Preparation of metal-carbon nanotube composite powders for thermal plasma spraying applications
Bibliographic record
Abstract
Extensive research efforts are underway to generate composite coatings including carbon nanotubes (CNT) in order to improve the properties of the bulk coat. The present project concentrates on possible methods that would allow the plasma spraying of CNT-based nano-composite coatings. Various methods for producing metal-CNT composite powders for thermal plasma spraying are studied in this project. The first method discussed is based on CNT dispersion followed by an agglomeration procedure using a polymer binder. The second and more promising technique gets around the problems associated with handling, separating, and transporting the nanotubes to the substrate through a direct synthesis of the CNT on the particles to be sprayed. In the present context, this synthesis is made on pretreated stainless steel SS304 powders by chemical vapor deposition (CVD). A parametric study is made for the various steps of the thermal CVD process by varying parameters such as the etching time in the pre-treatment sequence, while the reaction temperature, the annealing temperature, the growth phase duration and the acetylene injection time are varied for the CVD sequence. The composite powders are analyzed using high resolution electron microscopy images (FEGSEM), thermogravimetric analyses (TGA), Raman spectroscopy, and BET surface area analyses. This study provided the parametric optimization of the thermal CVD procedure with respect to CNT production. The composite powders generated show a dense, fully covered, and uniform CNT forest with a purity of CNT to amorphous carbon of 86% based on the micro-Raman spectra obtained.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".