Sodium Alginate-Pomegranate Peel Hydrogels for the Remediation of Heavy Metals from Water
Bibliographic record
Abstract
The use of agrochemicals in agriculture is common around the world as it allows for increased crop production. However, they also contain heavy metals such as copper and nickel, which can leach into the drinking water and harm the environment and human health. As such, it is important to remediate them from drinking water. One way to do so is via adsorption using biosorbents. This study aimed to synthesize and characterize environmentally friendly hydrogels from sodium alginate and pomegranate peel. The effect of organic vs commercial pomegranate peel was also tested. Moreover, the gel’s ability to remediate copper and nickel from contaminated water was tested. The gels were characterized using FTIR, SEM and water uptake tests. These results were promising as FTIR showed that the expected functional groups were present, SEM showed that incorporating organic pomegranate peel enhanced the roughness and porosity of the gels, and gels with organic pomegranate peel incorporations were able to take up 1.58x more water than sodium alginate only gels. Moreover, the effect of contact time, pH and adsorbent amount was tested for copper and showed that the optimal contact time was 60 minutes, the optimal pH was ~5, and increasing adsorbent amount decreases adsorption. The effect of contact time was tested for nickel and showed that the optimal contact time was 5 minutes. Overall, these hydrogels show promising results for the remediation of copper and nickel from contaminated water.
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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".