Decoration of plasmonic Mg nanoparticles by partial galvanic replacement
Bibliographic record
Abstract
Plasmonic structures have attracted much interest in science and engineering disciplines, exploring a myriad of potential applications owing to their strong light-matter interactions. Recently, the plasmonic concentration of energy in subwavelength volumes has been used to initiate chemical reactions, for instance by combining plasmonic materials with catalytic metals. In this work, we demonstrate that plasmonic nanoparticles of earth-abundant Mg can undergo galvanic replacement in a nonaqueous solvent to produce decorated structures. This method yields bimetallic architectures where partially oxidized 200-300 nm Mg nanoplates and nanorods support many smaller Au, Ag, Pd, or Fe nanoparticles, with potential for a stepwise process introducing multiple decoration compositions on a single Mg particle. We investigated this mechanism by electron-beam imaging and local composition mapping with energy-dispersive X-ray spectroscopy as well as, at the ensemble level, by inductively coupled plasma mass spectrometry. High-resolution scanning transmission electron microscopy further supported the bimetallic nature of the particles and provided details of the interface geometry, which includes a Mg oxide separation layer between Mg and the other metal. Depending on the composition of the metallic decorations, strong plasmonic optical signals characteristic of plasmon resonances were observed in the bulk with ultraviolet-visible spectrometry and at the single particle level with darkfield scattering. These novel bimetallic and multimetallic designs open up an exciting array of applications where one or multiple plasmonic structures could interact in the near-field of earth-abundant Mg and couple with catalytic nanoparticles for applications in sensing and plasmon-assisted catalysis.
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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".