Templated synthesis of nickel nanoparticles embedded in a carbon layer within silica capsules
Bibliographic record
Abstract
The encapsulation of small non-noble metal nanoparticles (NPs) within an inorganic layer has received considerable attention owing to their enhanced stability and high catalytic activity. Using a combination of emulsion-free polymerization, inner RF-Ni2+ and outer SiO2 coating, and subsequent carbonization treatment, herein, we have fabricated worm-like structured Ni-based composites in which a high density of nickel NPs are embedded in a carbon layer and also entrapped by SiO2 nanocages. We find that the carbonization temperature plays a vital role in adjusting the size of the Ni NPs. A detailed examination of the encapsulated nickel particles synthesized at 700 °C exhibited the best performance on the catalysis of the reduction of 4-NPs. Moreover, owing to the good alloying ability of the Ni NPs with noble metal NPs, the Ni-Pd alloy NPs are also entrapped in the SiO2 nanocages, which exhibit better performance on the catalysis than the Ni-based composites. The encapsulation of Ni-Pd alloys within SiO2 nanocages also improves stability against agglomeration and metal separation during catalytic operation.
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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".