Sustainable and Green Synthesis of Nano Zero-Valent Iron for Hexavalent Chromium Adsorption
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide This research aimed to assess the potential of Ethiopian coffee extracts in synthesizing nano zerovalent iron (nZVI) from ferric chloride hexahydrate (FeCl 3 ·6H 2 O) and to explore their ability to adsorb hexavalent chromium (Cr (VI)) from solution. The resulting nano zerovalent iron (nZVI) was analyzed using transmission electron microscopy (TEM) and Fourier-transform infrared (FTIR) spectroscopy. The particle sizes of nZVI derived from Wenbera (W-nZVI) and Sidama (S-nZVI) coffee extracts were below 10 nm. Cr (VI) adsorption to W-nZVI and S-nZVI were 91 and 94%, respectively, achieved at a pH of 3, an initial Cr (VI) concentration of 10 mg/L, and a temperature of 30 °C. The results confirmed that both W-nZVI and S-nZVI adsorbed significant amounts of Cr (VI). Adsorption isotherm models indicate that favorable removal of Cr (VI) by W-nZVI followed the Langmuir equation ( R 2 = 0.9913; 0 < R L < 1) better than the S-nZVI adsorption data ( R 2 = 0.9795). However, adsorption behavior for S-nZVI fitted the Freundlich isotherm model better ( R 2 = 0.9999; n = 2.029) compared with the W-nZVI adsorption data ( R 2 = 0.999; n = 0.559). The maximum adsorption capacities for W-nZVI and S-nZVI were 35 and 38 mg/g, respectively. The adsorption of Cr (VI) from aqueous solutions was spontaneous, accompanied by a negative Gibbs energy and exothermic, accompanied by a negative enthalpy of adsorption. The adsorption kinetics were better described by the pseudo-second-order model for both adsorbents. Taken together, nZVI synthesized by using coffee extract may provide a sustainable approach to remove hexavalent chromium from solution.
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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 | 0.000 | 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 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".