An Epoxy‐Free Sample Preparation Approach to Enable Imaging of Ionomer and Carbon in Polymer Electrolyte Membrane Fuel Cells
Bibliographic record
Abstract
Abstract Performance and durability of electrodes in proton exchange membrane fuel cells (PEMFCs), as one of the most promising zero‐emission power generation technologies, depend on the composition, microstructure, and distribution of its components—metal catalyst, carbon support, and ionomer. Their improvement requires an in‐depth understanding of the electrodes’ structure‐property‐performance relationship, for which transmission electron microscopy (TEM) has been an invaluable tool. However, the conventional TEM sample preparation, namely epoxy‐embedding ultramicrotomy, poses impediments in imaging ionomer and distinguishing it from carbon. Therefore, in this research, an epoxy‐free ultramicrotome technique is implemented on beginning‐of‐life (BOL) and end‐of‐life (EOL) PEMFC samples. For the first time, TEM and electron tomography‐TEM images reveals fascinating details of the ionomer network, carbon particles’ structure, and Pt distribution in BOL, as well as their structural changes after the cell degradation. Finally, the structural descriptors, extracted by a proprietary quantification method, are correlated with visual observations.
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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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".