Applicability of Natural Coffee Husk as a Mesoporous Adsorbent for Removal of Chromium (VI) from Aquatic Environments
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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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) | 0.000 | 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".