Kinetic Study of a Direct Water Synthesis over Silica-Supported Gold Nanoparticles
Bibliographic record
Abstract
The reaction mechanism of water formation from H2 and O2 was studied over a series of silica-supported gold nanoparticles. The metal particle size distributions were estimated with TEM and XRD measurements. Hydrogen and oxygen adsorption calorimetry was used to probe the nature and properties of surface species formed by these molecules. DFT calculations with Au5, Au13, and Au55 clusters and with Au(111) and Au(211) periodic slabs were performed to estimate the thermodynamic stability and reactivity of surface species. Kinetic measurements were performed by varying the reactant partial pressures at 433 K and by varying the temperature from 383 to 483 K at 2.5 kPa of O2 and 5 kPa of H2. The measured apparent power law kinetic parameters were similar for all catalysts in this study: hydrogen order of 0.7-0.8, oxygen order of 0.1-0.2, and activation energy of 37-41 kJ/mol. Catalysts with Si-MFI (Silicalite-1) and Ti-MFI (TS-1 with 1 wt % Ti) exhibited similar activities. The activities of these catalysts with the MFI crystalline supports were 60-70 times higher than that of an analogous catalyst with an amorphous silica support. Water addition in the inlet stream at 3 vol % did not affect the reaction rates. The mechanism of water formation over gold is proposed to proceed through the formation of OOH and H2O2 intermediates. A rate expression derived based on this mechanism accurately describes the experimental kinetic data. The higher activity of the MFI-supported catalysts is attributed to a higher concentration of gold particles comparable in size to Au13, which can fit inside MFI pores. DFT results suggest that such intermediate-size gold particles are most reactive toward water formation. Smaller particles are proposed to be less reactive due to the instability of the OOH intermediate whereas larger particles are less reactive due to the instability of adsorbed oxygen.
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 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".