Refractory Plasmonics of Reactively Sputtered Hafnium Nitride Nanoparticles: Pushing Limits
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract High‐temperature plasmonics deals with optically active nanostructures that can withstand high temperatures. A conventional approach relying on standalone noble metal nanoparticles fails to deliver refractory plasmonic nanomaterials, and an alternative route envisions metal nitrides. The main challenge remains the development of advanced synthesis techniques and the insight into thermal stability under real‐life application conditions. Here, hafnium nitride nanoparticles (HfN NPs) can be produced by gas aggregation using reactive magnetron sputtering, a technique with a small environmental footprint are shown. As‐deposited NPs are of 10 nm mean size and consist of stoichiometric, crystalline fcc HfN. They are characterized by optical absorption below 500 nm caused by interband transitions and in the red/near‐infrared (NIR) region due to intraband transitions and localized surface plasmon resonance (LSPR). The optical response can be engineered by tuning the NP composition as predicted by finite‐difference time‐domain (FDTD) calculations. Going beyond the state‐of‐the‐art, the HfN NP thermal stability is focued under ultrahigh vacuum (UHV) and in air. During UHV annealing to 850 °C, the NPs retain their morphology, chemical and optical properties, which makes them attractive in space mission and other applications. During air annealing to 800 °C, HfN NPs remain stable until 250 °C, which sets a limit for air‐mediated use.
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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 | 0.000 |
| 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 it