Nano-Porous Composites of Activated Carbon–Metal Organic Frameworks (Fe-BDC@AC) for Rapid Removal of Cr (VI): Synthesis, Adsorption, Mechanism, and Kinetics Studies
Bibliographic record
Abstract
Abstract Metal–organic frameworks (MOFs) are a group of porous materials that display potential in the elimination of toxic industrial compounds (TICs) from polluted water streams. However, their applications have so far been held up by issues due to their physical nature and cost. In this study, activated carbon (AC) is modified with an Fe-based MOF, iron terephthalate (Fe-BDC). A facile and cost-effective impregnation method is used for enhanced removal from aqueous solutions. The new adsorbent is characterized by SEM, FTIR, PXRD, and BET. The composite displays excellent uptake of Cr (VI) when compared to un-impregnated AC with a maximum monolayer adsorption capacity of 100 mg·g−1. The experimental data shows a high correlation to the Langmuir adsorption model. The adsorption kinetic study reveals that the adsorption of Cr (VI) to Fe-BDC@AC obeys the pseudo-first-order equation. The composite shows high reusability after five cycles and high adsorption rates reaching equilibrium in just 50 min. Such properties make the nanocomposite promising for water decontamination on larger scales compared to powder-based alternatives, such as individual MOF crystals.
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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".