Following the Thermal Activation of Au<sub>25</sub>(SR)<sub>18</sub> Clusters for Catalysis by X-ray Absorption Spectroscopy
Bibliographic record
Abstract
We show the thermal activation of phenylethanethiolate (L = SC 8 H 9 ) and hexanethiolate (L = SC 6 H 13 ) Au 25 L 18 monolayer protected clusters (MPCs) on carbon black supports, followed by characterization with extended X-ray absorption fine structure (EXAFS) spectroscopy and transmission electron microscopy (TEM). EXAFS analysis shows that the thiolate stabilizers can be partially removed from the surface under mild heating conditions without significant changes in the cluster size. The resulting clusters are shown to be catalytically active for the reduction catalysis of 4-nitrophenol. EXAFS and TEM data show that thermal treatment under air at 200 °C leads to nearly complete removal of all of the thiolate stabilizers with little to no growth in cluster size, while cluster sintering was seen at higher calcination temperatures. The maximum catalytic activity for Au 25 (SC 8 H 9 ) 18 MPCs was seen at 250 °C activation conditions. These results are consistent with results reported earlier for Au 25 (SR) 18 clusters on ceria by Jin et al., who suggested that cluster integrity was maintained during mild activation conditions. Here, EXAFS data unambiguously show that while the cluster size does not grow significantly, such mild heating conditions do lead to nearly complete removal of the thiolate stabilizers.
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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) | 0.002 | 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".