Modeling of Microfluidic Fuel Cells With Flow-Through Porous Electrodes
Bibliographic record
Abstract
This paper presents a modeling of a microfluidic fuel cell with flow-through porous electrodes using vanadium redox couples as the fuel and oxidant. There are advantages associated with the use of vanadium redox species in microfluidic fuel cell: 1) vanadium redox couples have the possibility of producing high open-circuit potential (up to 1.7 V at uniform PH [1]); 2) they have high solubility (up to 5.4 M) which causes more species available to the electrodes; 3) they do not require metal catalyst for electrochemical reactions so the reactions take place on the bare carbon electrodes. This characteristic of the vanadium redox couple make them a great candidate as reactants as they do not need expensive catalyst coatings on the electrodes. The fuel and the oxidant can be brought into contact with the electrode in two different ways: flowing over the electrodes or flowing through the electrodes. In the presented fuel cell design, the vanadium redox species are forced to flow through the porous electrodes. They finally come to meet each other in the middle microchannel and establish a side-by-side co-laminar flow traveling down the channel. In this paper, the effect of the inlet velocity and electrode porosity has been investigated. As it is expected, the higher velocity results in the higher power densities. For the porosity, however, there is an optimum value. In essence, there is a trade-off between the available electrode surface area and electric conductivity of the solid phase (i.e., the porous carbon electrode). The modeling shows that a porous electrode with a 67% porosity results in the highest power output.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| 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".