Phonon and Defect Induced Transparencies in the Mid-Infrared Spectrum of Grafted Single Layer Graphene
Bibliographic record
Abstract
The Drude-like response of graphene in the terahertz and infrared region of the spectrum has made it attractive for optoelectronic applications in this range, because the response can be controlled by gating and doping. [1] Graphene infrared response can further be tailored for photonics and plasmonics, as the patterned material harbors low energy plasmon modes. [2] However, the infrared spectrum of pristine single layer graphene (SLG) is monotonous; in contrast to Raman, there are no infrared-active phonon modes, while bilayer graphene displays a Fano resonance in the infrared at ~1600 cm-1. [3] In a first time, we show experimentally that grafting SLG with halogenophenyl moieties induces optical transparencies at two specific energies: 1250 and 1600 cm-1 [4], in close similarity to the bands that can also be observed in carbon nanotubes as Fano resonances. [5] Unlike bands caused by the absorption of light by vibrational modes, these antiresonances show a decrease of the absorbance, an optical transparency effect. Moreover, we show that the amplitude of the transparencies can be modulated by changing the charge carrier density through doping, and by the defect density through controlled grafting. In as second time, we will present a theory based on quantum mechanics to calculate the optical conductivity of grafted SLG. [4] The model puts into play phonon modes with momenta different from Γ that can be addressed through scattering on defects. Numerical simulations reproduce the experimental data with good agreement. The theory also captures the dependence of the signal on charge carrier density and defect density. Our findings bring a new understanding for the physics behind the infrared activity of nanostructures, while opening new capabilities for tailoring the optical spectrum of nanomaterials. References [1] Horng et al. (2011) Phys Rev B 83:165113 [2] Low & Avouris (2014) ACS Nano 8:1086 [3] Kuzmenko et al. (2009) Phys Rev Lett 103:116804 [4] Rousseau et al. (2014) arXiv preprint arxiv:1407.8141 [5] Lapointe et al. (2012) Phys Rev Lett 109:097402
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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 | 0.001 |
| 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".