Bibliographic record
Abstract
Two approaches for the large scale synthesis of graphene were investigated with the objective of achieving high-quality graphene for practical applications: (1) non-covalent solution based exfoliation of graphite, and (2) chemical vapour deposition of graphene on copper. As the processing conditions, structure, and properties of graphene are inherently connected, the aim of the study was to gain fundamental insights on the critical mechanisms that govern the properties and device performance of graphene. The first part of this thesis describes the efficient non-covalent exfoliation of graphite to produce few-layer-graphene dispersion in N-methylpyrrolidone and large-scale thin film deposition using the Langmuir-Blodgett assembly. In the second part, the chemical vapour deposition of polycrystalline graphene films on copper was investigated as it has emerged as the most promising route toward large scale synthesis of monolayer graphene for optoelectronics applications due to its properties approaching that of ideal graphene and the relatively low cost of copper. An extensive range of growth parameters was employed to develop a model of two-dimensional nucleation and self-limited growth of graphene on the surface of copper. The analysis of the nucleation and growth kinetics has revealed the relationship between atomic processes that impart two distinct temperature regimes of nucleation, whereas the growth of the individual nuclei was shown to be rate-limited by the carbon attachment at the nuclei edges. Moreover, the growth on high index copper surfaces has shown that graphene nanostructures of controlled shapes, density, and dimensions can be produced, depending on the Cu crystal orientation. Interestingly, few-layer-graphene grown on Cu frequently exhibits AA stacking with interlayer spacing of ~3.6 Å , with a preserved linear dispersion relationship. This work thus provides practical guidelines for achieving wafer scale single crystal graphene as well as the control over the mesoscale structure by the careful selection of the growth parameters.
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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".