Bio-renewable hydrogel sorbents for removal of heavy metals from water
Bibliographic record
Abstract
Water is essential for life, yet water scarcity from heavy metal pollution is a growing problem severely affecting resource-limited areas where drinking water is already lacking. Sorption is the simplest and most economically feasible technique for heavy metal removal; however, many commercial sorbents are powders which have safety concerns, present processing and handling difficulty with low removal efficiency, and potential secondary pollution. To overcome these issues, the goal of this work was to develop an inexpensive, renewable, and biodegradable hydrogel able to efficiently bind heavy metals while practicing the principles of green chemistry. Therefore, we used cellulose, the most abundant and easily degradable biopolymer on Earth. Cellulose derivatives — hydroxyethyl cellulose (HEC), which provides structural support, and carboxymethyl cellulose (CMC), which efficiently binds heavy metals with its wealth of carboxylate groups — were modified with aromatic aldehydes (aa-HEC and aa-CMC). These functionalized cellulose derivatives were covalently crosslinked with an ethylenediaminetetraacetic acid (EDTA)-based crosslinker modified with four hydrazide groups (4h-EDTA) to construct hydrazone crosslinked hydrogels. In Chapter 2, rheology, a method to quantify mechanical strength, was used to optimize the aa-HEC/aa-CMC/4h-EDTA cellulose hydrogels for their crosslinking ratio and composition, determined to be 1:2 aldehyde:hydrazide (a:h) and 2 wt% 1:3 aa-HEC/aa-CMC (1:3 H:C), respectively. This optimal 1:3 H:C hydrogel exhibited a storage modulus (G’) of 200 Pa and a maximum sorption capacity of 102 mg/g for Cu2+, comparable to current bio-based sorbents. The findings from Chapter 2 provided us with a better understanding of our cellulose-based hydrogels and highlighted the need to enhance their mechanical strength. Thus, in Chapter 3 we explored (modified)-cellulose nanocrystals (CNCs) as rigid green nano-additives in place of a portion of the flexible cellulose derivatives to improve the hydrogel’s mechanical integrity. Specifically, we studied the incorporation of (modified)-CNCs at a 2 wt% 1:1:1 aa-HEC/aa-CMC/(modified)-CNC ratio using our 4h-EDTA crosslinker to form hydrazone bonds at the 1:2 a:h crosslinking ratio. The control condition used native CNCs, and the modified-CNCs included aromatic aldehyde modified-CNCs (aa-CNCs) and carboxylated CNCs (T-CNCs). All nanocomposite hydrogels (1:1:1 CNC, 1:1:1 aa-CNC, and 1:1:1 T-CNC) showed decreased swelling and greater mechanical strength compared to the 1:3 H:C hydrogel after salt/buffer incubation. Additionally, after incubation in excess Cu2+, all hydrogel compositions experienced shrinking which significantly enhanced their mechanical strength — the 1:1:1 T-CNC gained the most strength (G’ of 150 Pa pre-incubation to 3100 Pa post-incubation in Cu2+). Furthermore, sorption studies revealed the 1:1:1 T-CNC composition had a binding capacity of 90 mg/g for Cu2+, comparable to our 1:3 H:C hydrogel and current bio-based sorbents. Overall, our findings provided us with a blueprint towards using functionalized cellulose derivatives and modified-CNCs to develop mechanically strong nanocomposite cellulose hydrogels. These cellulose-based hydrogels have the potential to serve as safe, sustainable, inexpensive, and easy-to-handle alternatives to powdered sorbents for water purification of heavy metals.
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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 | 0.001 |
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".