Preparation of metal–organic framework material: Experiment and simulation analysis on removing organic dyes from wastewater
Bibliographic record
Abstract
Abstract Dye is an organic substance that seriously threatens the water environment. Using a solvothermal technique, MIL‐100(Fe) and M‐Fe have been synthesized to effectively remove methylene blue (MB) and methyl orange dyes from wastewater. The microstructures of the two products were analyzed using scanning electron microscopy, Fourier transform infrared, Brunauer–Emmett–Teller (BET), thermogravimetric (TG), and X‐ray diffractometer (XPS) test. Optimized the optimal experimental parameters through batch processing experiments, when the pH was 5 and 2, the amount of adsorbent was 50 mg, the temperature was 25°C, and the dye concentration was 30 mg/L, MIL‐100(Fe) and M‐Fe had the highest removal rates of MB, at 98.95% and 94.11%, respectively. Adsorption process was well described by the Freundlich isotherm and pseudo‐second‐order kinetics. After three regeneration experiments, the removal rate was still high. The removal efficiencies of the two adsorbents for MB were similar. However, the regeneration performance of M‐Fe was better. Through the simulation of materials studio, the adsorption energy of the product and MB is −0.93 eV, which is favorable for adsorption. The results show that the two adsorbents can effectively remove MB from wastewater and have broad application prospects.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.002 | 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 teacher head, 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".