Au Nanochains Anchored on 3D Polyaniline/Reduced Graphene Oxide Nanocomposites as a High‐Performance Catalyst for Ethanol Electrooxidation
Bibliographic record
Abstract
Abstract A nanocomposite containing Au, polyaniline (PANI) and reduced graphene oxide (RGO) has been synthesized by a two‐step method. The PANI can intermix with graphene to build a three‐dimensional (3D) structure, which is beneficial for uniform dispersion of Au networks with a mean diameter of 5.6 nm. In addition to its role as the support of Au, the presence of PANI is also favorable for avoiding the heavy agglomeration of graphene when the reduction occurs. Additionally, the introduction of graphene can not only boost the connections with PANI, but also accelerate the electron transfer between the electrolyte solution and catalysts. Electrochemical tests indicate that the Au/PANI/RGO hybrid exhibits high catalytic activity and stability for ethanol electrooxidation in alkaline conditions. The superior performance can be ascribed to the uniform dispersion of the Au nanocatalyst on the PANI/RGO support with a particular 3D structure, resulting in an increase of the electrochemically active surface area together with a synergic effect between the PANI, graphene and Au.
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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 | 0.000 |
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".