Controlling Short-Range Interactions by Tuning Surface Chemistry in HDPE/Graphene Nanoribbon Nanocomposites
Bibliographic record
Abstract
Unique dispersion states of nanoparticles in polymeric matrices have the potential to create composites with enhanced mechanical, thermal, and electrical properties. The present work aims to determine the state of dispersion from the melt-state rheological behavior of nanocomposites based on carbon nanotube and graphene nanoribbon (GNR) nanomaterials. GNRs were synthesized from nitrogen-doped carbon nanotubes via a chemical route using potassium permanganate and some second acids. High-density polyethylene (HDPE)/GNR nanocomposite samples were then prepared through a solution mixing procedure. Different nanocomposite dispersion states were achieved using different GNR synthesis methods providing different surface chemistry, interparticle interactions, and internal compartments. Prolonged relaxation of flow induced molecular orientation was observed due to the presence of both carbon nanotubes and GNRs. Based on the results of this work, due to relatively weak interactions between the polymer and the nanofillers, it is expected that short-range interactions between nanofillers play the key role in the final dispersion state.
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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".