Kinetic of Hexavalent Chromium (Cr(VI)) Removal by Corn Cob-Based Activated Carbon Modified with Nitric Acid
Bibliographic record
Abstract
The presence of heavy metal Cr(VI) in water indicates environmental pollution. Heavy metal Cr(VI) that exceeds the standard can be harmful to health because it is toxic and carcinogenic. Activated carbon can be used as a heavy metal adsorbent. Modification of activated carbon using nitric acid can increase metal adsorption capacity. Therefore, this study aims to determine the kinetic of hexavalent chromium (Cr(VI)) removal by corn cob-based activated carbon modified with nitric acid. The modified activated carbon was characterized by Boehm titration and FTIR spectrophotometer. The adsorption capacity was identified in various parameters, involving the initial concentration of Cr(VI), pH value, contact time, and concentration of the adsorbent to obtain the optimal Cr(VI) removal efficiency value. The most optimal Cr(VI) adsorption was obtained at an activated carbon dosage of 3 g/L, pH value of 1, contact time of 140 minutes, and 100 mg/l Cr(VI) concentration. Based on adsorption kinetics data, the pseudo-second-order equation was obtained (R2 =0.994). The adsorption phenomenon followed the Langmuir isotherm model (R2 = 0.998) with an optimum adsorption capacity of 28.32 mg/g. Corn cob-activated carbon modified with nitric acid has many acidic groups that act as effective active sites for reducing Cr(VI) from water.
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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) | 0.001 | 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".