BIOSORPTION OF DISSOLVED Pb(II) IN DILUTE AQUEOUS SOLUTIONS BY USING AGRO-WASTE PRODUCTS
Bibliographic record
Abstract
The potential to remove Pb(II) from aqueous solutions through biosorption using four raw dead plant biomasses (karab, bardie, rice hulls and corn-cobs) was investigated in batch tests and compared with that for the PAC. From tests, it was found that the four types of dead-plant biomasses had shown high removal efficiency with the descending order (karab, bardie, rice hulls and corn cobs). Their percent removal (% R) of Pb(II) were (98.76, 96.10, 95.16 and 94.70) respectively at pH 4 with 0.2 g of sorbent/100 ml of 10 ppm lead solution , while it was (99.8 %) for PAC. Generally through batch system at a laboratory scale, karab has proved to be an efficient biosorbent for the removal of Pb(II) from aqueous solutions with low initial ion concentration (1–10 mg Pb(II)/ℓ) at pH (4 - 4.5). The EPA discharge limit (0.1 ppm) for lead was achieved. The biosorption rate is quite rapid and within 5 min of mixing more than 90 % of Pb(II) ions were removed by the karab biomass. Varying agitation speed has no influence on the rate of uptake and the Pb(II) uptake was not affected by karab particle size. The Freundlich and Langmuir isotherms described the data well. According to the evaluation using Langmuir equation, the maximum capacity q max obtined from equilibrium biosorption isotherm test was 13.2 mg/l for pb (II) . The ultimate sorption capacity KF in the Frendlich model was 3.1 .
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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) | 0.001 | 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".