Graphene Thin Films and Graphene Decorated with Metal Nanoparticles
Bibliographic record
Abstract
The electronic, thermal, and optical properties of graphene-based materials depend strongly on the fabrication method used and can be further manipulated through the use of metal nanoparticles deposited on the graphene surface. Metals that strongly interact with graphene such as Co and Ni can form strong chemical bonds which may significantly alter the band structure of graphene near the Dirac point. Weakly interacting metals such as Au and Cu can be used to induce shifts in the graphene Fermi energy, resulting in doping without significant alteration to the graphene band structure. The deposition and nucleation conditions such as deposition rate, annealing temperature and time, and annealing atmosphere can be used to control the size and distribution of metal nanoparticles. Under ideal conditions, self-assembled arrays of nanoparticles can be obtained on graphene-based films for use in new types of nano-devices such as evanescent waveguides.
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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) | 0.002 | 0.001 |
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".