Solid-State Synthesis of UV-Plasmonic Cr2N Nanoparticles
Bibliographic record
Abstract
Materials that exhibit plasmonic response in the UV region can be advantageous for many applications such as biological photodegradation, photocatalysis, disinfection, and bioimaging. Transition metal nitrides have recently emerged as chemically and thermally stable alternatives to metal-based plasmonics materials. However, most free-standing nitride nanostructures explored so far have plasmonic response in the visible and near-IR region. Herein, we report the synthesis of UV-plasmonic Cr2N nanoparticles using a solid-state nitridation reaction. The nanoparticles had an average diameter of 9 ± 5 nm and a positively charged surface that yields stable colloidal suspension. The particles were composed of a crystalline nitride core and an amorphous oxide/oxynitride shell whose thickness varied between 1 - 7 nm. Calculations performed using finite element method predicted the localized surface plasmon resonance (LSPR) for these nanoparticles to be in the UV-C region (100 - 280 nm). While a distinctive LSPR peak could not be observed using absorbance measurements, low-loss electron energy loss spectroscopy showed the presence of surface plasmons between 80 - 250 nm and bulk plasmons centered around 50 - 60 nm. Plasmonic coupling was also observed between the nanoparticles resulting in resonances between 250 - 400 nm.
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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) | 0.001 | 0.001 |
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".