Applicability of Natural Coffee Husk as a Mesoporous Adsorbent for Removal of Chromium (VI) from Aquatic Environments
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Bibliographic record
Abstract
Chromium (Cr) is an essential element in plant and animal metabolism, but high accumulation of this metal ion in soil and water from industrial sources has caused concern. This study investigated the removal of Cr (VI), the most mobile and toxic species, using coffee husk biosorption from aqueous solutions. Coffee husk was characterized using Brunauer Emmett Teller (BET) and Fourier Transform Infrared (FTIR) analysis. The effects of pH, adsorbent dosage, contact time, effect of KNO3 concentration, and temperature during the adsorption of Cr (VI) were studied. Infrared spectral studies revealed the presence of functional groups, such as hydroxyl and carboxyl groups, which facilitated the biosorption of Cr (VI). The maximum adsorption capacity reached 87 % at pH~2. Moreover, maximum adsorption capacity of Cr (VI) onto coffee husk 0.660 mg g-1 was recorded after 60 min of using 40 mg L-1 of Cr (VI) with no addition of KNO3 at 40 0C. The kinetic data followed the pseudo-second-order model with regression coefficients R2 = 0.99. The equilibrium data for the adsorption of Cr (VI) onto coffee husk biosorption were fitted into Langmuir and Temkin adsorption isotherm models with R2 = 0.98 corresponding to each model. Thermodynamic studies showed that the coffee husk-Cr (VI) adsorption system was spontaneous and endothermic because negative and positive values were obtained for ΔG° and Δ°H, respectively. The coffee husk was applicable to remove Cr (VI) after three recycling and using different environmental water samples. In conclusion, coffee husk can be used effectively as an adsorption system for Cr (VI) at different polluted sites.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it