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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 teacher head, 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".