The Effect of Crosslinker and Pore Generated on Selective Adsorbent (Cu2+) based on Grafting of Acrylic Acid onto Cassava Starch
Bibliographic record
Abstract
The technology development in many industries nowadays, such as electronic industry produces heavy metal wastes which may pollute our environment. The use of adsorbent as a heavy metal removal from soil and water is one of the efficient process which can be considered to be used. In addition the release of the adsorbate becomes an important way as well because usually those heavy metals still have a high value. The objective of this research is to develop adsorbent based on cassava starch. So, the release process will become easier and will not produce another waste. The adsorbent was produced through the grafting of acrylic acid onto cassava starch by using Fenton initiator. To construct a stable 3-D network, the crosslinker (CL) N,N’- methylenebisacrylamide was added. The variable observed were the amount of CL added (0.5%; 1.5%; 2.5% and 3.5%) and the treatment of generating more pores on starch copolymer. The treatments on starch copolymer observed were single freezing, second freezing, and citric acid modification and carbonization methods. Analysis performed on the adsorbent was % add-on, water absorption and metal adsorption (especially Cu2+ ion) capacity. The result showed that the used of 2.5% CL produced the highest add-on (47.66 %), the highest water absorption capacity and the highest metal adsorption capacity (0.29g Cu2+/g adsorbent) The citric acid modification also produced the highest pores on the adsorbent.
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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".