Stand-Alone Sponge-Shaped Mesoporous AuPt Alloy Nanosponges as Visible Photocatalyst for the Selective Reduction of CO<sub>2</sub> to CO
Bibliographic record
Abstract
Bulk Au is chemically inert and a relatively poor catalyst while Pt is catalytically active. Nanostructured Au is an effective photocatalyst due to hot electrons generated by interband- and intraband damping of surface plasmons. A bimetallic, mesoporous AuPt catalyst with a large specific surface area is highly desirable due to the combination of hot carrier and catalytic promoter effects together with a large number of active reaction sites. Sponge-shaped AuPt alloy nanoparticles were grown by spontaneous thermal dewetting of ultrathin AuPtAg films followed by dealloying through removal of Ag. Selective etching of silver from AuPtAg alloy precursors formed a porous AuPt sheet-like nanostructure with mesopores with an average size of 3.72 nm. Under AM1.5G one sun illumination, the AuPt nanosponge acted as a stand-alone photocatalyst for CO 2 reduction by evolving CO at a rate of 1010 μmol g –1 h –1 with 100% selectivity. Scavenger experiments confirmed photogenerated electrons to be the driving force for chemical transformation as opposed to holes. AuPt nanosponge significantly outperformed bare Au nanosponge in both visible light-stimulated CO 2 photoreduction and photooxidative degradation of methylene blue dye, which is attributed to a non-negligible photoinduced electron transfer from Au to Pt followed by electron transfer to reactants.
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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".