Separation of Heavy Metals Copper (Cu) and Nickel (Ni) from Industrial Wastewater by Adsorption Using Chitosan Shrimp Shell
Bibliographic record
Abstract
Shrimp shell contains chittin that can be processed become chitosan. Chitosan can be used as bioadsorbent totreat heavy metals content in wastewater. The purposes of this research are to find deacethylation degree ofchitosan from shrimp shell, the constant value of adsorption affinity (k) and adsorption efficiency for variousvariation mass and size of chitosan, heavy metal concentration (solute) in wastewater and to compare adsorptionefficiency between syntetic solution and industrial wastewater. The size variation of the chitosan are 20 meshand 40 mesh. The type of adsorption used is batch until 4 hours with 5 rpm as agitation rate. Deasethylizationdegree for chitosan 20 mesh and 40 mesh are resulted as 75,61% and 77,71 %. More amount of chitosan usedand the smaller size of chitosan make the adsorption efficiency higher as 92,52%. A synthetic solution and PTSIER industrial wastewater are types of wastewater used. PT SIER wastewater contains other metals that canhamper the adsorption of desired metals. Ni is easier to adsorp with 92,52% efficiency than Cu which hasefficiency of 88,52%, because atomic radius of Ni is smaller than Cu. Adsorption affinity constant is influencedby size of the chitosan. The smaller size of chitosan make adsorption affinity constant higher than the bigger size(which is 0,13).
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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".