Control of the average spacing between aligned gold nanoparticles by varying the FIB dose
Bibliographic record
Abstract
This work presents a new method to align gold nanoparticles (Au NPs), based on three well-known techniques: self-assembled monolayer (SAM) formation, focused ion beam (FIB) lithography, and organo-metallic chemical vapour deposition (OMCVD). Silicon substrates are coated with CH(3)-terminated silane SAMs as resists. A fine beam of Ga(+) ions, applying different doses, damages/removes these SAMs to correspondingly form a pattern containing sets of lines. Atomic force microscopy (AFM) and time-of-flight secondary ion mass spectroscopy (ToF-SIMS) are used to study the SAM removal process. The FIB nano-lithographically patterned SAMs are re-filled with an SH-terminated silane SAM. An OMCVD process is carried out to grow Au NPs onto the SH-groups in the lines. The average spacing between the Au NPs is demonstrated to be controlled by varying the FIB dose. Scanning electron microscopy (SEM) image analysis indicates that the average spacing decreases exponentially with increasing the dose, up to a predefined threshold. In addition, the formation of OMCVD Au NPs spacing and its dose-dependence in the absence of the SH-terminated SAMs is studied.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".