Gas and Solution Uptake Properties of Graphene Oxide-Based Composite Materials: Organic vs. Inorganic Cross-Linkers
Bibliographic record
Abstract
This study focused on a comparison of the adsorption properties of graphene oxide (GO) and its composites that were prepared via cross-linking with chitosan (CTS) or Al3+ species, respectively. Comparative material characterization was achieved by several complementary methods: SEM, NMR spectroscopy, zeta-potential, dye-based adsorption, and gas adsorption at equilibrium and dynamic conditions. SEM, solids NMR, and zeta-potential results provided supporting evidence for cross-linking between GO and the respective cross-linker units. The zeta-potential of GO composites decreased upon cross-linking due to electrostatic interactions and charge neutralization. Equilibrium and kinetic adsorption profiles of the GO composites with methylene blue (MB) in aqueous media revealed superior uptake over pristine GO. The monolayer adsorption capacity (mg g−1) of MB are listed in descending order for each material: GO–CTS (408.6) > GO–Al (351.4) > GO (267.1). The gas adsorption results showed parallel trends, where the surface area and pore structure of the composites exceeded that for GO due to pillaring effects upon cross-linking. The green strategy reported herein for the preparation of tunable GO-based composites revealed versatile adsorption properties for diverse heterogeneous adsorption processes.
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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".