Vertically Stacked Boron Nitride/Graphene Heterostructure for Tunable Antiresonant Hollow-Core Fiber
Bibliographic record
Abstract
Incorporating atomically thin two-dimensional (2D) materials with optical fibers expands their potential for optoelectronic applications. Recent advancements in chemical vapor deposition have enabled the batch production of these hybrid fibers, paving the way for practical implementation. However, their functionality remains constrained by the integration of a single 2D material, restricting their versatile performance. Here, we introduce a boron nitride/graphene (BN/Gr) heterostructure in the antiresonant hollow-core fiber (ARF) to modulate its optical resonance and thus enhance graphene nonlinearity by controlling the BN thickness. Hydroxyl-rich methanol is employed to improve the flatness and crystallinity of graphene, promoting the vertical epitaxy of BN with a controllable thickness ranging from 5 to 50 nm. The engineered optical resonance notably tunes the light-graphene interaction within the BN/Gr-ARF, increasing the depth of nonlinear optical modulation from 4% to 10% and enhancing all-optical modulation performance by 75%. Our methodology opens possibilities for tunable optical waveguides via the direct growth of functional 2D material-based heterostructures, offering a robust platform for the development of highly integrated photonic devices.
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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) | 0.000 | 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".