Poly(pentafluorstyrene) based ionomers for electrochemical hydrogen pumps II – Probing the electrode processes by distribution of relaxation times
Bibliographic record
Abstract
The properties of different poly (pentafluorstyrene) (PPFSt) based ionomers implemented into full-cell electrochemical hydrogen pumps (EHPs) are investigated by electrochemical impedance spectroscopy (EIS) coupled with the distribution of relaxation times (DRT) analysis. Thereby, the resistances of the proton transport (PT) in the electrodes, the hydrogen evolution reaction (HER) on the cathode, the hydrogen oxidation reaction (HOR) on the anode, and the mass transport (MT) in each EHP are quantified. Phosphonated PPFSt (PWN70) ionomer exhibits superior performance with a polybenzimidazole membrane, demonstrating excellent PT and kinetics. A novel PPFSt ionomer functionalized with an imidazole group also outperforms commonly utilized PTFE binder by improving the PT and the HER, reducing the power consumption by 25 % at 1.0 A cm −2 and 200 °C. Both ionomers demonstrate a MT resistance comparable to the PTFE catalyst layer, caused by the high porosity of PWN70 and the high hydrophobicity of the imidazole binder. • Impedance study of high-temperature ionomers in electrochemical hydrogen pumps. • Resistance evaluation by distribution of relaxation times. • PWN70 and PPFSt-Imi outperform PTFE as binders in combination with the PBI membrane. • Ionomers reduce proton transport and HER resistances. • 100 % H 2 recovery and 95 % power efficiency at 0.2 A cm −2 .
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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 | 0.000 | 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) | 0.001 | 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".