Graphene and MWCNT: Potential Candidate for Microwave Absorbing Materials
Bibliographic record
Abstract
Currently a wide range of materials are used for the design and development of microwave absorbing material or RADAR absorbing materials (RAMs). The microwave has two components, electric and magnetic which are acting perpendicular to each other. So, in order to make materials microwave invisible, it is required to cancel out both of these components, when material is exposed to microwave i.e. reduction of radar cross section (RCS). The RAMs should capable of cancelling out both the magnetic and electrical components of the electromagnetic radiation for an effective absorption. Generally this has been achieved by incorporating magnetic and electrically conducting fillers into a matrix. But here we want to study the microwave absorption ability of Graphene/MWCNT itself in TPU matrix. We prepared the material with 10% loading and sample thickness kept at 2 mm. Field Emission Scanning electron microscopy (FESEM) and Transmission Electron Microscopy (TEM) used for morphological study and scattering parameters were measured in X-band region by using a Vector Network Analyzer. Result showed that Graphene has better absorption capability than MWCNT.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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".