Biosorption of Cr(III), Fe(II), Cu(II), Zn(II) Ions from Liquid Laboratory Chemical Waste by Pleurotus ostreatus
Bibliographic record
Abstract
Heavy metals present in the liquid laboratory chemical waste should be removed due to their hazards to human health and environment. Among them, Cr(III), Cu(II), Fe(II), Zn(II) are usually exist. The conventional treatments in the heavy metals removal have several limitations which encourage researchers to search for alternative treatment methods. One of possible method in the removal of heavy metal ion is through biosorption. Therefore, the present study aims to evaluate the effectiveness of white root fungus (mushroom) viz., P.ostreatus to absorb Cr(III), Cu(II), Fe(II), Zn(II) from liquid laboratory chemical waste. It was found that the best operating treatment process was at pH above 4.0. The highest biosorption efficiency for Fe(II) and Cu(II) was shown at pH 6 with 80.52% and 45.20% while Zn(II) at pH 4 (5.04%) and Cr(III) at pH 5 with 21.14% at agitation speed of 150 rpm and temperature of 25°C. Throughout the research, the percentage of removal was found to be increased with the increasing of contact time between P.ostreatus and liquid laboratory chemical waste. Almost 17.02% of Cr( III ), 55.35% of Fe(II), 15.34% of Cu(II) and 13.34% of Zn(II) were removed from chemical waste. This validates that P.ostreatus is potential as biosorbent for liquid laboratory chemical waste treatment.
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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".