AFM and Cu Electrodeposition Studies of Reduced Graphene Oxide Modified Au(111) Facets Prepared using Electrodeposition and Post-Deposition Pulse Treatment
Bibliographic record
Abstract
The structural and electrochemical characteristics of electrodeposited graphene oxide (GO) onto a single crystal gold bead electrode was studied using AFM and Cu electrodeposition. Optimal deposition uniformity was realized by rotation of the electrode, suggesting an initial electrophoretic deposition occurs followed by some reduction of the GO. Potential pulse treatments to reducing potentials for a short time significantly improve the electrochemical characteristics of the electrochemical reduced graphene oxide (ERGO). Both under-potential and over-potential Cu electrodeposition was used to characterize the quality of the ERGO deposit and showed that the gold surface was completely coated with ERGO only after potential pulsing treatments. AFM analysis of ERGO modified Au(111) facet on the gold bead electrode revealed the deposition was composed of sheets of GO/ERGO as well as much rougher deposits. AFM showed that the potential pulsing treatment resulted in the nucleation and growth of electrodeposited Cu nanoparticles over much of the ERGO surface, in contrast to the limited deposition of nanoparticles on the untreated GO deposit. The ERGO modified gold surface prepared using electrodeposition followed by potential pulse treatments results in conductive and electrochemically active ERGO surfaces.
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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) | 0.002 | 0.000 |
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".