High temperature fuel cell membranes based on poly(arylene ether)s containing benzimidazole groups
Bibliographic record
Abstract
Development of new high-performance polymer membranes that retain their proton conductivity under low humidity conditions is one of the most critical requirements to commercialize PEMFC systems. Current sulfonated proton exchange membranes acquire proton conductivity by water that solvates ion and carries proton. Consequently, a loss of water under low RH conditions immediately results in a loss of proton conductivity. One approach to maintain proton conductivity under low RH conditions is to replace water with a less volatile proton solvent. Kreuer has pointed out the possibility to develop fully polymeric proton-conducting membranes based on nitrogen-containing heterocycles such as imidazole, benzimidazole, and pyrazole. We have attempted to blend those less volatile proton solvent with sulfonated copolymers such as polystyrene sulfonic acid, Nafion, poly(arylene ether sulfone, BPSH-xx). [Ref. DOE review meeting 2007 and 2008] However, we observed that imidazole was slowly sublimated out as temperature and humidity increases which could cause poisoning of electro-catalyst, corrosion and losing conductivity. In this presentation, we report the synthesis of novel poly(arylene ether sulfone)s containing benzimidazole groups These benzimidazole containing polymer was blended with sulfonated poly(arylene ether sulfone). In the blend system, benzimidazole group attached to the polysulfone acts as a medium through the basic nitrogen for transfer of protons between the sulfonic acid groups. Proton conductivity of the blend membranes was investigated as a function of water content at 80 C and compared the performance with water based proton conduction system.
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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 | 0.001 |
| 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".