Preparation And Characterization Of Graphene Oxide Cross-Linked Composites
Bibliographic record
Abstract
Graphene oxide (GO)-based materials have been studied for applications in adsorption and water treatment.Experimental results revealed that GO is a promising adsorbent due to its low-cost production, large surface area, and strong interaction with a wide range of dyes in an aqueous phase.GO chemical structure has the potential to be tuned using chemical methods such as cross-linking to produce a framework material.Therefore, in this study, cross-linking of GO structure using chitosan biopolymer as a cross-linker agent was investigated.Cross-linked GO composites were prepared through a green solution-based chemistry approach.Chemical structural, morphological and thermal changes in the crosslinked GO composites were investigated using Fourier transform infrared (FTIR) spectroscopy, scanning electron microscopy (SEM), and thermal gravimetric analysis (TGA).Also, adsorption properties of samples were obtained using methylene blue (MB) as a cationic probe, in solution phase.According to the spectroscopy results, cross-linked composites suggested interaction between the GO sheets with chitosan through the formation of amide linkages.SEM results showed irregular layer shapes connected to each other with higher surface roughness and porosity in cross-linked samples.Changes in the thermal stability of cross-linked samples can be ascribed to the cross-linking effect.Kinetic adsorption studies indicated higher sorption capacity of cross-linked samples toward MB in aqueous phase compare to pure GO and chitosan.
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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 | 0.001 | 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) | 0.001 | 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".