Stability and Surface Functionalization of Plasmonic Group 4 Transition Metal Nitrides
Bibliographic record
Abstract
Abstract Plasmonic transition metal nitrides (TMNs) have emerged as a low‐cost and thermally and chemically robust alternatives to noble metals. While their superior thermal properties have been established, their chemical properties on the nanoscale haven't been as well investigated. Herein, the oxidative stability over time under ambient conditions and colloidal stability as function of pH was explored for plasmonic TiN, ZrN, and HfN nanoparticles. It was discovered that the TMN nanoparticles made via solid‐state method had a narrow pH stability range between 2–3. Under highly acidic conditions, the particles underwent dissolution and at pH ≥4, they aggregate and precipitate from the solution. Additionally, TiN nanoparticles had poor oxidative stability and oxidized to TiO 2 after ~40 days. 3‐Aminopropyltriethoxysilane (APTES) and dimethylsilane coated TMNs were synthesized to yield water and organic solvent dispersible particles, respectively. These functionalized colloidal suspensions showed enhanced oxidative stability over 60 days and the APTES coating widened the pH stability window of TMNs to include physiological pH. This study shows that surface functionalization using M−O−Si linkages (where M=Ti, Zr, or Hf) can greatly enhance the stability, dispersibility and therefore applicability of plasmonic TMN nanoparticles.
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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 | 0.000 |
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".