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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 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".