Enhancing the removal of methylene blue by modified ZnO nanoparticles: kinetics and equilibrium studies
Bibliographic record
Abstract
This research aims to use modified ZnO nanoparticles to enhance the removal rate of the methylene blue (MB) dye. ZnO nanoparticles are modified by coating their surface with Congo red (CR) dye, henceforth referred to as ZnO/CR. This process is used to produce a Lewis acid on the surface of ZnO to attract any Lewis base such as a MB dye (MB+). Therefore, the stability of ZnO/CR improved, and it resists the change in pH value (from 3 to 9). Several analysis techniques such as scanning electron microscopy, X-ray diffraction, FTIR, and BET method were used to characterize ZnO/CR. Nonlinear and linear regressions of pseudo first-order, pseudo second-order, and Elovich models were used to calculate the kinetic parameters of the adsorption process. The best-fit kinetic equation was investigated using three functions of error analysis: the sum of the squares of the errors, chi-square analysis, and the coefficient of determination. The intraparticle diffusion equation was used to study the diffusion process. The adsorption process of the MB followed the Langmuir model with a maximum capacity (qm) value of 43.5 mg/g. This value is six times greater than the value calculated with pure ZnO. Thermodynamic parameters ΔS•, ΔH•, and ΔG• were investigated at four temperatures (10, 20, 30, and 40 °C). The uptake process of the MB occurs spontaneously following endothermic process and an increase in the system disorder. The rate of adsorption was controlled mainly by a Lewis acid–base interaction and H bonding. Furthermore, the removal of the MB by ZnO/CR powder worked well as a chemical and physical adsorption process.
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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".