Towards Practical Applications of EQCN Experiments to Study Pt Anchor Sites on Carbon Surfaces
Bibliographic record
Abstract
This work investigates the viability and outlines the current challenges in electrochemical quartz crystal nanobalance (EQCN) experiments on supported Pt catalysts. EQCN experiments involving Pt supported on 2-D “surface-treated graphite sputtered onto quartz crystal” (Pt/MFG-H) catalysts were compared to standard polycrystalline Pt (Ptpoly), which showed similarities in frequency versus potential trends; however, the Pt/MFG-H catalysts obtained higher frequencies due to the support capacitance. The physical characterizations (XRD and XPS) and electrochemical responses, mainly cyclic voltammetry in acidic media and the ferri/ferrocyanide couple, of the 2-D Pt/MFG-H were compared to the representative 2-D Pt supported on treated highly orientated pyrolytic graphite (Pt/HOPG-H), in order to make assertions on the similarities between the two catalysts. The XRD diffraction patterns and the XPS valence band structure for the treated and untreated MFG (-H and -P, respectively) and HOPG (-H and -P, respectively) demonstrated similarities. Nevertheless, the cyclic voltammograms and peak positions of the ferri/ferrocyanide couple between the treated and untreated MFG and HOPG catalysts were dissimilar. However, EQCN may be used qualitatively between the two different 2-D catalysts since the same trends in electrochemical responses before and after treatment of the MFG and HOPG catalysts were seen. Hence, the EQCN technique can be used in future studies as an alternative method to study degradation mechanisms of Pt and carbon for PEFCs.
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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 | 0.002 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".