A strategy for fabricating nanoporous gold films through chemical dealloying of electrochemically deposited Au-Sn alloys
Bibliographic record
Abstract
We report a novel strategy for the fabrication of nanoporous gold (NPG) films. The fabrication process involves the electrodeposition of a gold-tin alloy, followed by subsequent chemical dealloying of tin. Scanning electron microscopy (SEM) images show a bicontinuous nanoporous structure formed on the substrates after chemical dealloying. Energy dispersive x-ray (EDX) analysis indicates that there are no impurities in the Au-Sn alloy film with an average composition of 58 at. % Au and 42 at. % Sn. After dealloying, only gold remains in the NPG film indicating the effectiveness of this technique. X-ray diffraction (XRD) results reveal that the as-prepared Au-Sn alloy film is composed of two phases (Au5Sn and AuSn), while the NPG film is composed of a single phase (Au). We demonstrate that this approach enables the fabrication of NPG films, either freestanding or supported on various conductive substrates such as copper foil, stainless steel sheet and nickel foam. The resulting NPG electrode exhibits enhanced electrocatalytic activity toward both H2O2 reduction and methanol oxidation compared to the polished Au disc electrode. Our strategy provides a general method to fabricate high quality NPG films on conductive substrates, which will broaden the application potential of NPG or NPG-based materials in various fields such as catalysis, optics and sensor technology.
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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 | 0.001 | 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".