Facile synthesis of zeolitic‐imidazole framework‐67 (<scp>ZIF</scp>‐67) for the adsorption of indigo carmine dye
Bibliographic record
Abstract
Abstract This study aims to synthesize ZIF‐67 for the adsorption of indigo carmine (I.C.) dye. ZIF‐67 materials were synthesized under different conditions and the synthesis condition of ZIF‐67 giving the best adsorption efficiency was obtained. In batch adsorption studies, pH, adsorbent amount, contact time, initial dye concentration, and temperature effects were investigated. Adsorption data were applied to Langmuir, Freundlich, and Temkin isotherm models. The best correlation value was obtained in Langmuir isotherm (R2 = 0.999). The adsorption of I.C. dye with ZIF‐67 occurred in a monolayer with a homogeneous surface according to Langmuir isotherm. The highest adsorption capacity value of ZIF‐67 metal–organic framework for I.C. dye was found as 370 mg/g. Kinetic model studies were also performed and pseudo first‐order, pseudo second‐order, and intraparticle diffusion models were used. Pseudo second‐order was the best fitting kinetic model to the data (R2 = 0.997). Adsorption mechanisms of ZIF‐67 for I.C. dye include electrostatic interaction, π–π interaction, and hydrogen bonding. Thermodynamic studies showed that I.C. removal by ZIF‐67 is an exothermic and spontaneous process. ZIF‐67 was characterized by X‐ray diffraction (XRD), Fourier transform infrared spectroscopy (FT‐IR), field emission scanning electron microscope (FE‐SEM), and N2 adsorption–desorption. The surface area of synthesized ZIF‐67 was found 1493 m2 g−1. The salt concentration effect on I.C. adsorption by ZIF‐67 was also investigated, showing an increase in adsorption capacities with increasing salt concentration. The regeneration study was carried out and after 5 cycles, the reduction in efficiency was 21%.
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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".