(Physical and Analytical Electrochemistry Division David S. Grahame Award Address) Nanoscale Templates and Scaffolds for Electrochemical Device Applications
Bibliographic record
Abstract
This talk will review some of our recent fuel cell-related research efforts, which have had a primary focus on increasing the lifetime and performance of both anode and cathode catalyst layers. In our recent work on PEM fuel cells, a new class of ordered, mesoporous carbon materials (both powders and free-standing scaffolds), with nano-engineered pore diameters and lengths, have been developed as support materials to better distribute and stabilize catalytic nanoparticles that are attached to their surface. These carbons have also been surface modified with a range of functional groups, showing that this can significantly alter their wettability, enhance their resistance to corrosion, better anchor catalytic nanoparticles, and are also beneficial in redox flow battery electrochemistry. These reproducibly ordered carbon scaffolds are also proving to be ideal for the investigation of the interactions of Nafion with Pt/carbon in relation to performance, especially as a function of carbon pore diameter, depth and surface hydrophilicity. In parallel research related to catalyst supports, we have constructed ordered metal oxide nanotubular arrays, then converting them to conducting oxy-nitride forms, primarily to replace carbon. These metal oxy-nitrides undergo interesting redox transitions that will be shown to correlate with the activity of these materials (after deposition of Pt nanoparticles) towards the oxygen reduction reaction. Ordered surface arrays of Zr oxide nanotubes are also very promising for use in novel, nano-structured, high temperature solid oxide fuel cells.
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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 | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.117 | 0.068 |
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".