Quantification of the Shell Thickness of Tin Oxide/Gold Core–Shell Nanoparticles by X-ray Photoelectron Spectroscopy
Bibliographic record
Abstract
Encapsulation of nanoparticles (NP) by a shell material provides characteristics and properties not achievable by the individual materials of the core and the shell. Therefore, the characterization of morphology parameters of core–shell nanoparticles (CSNP) is critical to determine their performance. In this work, an analysis to determine the shell thickness of CSNP has been conducted using a simple and noniterative procedure known as Shard’s methodology, where the conversion of XPS peak intensities into shell thickness can be performed. Implementing the Simulation of Electron Spectra for Surface Analysis (SESSA), we acquired an insight into the morphology and composition of the CSNP by simulating the spectra from XPS to extract the parameters (Shard’s methodology) and confirm the core–shell ratio with experimental spectra to quantify the shell thickness. The CSNPs were synthesized with reverse micelle templating using diblock copolymer micellar templates, using various ratios of tin and gold precursors to form SnO 2 @Au core–shell nanoparticles. Considering elastic-scattering effects, a good agreement was found in the quantification of the shell thicknesses with a variation inside the ±10% regime of the effective attenuation length (EAL). This work demonstrates the use of simulation software as a complementary tool to a multitechnique analysis for a detailed understanding of CSNP.
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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".