Multiscale Understanding of Anion Exchange Membrane Fuel Cells: Mechanisms, Electrocatalysts, Polymers, and Cell Management
Bibliographic record
Abstract
Abstract Anion exchange membrane fuel cells (AEMFCs) are among the most promising sustainable electrochemical technologies to help solve energy challenges. Compared to proton exchange membrane fuel cells (PEMFCs), AEMFCs offer a broader choice of catalyst materials and a less corrosive operating environment for the bipolar plates and the membrane. This can lead to potentially lower costs and longer operational life than PEMFCs. These significant advantages have made AEMFCs highly competitive in the future fuel cell market, particularly after advancements in developing non‐platinum‐group‐metal anode electrocatalysts, anion exchange membranes and ionomers, and in understanding the relationships between cell operating conditions and mass transport in AEMFCs. This review aims to compile recent literature to provide a comprehensive understanding of AEMFCs in three key areas: i) the mechanisms of the hydrogen oxidation reaction (HOR) and the oxygen reduction reaction (ORR) in alkaline media; ii) recent advancements in the synthesis routes and structure‐property relationships of cutting‐edge HOR and ORR electrocatalysts, as well as anion exchange membranes and ionomers; and iii) fuel cell operating conditions, including water management and impact of CO2. Finally, based on these aspects, the future development and perspectives of AEMFCs are proposed.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".