Innovative Membrane Electrode Assemblies For Polymer Electrolyte Membrane Fuel Cells
Bibliographic record
Abstract
Polymer electrolyte membrane fuel cell (PEMFC) is regarded as a promising technology for both automotive and stationary applications. Two significant challenges that hamper its commercialization are its high cost and insufficient durability. Catalyst layer (the region where fuel and oxidant convert to products) has a vital importance to be able to mitigate the above challenges. This dissertation reports a systematic study of using niobium (Nb)-doped titanium dioxide nanofibers as a corrosion-resistance catalyst support for PEMFCs, along with the study on the control of physical and electrochemical properties to create durable and still active platinum catalysts, and a new strategy to optimize ionomer phase (Nafion) loadings in the catalyst layers. It also proposed a simple wet coating process of Nb-doped titanium dioxide (TiO2) sols onto carbon papers to protect the interface between gas diffusion backing layer and catalyst layer for unitized regenerative fuel cell applications. Oxidative treatment and dip coating of carbon paper with Nb-doped TiO2 sol was shown to increase the corrosion resistance of carbon paper at the interface between catalyst layer and gas diffusion backing layer (Chapter 2). Anatase phase Nb-doped TiO2 nanofibers were synthesized by using the upscalable method of electrospinning to find a more durable alternative catalyst support, to substitute not corrosion resistant pure carbon-based catalyst supports (Chapter 3). More electronically conductive and high surface area rutile phase Nb-doped TiO2 nanofibers were synthesized through embedding carbon in between rutile crystallites using an innovative strategy called “in-situ reductive embedment (ISRE)” (Chapter 4). However, the ORR mass activities of the Pt catalysts that were supported by carbon-embedded Nb-doped TiO2 nanofibers were still slightly lower than pure carbon black based Pt catalysts. Instead, electronically more conductive and high surface area catalyst supports were synthesized by physically mixing of commercial carbon blacks with carbon-embedded Nb-doped TiO2 nanofibers (Chapter 5) and the effect of catalyst layer preparation method on the distribution of catalyst layer components has been investigated (Chapter 6).
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".