Enhanced methyl green adsorption of ZIF-8 metal-organic framework: Insights from different solvents
Bibliographic record
Abstract
This study investigates the effect of solvent type on the structural properties and adsorption performance of the ZIF-8 metal-organic framework for removing various dyes including, methyl green (MG), methylene blue (MB), and methyl orange (MO) from water in acidic and alkaline environments. ZIF-8 samples were synthesized using zinc nitrate, methylimidazole, and three different solvents including, water, methanol, and ethanol under atmospheric pressure and 70 °C. Characterization using BET, XRD, FT-IR, and TGA techniques sheds light on the structural, chemical, and thermal properties of ZIF-8 samples. Among the samples, ZIF-8/M, synthesized using methanol, stands out, demonstrating the high surface area of 2172.7 m2/g, large total pore volume of 1.5412 cm3/g, and high crystallinity of 31.9% with improved thermal stability. Furthermore, ZIF-8/M shows better adsorption performance for methyl green with a removal percentage of 81.9%, 87.1%, and an adsorption capacity of 20.5 mg/g and 21.8 mg/g, in acidic and alkaline environments, respectively. Enhanced dye adsorption of ZIF-8/M is associated with both physical and effective chemical adsorption mechanisms via tuning the environment's acidity.
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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) | 0.000 | 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".