Up-Scaled Microfluidic Fuel Cells With Porous Flow-Through Electrodes
Bibliographic record
Abstract
In this work, an experimental microfluidic fuel cell is presented with a novel up-scaled porous electrode architecture that provides higher available surface area compared to conventional microfluidic fuel cells, providing the potential for higher overall power outputs. Our proof-of-concept architecture is an up-scaled flow-through fuel cell with more than nine times the active electrode surface area of the flow-through architecture first proposed by Kjeang et al. (2008, “A Microfluidic Fuel Cell With Flow-Through Porous Electrodes,” J. Am. Chem. Soc., 130, pp. 4000–4006). Formic acid and potassium permanganate were employed as the fuel and oxidant, respectively, both dissolved in a sulfuric acid electrolyte. Platinum black was employed as the catalyst for both anode and cathode, and the performances of carbon-based porous electrodes including cloth, fiber, and foam were compared to that of traditional Toray carbon paper (TGP-H-120). The effects of catalyst loading were investigated in a microfluidic fuel cell containing 80 pores per linear inch carbon foam electrodes. A discussion is also provided of current density normalization techniques via projected electrode surface area and electrode volume, the latter of which is a highly informative means for comparing flow-through architectures.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".