Synthesis of Pectin Hydrogels from Grapefruit Peel for the Adsorption of Heavy Metals from Water
Bibliographic record
Abstract
The increasing presence of heavy metals in aquatic environments, driven by the production of industrial waste and consumer products, poses serious environmental and health risks due to their toxicity and persistence. Copper (Cu(II)) and nickel (Ni(II)) are particularly harmful, with high concentrations linked to neurological, dermatological and carcinogenic effects. This proof-of-concept study explores the synthesis of sustainable hydrogels derived from grapefruit peel (biosorbents) for the adsorption of Cu(II) and Ni(II) from aqueous solutions. Pectin was extracted from the peels and was used to synthesize pectin-based hydrogels (PH) and pectin hydrogel metal–organic frameworks (PHM composites). The hydrogels were characterized using FT-IR, SEM, diameter size and water absorption capacity. Lyophilized hydrogels were significantly smaller than their wet counterparts, and adsorption performance was analyzed using FAAS. PHs demonstrated high Cu(II) removal efficiency, achieving 95.11% adsorption and 97.75 mg/g capacity at pH 5. PHM composites showed comparable Cu(II) adsorption with a maximum capacity of 67.53 mg/g. Notably, PHs also exhibited rapid Ni(II) adsorption, reaching 92.62% efficiency and 28.189 mg/g capacity within one minute. These findings highlight the potential of pectin-based hydrogels as an effective, low-cost and environmentally friendly method for heavy metal remediation in 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".