Covalent Organic Framework-Templated <i>N</i>-Heterocyclic Carbene-Functionalized Gold Nanoparticles for the Catalytic Reduction of Nitrophenol
Bibliographic record
Abstract
Herein, a hybrid material based on a covalent organic framework (COF) and N- heterocyclic carbene ( N HC)-functionalized gold nanoparticles (AuNPs) was developed. The synthesis starts with the formation of an N HC@Au(I) complex, serving as the precursor for the AuNPs. This compound was either entrapped within the pores of the COF during its assembly (method 1) or infiltrated inside its pores after its formation (method 2). Our results demonstrate that method 1 yields AuNPs smaller than those produced by method 2. Electron microscopy analysis confirmed the successful embedding of AuNPs into the COF, with well-distributed NPs of smaller than 5 nm for method 1 and larger, agglomerated AuNPs (over 5 nm) for method 2. Additionally, nitrogen adsorption–desorption isotherms (BET analysis) indicated a significant reduction in surface area after gold integration, decreasing from an initial value of 1885 to 1106 m 2 /g and 910 m 2 /g for the two methods, respectively. The synthesized heterogeneous AuNP catalysts effectively facilitated the reduction of nitrophenol at ambient temperature, exhibiting rapid and efficient catalysis. Notably, the smaller AuNPs embedded within the COF showed enhanced catalytic performance compared to larger NPs.
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 | 0.001 | 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".