Graphene Nanoplatelets Prepared by Electric Heating Acid-Treated Graphite in a Vacuum Chamber and Their Use as Additives in Organic Semiconductors
Bibliographic record
Abstract
Graphene nanoplatelets (GNPs) were prepared from acid-treated expandable graphite using a novel method of electric heating the graphite in an evaporation chamber under high vacuum, followed by solvent exfoliation. Such prepared graphene nanoplatelets, the eGNPs, were compared to GNPs prepared from two conventional methods: thermal expansion in an isothermal oven followed by solvent exfoliation (oGNPs), and direct solvent exfoliation (sGNPs), using various characterization techniques including UV-vis spectroscopy, scanning electron microscopy, and atomic force microscopy. It was found that the eGNPs were very thin, with a thickness of 4-16 nm, and showed no oxidation. On the other hand, oGNPs exhibited much thicker sheets, upward of 40 nm, and the sGNPs showed a high degree of oxidation. Utilizing the high purity eGNPs as an additive in PQT-12 semiconductor layer has been shown to improve the mobility by a factor of 2 in thin-film transistor devices.
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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) | 0.000 | 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 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".