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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".