Exploration of the growth process of ultrathin silica shells on the surface of gold nanorods by the localized surface plasmon resonance
Bibliographic record
Abstract
Ultrathin silica coating (UTSC) has emerged as an effective way to improve the compatibility and stability of nanoparticles without attenuating their intrinsic optical properties. Exploration strategies to probe the growth process of ultrathin silica shells on the surface of nanoparticles would represent a valuable innovation that would benefit the development of ultrathin silica coated nanoparticles and their relevant applications. In this work, we report a unique, very effective and straightforward strategy for probing the growth of ultrathin silica shells on the surface of gold nanorods (Au NRs), which exploits the localized surface plasmon resonance (LSPR) as a reporting signal. The thickness of the ultrathin silica shells on the surface of Au NRs can be quantitatively measured and predicted in the range of 0.5-3.5 nm. It is demonstrated that the LSPR shift accurately reflects the real-time change in the thickness of the ultrathin silica shells on Au NRs during the growth process. By using the developed strategy, we further analyze the growth of UTSC on the surface of Au NRs via feeding of Na2SiO3 in a stepwise manner. The responsiveness analysis of LSPR also provides important insight into the shielding effect of UTSC on the surface of Au NRs that is not accessible with conventional strategies. This LSPR-based strategy permits exploration of the surface-mediated sol-gel reactions of silica from a new point of view.
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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".