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Lignin acrylic acid polymer as an emulsifier for oil-water emulsions
Get PDFOil-water emulsions are applied in various fields such as food, cosmetics, pharmaceuticals, petrochemicals, and agricultural products. Currently, synthetic-based emulsifiers are extensively used
in oil-water emulsions, but the use of synthetic emulsifiers is discouraged due to the adverse environmental impacts related to their production and use. Alternatively, biobased emulsifiers can
serve such a purpose. In this regard, lignin, which is the main component of lignocelluloses materials, can be converted to emulsifiers for oil-water emulsions. In this regard, the production of
lignin-based emulsifiers has not been explored comprehensively. In this thesis, lignin-derived
emulsifiers were experimentally produced via polymerizing lignin and acrylic acid (AA), and its
impacts on emulsions produced by mixing water and three industrially used oil of xylene, cyclohexane, and decane was studied comprehensively using a tensiometer, and Quartz crystal microbalance. [...
Changing channels: altering the dis-course of “invasive” species education
Get PDFThis portfolio is focused on two discourses around "invasive" species, namely the
dominant Western science narratives that emphasize elimination, control, and management and
an Indigenous perspective that takes a longer view that recognizes perpetual change in
ecosystems. Braiding these worldviews together may offer a more humane and healthier
approach to not only conservation science but also "invasive" species education. There are four
tasks in this portfolio. The first is a literature review that provides an overview of Western and
Indigenous epistemologies and ontologies in regards to conservation science, relationships to
Land, and other beings with whom we share a life, zeroing in on "invasive" species. Some of the
educational implications of these discourses are woven throughout the literature review. The
second task in my portfolio focuses explicitly on education and involves a review of current
“invasive” species education in the Ontario environmental science curriculum, as well as
observations from my experiences as an interpreter about how "invasive" species are discussed.
The third task is an interpretive program focused on "invasive" species that applies ideas from
the literature review in a practical way. The fourth and final task is a reflection paper on my
learning journey
Interspecific interactions modulate social foraging behaviour and habitat use in a neotropical migratory warbler species during its nonbreeding period
Get PDFSpatial and temporal variation in biotic and abiotic conditions in any foraging
environment prompts individuals to modify their strategies of space use and behavioral
responses such as going from solitary to social foraging, as it occurs with flocking in forest
birds. While asymmetric intraspecific competition determines differential habitat selection, the
occurrence and foraging activity of others, conspecifics or even congeners, can also inform on
fitness prospects and provide floaters or flock attendants additional foraging opportunities when
searching for alternative habitats. Whether interspecific interactions between congeneric
species modulate habitat use in a species that occurs at low densities during the non-breeding
period remains less known. Here, I study two Neotropical migratory bird species that join
flocks: the endangered Golden-cheeked warbler (GCWA; Setophaga chrysoparia) and the
Townsend’s warbler (TOWA; S. townsendi). The main objective was to describe social
mechanisms underlying habitat preference in the former species by looking at foraging
strategies and interactions with its congener that might influence its habitat selection. A
secondary goal was to provide knowledge on the nonbreeding ecology of GCWA toward
conservation recommendations. [...
Expansion of the applicability of the Truce-Smiles rearrangement
Get PDFThe Truce-Smiles rearrangement is a synthetically useful and easily performed reaction
which can be used to condense multiple steps of a synthesis. The nucleophilic aromatic
substitution in this reaction produces a chiral center in the rearrangement product. A variety
of rearrangement substrates has been prepared and investigated. Investigations into tether
functionalization, tether length, pyridyl ring systems and introduction of a second heteroatom
into the tether are reported. It has been shown that the rearrangement prefers nitrile tether
functionalization. For ethyl ester tethers that perform the rearrangement, there is a
secondary cyclization that results in the formation of an aryl lactone. The rearrangement
favours a tether length that proceeds through a 5-membered ring intermediate.
Rearrangement was successfully reported for a substrate which utilizes two heteroatoms in
the tether, something which has not appeared in the literature previously. Use of a
microwave reactor resulted in increased rearrangement yields, in addition to facilitating
rearrangements that were previously unsuccessful using conventional heating with an oil
bath. Use of chiral ionic liquids (CILs) is an excellent approach toward green chemistry due
to their high solubility power, coupled with their ability to be recycled and reused over
multiple reactions. Over recent years, there has been an increasing interest in investigating
the use of CILs as solvent systems to selectively induce chirality in reactions; resulting in
the enantioselective formation of products and reduced waste. A variety of CILs have been
prepared and tested for their ability to serve as solvents and impart chirality on the reaction.
The CILs were successfully used as reaction solvents, however, there is no strong chiral
induction observed
Evaluating the combined effect of thermoplastic polyurethane material in Ice hockey goaltender helmets and cervical muscle strength in mitigating concussion risk during simulated horizontal head collisions
Get PDFConcussions, or mild traumatic brain injuries (mTBI), occur due to an impact on the head
and are the most common type of brain injury for goaltenders in the sport of ice hockey. The two
main techniques used to mitigate concussion risk for ice hockey goaltenders include improving
the impact absorption capabilities of the goaltender helmets and increasing the athlete’s cervical
muscle strength. Based on these two strategies, this study examined the effect of thermoplastic
polyurethane (TPU) as a goaltender helmet liner material to mitigate concussion risk for
individuals with different neck strength levels during simulated horizontal head collisions.
To address the purpose of this study, static testing was conducted to examine the material
properties of the goaltender helmet liners, which was then used to identify the best TPU liner
design that improved the performance of the goaltender helmet technology. One particular TPU
liner design was found to weigh 2.7 times more on average than the standard liner; however, it
was capable of statically absorbing 10.8 times more energy per kilogram than the standard liner.
The researcher selected this TPU design and used it in repeated impact testing to further gauge
the performance of the TPU and standard liner materials compared to a bare head without liner
protection. Repeated dynamic impact trials revealed that the TPU liner mitigated impacts
similarly to the standard liner, providing evidence of the TPU effectiveness as a possible helmet
liner. [...
Multi-timeframe algorithmic trading bots using thick data heuristics with deep reinforcement learning
Get PDFThis thesis presents an augmented Artificial Intelligence (AI) algorithmic trading
approach that combines Thick Data Heuristics (TDH), with Deep Reinforcement Learning
(DRL), to successfully learn trading execution timing policies. In this thesis, combining the
augmented AI human trader’s intuition and heuristics with DRL techniques to provide more
focused drivers for trading order execution timing is explored. In this financial technology
(Fintech) research, the goal is to solve the sequential decision-making problem of AI for
profitable day and swing trading order timing executions. Enabling trading bots with cognitive
intelligence and common-sense heuristics will offer traders including automatic traders an
insight to understand the day-to-day swing trading timeframes indicators and arrive at mature
trading decision-making. This thesis examines the performance of bots with Nasdaq and NYSE
stocks that have a strong catalyst (info. which increases directional momentum) to find that they
outperform benchmark algorithmic trading approaches. The thesis illustrates to the reader how to
combine TDH and Deep Q-networks (DQN) into a TDH-DQN augmented AI trading bot. The
bot learns through test data to predict the optimal timing of order executions autonomously on
idealized trading time series data. The results show the TDH-DQN bot outperformed the buy and
hold strategy plus two out of the three benchmark algorithmic trading strategies
Process design and feasibility study of synthetic crude production by the combination of methane decomposition, reverse water gas shift reaction, and Fischer-Tropsch synthesis
Get PDFIn this dissertation, the conversion of CO2 to gas-to liquid (GTL) products was investigated for the
production of 30000 bbl per day syncrude. The GTL plant consisted of four main units: hydrogen
production by catalytic thermal decomposition of methane in a Cu-Bi molten media, syngas
production by the reverse water gas shift (RWGS) reaction using a nickel-based catalyst, syncrude
production by the low temperature Fischer-Tropsch (LTFT) synthesis over a cobalt-based catalyst,
and an energy recovery unit for electricity generation. The plant was simulated by the coupling of
HYSYS and MATLAB to simulate the RWGS and FT reactors and converge their recycle streams.
150 alkanes and 149 alkenes were included in the simulation to accurately estimate the product
distribution of the FT reactor. The fixed capital investment of the plant and the manufacturing cost
of syncrude were 137 bbl-1
, respectively. It was found that hydrogen production
by methane decomposition reduced the manufacturing cost of syncrude by 32% when compared
to GTL plants that sourced their hydrogen from water electrolysis. The profitability analysis
showed the plant could not be economically viable without selling the produced solid carbon. The
breakeven price of the produced solid carbon was estimated to be 59.31 bbl-1
. The economic performance of the plant was highly favourable at
syncrude selling prices higher than $80 bbl-1
. It was determined that the plant was a net emitter of
CO2 at a rate of 19.92 g CO2 per 1 MJ of syncrude, which was lower than the reported values for
different types of natural-gas based GTL plants, but higher than water electrolysis-FT plants
The design of Zr metal-organic frameworks in the detection and neutralization of organophosphorus based nerve agents and pesticides
Get PDFOrganophosphates are ubiquitous in uses ranging from chemical warfare agents
(CWA’s) to detergents. In between these two extreme uses lie organophosphorus-based
agrichemicals, primarily pesticides, which are applied in multi-tonne amounts each year.
Metal-organic frameworks, especially the UiO-6x family, are known to interact, sequester,
and/or break down organophosphate nerve gases.
This thesis presents the synthesis and characterization of UiO-6x MOFs,
comparing standing solvothermal with microwave methods, which appear (by powder Xray diffraction, X-ray photoelectron spectroscopy, and solid-state nuclear magnetic
resonance (SS-NMR) analysis) to give equally high-quality products. Preparation of the
UiO-6x MOFs were conducted using 1 molar equivalent of ZrCl4 to 1 equivalent of organic
ligand in the presence of equal volumes of dimethyl formamide (DMF) and glacial acetic
acid (GAA) for both solvothermal and microwave methods. Yields ranged from 41-63%
for our synthesized MOFs (UiO-66, UiO-67 and UiO-67-bipy).
The prepared MOFs are then reacted with organophosphate nerve agent
simulants and agrichemicals in reactions followed by GC or HPLC; it appears that the
studied agrichemicals are less-reactive than their chemical warfare agent equivalents
with only (2-chloroethyl) phosphonic acid and glyphosate showing reactivity with UiO-67
and UiO-66 respectively whereas UiO-67-bipy was shown to nearly completely
degrade/sequester dimethyl methylphosphonate. Also presented are preliminary SSNMR spectra on UiO-66 post-reaction with glyphosate. The line-broadening and
restricted rotation that occurs on the combined spectrum suggests the pesticide is
incorporated whole into the MOF without degrading
Concrete structure damage classification and detection using convolutional neural network
Get PDFA resilient infrastructure system remains a top priority for Canada as it is hinged to strong
economies. Corrosion, ageing, aggressive environments, material defects, and unforeseen
mechanical loads can compromise the serviceability and safety of existing infrastructures.
Introducing Artificial Intelligence (AI) in smart structural health monitoring (SHM) can assist in
building an automated and efficient infrastructure condition monitoring method to facilitate an
effortless inspection and accurate evaluation of deteriorated infrastructure. Over the last decades,
vision-based AI has proven successful in pattern recognition applications and motivated this
current research to assemble a data-driven damage detection technique. To further explore the
possibilities of deep-learning (DL) applications, this dissertation research aims to develop an
autonomous damage assessment process using DL techniques to classify and detect two types of
defects- crack and spalling on concrete structures. This research started with reviewing existing
application of various DL-based technologies for damage detection of concrete structures and
identifying the challenges and limitations. One major challenge in DL-based SHM technique is
the lack of adequate real image database obtained from field inspection. To address these
challenges, this research has created a diverse dataset with concrete crack (4087) and spalling
(1100) images and used it for damage detection and classification by applying convolutional neural
network (CNN) algorithms. For defects classification VGG19, ResNet50, InceptionV3,
MobileNetV2, and Xception CNN models were used, whereas semantic segmentation process
adopted Encoder-Decoder Models- U-Net and PSPnet. For both cases a detailed sensitivity
analysis of hyper-parameters (i.e., batch size, optimizers, learning rate) was performed to compare
their performances and identify the best-performed model. After assessing all the criteria, the best
performance for defects classification was achieved by InceptionV3. On the other hand, for crack
and spalling segmentation U-net outperformed the other models. Overall, the developed algorithms
achieved an excellent performance in damage classification and localization and proved to be
successful enough to offer an automated inspection platform for ageing infrastructures. The
outcomes of this study signify that the data-driven CNN methods could be a promising solution
for the condition assessment of deteriorating concrete structures. The research outcomes can be
implemented by practitioners for condition assessment of existing infrastructure and
recommending proper rehabilitation measures
Road-network location heuristics for the tactical harvest-scheduling model
Get PDFIn tactical planning in hierarchical forest management, cut-blocks are selected
for maximizing revenue and road networks are allocated at minimal cost in order to
maximize profit. The selected cut-block set and requisite road-network, connecting the
cut-blocks, therefore have an interdependent relationship. The location of these two
elements in tactical planning must therefore be considered simultaneously in a tactical
harvest-scheduling model. This integration presents a major computational challenge,
especially with regard to the execution time required to find an optimal solution to the
tactical harvest-scheduling model.
The objective of this thesis isto explore the influence of different road location
heuristics, used within the tactical harvest scheduling model, upon the model’s
execution time and solution quality. We nested the three different types of roadlocation heuristics within the harvest-scheduling model in order to evaluate their
effectiveness by three criteria: execution time, road construction cost and objective
function value. In addition, after the tactical model was run, we executed and evaluated
the usefulness of a road network repair algorithm, designed to improve further the
solution of the road-network location generated by the tactical harvest-scheduling
model. The thre heuristics were evaluated on a real-world dataset, representing a
section of the Kenogami forest in Ontario, Canada. Our result show: i) that the Shortest
Path Origin Heuristic (SPOH) achieved the fastest execution time and lowest
construction cost when integrated within the tactical harvest-scheduling model; and ii)
that the road network repair algorithm successfully lowered the road network costs and
thereby increased the objective function value of all solutions generated using the
tactical planning model. These results are significant for two reasons: first, they
show that the choice of the road network heuristic used within a tactical planning
model can have a major influence on the model’s solution quality; and second, that the
use of a road repair algorithm, on the solution generated using a tactical model, is of
major economic value in forest management planning