Nonconformal Particles of Hyperbranched Sulfonated Phenylated Poly(phenylene) Ionomers as Proton-Conducting Pathways in Proton Exchange Membrane Fuel Cell Catalyst Layers
Bibliographic record
Abstract
Characteristic poor electrochemical kinetics, high ionic resistance, and high mass transport resistance within the catalyst layer (CL) are chief among parameters that cause poor performance of proton exchange membrane fuel cells (PEMFCs) utilizing hydrocarbon-based proton-conducting ionomers. Herein, the design and addition of nondimensionally swellable, nonconformal, hyperbranched sulfo-phenylated poly(phenylene) ionomer particles (HB-sPPT-H + ) are reported to introduce a direct pathway for proton conduction in hydrocarbon ionomer-based CLs, resulting in an eight times reduction in ionic resistance of the CL, a 71% increase in catalyst mass activity, and a >90% increase in power at 0.6 V (H 2 /air) compared to state-of-the-art hydrocarbon ionomer-based CLs. The benefits of incorporating HB-sPPT-H + ionomer particles are also shown when employed in perfluorosulfonic acid (PFSA) ionomer-based PEMFCs. These results dispel a commonly held conception that hydrocarbon ionomers possess limitations of gas permeability and electrochemical activity and open up previously unexplored avenues of ionomer development for nonfluorous, wholly hydrocarbon PEMFCs.
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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 | 0.000 |
| 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 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".