Predicting Liquid Organic Hydrogen Carrier Saturation in Dehydrogenation Cell Gas Diffusion Layers for Hydrogen Storage
Bibliographic record
Abstract
Abstract Dehydrogenating methylcyclohexane (MCH) as a liquid organic hydrogen carrier offers a promising method for producing stored hydrogen. However, the transport properties of the gas diffusion layers (GDLs) in dehydrogenation cells (D‐cells) have not yet been optimized for high reactant saturation at the GDL‐catalyst layer (CL) interface, which is crucial for increasing hydrogen production. We applied pore network modeling (PNM) to quantify the anisotropic transport properties and local saturation of MCH in GDLs with distinct microstructures. We demonstrate that GDLs with larger mean pore diameters and lower tortuosity exhibit higher MCH permeability and diffusivity. Moreover, a high porosity at the GDL‐CL interface increases MCH saturation (from 0.05 to 0.11), highlighting the impact of local GDL porosity on MCH supply to the catalyst. The results of the invasion percolation simulation revealed that smaller pore sizes lead to a longer MCH transport pathway to the GDL‐CL interface, thereby reducing MCH saturation at this interface (by more than twofold), which hinders reactant availability for hydrogen production. Therefore, we recommend a GDL that combines large pores for efficient MCH flow and small pores close to the CL for liquid retention to enhance MCH utilization in the anode of D‐cell, particularly at high current densities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".