A Mathematical Model of a Molten Carbonate Direct Carbon Fuel Cell
Bibliographic record
Abstract
A one-dimensional (1D) homogeneous unit cell model was developed to study the performance of the molten carbonate direct carbon fuel cell (DCFC), which uses solid carbon as fuel and molten carbonate as electrolyte. It is the first unit cell model for the molten carbonate DCFC in which both 4-electron carbon oxidation and 2-electron CO oxidation reactions, as well as the reverse Boudouard reaction, are considered. The simulation results verify that, besides the relatively sluggish kinetics of the anodic reactions, cell performance is mainly limited by ohmic losses in the anode. Further modeling exploration reveals that a minimum effective electronic conductivity of around 0.56 S/cm is required to facilitate proper electrical conduction in the cathode to attain high DCFC performance. It was found that there are optimal volume fractions for the carbon fuel and liquid electrolyte in the anode. If the effective electronic conductivity of the cathode falls to 0.56 S/cm, optimal volume fractions also exist for the solid material and liquid electrolyte in the cathode. The detailed modeling analysis showed that performance improvement at high operating temperature was mainly attributed to improvement of anodic kinetics and reduction of ohmic loss in the electrolyte of electrodes and electrolyte matrix.
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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".