Super-high-frequency shielding properties of excimer-laser-synthesized-single-wall-carbon-nanotubes/polyurethane nanocomposite films
Bibliographic record
Abstract
Electromagnetic shielding attenuation (ESA) properties of carbon nanotubes/polymer nanocomposite films, in the super high frequency (SHF) X-band (7–12 GHz) domain are studied. The nanocomposite films consisted of thermoset polyurethane (PU) resin blended with single-walled carbon nanotubes (SWCNTs) mats, and deposited on fused quartz substrates. Two different approaches were used to achieve the nanocomposite films, namely (i) through the on-substrate “all-laser” growth approach of SWCNTs directly onto substrate, followed by their infiltration by the PU resin, and (ii) by appropriately dispersing the chemically-purified SWCNTs (in the soot form) into the PU matrix and their subsequent deposition onto quartz substrates by means of a solvent casting process. Characterizations of the ESA properties of the developed nanocomposite films show that they exhibit systematically a deep shielding band, centered at around 9.5 GHz, with an attenuation as high as |− 30| dB, recorded for SWCNT loads of 2.5 wt. % and above. A direct correlation is established between the electrical conductivity of the nanocomposite films and their electromagnetic shielding capacity. The SWCNTs/PU nanocomposites developed here are highly promising shielding materials as SHF notch filters, as their ESA capacity largely exceeds the target value of |− 20| dB generally requested for commercial applications.
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) | 0.001 | 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".