells eliminate the membrane by utilizing parallel colaminar flow of electrolyte between the anode and cathode electrodes. When operated on vanadium redox electrolyte, these cells also eliminate the need for catalyst. Hence, microfluidic fuel cells are promising contenders in terms of achieving useful performance levels for commercial applications while being cost-effective on a commercial scale. However, due to the inherent size of these devices the power output is relatively low and scale-up is a major challenge. In the present article, two planar cell multiplexing strategies are introduced, featuring a nonsymmetric unilateral design and a symmetric bilateral device architecture, both of which employ two cells with shared fluidic inlet ports. The fuel cell design is based on flow-through porous carbon electrodes using vanadium redox electrolytes as reactants. In both array architectures, the two cells are fluidically connected in parallel and electrically in series. The main challenge of achieving uniform flow distribution is assessed using laminar flow theory and computational fluid dynamics and validated experimentally. The normalized performance obtained with the two prototype array cells is found to be equivalent to previously reported data for single cells, in this case doubling the device level voltage and power output and reaching 820 and 1200 mW/cm2 peak power density for the nonsymmetric unilateral and symmetric bilateral array designs, respectively. It is, thus, demonstrated that both unilateral and bilateral planar multiplexing strategies are feasible for microfluidic fuel cell technologies and are shown to be particularly effective when the flow sharing between different cells is equal.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".