Study of Adsorption Mechanism of Congo Red on Graphene Oxide/PAMAM Nanocomposite
Bibliographic record
Abstract
. The kinetics and thermodynamics of adsorption were investigated to elucidate the effects of pH, temperature, shaking rate, ionic strength, and contact time. Kinetic data were analyzed by the KASRA model and the KASRA, ISO, and pore-diffusion equations. Adsorption adsorption isotherms were studied by the ARIAN model and the Henry, Langmuir, and Temkin equations. It was shown that adsorption sites of GO/PAMAM at experimental conditions were phenolic hydroxyl groups of GO sheets and terminal amine groups of PAMAM dendrimer. Analysis of kinetic data indicated that amine sites were located on the surface, and that hydroxyl sites were placed in the pores of adsorbent. CR molecules interacted with the adsorption sites via hydrogen bonds. The molecules were adsorbed firstly on the amine sites, and then on the internal hydroxyl sites. Adsorption kinetic parameters indicated that the interaction of CR to the -NH₃⁺ sites was the rate-controlling step of adsorption of CR on this site and adsorption activation energies calculated for different parts of this step. On the other hand, kinetic parameters showed that the intraparticle diffusion was the rate-controlling step during the interaction of CR molecules to -OH sites and activation energy of this step was not calculable. Finally, the used GO/PAMAM was completely regenerated by using ethylenediamine.
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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".