Computational investigation of the plasmonic properties of TiN, ZrN, and HfN nanoparticles: the role of particle size, medium, and surface oxidation
Bibliographic record
Abstract
Group 4 transition metal nitride (TMN) nanoparticles (NPs) display strong plasmonic responses in the visible and near-infrared regimes, exhibit high melting points and significant chemical stability, and thus are potential earth-abundant alternatives to Au and Ag based plasmonic applications. However, a detailed understanding of the relationship between TMN NP physical properties and plasmonic response is required to maximize their utility. In this study, the localized surface plasmon resonance (LSPR) frequency, bandwidth, and extinction of titanium nitride (TiN), zirconium nitride (ZrN), and hafnium nitride (HfN) NPs were examined as a function of the particle size, surface oxidation, and refractive index of the surrounding medium using finite element method (FEM). A linear redshift in the LSPR frequency and a linear increase in the associated full width at half maximum (FWHM) was observed with increasing the particle size, oxidation layer thickness, and medium refractive index. We show that the effect of surface oxidation on plasmonic properties of TMN NPs is strongly size-dependent with a significant LSPR redshift, intensity reduction, and broadening in small NPs compared with larger NPs. Furthermore, the performance and efficiency of HfN, ZrN, and TiN, as well as Au NPs for narrowband (photothermal therapy, PTT) and broadband (solar energy conversion) applications, was investigated in detail. The results indicate that narrowband and broadband photothermal performance of NPs strongly depend on the particle size, surface properties, and in case of narrowband absorption, excitation wavelength.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".