A review of molecular dynamic simulation on polymer electrolyte membrane fuel cell
Bibliographic record
Abstract
Polymer electrolyte membrane fuel cells (PEMFCs) offer high-efficiency clean energy generation. However, their commercialization is hindered by performance degradation, high material costs, and short operational lifespans. Addressing these challenges requires the development of advanced materials and optimized component architectures, including membrane, catalyst layer (CL) and their interfaces. Due to the nanoscale complexity of PEMFC components, molecular dynamics (MD) simulations offer a powerful approach to explore critical structural and transport phenomena at the molecular level. This review identifies five key research themes: catalyst and carbon support architecture, structural analysis of ionomer and water clusters, mass transfer, thermal conductivity, and mechanical properties in membrane, CL, and triple-phase boundary (TPB). Unlike previous MD reviews, this work encompasses a broader spectrum, including membrane, CL, and TPB studies from thermomechanical, structural, and mass transfer insights. The influencing parameters, such as hydration level, temperature, polymer type, and the polymer's side chain impact are explored in each section. By compiling the literature data from various MD analyses, a comparative discussion of applied methods and investigated parameters is given. Finally, by discussing the available gaps and future prospects in MD simulations of PEMFCs, this review provides a roadmap and valuable insights for researchers in this field.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".