Development of Molten Carbonate Fuel Cell Based on Lignin Fuel Consumption (MC-LFC): Correlation Between Modeling and Experimental Results
Bibliographic record
Abstract
COMSOL software was used for the modeling of the performance parameters of the Molten Carbonate -Lignin Fuel Cell (MC-LFC), as it is a flexible tool, able to handle different physical approaches.The model developed includes the following processes: electronic and ionic charge balance (Ohm's law), Butler-Volmer charge transfer kinetics, diffusion gas flow in porous electrodes (Brinkman's equation), gas-phase mass balances in fuel and oxygen distribution channels, and porous electrodes (Maxwell-Stefan diffusion and convection).A parametric analysis was performed to evaluate the effect of material properties, pressure, and temperature, on cell performance.The results show that for a better performance of the MC-LFC cell, the exchange current density (A/m 2 ) in the anode and cathode compartment must be respected in the interval [0.075, 0.75] and [1.58, 15.8].The electrical conductivity of the electrolyte (S/m), anodic and cathodic materials can be respectively in the interval [26, 265], [25, 250], and [19, 60].It is also noted that the increase in temperature from 700 K to 1000 K generates a drop in the maximum power density of the battery (approximately 1500 mW/cm 2 to 1260 mW/cm 2 ).It is in every interest to operate the Cell MC-LFC under 500 °C.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".