Alkali ion-promoted palladium subnanoclusters stabilized on porous alumina nanosheets with enhanced catalytic activity for benzene oxidation
Bibliographic record
Abstract
Catalytic C-H bond activation is one of the backbones of the chemical industry. Supported metal subnanoclusters consisting of a few atoms have shown attractive properties for heterogeneous catalysis. However, the creation of such catalyst systems with high activity and excellent anti-sintering ability remains a grand challenge. Here, we report on alkali ion-promoted Pd subnanoclusters supported over defective γ-Al2O3 nanosheets, which display exceptional catalytic activity for C-H bond activation in the benzene oxidation reaction. The presence of Pd subnanoclusters is verified by aberration-corrected high-angle annular dark-field scanning transmission electron microscopy, X-ray absorption spectroscopy, and X-ray photoelectron spectroscopy. This catalyst shows excellent catalytic activity, with a turnover frequency of 280 h−1 and yield of 98%, in benzene oxidation reaction to give phenol under mild conditions. Moreover, the introduction of alkali ion greatly retards the diffusion and migration of metal atoms when tested under high-temperature sintering conditions. Density functional theory (DFT) calculations reveal that the addition of alkali ion to Pd nanoclusters can significantly impact the catalyst’s structure and electronic properties, and eventually promote its activity and stability. This work sheds light on the facile and scalable synthesis of highly active and stable catalyst systems with alkali additives for industrially important reactions.
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) | 0.000 | 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 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".