N-Heterocyclic Carbene Deposition on a Copper Powder Surface Using Mechanochemistry
Bibliographic record
Abstract
Metal powders are crucial precursors for manufacturing surfaces through thermal spraying, cold spraying, and 3D printing methods. However, surface oxidation of these precursors poses a challenge to the coherence of the metallic materials during manufacturing processes. Herein, we introduce a method for surface modification of copper powder with N-heterocyclic carbenes (NHCs) using mechanochemistry to mitigate surface oxidation. A resonant acoustic mixer was used to deposit five different carbenes on copper powders using benzimidazolium hydrogen carbonate precursors and a trace amount of solvent. Significant oxide reduction was observed by X-ray photoelectron spectroscopy (XPS), and the immobilization of NHCs on the powder was confirmed by mass spectrometry and XPS. The consistent morphology of the modified copper powder minimizes any potential impact on subsequent manufacturing processes. Moreover, a life cycle assessment indicates the potential environmental hotspots, leading to recommendations to develop lower-footprint processes. Overall, the mechanochemical method to produce NHC-modified metal powders with a higher metallic content provides great prospects for powder precursors to produce coatings from thermal spray, cold spray, and additive manufacturing processes.
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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".