(Invited) Atomic Resolution, Coordinated Electron Microscopy Characterization of 2D Layers Synthesized By Confinement Heteroepitaxy (CHet) Technique
Bibliographic record
Abstract
Since the discovery of graphene in 2004 [1] there has been a flurry of activity in investigating two dimensional (2D) materials, because of their superlative properties: spin-orbit coupling, massless Dirac fermions, mechanical strength, thermal and chemical stability [2]. Remarkably, the in-plane hexagonal symmetry of graphene is largely responsible for its behavior. However, the zero-band gap of graphene presents hurdles to its use as a semiconductor. This has spurred interest in synthesis other van der Waals 2D structures, such as h-BN [3] and more recently 2D Gallium Nitride (2D GaN) [4]. The synthesis of 2D Nitrides was first reported in 2016 by applying Confinement Heteroepitaxy (CHet) process to form a 2D Gallium Nitride (GaN) layer with 1-2 atom thickness. In this technique, a silicon carbide (SiC) substrate is graphitized and deliberately damaged using oxygen plasma to provide a two-step intercalation path. First a metal (like Gallium, or other group V metal) through evaporation is intercalated between the graphene and SiC substrate. Then, to complete the half reaction, nitrogen is introduced by ammonia annealing at 700 °C to form a 1-layer thick III-nitride. In this study, the growth method is extended from 2D GaN to variety of other 2D metals (Indium), Nitrides (InN) and alloys (InGa alloys). We employ a coordinated approach to understand the surface in both plan and cross-section view – linking through a variety of electron microscopy techniques, including auger electron spectroscopy (AES), scanning electron microscopy (SEM), and transmission electron microscopy (TEM). Bright-field, Dark-field, aberration-corrected STEM-HAADF imaging, and atomic-scale electron energy loss spectroscopy (EELS) mapping using a Gatan K2 SI direct electron detector and other techniques mapping were all applied to characterize the SiC/2D-layer/graphene interface. These electron microscopy techniques allow a precise, local measurement of the atomic structure of these new materials, including such growth parameters as the effect of graphene on the 2D layer stability, the effect of the SiC step edges on the layers structure, and the effect of the plasma type and source used in damaging the graphene on the 2D layer chemistry. Plasmon mapping also describes the unique bonding in the materials and their electronic structure. Ultimately, the measurements presented here characterize a whole new class of nanomaterials and provide insight on the fundamentals of the intercalation growth process. 1. Novoselov, K.S., et al., Electric field effect in atomically thin carbon films. Science, 2004. 306(5696): p. 666-669. 2. Soldano, C., A. Mahmood, and E. Dujardin, Production, properties and potential of graphene. Carbon, 2010. 48(8): p. 2127-2150. 3. Watanabe, K., T. Taniguchi, and H. Kanda, Direct-bandgap properties and evidence for ultraviolet lasing of hexagonal boron nitride single crystal. Nature Materials, 2004. 3(6): p. 404-409 4. Al Balushi, Z.Y., et al., Two-dimensional gallium nitride realized via graphene encapsulation. Nature Materials, 2016. 15(11): p. 1166-1171. Figure 1
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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) | 0.003 | 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".