Bio-renewable hydrogel sorbents for removal of heavy metals from water
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».