A Statistically Based Thermal Conductivity Model for PEMFC Gas Diffusion Layers
Bibliographic record
Abstract
Little attention has been paid to the geometrical parameters of Gas Diffusion Layers (GDLs), as many researchers have reported the GDL transport properties, like thermal conductivity, as a function of only one parameter; porosity. In this paper, a statistical unit cell approach is presented for estimation of the thermal conductivity on the basis of measured geometrical data such as the angles and also the distance between fibers (aspect ratio) and fiber diameter. The dependency of the thermal conductivity on each of these parameters is considered to obtain the best GDL structure. It is discussed, for the first time in this paper, that the parameter of the aspect ratio can usually be as important as porosity, and the traditional thought that a porous media with higher porosity has a lower thermal conductivity cannot always be correct. It is also concluded that the maximum thermal conductivity of a GDL is not a function of the aspect ratio and fiber diameter and only dependent on porosity and fiber angle. In general, the geometrical concepts and the optically measured data presented in this paper can be employed for estimation of other GDL transport properties such as permeability.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".