Functionalized Graphene Nanoribbon Films as a Radiofrequency and Optically Transparent Material
Bibliographic record
Abstract
We report that conductive films made from hexadecylated graphene nanoribbons (HD-GNRs) can have high transparency to radiofrequency (RF) waves even at very high incident power density. Nanoscale-thick HD-GNR films with an area of several square centimeters were found to transmit up to 390 W (2 × 10(5) W/m(2)) of RF power with negligible loss, at an RF transmittance of ∼99%. The HD-GNR films conformed to electromagnetic skin depth theory, which effectively accounts for the RF transmission. The HD-GNR films also exhibited sufficient optical transparency for tinted glass applications, with efficient voltage-induced deicing of surfaces. The dispersion of the HD-GNRs afforded by their edge functionalization enables spray-, spin-, or blade-coating on almost any substrate, thus facilitating flexible, conformal, and large-scale film production. In addition to use in antennas and radomes where RF transparency is crucial, these capabilities bode well for the use of the HD-GNR films in automotive and general glass applications where both optical and RF transparencies are desired.
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".