A Mathematical Model of a Direct Propane Fuel Cell
Bibliographic record
Abstract
A rigorous mathematical model for direct propane fuel cells (DPFCs) was developed. Compared to previous models, it provides better values for the current density and the propane concentration at the exit from the anode. This is the first DPFC model to correctly account for proton transport based on the combination of the chemical potential gradient and the electrical potential gradient. The force per unit charge from the chemical potential gradient (concentration gradient) that pushes protons from the anode to the cathode is greater than that from the electrical potential gradient that pushes them in the opposite direction. By including the chemical potential gradient, we learn that the proton concentration gradient is really much different than that predicted using the previous models that neglected the chemical potential gradient. Also inclusion of the chemical potential gradient made this model the first one having an overpotential gradient (calculated from the electrical potential gradient) with the correct slope. That is important because the overpotential is exponentially related to the reaction rate (current density). The model described here provides a relationship between the conditions inside the fuel cell (proton concentration, overpotential) and its performance as measured externally by current density and propane concentration.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".