Pd@PdBO <i> <sub>x</sub> </i> heterostructure with strong electronic interaction to promote C─H bond activation for glycerol oxidation reaction
Bibliographic record
Abstract
Glycerol oxidation reaction (GOR) represents an economical pathway for transforming renewable feedstock to value-added chemicals. However, the inertness of C(sp 3 )─H bonds of glycerol and intermediates results in the high energy barrier of the dehydrogenation step, relating to poor product selectivity at high glycerol conversion. Here, a carbon nanotube–supported PdBO x @Pd heterostructure catalyst (PdBO x @Pd/CNTs) was synthesized in which in situ–exsoluted PdBO x clusters covalently covered Pd nanoparticles, thus yielding strong electronic interaction between Pd nanoparticles and PdBO x clusters. The strong electronic interaction in PdBO x @Pd/CNTs induces the hybridization between Pd(d), B(s, p), and O(s, p) atom orbits, optimizing the adsorption of reactants and intermediates, thus enhancing the activity for the GOR. The density functional theory calculation result reveals that the strong electronic interaction in PdBO x @Pd/CNTs facilitates the hydrogen transfer in the primary C─H bond of the CH 2 OHCHOHCH 2 O* intermediate, thus reducing the energy barrier of the rate-determining step and improving glyceric acid selectivity toward the GOR.
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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".