Notice bibliographique
Résumé
Advancements in technology are accompanied with introduction of new pollutants into the environment, and especially our waters. Accumulation of these contaminants have made out water sources very polluted and unsafe to drink. According to Global Affairs Canada and the United Nations (UN), 40% of world’s current population lacks access to clean water and 80% of illnesses are linked to unsafe water from poor sanitation. Shortcomings in water treatment demand increasingly efficient materials. The utilization of graphene oxide (GO) and reduced graphene oxide (rGO) in the field of materials design has been the focus of many recent studies. As two of graphene’s derivatives, GO and rGO are 2D atomically thin nanosheets that are decorated with many to few oxygen-containing moieties, and great candidates for adsorption applications. However, GO and rGO are also extremely stable when dispersed in water. This becomes a challenge when using these nanomaterials for contaminant removal as it is difficult to recover the ‘used’ contaminant-loaded GO from water. Therefore, GO nanosheets are often reduced to obtain a three-dimensional porous macrsostructure (3DPM) for contaminant removal. In this PhD thesis, first we evaluate the different factors affecting the performance of graphene oxide-coated 3DPMs in removal of soluble methylene blue (MB) and colloidal nanoplastics from water. Same 3DPMs are also used to adsorb various organic solvents. Factors studied here include surface chemistry (pristine polymeric foams, and GO-coated and/or rGO-coated foams) and pore structure of the 3DPMs as well as the water chemistry of media (various pH ranges studied) and organic solvents’ properties (various polarity indices and dynamic viscosities). In the second part of this thesis, a combination of GO with cellulose nanocrystals (CNCs) is used to design and fabricate a new class of porous materials by templating an oil-in-water emulsion system without the need of polymerization. While maintaining the combined mass of GO and CNC, varying ratios of GO:CNC showed that a higher CNC content leads to an increase in sponges’ storage moduli. The emulsion-templated rGO-CNC sponge, whose pore architecture is comparable to the emulsions’ droplet size distribution, shows superior (270% greater) performance when compared to granular activated carbon (GAC) in removal of methylene blue (MB) as a model dye contaminant. The sponges maintained their performance at varied but relevant water chemistry conditions (pH range of 2.6 – 7.6, addition of 0.1 M NaCl) evaluated.In a follow-up study, surface chemistry of the emulsion-templated rGO-CNC sponge was modified with polymer of 5,5-dimethyl-3-(3′-triethoxysilylpropyl) hydantoin (PSPH). Upon halogenation, where the nitrogen atom(s) in N-halamine polymers gets covalently bonded to a halide atom (in this case, chlorine), the PSPH/Cl becomes an active bactericidal agent. The chlorinated functionalized rGO-CNC sponges (sp-PSPH/Cl) made in this study showed high efficiency and reusability in inactivating both Gram-negative Escherichia coli (E. coli K12) and Gram-positive Bacillus subtilis (B. subtilis ATCC 6633). The presented thesis provides insights in forming a structure-property correlation for graphene-based 3DPMs and their performance in removing various types of contaminants from water. We also present new efficient forms of graphene-based porous materials for removal of a wide range of emerging contaminants – from small organic molecules to nanoplastics, as well as inactivating both Gram-negative Escherichia coli (E. coli K12) and Gram-positive Bacillus subtilis (B. subtilis ATCC 6633)
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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) | 0,002 | 0,000 |
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 ».