Mechanically Robust and Highly Conductive Polybenzimidazole(PBI)-Based Reinforced Membrane for High Temperature Polymer Electrolyte Membrane Fuel Cell
Bibliographic record
Abstract
Operating polymer electrolyte membrane fuel cell(PEMFC) at high temperatures (HT, 100oC~200oC) is beneficial owing to fast kinetics for electrochemical reactions and high tolerance to carbon monoxide(CO) poisoning. Also, the HT operation allows a simpler system design by eliminating water management and PROX. However, current HT-PEMFCs still suffer from insufficient power performance partly originating from large membrane resistance. Typically, HT-PEMFC employs the phosphoric acid-doped PBI membrane and its proton conductivity depends on the acid doping level. Therefore, increasing the doping level is prerequisite for high power performance of HT-PEMFC. However, phosphoric acid doping level is practically limited by a decreased mechanical stability of the membrane with acid doping. To mitigate the problems caused by the loss of low mechanical strength with the acid doping, a thick PBI membrane (>100um) is conventionally employed although its large membrane resistance and consequent negative influence on power performance. Against this backdrop, in this study, we present a novel approach for addressing the trade-off between proton conduction and mechanical robustness of PBI based HT membrane. For fabricating an reinforced PBI membrane coupled with a porous PTFE membrane, PBI filling in a porous PTFE membrane is highly challenging due to low compatibility between hydrophobic PTFE and hydrophilic PBI. In this work, to make the PTFE surface hydrophilic, a porous PTFE membrane is treated with dopamine which is as known nature-inspired adhesive and self-polymerized into polydopamine via a pH-induced oxidation. A polydopamine layer can be formed on the PTFE surface by simply immersing the porous PTFE membrane in the aqueous buffered dopamine solution at room temperature. Compared to the pristine PTFE membrane, the polydopamine-treated PTFE membrane is more densely filled with PBI after the impregnation of PBI solution, indicating an effective intrusion of the PBI solution into the modified hydrophilic pores. The reinforced membrane shows a higher mechanical strength after phosphoric acid doping process even at a thin membrane thickness(~50um) in comparison with a 100 micron thick un-reinforced PBI membrane. The second feature of this approach is the introduction of plasticizer in the PBI matrix for increasing the acid doping level for the reinforced PBI/PTFE membrane. In general, mechanical supports suppress swelling of the PBI matrix with phosphoric acid solution, resulting in a lower acid doping level compared to that of pristine PBI membrane. However, poly(ethylene glycol) (PEG), which can be exchanged with phosphoric acid, provides an additional space for accommodating phosphoric acid in the PBI matrix when incorporated in the PBI matrix. PEG and PBI forms a miscible blend and PEG can be extracted out during the acid doping process, being exchanged with phosphoric acid as confirmed by DSC and FT-IR analysis. The PEG/PBI blend exhibits a higher mechanical stability owing to its lower expansion but a higher proton conductivity owing to its high acid doping level after the acid doping, clearly demonstrating the benefit of the approach. When the PEG-plasticized PBI matrix and polydopamine-treated PTFE support are combined, the mechanical stability can be significantly improved compared with the PEG-plasticized PBI membrane and the proton conductivity be enhanced compared with the PTFE-reinforced PBI membrane. The power performance of an in-house membrane electrode assembly (MEA) employing the novel reinforced membrane is comparable to that of a commercial HT-MEA. Therefore, the combined strategy can overcome the trade-off between mechanical stability and proton conductivity in the design of HT-PBI membranes.
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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 | 0.001 | 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".