Biodegradable and Reusable Sponge Material Prepared from Pea Protein for the Effective Removal of Heavy Metals
Bibliographic record
Abstract
There is a growing demand for biodegradable and sustainable materials, particularly those sourced from agricultural or industrial byproducts, for use in wastewater treatment. This study introduces an approach for developing a sponge-like adsorbent derived exclusively from pea protein (Pea AS -PEI) using liquid foam templating for the removal of heavy metals. The pore size, surface area, and mechanical strength of the sponge were modulated by the extent of protein hydrophobic aggregation induced by ammonium sulfate (AS) immersion based on the salting-out effect. Raising AS concentration from 10 to 20% led to an increase in surface area from 30.69 to 127.41 m 2 /g and compressive stress from 151.02 ± 8.73 kPa to 316.10 ± 13.87 kPa at 90% strain. Polyethylenimine (PEI) grafting introduced additional amine groups for heavy metal adsorption, and the porosity of the sponge increased from 86.9 to 90.3% upon surface modification. As a result, the PEI-modified sponge showed favorable adsorption performance of Cu(II), Zn(II), and Ni(II) ions at pH 5 with maximum sorption capacities of 67.07, 115.61, and 55.86 mg/g, respectively. The adsorption kinetics and isotherm study suggested that the adsorption process was primarily chemisorption and occurred with monolayer interactions. The negative Gibbs free energy change (Δ G < 0) confirmed that the adsorption was thermodynamically spontaneous. Reusability tests for the Pea AS -PEI sponge revealed that its adsorption capacity could be well maintained over five successive adsorption–desorption cycles, with the removal efficiency of Cu(II) over 95%. The pea protein sponge also exhibited excellent biodegradability in soil within 28 days, with weight losses of 86.1% and 67.5% before and after PEI grafting, respectively. Together, these results indicate the great potential of affordable and sustainable PEI-modified pea protein sponges for the remediation of water polluted with copper, zinc, and nickel ions.
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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".