Surfactant-Assisted Chemical Vapour Deposition of Gold Nanoplates with Highly Smooth Surface
Bibliographic record
Abstract
Tetrahydrothiophene (THT), as a surfactant, was studied for the shape control of gold nanoplates via chemical vapour deposition (CVD).Dense gold nanoplates (6 μm) with a highly smooth surface were deposited by using 1,3-diisopropyl-imidazol-2-ylidene gold (I) hexamethyldisilazide at 370˚C, with 45 mtorr of THT.Similar but isolated nanoplates were attained with 15-35 mtorr of THT at 430˚C.Along with single-crystalline structure, a {111} plane of the nanoplates was confirmed by determining the gold stacking fault, 1 3 {422} diffractions in selected area electron diffraction (SAED) patterns.Purity of the nanoplates was shown by energy dispersive X-ray spectroscopy (EDS) and X-ray photoelectron spectroscopy (XPS) analysis, both showing gold metal without significant sulfur impurities.THT preferably capped the gold {111} plane which lowered the surface energy, leading to a smooth surface and large size.Gold precursor supply influenced the particle size and mechanism of the crystal growth, as well as the particle density.iii Acknowledgements First of all, I would like to give my most sincerely thank to my parents for the support of my study abroad, no matter on spirit or finance.I love you, forever.Second, thanks, Seá n, my supervisor, as well as the first Canadian I meet.I will never forget your professional attitudes to science
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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".