Evaluating the effect of graphene oxide <scp>PEGylation</scp> on the properties of chitosan‐graphene oxide nanocomposite scaffold
Bibliographic record
Abstract
In this study, graphene oxide (GO) was functionalized with polyethylene glycol (PEG) to understand the effect of PEGlayted GO on properties of chitosan-based nanocomposite scaffold. GO was synthesized according to modified Hummer's method and covalently linked to polymeric chains of PEG to produce polyethylene glycolated GO (PGO). Successful preparation of GO and PGO was confirmed by FT-IR and Raman techniques, where the chemical bonding of PEG and GO nanosheets were concluded based on PGOs' lower zeta potential compared to GO. Nanocomposite scaffolds were prepared by adding equal amounts of GO and PGO into 2% (w/v) chitosan (Cs) solutions. The highly porous scaffolds were developed by lyophilization of solutions. Incorporation of GO and PGO into chitosan scaffold network resulted in uniform and spherical pores. Modified samples offered higher porosity and density, indicating adequate scaffold structure. Improvements in the physical properties of prepared chitosan scaffolds were concluded through higher water absorption and retention values. Compressive strength measurement showed 6.33 and 5.5 times improvement respectively for Cs-GO and Cs-PGO samples compared to Cs scaffold. The Cs-GO scaffolds showed minimum susceptibility toward enzymatic degradation and higher degrees of protein adsorption (26% and 23% improvement in value of adsorbed protein respectively for Cs-GO and Cs-PGO compared to Cs scaffold) and biomineral formation on scaffold surface. Also, Cs-PGO sample showed the highest degree of cell viability and lower hemolysis than both Cs and Cs-GO scaffolds. Investigations showed that cell infiltration into scaffold porous structure was more prominent in Cs-PGO scaffolds than in Cs and Cs-GO scaffolds.
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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 | 0.012 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".