Enhancing Galvanic Replacement in Plasmonic Hollow Nanoparticles: Understanding the Role of the Speciation of Metal Ion Precursors
Bibliographic record
Abstract
Abstract Hollow nanostructures offer great potential for plasmonic applications due to their strong and highly tunable localized surface plasmon resonance. The relationship between the plasmonic properties and geometry of hollow nanoparticles, such as core‐shell size ratio, concentricity of the cavity and porosity of the wall, is well documented. Nanoscale galvanic replacement provides a simple, versatile and powerful route for the preparation of such hollow structures. Here we demonstrate how the efficiency of reductant‐assisted galvanic replacement processes can be enhanced by controlling the degree of hydration and hydrolysis of the metal ion precursor using pH and pL as key control parameters (by analogy to pH, the letter p in the expression pL is used to indicate the decimal cologarithm associated with the concentration of the ligand L). Adjusting precursor speciation prior to the sacrificial template's hollowing process offers a new strategy to tune the morphology and optical properties of plasmonic hollow nanostructures.
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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".