Bibliographic record
Abstract
Write your name on a piece of paper with a pencil and you have just created the hottest new material in physics, namely graphene. Graphene is a single atomic layer of graphite arranged in a perfect network of repeating hexagons. It was discovered in 2004 by Andre Geim and Konstantin Novoselov, who received the 2010 Nobel prize in physics. Because of graphene’s unique two-dimensional nature, it has a variety of interesting properties. For example, graphene’s high crystal quality is the result of extremely flexible interatomic bonds, which create a substance stronger in plane than diamond yet allows planes to bend when a force is applied perpendicular to this plane. The current challenge in this area of study is to make uniform large area films of graphene. A promising method is chemical vapour deposition (CVD) on metal substrates, particularly copper (Cu). An apparatus for CVD of graphene was built and tested. Using a variety of different experimental conditions, the growth of graphene was investigated. Scanning electron microscopy was used as a preliminary diagnostic tool to determine the presence of graphene. The graphene was then transferred from Cu onto silicon dioxide in order to image the sample using optical and Raman spectroscopy. These methods both confirmed that graphene is present. Further work is being done to optimize the growth and transfer methods as well as to test some of graphene’s interesting electrical and mechanical properties.
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 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".