Enhanced Wettability by Copper Electroless Coating of Carbon Nanotubes
Bibliographic record
Abstract
In recent years, carbon nanotubes have raised scientific interest due to their unmatched properties and their numerous potential applications. Their exceptional mechanical properties especially make them candidates for superstrong and lightweight nanocomposites. The present study aims to test the possibility of fabricating nanocomposites, with aluminum as the matrix and carbon nanotubes as the reinforcing phase, using liquid vacuum infiltration. Before infiltrating nanotubes by molten aluminum, it is necessary to enhance their wettability. For this purpose, an electroless plating of copper was carried out to ensure a good quality of the matrix / nanotubes interface. Three steps were needed to make this plating. Firstly, the carbon nanotubes were oxidized in a mixture of strong acids in order to improve their chemical reactivity. Secondly, their surface was activated by the deposition of some catalytic nuclei of palladium. Finally, the electroless copper-plating step was performed. Field Emission Scanning Electron Microscopy, Field Emission Transmission Electron Microscopy, Transmission Electron Microscopy, X-ray microanalysis, and Fourier-Transform Infra Red spectroscopy were used for chemical and microstructural characterization during the different steps of the process with emphasis on studying the interface.
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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".