Removal of lead ions from aqueous solutions using powdered corn cobs
Bibliographic record
Abstract
Abstract This paper is a report on an investigation conducted on adsorption of lead ions (Pb2+) onto powdered corn cobs (PCC). Corn cobs were collected from a selected location in Nigeria. The corn cobs were crushed and pulverised into different particle sizes. Its compositions and adsorption properties of Pb2+ onto PCC were studied. The effects of pH, particle size of PCC, and initial concentration of Pb2+ on the adsorption properties were monitored. The adsorption capacities were analysed by using standard adsorption models. The models were evaluated statistically (total error, coefficient of determination (CD), model of selection criterion (MSC), and root mean square error). The study revealed that PCC contained 2.33%, 86.89%, 10.78%, 0.52%, and 4.56% ash, volatile, moisture, solubility in water, and solubility in 0.25 M of HCl by mass, respectively. Acid digestion of a gram of PCC indicated that PCC contained no chromium and lead, but contained 131 mg Fe, 54.79% carbon, 8.03% hydrogen, 0.41% nitrogen, 0.010 mg of Al, and 1.70 mg calcium. The isotherm models parameters were 28.509 L/mg and 0.141 mg/g; 0.138 mg/g and 11.494 L/mg; 0.142 L/mg and 0.013 mg/g; 0.129 mg/g and 25.641 L/mg, 28.509 L/mg, 3.795 mg/g and 2.336 for Langmuir, Freundlich, Temkin, activated sludge, and Redlich–Peterson. The PCC particle size, initial pH, and initial Pb2+ concentration had effects on the adsorption parameters. The statistical evaluations showed that the best model for adsorption of lead ions from raw water onto PCC based on lower errors, high CD (0.88), reliability (97.5%), and MSC (1.86) was Freundlich and followed by activated sludge model. It was concluded that PCC is a good adsorbent like powdered eggshell and other carbon‐based materials.
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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) | 0.001 | 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) | 0.001 | 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".