A Comparison of the Impacts of Lithium Extraction and Lithium Recycling Processes

Lithium is used in the cathode and electrolyte of rechargeable batteries in many portable electronics and electric vehicles, and is thus seen as a critical component of modern technology (Gruber et al., 2011). Electric vehicles are promoted as a way to reduce carbon emissions associated with the transportation sector, which accounts for 14.3% of anthropogenic greenhouse gas emissions (OECD International Transport Forum, 2010). However, the sustainability of lithium procurement will influence the overall environmental impact of this proposed “green” solution. It is estimated that 66% of the world’s lithium resource is contained in natural brines, 24% in pegmatites, and 8% in sedimentary rocks such as hectorite clays (Gruber et al., 2011). It has been shown that “[r]ecycling of lithium from Li-ion batteries may be a critical factor in balancing the supply of lithium with future demand” (Gruber et al., 2011). In an attempt to quantify energy and materials consumption associated with production of a unit of useful lithium compounds, industry reports and peer-reviewed scientific literature concerning lithium mining and lithium recycling were reviewed and compared. Other aspects of sustainability, such as waste or by-products produced in the production of a unit of useful lithium, were also explored. Thus, this paper will serve to further the evaluation of the comparative environmental consequences associated with lithium production via extraction versus recycling. Efficiencies must be made in both processes to maximize productivity while minimizing ecological harm.

Detection and Grading of Prostate Cancer in Temporal Enhanced Ultrasound using Convolutional Neural Networks

Prostate cancer is the most common non-dermatological cancer amongst men in the developed world. The current definitive diagnosis is core needle biopsy guided by transrectal ultrasound. However, this method suffers from low sensitivity and specificity in detecting cancer. Recently, a new ultrasound based tissue typing approach has been proposed, known as temporal enhanced ultrasound (TeUS). In this approach, a set of temporal ultrasound frames is collected from a stationary tissue location without any intentional mechanical excitation. The main aim of this thesis is to implement a deep learning-based solution for prostate cancer detection and grading using TeUS data. In the proposed solution, convolutional neural networks are trained to extract high-level features from time domain TeUS data in temporally and spatially adjacent frames in nine in vivo prostatectomy cases. This approach avoids information loss due to feature extraction and also improves cancer detection rate. The output likelihoods of two TeUS arrangements are then combined to form our novel decision support system. This deep learning-based approach results in the area under the receiver operating characteristic curve (AUC) of 0.80 and 0.73 for prostate cancer detection and grading, respectively, in leave-one-patient-out cross-validation. Recently, multi-parametric magnetic resonance imaging (mp-MRI) has been utilized to improve detection rate of aggressive prostate cancer. In this thesis, for the first time, we present the fusion of mp-MRI and TeUS for characterization of prostate cancer to compensates the deficiencies of each image modalities and improve cancer detection rate. The results obtained using TeUS are fused with those attained using consolidated mp-MRI maps from multiple MR modalities and cancer delineations on those by multiple clinicians. The proposed fusion approach yields the AUC of 0.86 in prostate cancer detection. The outcomes of this thesis emphasize the viable potential of TeUS as a tissue typing method. Employing this ultrasound-based intervention, which is non-invasive and inexpensive, can be a valuable and practical addition to enhance the current prostate cancer detection.

The SHIVir Survey: A Dynamical Catalogue of Virgo Cluster Galaxies and their Scaling Relations

By virtue of its proximity and richness, the Virgo galaxy cluster is a perfect testing ground to expand our understanding of structure formation in the Universe. Here, we present a comprehensive dynamical catalogue based on 190 Virgo cluster galaxies (VCGs) in the "Spectroscopy and H-band Imaging of the Virgo cluster" (SHIVir) survey, including kinematics and dynamical masses. Spectroscopy collected over a multi-year campaign on 4-8m telescopes was joined with optical and near-infrared imaging to create a cosmologically-representative overview of parameter distributions and scaling relations describing galaxy evolution in a rich cluster environment. The use of long-slit spectroscopy has allowed the extraction and systematic analysis of resolved kinematic profiles: Halpha rotation curves for late-type galaxies (LTGs), and velocity dispersion profiles for early-type galaxies (ETGs). The latter are shown to span a wide range of profile shapes which correlate with structural, morphological, and photometric parameters. A study of the distributions of surface brightnesses and circular velocities for ETGs and LTGs considered separately show them all to be strongly bimodal, hinting at the existence of dynamically unstable modes where the baryon and dark matter fractions may be comparable within the inner regions of galaxies. Both our Tully-Fisher relation for LTGs and Fundamental Plane analysis for ETGs exhibit the smallest scatter when a velocity metric probing the galaxy at larger radii (where the baryonic fraction becomes sub-dominant) is used: rotational velocity measured in the outer disc at the 23.5 i-mag arcsec^{-2} level, and velocity dispersion measured within an aperture of 2 effective radii, respectively. Dynamical estimates for gas-poor and gas-rich VCGs are merged into a joint analysis of the stellar-to-total mass relation (STMR), stellar TFR, and Mass-Size relation. These relations are all found to contain strong bimodalities or dichotomies between the ETG and LTG samples, alluding to a "mixed scenario'' evolutionary sequence between morphological/dynamical classes that involves both quenching and dry mergers. The unmistakable differentiation between these two galaxy classes appears robust against different classification schemes, and supports the notion that they are driven by different evolutionary histories. Future observations using integral field spectroscopy and including lower-mass galaxies should solidify this hypothesis.