Optimization of Malachite Green Dye Removal Efficiency via Factorial Design Analysis Using Metal-Organic Frameworks
Bibliographic record
Abstract
In this study, we investigate the adsorption of the cationic dye Malachite Green (MG) onto two metal-organic frameworks, ZIF-8 and Fe-BTC.We effectively prepared beads using a polymer coating process with sodium alginate.To study the effect of different factors on adsorption capacity and removal %age of Malachite Green in a batch process, we employed a 2 3 factorial design study analysis.The investigation involved three primary factors, each with two levels: MOF type (A: Fe-BTC and ZIF-8), MOF bead dosage (B: 50 mg to 100 mg), and initial concentration (C: 5 mg/L to 17 mg/L).The primary effects of these variables and their interactions were examined using Response Surface Methodology (RSM), main effect, interaction effect, and Pareto chart.The factorial design analysis, conducted using analysis of variance (ANOVA), revealed that the most significant factor influencing adsorption capacity was the initial concentration of MG, followed by the dosage of MOF beads and the type of MOFs.The study demonstrates that SA@ZIF-8 beads have achieved the highest removal rate of 96%, in comparison to SA@Fe-BTC which reaches 90%.Notably, the initial concentration of MG demonstrates a positive effect, MOF dosage exhibits a negative effect, while the MOF type presents a positive effect, favoring SA@ZIF-8 for higher adsorption capacity.Moreover, significant two-way and three-way interactions were identified.The optimum conditions for the maximum removal of MG dye using SA@ZIF-8 were reported as follows: adsorbent dosage = 50 mg; MG initial concentration = 17 mg/L.The R 2 value > 98.8% for MG dye underscores the potency of the factorial design model, thereby encouraging further exploration and application of SA@ZIF-8 metal-organic framework to eliminate pollutants from aqueous solutions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".