Advances in Quantum Properties of Graphene and Derivatives Applied to Functional Nanomaterials and Metamaterials
Bibliographic record
Abstract
In the present work we describe the most important quantum properties of graphene and derivatives recently published. We discuss how these properties were incorporated into varied hybrid materials such as substrates for specific tuning of matter to track varied quantum signals. Their potential applications were analyzed from nanomaterial and nanotechnology. In particular graphene, its derivatives and other carbon-based allotropes were chosen due to their special chemical structure and properties from the nanoscale to larger lengths, according to specific applications. As expected, these carbon-based and related materials’ highly ordered and condensed electronic configuration showed particular electronic properties below the nanoscale. Thus, we discussed the generation of pseudo-electromagnetic fields and conduction bands. This particular property could also interact with different quantized energy levels and quantum properties, such as those focused on: i) Dirac electron interaction and conduction, ii) anomalous quantized hall effects, iii) magnetic effects, iv) excitons, v) polaron generations, d vi) Fermi and Landau levels. These different phenomena were discussed about the particular topological states of graphene by tuning their 3D chemical structures. Therefore, quantum phenomena and their possible modifications such as quantum interference, potential improvements and encrypted signal transduction were considered for applications.
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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 | 0.001 |
| 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 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".