Phytoremediation Study of Water Hyacinth (Eichhornia Crassipes) on Zinc Metal Ion (Zn2+)
Bibliographic record
Abstract
The high level of community activity around the river has resulted in high levels of heavy metals contained in river water. Commonly found heavy metals such as zinc (Zn), mercury (Hg), and copper (Cu). To reduce or remove heavy metals in the waters, phytoremediation techniques are used. The purpose of this study was to determine the ability of water hyacinth plants (E. crassipes) in remediating Zn2+ metal in contaminated Lake Tempe water. The research method used includes the sampling process, acclimatization, physicochemical analysis, Zn analysis, Bio-Concentration Factor (BCF), and infrared (IR) analysis. The Zn concentration in Lake Tempe water decreased after 30 days of the phytoremediation process. And the highest adsorbed Zn (II) metal ions on water hyacinth plants was 77,257 ppm on the 30th day. It was found that there is a relationship between the BCF value and the phytoremediation time, the higher the phytoremediation time, the higher the BCF value obtained. IR data show the presence of Zn metal bonds in plants involving the functional groups C=S, C=N, and OH. This indicates that water hyacinth (E. crassipes) has the potential to be used as a phytoremediation agent in adsorbing Zn in Lake Tempe waters.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".