Valorization of Groundnut Shell Biomass for Biosorption of Hexavalent Chromium from Aqueous Solution
Bibliographic record
Abstract
Biosorption is a cost-effective biotechnological innovation for the removal of heavy metals from aqueous solutions. There is widespread research into ways of utilizing agricultural residues to achieve zero waste. Groundnut shells are biodegradable waste available in large quantities. This study investigated the use of groundnut shells for the biosorption of chromium (VI) ions from an aqueous solution. Groundnut shells were cleaned and crushed to make fractions of particle size in the range of 90-1000 µm. The point of zero charge (pHPZC) and the distribution of oxygenated acidic and basic surface functional groups were determined. In batch experiments, the effect of acid pre-treatment, initial metal concentration, biosorbent particle size, biosorbent dosage, and contact time on biosorption was investigated. The biomass was found to have a pHPZC value of 6 and was dominated by acidic groups. The best biosorption activity was observed at 20 mg/L initial metal concentration and a biosorbent dosage of 10%. The effect of contact time was dependent on the initial chromium (VI) concentration. At 20 mg/L initial chromium (VI) concentration, the biosorption process reached equilibrium within 60 minutes whilst at high (>80 mg) chromium (VI) concentration equilibrium was not reached, even after 240 minutes. The best biosorption activity was observed with acid-treated biomass of particle size 300 µm. The adsorption fitted best with the Langmuir isotherm model and the pseudo-second-order kinetic model (R2 > 0.9982). Groundnut shell biomass has the potential for the removal of chromium (VI) ions from aqueous solutions and possibly from chromium-polluted effluents on an industrial scale.
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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) | 0.001 | 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 | 0.000 | 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".