Selective Plasmonic Sensing and Highly Ordered Metallodielectrics via Encapsulation of Plasmonic Metal Nanoparticles with Metal Oxides
Bibliographic record
Abstract
Abstract Novel materials for sensing and metallodielectric arrays have been prepared by encapsulation of gold-protected shape-selected silver nanoparticles with a diverse range of shells including iron, manganese, and iridium oxides using a versatile deposition procedure. These shells of varying thickness, porosity, and smoothness encapsulating metal nanoparticles advantageously combine functionalities of plasmonic cores and oxide shells into resulting functional materials. In particular, the size-uniform metal oxide encapsulated metal nanoparticles (MO-MNPs) assemble into well-ordered metallodielectric arrays for plasmonic applications. The shape and the localized surface plasmon resonance (LSPR) of the metal cores are well preserved, while the chemical and colloidal stability are enhanced by the formation of oxide shells. Metal oxide shells impart the selectivity of detection in surface-enhanced Raman spectroscopy (SERS) and surface plasmon resonance (SPR) sensing. Notably, selective sub-millimolar SPR detection of phosphate ions has been demonstrated.
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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) | 0.000 | 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 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".