Graphene Oxide–Chitosan Composite Material for Treatment of a Model Dye Effluent
Bibliographic record
Abstract
Abstract Graphene oxide (GO) was cross-linked with chitosan to yield a composite (GO-LCTS) with variable morphology, enhanced surface area, and notably high methylene blue (MB) adsorption capacity. The materials were structurally characterized using thermogravimetric analysis and spectroscopic methods (X-ray diffraction, Fourier transform infrared spectroscopy, Raman spectroscopy, and 13C solid-state NMR) to support that cross-linking occurs between the amine groups of chitosan and the −COOH groups of GO. Equilibrium swelling studies provide support for the enhanced structural stability of GO-cross-linked materials over the synthetic precursors. Scanning electron microscopy studies reveal the enhanced surface area and variable morphology of the cross-linked GO materials, along with equilibrium and kinetic uptake results with MB dye in aqueous media, revealing greater uptake of GO-LCTS composites over pristine GO. The monolayer uptake capacity (Qm; mg g–1) with MB reveals twofold variation for Qm, where GO-LCTS (402.6 mg g–1) > GO (286.9 mg g–1). The kinetic uptake profiles of MB follow a pseudo-second-order trend, where the GO composite shows more rapid uptake over GO. This study reveals that the sorption properties of GO are markedly improved upon formation of a GO–chitosan composite. The facile cross-linking strategy of GO reveals that its physicochemical properties are tunable and versatile for a wider field of application for contaminant removal, especially over multiple adsorption–desorption cycles when compared against pristine GO in its highly dispersed nanoparticle form.
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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) | 0.001 | 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".