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Modelling topological and magnetic materials for charge and spin-based devices
No full textThe imminent halt of Moore’s law and discontinuation of scaling of transistors based on
three-dimensional materials, e.g., silicon, has prompted researchers to look for different ma-
terials and device systems apart from the conventional ones to form the backbone of the
electronics industry of the future. Topological insulators (TIs) open a vast avenue to realize
devices with high ON current and low power consumption. TIs are a class of materials with
topologically protected edge states which are spin-polarized and robust against impurity
scattering. The possibility of spin-polarization in TIs and efficient transfer of spin-current in
soft-layered magnets opens another avenue of research for realizing fast memory devices. In
this dissertation, first, we model carrier transport through imperfect two-dimensional (2D)
TI ribbons. In particular, we investigate the impact of vacancy defects on the carrier trans-
port of 2D TIs. We show that carrier transport through the topologically protected edge
states is robust against a high percentage of defects (up to 2%), whereas the carrier trans-
port through the bulk state is strongly suppressed due to backscattering. We show that the
suppression of bulk transport in 2D TIs can be used to design devices using 2D TI ribbons.
Next, we develop a computational method to model the magnetic interactions in layered
magnetic materials and calculate their critical temperature from the first principles, taking
into account both the magnetic anisotropy as well as the out-of-plane interactions. We ap-
ply our method on Cr-compounds: CrI3, CrBr3, and CrGeTe3, and FeCl2, and show that
our predictions match well with experimental values. Using the same model we next inves-
tigate the magnetic order in two-dimensional (2D) transition-metal-dichalcogenide (TMD)
monolayers: MoS2, MoSe2, MoTe2, WSe2 , and WS2 substitutionally doped with period-four
transition-metals (Ti, V, Cr, Mn, Fe, Co, Ni). We show that five distinct magnetically or-
dered states can exist among the 35 distinct TMD-dopant combinations including the non-
magnetic (NM), the ferromagnetic (FM) with out-of-plane spin polarization (Z FM), the
out-of-plane polarized clustered FMs (clustered Z FM), the in-plane polarized FMs (X–Y
FM), and the anti-ferromagnetic (AFM) state. Most remarkably, we find from our study
that V-doped MoSe2 and WSe2, and Mn-doped MoS2, are the most suitable candidates for
realizing a room-temperature FM at a 16–18% atomic substitution. We then compare three
first-principles methods (the MC, the Green’s function, and the RNSW) of calculating the
Curie temperature in 2D FMs in the presence of exchange anisotropy, modeled using the
Heisenberg model. We find that the Curie temperature obtained from the Green’s function
in high-anisotropy regimes is higher compared to MC, whereas the Curie temperature cal-
culated using the renormalized spin-waves (RNSW) is lower compared to the MC and the
Green’s function for all anisotropies. Finally, we present a theoretical model to simulate spin-
dynamics and spin-induced switching in a semiconductor-ferromagnet heterostructure. Our
theoretical model combines the non-equilibrium Green’s function method for spin-dependent
electron transport and time-quantified Monte-Carlo for simulating magnetization dynamics.
We use the adiabatic approximation for combining the electron dynamics and the magne-
tization dynamics. We study spin-induced switching in a 2D TI-FM interface. Finally, we
show that for a certain range of magnetic exchange parameters (or certain materials), it is
possible to change magnetic domains in a 2D FM using spin-torque from TIs, which can be
used for designing high-speed memories
Phonological Organization in the Production of Novel Words in Typical and Atypical Language Development
No full textA core deficit in preschoolers with developmental language disorder (DLD) is in learning words.
Words incorporate both sound patterns and the meanings to which they are mapped. Children
with DLD show particular deficits in word form rather semantic aspects of word learning
(Dollaghan, 1987; Leonard et al., 2019). Deficits are characterized by increased sound errors
relative to peers with typical language development (TD); however, little is known about the
stability of these errors over multiple productions of the same word. The objective of this project
was to investigate two aspects of phonological word form learning—the variability of syllable
sequences and the systematicity of sound feature organization—in 4-5-year-olds with DLD, agematched TD peers, and 2-year-olds with typical language. It was predicted that both children
with DLD and younger toddlers would show high levels of phonological variability (e.g.,
Ferguson & Farwell, 1975). In contrast, toddlers, but not children with DLD, were expected to
show systematicity in their errors (e.g., Vihman, 1996). A major aim was to determine whether
sound pattern deficits attested in DLD represent a qualitative deficit or are developmental in
nature. Toddlers are a critical control group for positioning the findings from children with DLD
because it is hypothesized that changes in vocabulary size influence both the variability and
systematicity of sound patterns. Therefore, this project also incorporates manipulations to assess
the lexical and phonological interface.
This dissertation includes comprehensive summaries of three manuscripts and two new studies
on phonological organization related to syllable sequence variability and sound feature
systematicity in two cohorts of children. Cohort 1 consists of 21 preschoolers with DLD and 21
peers with TD. Cohort 2 consists of 13 two-year-olds with TD and 16 four-year-olds with TD.
All children participated in a word learning task in which they imitated multiple repetitions of
novel word forms with and without a meaningful object referent. Cohort 2 also produced word
forms with a relatively simple phonological structure. Comprehension probes were included to
assess learning.
Based on broad phonetic transcriptions of each production, segmental accuracy and sound
feature accuracy of each form were computed. In addition, novel methods based on network
science were developed to assess the variability and systematicity of phonological elements at
two different levels: the sequential organization of syllables, and the bundling of phonetic
features within syllables.
Findings from this project reveal that both TD toddlers and preschoolers with DLD show
relatively high levels of syllable sequence variability when compared with TD preschoolers,
suggesting that sequential errors in the nonword productions of children with DLD are
developmental in origin. The variability in sound organization is also not explained by
differences in sound feature systematicity, which was similar across all groups studied. That is,
toddlers, preschoolers with DLD, and preschoolers with typical language showed similar patterns
of sound feature bundling within syllables. Toddlers showed higher variability than TD
preschoolers in words with both complex and relatively simpler structure, suggesting that
variability is a core feature of their novel word productions even with varying phonological
structure. Further, the variability and systematicity of toddlers’ productions were unaffected by
the inclusion of a meaningful referent; however, children with DLD showed increases in the
stability of syllable sequences and of sound feature organization when a meaningful referent was
added. Thus, children with DLD, but not toddlers, showed interactivity across semantic and
phonological levels. These findings suggest a similar developmental trajectory for children with
DLD and younger toddlers in both the variability and systematicity of phonological form. In
contrast, children with DLD are affected by the inclusion of a referent while toddlers are not,
suggesting that this aspect of lexical and phonological interaction is not developmental
Tensile Deformation of Metallic Glass: Understanding the Effects of Specimen Size, Structural State and Testing Temperature
No full textThe disordered or amorphous structure of metallic glasses results in a unique physical and
chemical feature, which makes them suitable for a variety of applications, such as precise metal
parts, sporting equipment, energy conversion technologies, transformer cores, etc. In particular,
micro- and nano-sized devices can benefit from the homogenous structure (down to nanoscale),
high strength (1-3 GPa), large elastic strain limit (2-3%), and remarkable thermoplastic formability
of metallic glasses. However, the disordered structure is also responsible for structural softening,
which results in negligible tensile ductility of metallic glasses at room temperature. The lower
tensile ductility is considered as a major drawback for limiting their applications. Due to the
structural softening, the plastic strain in metallic glasses is localized in narrow shear bands (~20–
40 nm in thickness), which results in catastrophic failure in tension. Numerous factors such as the
elastic constants, testing temperature, strain rate, sample size, and cooling rate affect the
development of the shear bands. To comprehend the origin of shear band formation and lack of
tensile ductility in metallic glasses, it is crucial to investigate the effects of these factors and
propose a comprehensive model. While the effect of many parameters on the deformation of
metallic glasses has been understood, the sample size effects have remained controversial. The
contradictory findings regarding the size effects of metallic glass are triggered by numerous
reasons, such as improper sample geometry, use of high-energy irradiation during sample
preparation and testing as well as the absence of statistically reliable data from in-situ testing. To
understand the tensile deformation behavior of metallic glass on the nanoscale, this study examines
the effect of sample diameter, structural state, and testing temperature on the shear band formation
process in a Pt57.5Cu14.7Ni5.3P22.5 (Pt-based) metallic glass. The novel thermoplastic drawing
method was developed to manufacture numerous dog-bone-shaped tensile specimens with
diameters ranging from 100 μm to 100 nm. A custom-built experimental setup was used to
fabricate and test hundreds of nanosized tensile samples from Pt-based metallic glass at different
temperatures. The fracture morphologies of the samples after tensile testing show a gradual shift
from zero ductility (shear band mediated) to ductile necking (homogenous deformation) with
decreasing sample diameter. Our observation indicates that a reduction in the testing temperature
has a similar effect on the deformation behavior in all stages as the sample diameter. With a smaller
sample size and/or lower temperature processing of metallic glass, the critical diameter of
homogenous deformation increases. The relationship between the sample size and testing
temperature in tensile fracture of metallic glasses can help in understanding the origin of ductility
in metallic glass. These findings are verified and analyzed using the current shear band formation
models for bulk specimens in metallic glasses. In addition, a comprehensive model for shear band
development in metallic glasses is proposed, which describes the effects of sample size, structural
state, and testing temperature on size-dependent changes in tensile deformation
Spatially Explicit Machine Learning Approaches for House Price Models
No full textSpatial data or georeferenced data are special in that it has spatial reference, meaning that it is
linked with geographic coordinates on Earth. The spatial component allows for the identification
of spatial patterns, relationships and trends among spatial objects. Spatial objects are usually not
randomly or independently distributed, but spatially autocorrelated. In spatial data analysis,
spatial autocorrelation has been well recognized with the advocate of spatial statistical
techniques, such as spatial clustering, spatial interpolation, spatial regression, and spatial
simulation. However, spatial effects or spatial context is largely absent in mainstream machine
learning methods. With the popularity of machine learning in various applications in both
industry and academia, a new research area has emerged in the spatial community: spatial
explicit machine learning. It refers to the use of machine learning algorithms to analyze and
predict spatial data with the explicit integration of spatial effects or patterns. It is expected to
improve the model accuracy and prediction by incorporating spatial relationships or patterns in
the data that have not been captured by traditional machine learning models and, subsequently, to
gain better understanding of the data generation mechanism.
This research utilizes Franklin County, OH residential house transaction data to explore three
different data-driven approaches to integrate spatial perspectives into traditional machine
learning algorithms: 1) imposing spatial constraints on unsupervised learning to delineate
spatially constrained housing submarkets ; 2) integrating spatial weights into the cost function of
supervised learning to improve house price prediction accuracy; and 3) enhancing data input
using spatial feature engineering in tree-based ensemble learning for modeling multiscale spatial
effects. It intends to contribute new insights for spatially explicit machine learning to the
literature.
Overall, three studies explore spatially explicit machine learning methods from three different
aspects, and the empirical results show that spatially explicit machine learning methods are
preferred over traditional machine learning methods when data have strong positive spatial
autocorrelation, or more general, data include spatial information that is important for
classification, clustering, or prediction tasks
The Role of Federal Agency Accounting Quality in Federal Budget Allocation: Evidence From Audit Opinions
No full textPoliticians allocate substantial resources among federal government agencies through budgeting,
yet little empirical evidence exists regarding whether accounting quality of federal agencies plays
a role in this political process. Using audit opinions on agency financial statements to measure
accounting quality, I find that when an agency receives a modified audit opinion, the president
proposes and Congress passes a lower budget for the agency, and the budget disagreement between
the president and Congress increases. These effects are stronger when politicians (the president
and Congress) have stronger incentives to demonstrate accountability in federal spending. To
enhance identification, I exploit the enactment of the Department of Homeland Security (DHS)
Audit Requirement Target Act, which mandated DHS to end its long-lasting modified audit
opinions. I find that the president proposes a higher budget for DHS, and the budget disagreement
for DHS decreases after the Act. These findings suggest that higher-quality federal agency
accounting plays an important role in federal budgeting by reducing information asymmetry
between bureaucrats (agencies) and politicians
Electrochemical Performance of Polymer Derived Carbon Nanofibers and Tungsten Compound/Carbon Composites
No full textCurrent energy demands and advancements in energy harvesting have driven research towards
developing storage devices with high energy and power densities to better store and deliver charge.
Battery devices fulfill many of these demands, but supercapacitor devices have garnered more
favor due to their rapid charge rate capabilities and their vast cyclability. The different
classifications of supercapcitors (electric double layer capacitors, pseudocapacitors, and hybrid
supercapcitors) rely on either physical charge storage mechanisms which provide rapid delivery
of charge, faradaic charge storage mechanisms which can be used to achieve how quantities of
charge, or a combination of the two. While advancements in the field have been great, they are
largely driven by trial-and-error approaches. To fully achieve the potential of supercapacitor
devices, a better fundamental understanding of the aspects of the electrode materials and their
influence over charge capabilities is needed.
Chapter 1 introduces supercapacitor devices, and some of the materials that can be used to make
them. It provides details on the production of activated carbon nanofibers (CNFs) derived from
electrospun polyacrylonitrile (PAN) and tungsten compounds that were used in this work.
Chapter 2 demonstrates the influence of miscibility in electrospun polymer blends on
electrochemical performance on devices with ionic liquid electrolyte. In comparison to blends of
PAN:polystyrene (PS) which have a miscibility parameter (MP) of 118, the studied blend of
PAN:poly(styrene-co-acrylonitrile) has a miscibility parameter of 79. This resulted in a distinct
morphology for phase separation of the blends, as well as a channel like morphology with
interspersed domains. Devices from the PAN:SAN blend achieved a maximum power densities of
approximately 17,500 W/kg when tested at 10 A/g in galvanostatic charge/discharge (GCD) and
achieved a maximum energy density of 81 Wh/kg at 1750 W/kg when tested at 1 A/g. This device
also boasted a capacitance retention of 82% after 3,500 cycles.
Chapter 3 describes the preparation of hybrid electrode materials derived from polymer fibers with
tungsten oxide nanoparticles and the influence of their degree of interaction on performance. A
novel synthesis was described for the preparation of WO2.72, which normally is synthesized
through a hydrothermal procedure in an autoclave, was prepared by in-situ synthesis of hybrid
tungsten compound at CNFs with CO2 activation. This hybrid material (WO@CNF) was compared
to pure WO2.72 and CNFs to determine the degree of interaction between the components in the
hybrid and determine that interactions influence on the performance of the device. The WO@CNF
material was found to have a low degree of interaction, but this still provided an improvement on
the energy storage capabilities of the material over the physical mixture composite.
Chapter 4 describes the modification of the synthesis used to make WO2.72 nanoparticles in carbon
fibers to produce WN nanoparticles and the comparison of their electrochemical performance.
Upon carbonization without activation, WN was found in the WO@CNF hybrid material. Metal
nitrides possess favorable qualities for pseudocapacitors, and hybrid supercapacitors compared to
metal oxides, and WN is not widely researched for these devices. Utilizing ammonia activation
drove the synthesis towards WN to form WN@CNF hybrid materials. The WN@CNF produced
carbons with a higher graphitic quality (Id:Ig ratio of 0.77 compared to 0.91 for WO@CNF), and
achieved comparable power densities, but possessed lower energy densities
Coarsening Droplet: Meniscus-mediated Spontaneous Droplet Climbing and Its Applications
No full textInspired by nature, fundamental investigations of droplet directional movement grow explosively
in recent years, aiming to generate energy, solve water shortage, and protect the environment. To
move the droplets, strategies were developed based on changing the property of the
droplet/substrate, and the substrate geometry. However, it is challenging to remove submicrometer
droplets from the surface, which limits the potential applications of water harvesting. In this
dissertation, I present a new spontaneous droplet movement on the hydrophilic slippery liquidinfused porous surface (SLIPS), named coarsening droplet. The coarsening effect can rapidly
remove droplets with a diameter less than 20 μm. As a mechanism, low interfacial tension of
hydrophilic SLIPS enables droplet climbing, while a high oil surface tension provides the driven
force. By applying the coarsening droplet for water harvesting, it re-evaluated the classical
condensation model since 1973, which neglected droplet disappearance (e.g., removed from the
surface) that can enhance the heat transfer. Our new model elucidates the comprehensive heat
transfer process, giving rise to a clear guideline of surface design for dropwise condensation. To
further apply our new condensation theory of rapid droplet removal, I present a vapor-liquid
separation surface to further enhance the water harvesting. By separating the water vapor with
condensed droplets, the surface is always fresh to new water nucleation and has a higher droplet
disappearance frequency with smaller droplets. In this dissertation, the coarsening droplet is
focused on the fundamental study to show the importance of droplet removal. As SLIPS is not
durable for water harvesting due to the loss of lubricants, I present a quasi-liquid surface (QLS)
by tethering flexible polymer on various solid substrates to solve the durability issue of SLIPS.
QLS shows excellent durability during water harvesting experiments, which could be further
applied to industrial applications.
In this dissertation, Chapter 1, I introduced the fundamental and current progress of droplet
directional removal. In Chapter 2, I reported the coarsening droplet and investigate the mechanism
of the coarsening droplet as surface tension force. In Chapter 3, I apply the coarsening droplet for
water harvesting. The self-propelled coarsening droplet on hydrophilic SLIPS shows rapid
removal of condensed submicrometer droplets regardless of surface orientations, showing a
promising approach in water harvesting. In Chapter 4, I re-evaluate the condensation model on a
hydrophilic slippery liquid-infused surface. I propose a modified condensation model by
considering the droplet coverage ratio and removal frequency, which can precisely predict the heat
flux. In Chapter 5 I further enhance the water harvesting by achieving vapor-liquid separation on
T-shape structures. In Chapter 6, a quasi-liquid surface (QLS) is investigated to solve the durability
issue of SLIPS. In Chapter 7, I summarized all my contributions and novelties
The Effects of Supply Uncertainty on Voluntary Disclosure of Demand
No full textSupply disruptions are increasing in magnitude and frequency imbuing our global economy with
greater levels of supply uncertainty. In Cournot duopoly, I examine how supply uncertainty
affects the voluntary disclosure of a firm’s private demand information. In addition to supply
uncertainty, I allow for variation in product substitutability (substitutability effect) and in the
degree to which private demand information affects the rival firm’s demand (spillover effect). I
show that the partial disclosure equilibrium depends on a firm’s competitive advantage
characterized by the relative importance of the spillover effect, substitutability effect, and the
firms’ supply uncertainty. In particular, I show that absent supply uncertainty the firm with
private information about product demand discloses good news (bad news) when the
substitutability effect (spillover effect) is most pronounced. By incorporating the role of supply
uncertainty in a firm’s disclosure decision, I demonstrate that supply uncertainty diminishes the
substitutability effect and enhances the spillover effect. I show that with supply uncertainty the
firm possessing private information about product demand discloses good news (bad news) when
the supply uncertainty does not (does) mute the relative dominance of the substitutability effect
over the spillover effect. I also show that the disclosure region for both good news and bad news,
in general, decreases with increases in supply uncertainty of both the firm and its rival
First Principles Studies on Oxide Semiconductors for Back-end-ofline Transistor Applications
No full textThe development of high-performance p-type and n-type oxides with good carrier mobilities and
wide band gaps is critical for the applications of metal oxide (MO) semiconductors in back-endof-line (BEOL) CMOS devices. [S. Salahuddin et al. Nat Electron. 1, 442 (2018)] Currently
available oxide semiconductors are limited to n-type conduction, and p-type oxides have inferior
performance due to carrier mobilities and dopability which are significantly lower than that of their
n-type counterparts. This thesis devotes to studying and identifying novel high mobility p-type
oxide candidates with wide band gaps and robust phase stabilities. Using first principles studies,
we have identified several promising candidates including Rb2Sn2O3, TiSnO3, Ta2SnO6, and
Sn5(PO5)2 that would be of interest as high-mobility p-type oxides. An engineering method is also
developed to enhance the bandgap and hole mobility in widely investigated p-type oxide SnO.
Amorphous phase engineering is revealed effectively improving the hole dopability and hole
transport. Electron transport study on n-type oxides sheds light on the defect controlling and film
density engineering in improving the n-type BEOL device performances. Our results provide
fundamental materials insights into rational design of high mobility oxides semiconductors and
serve as a guide for experimental realization of oxide semiconductors based BEOL transistors
Role of Noradrenergic Signaling in the Medial Prefrontal Cortex in Extinction Enhancement Produced by Vagus Nerve Stimulation
No full textExposure-based therapy is the gold standard treatment for Post-Traumatic Stress Disorder (PTSD).
This therapy relies on extinguishing traumatic fears by creating new safe associations that
outcompete maladaptive conditioned fear responses. Although being an efficacious practice,
exposure-based therapy can be improved upon. Vagus nerve stimulation (VNS) shows promise as
an adjunctive strategy due to its ability to enhance fear extinction. Despite this promise, the precise
neural mechanisms by which VNS-paired extinction training enhances fear extinction remain
largely unclear. Elucidation of such mechanisms could enable opportunities to fine tune VNS
parameters and improve the efficacy of exposure-based therapy. The present study investigated
the role of noradrenergic signaling in the medial prefrontal cortex (PFC) on extinction
enhancement produced by vagus nerve stimulation. Adult male Sprague-Dawley rats underwent 2
days of auditory fear conditioning (AFC), followed by the implantation of a vagus nerve
stimulation device and the infusion of the immunotoxin saporin (SAP) in the medial prefrontal
cortex to selectively lesion incoming noradrenergic projections to that region. After recovery, rats
underwent 8 days of extinction training paired with VNS or SHAM stimulation before extinction
memory recall was tested and anxiety was examined. Extinction training results showed that SAP
lesions in the PFC led to a negation of VNS-enhanced extinction effects. Extinction recall results
showed rats that received VNS-paired extinction without SAP lesions did not exhibit an increase
in conditioned fear response when tested over 2 weeks after extinction training. No differences in
anxiety-like behaviors were observed when rats were examined in the Open field test 2 days after
extinction training. Together, the results of this study underscore the importance of noradrenergic
signaling in the medial prefrontal cortex on VNS-paired extinction training. Further research needs
to be conducted on the role noradrenergic signaling in the medial prefrontal cortex in the context
of generalized anxiolysis produced by VNS-paired extinction training