Quality evaluation of ultra‐thin samples: Application to graphene
Bibliographic record
Abstract
Abstract Many new materials emerging are strictly two dimensional (2D), often only one or two monolayers thick. They include transition metal dichalcogenides, such as MoS 2 , and graphene. Graphene in particular appears to have many potential applications. Typically the crystalline film without contamination is of interest. Therefore, a reliable method is needed to routinely evaluate the quality of the synthesized samples. Here, we present one such candidate method that utilizes standard electron diffraction and low/medium magnification imaging in a rudimentary transmission electron microscope. The electron irradiation dose is very low thus reducing electron irradiation damage of the investigated samples. As an example, the method was applied to the evaluation of as‐grown graphene sample quality and a study on heating‐induced change in graphene. It can be used to evaluate the volume and areal ratio of crystalline to noncrystalline component. The method is amiable to automated film quality evaluation.
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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 | 0.011 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".