Effect of the Functional Groups of Polymers on Their Adsorption Behavior on Graphene Oxide Nanosheets
Bibliographic record
Abstract
Abstract Graphene‐based polymer nanocomposites are emerging materials for both fundamental research and industrial applications. The influence of polymer functional groups on their adsorption behavior onto graphene oxide (GO) nanosheets is investigated, with poly(methyl methacrylate) (PMMA), poly(methyl methacrylate‐co‐methacrylic acid) (PMMA‐co‐MAA), and poly(methacrylic acid) (PMAA) having the same backbone but different functional side groups selected as the model polymers. Fourier transform infrared spectroscopy and X‐ray diffraction results confirm the interfacial interaction between the polymer and GO. Thermogravimetric analysis reveals notable enhancements in the amount of the adsorbed polymer up to 30.6 wt.% for PMMA‐co‐MAA/GO and 49.7 wt.% PMAA/GO compared with 18.7 wt.% for PMMA/GO. The water contact angle decreases from 71.3o for PMMA/GO to 69.1° for PMMA‐co‐MAA/GO and to 61.2° for PMAA/GO. The further washing process reduces the adsorption amount for the polymer/GO hybrid. Overall, the polar functional groups of the polymer directly influence the polymer adsorption behavior onto GO.
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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) | 0.002 | 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".