Size-Dependent Catalytic Activity of Palladium Nanoparticles Decorated on Core–Shell Magnetic Microporous Organic Networks
Bibliographic record
Abstract
It is of supreme importance to develop recyclable and green heterogeneous catalysts due to their potential to reduce environmental impact and reduce costs associated with their use. Hence, in this research, an initial development was made of a core–shell magnetic structure composed of Fe 3 O 4 and a microporous organic network (MON), followed by the physical absorption of palladium nanoparticles (NPs) onto the outer surface of this shell substance. The results of TEM and EDX analyses indicated that Pd NPs of varying sizes, which were produced by using different reducing agents, were evenly bound to the MON shell materials. The use of PiFM and XPS analyses provided evidence that there was effective bonding between Pd nanoparticles and the amine groups present on the surface of MON materials. Besides, the successful immobilization of palladium NPs was also approved by N 2 adsorption/desorption analyses with the decrease of the surface from 328 to 227 m 2 /g after palladium inclusion. The prepared nanoheterogeneous catalysts can catalyze the C–C formation in mild conditions, green solvents, and short reaction times with good to excellent yields. The results revealed that the catalyst with smaller palladium NPs sized 1–5 nm and in medium abundance has better catalytic performance toward C–C formation, compared to those with bigger Pd NPs sized 5–15 nm and in both low and high Pd contents.
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".