Probing the Surface Chemistry of Nanoporous Gold via Electrochemical Characterization and Atom Probe Tomography
Bibliographic record
Abstract
Surface chemistry information is crucial in understanding catalytic and sensing mechanisms. However, resolving the outermost monolayer composition of metallic nanoporous materials is challenging due to the high tortuosity of their morphology. In this study, we first elaborate on the capabilities and limitations of atom probe tomography (APT) in resolving interfaces. Subsequently, an electrochemical approach is designed to characterize the surface composition of nanoporous gold (NPG), developed from dealloying an inexpensive precursor (95 at. % Ag, 5 at. % Au), by the means of aqueous electrochemical measurements of the selective electrosorption of sulfide ions, which react strongly with Ag, but to a significantly lesser extent with Au. Accordingly, cyclic voltammetry was performed at various scan rates on NPG in alkaline aqueous solutions (0.2 M NaOH; pH 13) in the presence and absence of 1 mM Na2S. Calibrations via similar voltammetric measurements on pure polycrystalline Ag and Au surfaces allowed for a quantitative estimation for the Ag surface coverage of NPG. The sensitivity threshold for the detection of the adsorbate–Ag interaction was assessed to be approximately 2% Ag surface coverage. As curves measured on NPG only showed featureless capacitive currents, no faradaic charge density associated with sulfide electrosorption could be detected. This study opens a new avenue to gain further insight into the monolayer surface coverage of metallic nanoporous materials and assists in enhancement of the interpretation of APT reconstructions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".