Investigation of Electromagnetic Wave Absorption Properties of Ni-Coand MWCNT Nanocomposites
Bibliographic record
Abstract
BACKGROUND: In recent years, severe electromagnetic interference among electronic devices has been caused by the unprecedented growth of communication systems. Therefore, microwave absorbing materials are required to relieve these problems by absorbing the unwanted microwave. In the design of microwave absorbers, magnetic nanomaterials have to be used as fine particles dispersed in an insulating matrix. Besides the intrinsic properties of these materials, the structure and morphology are also crucial to the microwave absorption performance of the composite. In this study, Ni-Co- MWCNT composites were synthesized, and the changes in electric permittivity, magnetic permeability, and reflectance loss of the samples were evaluated at frequencies of 2 to 18 GHz. METHODS: Nickel-Cobalt-Multi Wall Carbon Nanotubes (MWCNT) composites were successfully synthesized by the co-precipitation chemical method. The structural, morphological, and magnetic properties of the samples were characterized and investigated by X-ray diffractometer (XRD), Scanning Electron Microscopy (SEM), Transmission Electron Microscopy (TEM), Vibrating Sample Magnetometer (VSM), and Vector Network Analyzer (VNA). RESULTS: The results revealed that the Ni-Co-MWCNT composite has the highest electromagnetic wave absorption rate with a reflectance loss of -70.22 dB at a frequency of 10.12 GHz with a thickness of 1.8 mm. The adequate absorption bandwidth (RL <-10 dB) was 6.9 GHz at the high-frequency region, exhibiting excellent microwave absorbing properties as a good microwave absorber patent. CONCLUSION: Based on this study, it can be argued that the Ni-Co-MWCNT composite can be a good candidate for making light absorbers of radar waves at frequencies 2- 18 GHz.
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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 | 0.001 | 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 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".