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Record W7037339329

Development of Novel High Temperature Mixed Matrix Membranes (MMMs) for Oil Sands Wastewater Treatment

2022· dissertation· en· W7037339329 on OpenAlexaboutno aff

Bibliographic record

VenueUniversity Library (University of Saskatchewan) · 2022
Typedissertation
Languageen
FieldAgricultural and Biological Sciences
TopicOrthoptera Research and Taxonomy
Canadian institutionsnot available
Fundersnot available
KeywordsMembraneFoulingWater treatmentReuseProduced waterCommercializationMembrane technologyNanoparticleEnhanced oil recovery
DOInot available

Abstract

fetched live from OpenAlex

While oil sands production plays a significant role in Canada’s economy, the rise in oil sands production leads to increasing water abstraction and pollution. Effective treatment and reuse of oilsands process-affected water (OSPW) can be considered a strategical solution to addressing these issues. Membrane technology is a promising option for OSPW treatment due to its high removal efficiency, small footprint, facile operation, installation and scaleup. Mixed matrix membranes (MMMs) prepared by mixing super-hydrophilic zwitterionic materials and inorganic nanoparticles into the host membrane are anticipated as a next membrane generation for OSPW treatment by achieving multi-functionalities beyond excellent fouling resistance, such as, improved water permeability, selectivity and mechanical strength. Reproducibility and feasibility for large-scale industrial applications are major challenges in the production of MMMs for OSPW treatment. There remain difficulties in adopting lab-scale membranes into industrial practice, along with ensuring their efficiency, stability and durability. This study aims to provide new insight on the performance, and stability of MMMs, outlooking to the commercialization prospect of MMMs. Molecular dynamics simulation (MDS) will be employed to provide in-depth information about complex atomic interactions between the membrane and the additives used, thus allow better understanding about structural and physical properties of MMMs in relation to their performance, fouling, and stability. \nThis thesis examined three major classes of zwitterionic material including carboxybetaine (CB), phosphatidylcholine (PC) and sulfobetaine (SB) for the modification of poly (vinylidene fluoride) PVDF membrane. Based on simulated data, PC is the most preferable zwitterion that leads to a more stable and hydrophilic mixed matrix membrane with enhanced oil-antifouling capacity. In addition, the chemistry and structural properties of zwitterionic material in relation to the performance of resultant modified membrane were investigated. In total 12 different zwitterionic structures with different polymer backbone (PB), spacer length (SL) and spacer chemistry (SC) were examined and compared. The results suggest that all PB, SL and SC influence the resultant MMM performance with SL the most impactful structural parameter on MMM stability and hydrophilicity. Long SL was demonstrated to reduce the ionic association of charged groups and increase their partial charges, thus promote higher inter-molecular interactions, resulting in well-connected polymer network. Moreover, the performance of modified membrane was examined at high temperatures (i.e., 25, 50, 70 and 90oC). Overall, high temperatures seemed to reduce the stability of modified membrane but to a smaller extent as compared to pristine PVDF membrane. Although the membrane hydrophilicity was greatly altered at high temperatures, no considerable impacts on MMM’s oil-antifouling capacity was observed. While the research outcomes can provide valuable knowledge for the design and development of high-quality membranes with the required characteristics for OSPW treatment applications, experimental studies are needed to validate the simulated data presented.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.510
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.013
GPT teacher head0.187
Teacher spread0.174 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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