Isolated Palladium Atoms Dispersed on Silicoaluminophosphate-31 (SAPO-31) for the Semihydrogenation of Alkynes
Bibliographic record
Abstract
Supported single-atom catalysts have become one of the most exciting frontiers in heterogeneous catalysis. The physicochemical properties of these materials are highly interesting; however, the generation of stable single atoms on solid supports is still challenging. Herein, we report an efficient and operationally simple postsynthesis method, consisting of spatial confining, freeze-drying, and reducing steps, for the construction of singly dispersed palladium atoms on SAPO-31 (Pd 1 /SAPO-31) with promising application prospects in the semihydrogenation of alkynes. A typical Pd–N–C single-atom catalyst derived from the ZIF-8-supported palladium complex was prepared and compared to Pd 1 /SAPO-31. The Pd 1 /SAPO-31 catalyst showed outstanding performance in the semihydrogenation of both phenylacetylene (85% selectivity at 99% conversion, 40 min) and 1-chloro-4-ethynylbenzene (95% selectivity at 99% conversion, 60 min). In addition, this single-atom palladium catalyst was highly active in triethoxysilane oxidation. Most importantly, this catalyst retained its atomic dispersion and catalytic activity after high-temperature thermal treatment in Ar at 700 °C. This study demonstrates how zeolites can be used to confine atomically dispersed metal species and tune the activity and stability of these materials to meet the needs of practical applications.
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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".