Direct Synthesis of Atomically Dispersed Palladium Atoms Supported on Graphitic Carbon Nitride for Efficient Selective Hydrogenation Reactions
Bibliographic record
Abstract
Heterogeneous catalysts with atomically precise metal sites have enabled unique insight into structure–property relationships in materials science. Herein, we report the construction and selective hydrogenation performance of a single-atom palladium catalyst by confining the palladium atoms into the six-fold N-coordinating cavities of graphitic carbon nitride (g-C 3 N 4 ) through a facile spatial confinement-reduction approach under mild reducing conditions. Spherical aberration correction electron microscopy and extended X-ray absorption fine structure measurements confirm the presence of atomically dispersed palladium atoms stabilized by the g-C 3 N 4 support. Its exceptional catalytic activity was demonstrated by the hydrogenation of styrene (98% conversion, 1.5 h) and furfural (conversion of 64% and selectivity of 99%, 4 h) and hydrodechlorination of 4-chlorophenol (99% conversion and 99% selectivity, 10 min). This palladium catalyst can be reused at least five times with negligible deterioration of its activity. Importantly, the palladium atoms retained their atomic dispersion following the thermal treatment. Moreover, this synthetic method can be scaled up while retaining similar catalytic activity. Fundamental insights are provided to elucidate how the material’s structure significantly impacts the catalytic performance at the atomic scale.
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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 | 0.001 | 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 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".