Graphite Nanoplatelets Produced by Oxidation and Thermal Exfoliation of Graphite and Electrical Conductivities of Their Epoxy Composites
Bibliographic record
Abstract
Graphite nanoplatelets were produced by sonication of thermally reduced graphite oxide produced from three precursor graphites. The thicknesses of the resulting graphite nanoplatlets were measured by X-ray diffraction and transmission electron microscopy. The type and size of the precursor graphite plays an important role in the final graphite nanoplatelet quality. The thinnest graphite nanoplatelets (average thickness of 4-7 nm) were obtained from Sri Lankan powdered graphite (average particle size of 0.1-0.2 mm). Thicker graphite nanoplatelets (average thickness of 30-60 nm), were obtained from a Canadian graphite (with an average flake size of 0.5-2 mm). Graphite nanoplatelets obtained by acid intercalation of Sri Lankan graphite were much thicker (an average thickness of 150 nm). Graphite nanoplatelet/epoxy composites containing 4 wt.% graphite nanoplatelets derived from Canadian or Sri Lankan natural graphite have electrical conductivities significantly above the percolation conductivity threshold. In contrast, corresponding composites, produced with (4 wt.%) commercial graphite nanoplatelets, either as-received or re-exfoliated, were electrically insulating. This behaviour is attributed to the highly wrinkled morphology, folded edges and abundant surface functional groups of the commercial graphite nanoplatelets. Thermal reduction of graphite oxide produced from natural flake graphite is therefore a promising route for producing graphite nanoplatelets fillers for electrically-conducting polymer composites.
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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) | 0.001 | 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".