Development and Application of Thin Wide-Band Screening Composite Materials
Bibliographic record
Abstract
The paper is dedicated to the development of effective composite coatings with the use of carbon fillers of different morphology, their research, and application in the broadband frequency range. Electromagnetic loss studies were performed according to international standards ASTM D4935, IEEE-STD-299, and the US Department of Defence standard MIL-STD 461F. The impact of hybrid carbon nanomaterial "graphene/nanotubes" on the electrophysical properties of the composite material has been analyzed. As a result, the research laboratory technologies of production of composite coating on water and non-water (alcohol) basis are developed based on the carbon fillers of various morphology and also magnetite. The shielding properties of most of the created composites are estimated in the frequency range from 50 MHz to 30 GHz. The state enterprise “All-Ukrainian center for standardization, metrology, certification, and consumers’ rights protection” (here and after “Ukrmetrteststandart”) conducted comparative tests of the developed coating (in the form of paint) with a protective coating # 842 MG Chemicals (Burlington, Ontario, Canada) based on silver microparticles. Developed water-based composites can be used for interior decoration, in the formation of electromagnetic screens, thin gradient coatings to protect people from electromagnetic radiation in the microwave range.
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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 | 0.001 | 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".