Synthesis and Characterization of Magnetic Nanoparticle-Decorated Multiwalled Carbon Nanotubes
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Multiwalled carbon nanotubes find applications in many fields due to their extraordinary properties. However, depending on their synthesis method, they show no or a poor response to the presence of a magnetic field. This limits their usability in magnetic applications. In this study, the maximum induced magnetization of multiwalled carbon nanotubes was increased by deposition of magnetic nanoparticles, which were produced by nanosecond pulsed laser deposition under inert low-pressure conditions using iron (Fe), magnetite (Fe 3 O 4 ), cobalt (Co), and nickel (Ni) targets. Extensive chemical and physical characterization of the added nanoparticles was performed. It was found that for the same synthesis conditions, Fe and Fe 3 O 4 targets resulted in the formation of larger, asymmetrical magnetic Fe nanoparticles with a Fe 3 O 4 shell (Fe@Fe 3 O 4 ) (3.2–8.6 nm) and Fe 3 O 4 (6.0–12.4 nm) nanoparticles, respectively. Smaller, more spherical Co@CoO (2.1–5.0 nm) and Ni@NiO (1.4–3.5 nm) nanoparticles were obtained from the Co and Ni targets, respectively. The highest increase in maximum induced magnetization was observed for multiwalled carbon nanotubes with Fe@Fe 3 O 4 (5.37 ± 0.15 emu/g) or Co@CoO nanoparticles (4.29 ± 0.01) compared to pristine multiwalled carbon nanotubes (2.46 ± 0.08 emu/g) and nanotubes with Fe 3 O 4 (3.79 ± 0.38 emu/g) or Ni@NiO nanoparticles (2.85 ± 0.06 emu/g). Finally, superior adhesion of the Fe@Fe 3 O 4 and Fe 3 O 4 nanoparticles to multiwalled carbon nanotubes compared to the Ni@NiO and Co@CoO nanoparticles was identified.
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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 | 0.001 | 0.000 |
| 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 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".