Tetrahydrazide-EDTA Cross-Linked Cellulose Hydrogels for Water Treatment by Heavy Metal Chelation
Bibliographic record
Abstract
Polluting heavy metals persist in the environment, leading to bioaccumulation and toxicity, which is a growing problem in developing countries. Various water filtration systems for heavy metal removal have been developed, with sorption being the simplest and most economically viable. However, many commercial sorbents are powders, leading to inefficient sorbent removal and secondary pollution. The goal of our research was to develop renewable, biodegradable, and cost-effective hydrogel sorbents able to bind heavy metals. This was accomplished using hydroxyethyl cellulose (HEC) and carboxymethyl cellulose (CMC) functionalized with aromatic aldehydes (aa-HEC and aa-CMC), and an ethylenediaminetetraacetic acid (EDTA)-based cross-linker modified with four hydrazide groups (4h-EDTA). By varying the ratio of aldehyde-to-hydrazide (a:h) groups in the aa-HEC/4h-EDTA hydrogel, a ratio of 1:2 a:h was found to have the maximum storage modulus ( G ′). This was used to make 2 wt % hydrogels with a composition of 25/75 aa-HEC/aa-CMC cross-linked with 4h-EDTA (aa-HEC/aa-CMC/4h-EDTA), with a G ′ of 200 Pa and a maximum sorption capacity of 102 mg of Cu 2+ per gram of hydrogel. The sorption capacity of the hydrogels was tested for Cu 2+, Ni 2+, Zn 2+, Co 2+, and Mg 2+ individually and as a mixture, with Cu 2+ showing the highest affinity. This work shows that cellulose-based hydrogels can be used as a green alternative for the removal of heavy metal pollutants from 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".