Plasma‐Made (Ni<sub>0.5</sub>Cu<sub>0.5</sub>)Fe<sub>2</sub>O<sub>4</sub> Nanoparticles for Alcohol Amination under Microwave Heating
Bibliographic record
Abstract
Abstract Amine N ‐alkylation is a process involved in the production of a wide range of chemicals. Here we describe the synthesis of well‐defined (Ni 0.5 Cu 0.5 )Fe 2 O 4 magnetic nanoparticles by plasma induction, and their successful application to amine N ‐alkylation using alcohols as coupling agents through a borrowing hydrogen pathway. Plasma induction allows precise morphology and size control over nanoparticle synthesis, while allowing the one‐pot production of decagram quantities of material. Up to date, such nanoparticles have never been applied for organic reactions. By coupling high‐end characterization techniques with catalytic optimization, we showed that small Cu(0) satellite nanoparticles played an essential role in alcohol oxidation, whereas both Ni and Cu were required for the last step of the reaction. Using elemental mapping, we demonstrated that catalyst deactivation occurred through a leaching/re‐deposition mechanism of Cu and Ni. The reactions were conducted under microwave conditions, which exerted a positive effect on catalytic activity. Finally, the catalyst was active at low metal loadings (2 mol%) even on the gram‐scale, and affording unprecedented TON for this reaction catalyzed by Ni/Cu bimetallic systems (19).
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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; both teacher heads agree on what is shown here.
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".