Electrocatalytic Oxygen Reduction Performance of Silver Nanoparticle Decorated Electrochemically Exfoliated Graphene
Bibliographic record
Abstract
We have developed a potentiostatic double-pulse technique for silver nanoparticle (Ag NP) deposition on graphene (GRn) with superior electronic and ionic conductivity. This approach yielded a two-dimensional electrocatalyst with a homogeneous Ag NP spatial distribution having remarkable performance in the oxygen reduction reaction (ORR). GRn sheets were reproducibly prepared by the electrochemical exfoliation of graphite (GRp) at high yield and purity with a low degree of oxidation. Polystyrenesulfonate added during exfoliation enhanced the stability of the GRn solution by preventing the restacking of the graphene sheets and increased its ionic conductivity. The potentiostatic double-pulse technique is generally used to electrodeposit Pt nanoparticles and remains challenging for silver metal that exhibits nucleation and growth potentials relatively close to each other. We judiciously exploited this narrow margin of potential, and for the first time we report Ag NP electrodeposited onto graphene with the subsequent ability to control both the density and the size of metallic nanoparticles. Considering the high activity along with the lower cost of Ag compared to Pt, these findings are highly relevant to the successful commercialization of fuel cells and other electrochemical energy devices.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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".