Ionomers and Electrocatalysts for Anion Exchange Membrane Fuel Cells
Bibliographic record
Abstract
Anion exchange membrane fuel cells (AEMFCs) are very promising devices that allow the reduction or elimination of noble metal electrocatalysts due to the faster kinetics of the oxygen reduction reaction (ORR) in a basic environment. Anion exchange ionomers (AEIs) influence the morphology and the performance of the catalyst layer in the ORR. They act as binder of the catalyst particles, creating additional pathways for hydroxide transport between the reaction sites to the catalyst and to the anion exchange membranes (AEMs). The poor homogeneity of the catalyst layer is a deterring factor of the fuel cell performance and provokes aggregation and low utilization of the catalyst reducing the electrode mechanical stability. We recently synthesized AEIs with various backbones and architectures.[1-2] Backbones of different hydrophilicity were used: polysulfone, the most hydrophilic, poly(2,6-dimethyl-1,4-phenyleneoxide) with intermediate hydrophilicity, and poly(alkylene biphenyl) the most hydrophobic. The polymers were functionalized with trimethylammonium moieties grafted on long (LC) or short (SC) side chains.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".