Graphene Oxide/Polyamidoamine G4 as a High Efficient and Eco-Friendly Adsorbent for Dichromate Ions
Bibliographic record
Abstract
In this study, graphene oxide/polyamidoamine G4 was used as a biocompatible nanocomposite adsorbent to adsorb dichromate ions. In alkaline solutions, dichromate ions changed to chromate ions which were not adsorbed on the surface of adsorbent. Thus, experiments were carried out in acidic and neutral water solution. Under these conditions, adsorption sites of adsorbent were protonated by primary and ternary amine groups of adsorbent shown as –NH3+ and –NHR2+ respectively that adsorbed dichromate ions through electrostatic interactions. Adsorption isotherms of dichromate on graphene oxide/polyamidoamine G4 were obtained under various ionic strengths, pHs and temperatures. Isotherms were analyzed by the adsorption isotherm regional analysis (ARIAN) model. The highest observed adsorption capacity of this process was 246.7 mg g–1 at pH = 0 and 318 K. Tests at pH = 2 showed that this process was endothermic. Adsorption kinetic experiments were carried out under various initial dichromate concentrations, pHs, temperatures, shaking rates and ionic strengths and were analyzed by the kinetics of adsorption study in the regions with constant adsorption acceleration (KASRA) model and intraparticle diffusion, ideal-second-order (ISO) and non-ideal process of adsorption kinetics (NIPPON) equations. The four-region ARIAN and KASRA models using a series of equations can interpret thermodynamics and kinetics of interactions of adsorbent and adsorbate under different conditions, respectively. Pb2+, Cd2+, Cr3+ and tannic acid were separated by graphene oxide/polyamidoamine G4 from dichromate ions and the graphene oxide/polyamidoamine G4 was recycled by using an alkaline solution.
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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 | 0.000 |
| 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) | 0.000 | 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 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".