Mechanistic understanding of the effects of graphene oxide on the thermal conductivity of polymer fuel cells
Bibliographic record
Abstract
Efficient thermal management is critical for the performance and durability of polymer electrolyte membrane fuel cells (PEMFCs), particularly under dry and high-temperature conditions. This study examines the impact of graphene oxide (GO) incorporation on the thermal conductivity (TC) of Nafion membranes using molecular dynamics (MD) simulations. To this end, the TC of 0.8 wt% GO-loaded Nafion membrane was compared with cast Nafion membranes at varying hydration levels. Additionally, the influence of temperature (300 K, 325 K, and 350 K) on TC was evaluated for both cast and GO-loaded membranes. It was found hydration level increment leads to a 14% TC enhancement, which was consistent with the literature results. On the other hand, the addition of GO reduced TC of the dry membrane by 4.7%. For dry membranes, temperature elevation induces slightly negative impacts on TC in both cast and GO-loaded samples. This reduction is primarily attributed to the fragmentation of connected water clusters, which weakens interchain heat transfer pathways. The water clusters’ fragmentation is more pronounced with the presence of GO. Further structural and interaction analysis revealed that the strong interactions of GO with water molecules prevented the formation of connected water clusters. These insights provide valuable guidance for optimizing the TC of PEMFC membranes, emphasizing that optimized GO dispersion, hydration control, and functionalization strategies are essential when incorporating GO-based PEMs to ensure efficient heat dissipation and long-term operational reliability.
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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 | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".