Toughened Membrane/Catalyst Layer Interface with Mechanical Nano-Fastener for Hydrocarbon Membrane Based Polymer Electrolyte Membrane Fuel Cell
Bibliographic record
Abstract
Polymer electrolyte fuel cells (PEMFCs) have been spotlighted as one of the promising eco-friendly energy technologies for stationary and automotive applications owing to zero CO2 emission, high energy density and moderate operation conditions. In this technology sector, polymer electrolyte membrane, one of the key components of PEMFC, has been intensely studied for several decades. Conventionally,perfluorinated sulfonic acid (PFSA) membranes like Nafion are used due to their high proton conductivity and mechanical stability. However, their high cost has been pointed out as a significant drawback interrupting mass commercialization of fuel cell electric vehicle. In this regard, hydrocarbon (HC) membranes, as cheaper alternatives, have been intensively studied in replacing PFSA membrane. However, until now, the challenge in adopting cost-effective HC membrane for PEMFCs has been the poor interfacial adhesion between catalyst layers (CLs) and HC membrane, which causes the membrane to delaminate easily, losing efficiency with use. Here, we present scalable mechanical nano-faster featured by three-dimensional interlocked interfacial structure between HC membrane and PFSA-based CL as a novel strategy to tackle the interfacial issue. It is realized by forming nano-porous skins on the both side of HC membrane and successively fiiling the pores with PFSA ionomer with scalable wet coating methods. The interlocking interface tightly binds the HC membrane and CL owing to its highly-interlocked ball and socket joint structure. The interfacial adhesion is dramatically enhanced by 37-fold with the nano-fastener. The membrane electrode assembly (MEA) with the three-dimensional interlocking interface exhibits 17 times higher durability than that with flat interface, paving a way to realize highly robust and cost-effective HC membrane-based PEMFCs for automotive use.
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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 | 0.001 | 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".