Bibliographic record
Abstract
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)
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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) | 0.002 | 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".