Core–shell titanium dioxide–titanium nitride nanotube arrays with near-infrared plasmon resonances
Bibliographic record
Abstract
Abstract Titanium nitride (TiN) is a ceramic with high electrical conductivity which in nanoparticle form, exhibits localized surface plasmon resonances (LSPRs) in the visible region of the solar spectrum. The ceramic nature of TiN coupled with its dielectric loss factor being comparable to that of gold, render it attractive for CMOS polarizers, refractory plasmonics, surface-enhanced Raman scattering and a whole host of sensing applications. We report core–shell TiO 2 –TiN nanotube arrays exhibiting LSPR peaks in the range 775–830 nm achieved by a simple, solution-based, low cost, large area-compatible fabrication route that does not involve laser-writing or lithography. Self-organized, highly ordered TiO 2 nanotube arrays were grown by electrochemical anodization of Ti thin films on fluorine-doped tin oxide-coated glass substrates and then conformally coated with a thin layer of TiN using atomic layer deposition. The effects of varying the TiN layer thickness and thermal annealing on the LSPR profiles were also investigated. Modeling the TiO 2 –TiN core–shell nanotube structure using two different approaches, one employing effective medium approximations coupled with Fresnel coefficients, resulted in calculated optical spectra that closely matched the experimentally measured spectra. Modeling provided the insight that the observed near-infrared resonance was not collective in nature, and was mainly attributable to the longitudinal resonance of annular nanotube-like TiN particles redshifted due to the presence of the higher permittivity TiO 2 matrix. The resulting TiO 2 –TiN core–shell nanotube structures also function as visible light responsive photocatalysts, as evidenced by their photoelectrochemical water-splitting performance under light emitting diode illumination using 400, 430 and 500 nm photons.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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 teacher head, 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".