Control of Density of Randomly Grown OMCVD Gold Nanoparticles by Means of Ion Irradiation
Bibliographic record
Abstract
A new method is presented, which statistically controls the density of randomly deposited gold nanoparticles (Au NPs), based on three well-known techniques: self-assembly, ion irradiation, and organometallic chemical vapor deposition (OMCVD). Silicon substrates were coated with a CH 3 -terminated self-assembled monolayer (SAM) as a resist. A beam of accelerated Cu − ions was applied in different doses to damage/remove the CH 3 -terminated SAM on half the sample area to provide an “empty” surface to self-assemble a mercapto-containing molecule allowing Au NP growth, while the other half is protected by a mask. Contact angle measurements and X-ray photoelectron spectroscopy (XPS) in both survey and high-resolution modes were implemented to study the dose-dependent removal process on the ion-irradiated sides, as well as the uniformity of the SAM coverage on the unirradiated sides. The second SAM deposition process with the mercapto moiety was performed on all samples for a selective recoating of the ion-irradiated sides. Au NPs were grown by OMCVD onto the SH groups. The amount dependence of the Au NPs on the ion dose was studied by Rutherford backscattering spectroscopy (RBS) and high-resolution XPS. Scanning electron microscopy (SEM) image analysis were used to investigate the changes in the density and in the average spacing of the OMCVD grown Au NPs with varying ion dose. In addition, the formation of OMCVD Au NP clusters and its dose dependence in the absence of the SH-terminated SAM was studied by RBS and SEM.
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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) | 0.001 | 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 | 0.001 | 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".