Mid-infrared Polaritonic Coupling between Boron Nitride Nanotubes and Graphene
Bibliographic record
Abstract
Boron nitride (BN) is considered to be a promising substrate for graphene-based devices in part because its large band gap can serve to insulate graphene in layered heterostructures. At mid-infrared frequencies, graphene supports surface plasmon polaritons (SPPs), whereas hexagonal-BN (h-BN) is found to support surface phonon polaritons (SPhPs). We report on the observation of infrared polaritonic coupling between graphene SPPs and boron nitride nanotube (BNNT) SPhPs. Infrared scattering type scanning near-field optical microscopy is used to obtain spatial distribution of the two types of polaritons at the nanoscale. The observation suggests that those polaritons interact at the nanoscale in a one-dimensional/two-dimensional (1D/2D) geometry, exchanging energy in a nonplanar configuration at the nanoscale. Control of the polaritonic interaction is achieved by adjustment of the graphene Fermi level through voltage gating. Our observation suggests that boron nitride nanotubes and graphene can interact at mid-infrared frequencies and coherently exchange their energies at the nanoscale through the overlap of mutual electric near field of surface phonon polaritons and surface plasmon polaritons. Such interaction enables the design of nano-optical devices based on BNNT-graphene polaritonics in the mid-infrared 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 | 0.000 | 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".