Sodium Alginate-Pomegranate Peel Hydrogels for the Remediation of Heavy Metals from Water
Bibliographic record
Abstract
The use of agrochemicals in agriculture is widespread globally, as it enables increased crop yields. 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 imperative that they are removed from drinking water. One way to achieve this is through adsorption using biosorbents. This proof-of-concept study aimed to synthesize and characterize environmentally friendly hydrogels from sodium alginate (SA) and pomegranate peel powder (PPP). The gels were characterized using Fourier-Transform Infrared Spectroscopy (FTIR), Scanning Electron Microscopy (SEM), and water uptake tests. The FTIR analysis confirmed the presence of the expected functional groups, SEM revealed that incorporating PPP enhanced the roughness and porosity of the gels, and gels with PPP incorporation were able to absorb 1.58 times more water than SA-only gels. Moreover, their ability to remediate copper and nickel from contaminated water was tested. Here, the effects of contact time, pH, and adsorbent amount were tested for copper, demonstrating that the optimal contact time was 60 min, the optimal pH was ~5, and 0.01 g of adsorbent was needed for optimal adsorption. The effect of contact time was tested for nickel, and it was found that the optimal contact time was 5 min. Overall, these gels 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".