Organosulfur-Functionalized Au, Pd, and Au−Pd Nanoparticles on 1D Silicon Nanowire Substrates: Preparation and XAFS Studies
Bibliographic record
Abstract
A hybrid preparative method was developed to prepare organosulfur-functionalized Au nanoparticles (NPs) on silicon nanowires (SiNWs) by reacting HAuCl(4) with SiNW in the presence of thiol. A number of organosulfur molecules-dodecanethiol, hexanethiol, 1,6-hexanedithiol, and tiopronin-were used to functionalize the Au surface. Size-selected NPs ranging from 1.6 to 7.5 nm were obtained by varying the S/Au ratio and the concentration of HAuCl(4). This method was further extended to the preparation Pd and Pd-Au bimetallic NPs on SiNWs. The morphology of the metal nanostructures was examined by transmission electron microscopy (TEM) and high-resolution transmission electron microscopy (HRTEM). The local structure and bonding of the SiNW-supported metal nanostructures were studied using X-ray absorption fine structures (XAFS) [including both X-ray near-edge structures (XANES) and extended X-ray absorption fine structures (EXAFS)] at the Au L(3)-, Pd K-, S K-, and Si K-edges. It was also found that the annealing of the thiol-capped Au NPs up to 500 degrees C transforms the surface of the thiol-capped NPs to gold sulfide, as identified using Au L(3)- and S K-edge XANES. We also illustrate that this preparative approach can be used to form size-controllable Au NPs on carbon nanotubes.
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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".