Membraneless Liquid-Fuel Microfluidic Fuel Cells: A Computational Study
Bibliographic record
Abstract
Presented in this paper is a computational analysis of a membraneless microfluidic fuel cell that uses the laminar nature of microflows to maintain the separation of fuel and oxidant streams. The fuel cell consists of a T-shaped microfluidic channel with liquid fuel and oxidant entering at separate inlets and flowing in parallel without turbulent or convective mixing. Electrodes are placed along the walls, and the resulting redox reactions provide the cell voltage and current. A concise electrochemical model of the key reactions and appropriate boundary conditions for the computational fluid dynamic (CFD) modelling of this system are developed and implemented into the numerical model. The coupled flow, species transport and chemical aspects of the microfluidic fuel cell are simulated. The effects of geometry and flow rates on fuel cell performance are investigated. Results indicate that the microfluidic fuel cell performance is limited by the transport of reactants through the concentration boundary layer to the electrodes. Three typical geometries were simulated, and it was found that increasing the aspect ratio of the channel cross-section from a square geometry to a rectangular one leads to more than a two-fold increase in fuel utilization. The two rectangular geometries simulated consist of a design with a high aspect ratio in the direction perpendicular to the plane of cross-stream diffusion as well as a design with a high aspect ratio in the direction parallel to the plane of cross-stream diffusion. The electrode placement and geometry play key roles with respect to mixing and fuel utilization. The design with a high aspect ratio in the direction perpendicular to the plane of cross-stream diffusion demonstrated relatively less cross-stream mixing compared to the other rectangular geometry, and had the potential for improved fuel utilization with appropriate electrode design. In addition, results suggest that fuel utilization can be increased from previous values by a factor of two or more. Decreasing the inlet velocity from 0.1 m/s to 0.02 m/s caused the fuel utilization to increase non-linearly from 8 % to 23 %, and only caused an increase of 3 % in cross-stream mixing at the outlet.
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) | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".