Tuning the Network Structure of Graphene/Epoxy Nanocomposites by Controlling Edge/Basal Localization of Functional Groups
Bibliographic record
Abstract
Successful formation of a three-dimensional (3D) network of incorporated conductive fillers in a polymer matrix leads to achieve an electrically conductive nanocomposite at low filler loading levels. In this work, one- to three-layer edge and basal-functionalized graphene oxide (GO) nanosheets were synthesized via a novel method. Raman spectroscopy was employed to investigate the localization of oxygen-containing groups through the GO nanosheets. Afterward, the synthesized GO nanosheets were dispersed in an aqueous epoxy suspension to produce electrically conductive polymer nanocomposites. The formation of the interconnected 3D network structure of GO nanosheets through the epoxy matrix was studied by employing rheological approaches and imaging techniques. We postulated that oxygen-containing groups’ localization can effectively impact the polymer–GO nanosheet interactions, which, in turn, affect the 3D network formation of the nanosheets through the polymeric medium. After in situ thermal reduction of polymer nanocomposites at 225 °C, electrical conductivity measurements revealed that nanocomposites containing basal-functionalized graphene nanosheets featured higher electrical conductivity values compared to those for samples containing edge-functionalized graphene nanosheets. Hence, these results shed light on the importance of functional groups localization that can dictate the final properties.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 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".