Synthesis of Isoreticular Metal Organic Framework-3 (IRMOF-3) Porous Nanostructure and Its Effect on Naphthalene Adsorption: Optimized by Response Surface Methodology
Bibliographic record
Abstract
Naphthalene is a carcinogenic compound and its environmental release poses a major risk to human and aquatic health. Therefore, the application of nanomaterial technologies for naphthalene removal from wastewater has attracted significant attention. In this research, for the first time, the performance of IRMOF-3 for naphthalene removal from aqueous media is evaluated. IRMOF-3 with a specific surface area of 718.11 m2·g−1 has the ability to absorb naphthalene from synthetic wastewater to a high extent. The structures and morphology of IRMOF-3 were determined by FT-IR, XRD, SEM and BET analyses. Thirty adsorption experiments were conducted to obtain the best conditions for naphthalene removal. An optimum naphthalene removal efficiency of 80.96% was obtained at IRMOF-3 amounts of 0.1 g·L−1, a solution concentration of 15 mg·L−1, a contact time of 60 min and a pH = 11. The results indicate that the lower the concentration of naphthalene, the higher its dispersion at the surface of the porous nanostructure. Increasing naphthalene concentration results in its accumulation on porous nanostructures that clog cavities. In addition, high contact time provides ample opportunity for naphthalene to penetrate the cavities and pores which facilitates crystallization phenomena deep in the pores. Finally, the results of this study revealed that IRMOF-3 is one of the most effective adsorbents for naphthalene removal from wastewater.
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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 | 0.001 |
| 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) | 0.000 | 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".