Rice University Research Repository
The Rice Research Repository (R-3) provides access to research produced at Rice University, including theses and dissertations, journal articles, research center publications, datasets, and academic journals. Managed by Fondren Library, R-3 is indexed by Google and Google Scholar, follows best practices for preservation, and provides DOIs to facilitate citation. Woodson Research Center collections, including Rice Images and Documents and the Task Force on Slavery, Segregation, and Racial Injustice, have moved here.
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Recent Submissions
New technological advances in scalable manufacturing and biophysical characterization of extracellular vesicles in biomedicine
(2024-04-16) Kapoor, Kshipra; Kalluri, Raghu; Kono, Junichiro
Extracellular Vesicles (EVs) have emerged as important mediators of intercellular
communication that package and disseminate biochemical signals. This newly recognized
mode of communication between the cells has brought unprecedented therapeutic
and diagnostic opportunities making them attractive nanocarriers for clinical and
industrial translation. As the EV industry rapidly grows, there is a rising demand for
strategies that facilitate EV manufacturing. One of the most vexing issues in the field
is a method of EV isolation that can offer reliability, purity, speed, and reproducibility
and meet the stringent manufacturing standards of the pharmaceutical industry.
To overcome this challenge, in the first part of my thesis, I propose a new highyield
and rapid (<20 min) real-time EV isolation method called Size Exclusion –
Fast Performance Liquid Chromatography (SE-FPLC). We show that our method
can effectively isolate EVs from multiple sources, including EVs derived from human
and mouse cells and biofluids. The results indicate that our SE-FPLC platform
can successfully remove highly abundant protein contaminants, such as albumin and
lipoprotein complexes, which currently represent a significant hurdle in the largescale
isolation of EVs for clinical translation. Additionally, the high-yield nature of
SE-FPLC allows for easy industrial upscaling of EV production for various clinical utilities. Moreover, SE-FPLC enables analysis of very small volumes of blood for use
in point-of-care diagnostics in the clinic. Collectively, our platform offers many advantages
over current EV isolation methods and offers rapid clinical utility potential.
Once the EVs are isolated, it is imperative to perform EV physicochemical characterization
as particle shape and particle charge is pivotal in immune cell interaction.
Bulk ensemble methods quantify EV composition but mask heterogeneity. Studying
single-vesicle heterogeneity is vital, especially given their emerging role as therapeutic
cargos. In the second part of my thesis, I developed a label-free method: to
image, perform high-quality biological segmentation using a custom pre-trained neural
network model, and quantify and classify single EVs purified from a diverse set of
samples. Evaluating the heterogeneity of EVs is crucial for unraveling their complex
actions and biodistribution. We identified consistent architectural heterogeneity of
EVs using cryogenic transmission electron microscopy (cryo-TEM). Imaging EVs isolated
using different methodologies from distinct sources such as cancer cells, normal
cells, and body fluids, we identify a structural atlas of their dominantly consistent
shapes. We identify EV architectural attributes by utilizing a segmentation neural
network model. In total, 7,600 individual EVs were imaged and quantified by our
computational pipeline. Across all 7,600 independent EVs, the average eccentricity
was 0.5366, and the average equivalent diameter was 132.43 nm. The architectural
heterogeneity was consistent across all sources of EVs, independent of purification
techniques, and compromised of single spherical (S. Spherical), rod-like or tubular,
and double shapes. This openly accessible data and computation toolkit will serve as
a reference foundation for high-resolution EV images and offer insights into potential
biological impact.
New technological advances in scalable manufacturing and biophysical characterization of extracellular vesicles in biomedicine
(2024-04-16) Kapoor, Kshipra; Kalluri, Raghu; Kono, Junichiro
Extracellular Vesicles (EVs) have emerged as important mediators of intercellular
communication that package and disseminate biochemical signals. This newly recognized
mode of communication between the cells has brought unprecedented therapeutic
and diagnostic opportunities making them attractive nanocarriers for clinical and
industrial translation. As the EV industry rapidly grows, there is a rising demand for
strategies that facilitate EV manufacturing. One of the most vexing issues in the field
is a method of EV isolation that can offer reliability, purity, speed, and reproducibility
and meet the stringent manufacturing standards of the pharmaceutical industry.
To overcome this challenge, in the first part of my thesis, I propose a new highyield
and rapid (<20 min) real-time EV isolation method called Size Exclusion –
Fast Performance Liquid Chromatography (SE-FPLC). We show that our method
can effectively isolate EVs from multiple sources, including EVs derived from human
and mouse cells and biofluids. The results indicate that our SE-FPLC platform
can successfully remove highly abundant protein contaminants, such as albumin and
lipoprotein complexes, which currently represent a significant hurdle in the largescale
isolation of EVs for clinical translation. Additionally, the high-yield nature of
SE-FPLC allows for easy industrial upscaling of EV production for various clinical utilities. Moreover, SE-FPLC enables analysis of very small volumes of blood for use
in point-of-care diagnostics in the clinic. Collectively, our platform offers many advantages
over current EV isolation methods and offers rapid clinical utility potential.
Once the EVs are isolated, it is imperative to perform EV physicochemical characterization
as particle shape and particle charge is pivotal in immune cell interaction.
Bulk ensemble methods quantify EV composition but mask heterogeneity. Studying
single-vesicle heterogeneity is vital, especially given their emerging role as therapeutic
cargos. In the second part of my thesis, I developed a label-free method: to
image, perform high-quality biological segmentation using a custom pre-trained neural
network model, and quantify and classify single EVs purified from a diverse set of
samples. Evaluating the heterogeneity of EVs is crucial for unraveling their complex
actions and biodistribution. We identified consistent architectural heterogeneity of
EVs using cryogenic transmission electron microscopy (cryo-TEM). Imaging EVs isolated
using different methodologies from distinct sources such as cancer cells, normal
cells, and body fluids, we identify a structural atlas of their dominantly consistent
shapes. We identify EV architectural attributes by utilizing a segmentation neural
network model. In total, 7,600 individual EVs were imaged and quantified by our
computational pipeline. Across all 7,600 independent EVs, the average eccentricity
was 0.5366, and the average equivalent diameter was 132.43 nm. The architectural
heterogeneity was consistent across all sources of EVs, independent of purification
techniques, and compromised of single spherical (S. Spherical), rod-like or tubular,
and double shapes. This openly accessible data and computation toolkit will serve as
a reference foundation for high-resolution EV images and offer insights into potential
biological impact.
Stochasticity, Stability, and Hysteresis in the Biogeochemical Cycling of Carbon and Silicon
(2024-04-18) Hou, Yi; Torres, Mark A
The cycling of elements between surface environments and the rock reservoir sets the chemistry of the atmosphere, natural waters, and soils. Carbon (C) and Silicon (Si) are particularly interesting as they are important in controlling our planet’s climate and habitability. However, the rates at which relevant biogeochemical processes drive and respond to environmental change remain uncertain. This thesis furthers the mechanistic understanding of two key processes, organic carbon (OC) burial and silicate weathering, to provide new, quantitative constraints on how biogeochemical cycles respond to environmental changes.
Chapter one evaluates how OC burial is affected by sedimentation dynamics. Due to the internal dynamics in sedimentary systems, sedimentation rates at a discrete location appear virtually random. To investigate the previously unknown effect of this stochasticity on OC burial, reactive-transport modeling was coupled with statistical methods. The results show that this stochasticity alone can profoundly alter OC burial efficiencies and create autogenic signals independent of climatic or environmental forcings. Likely, these autogenic signals are prevalent in observed chemostratigraphic records.
Chapter two demonstrates how silicate weathering responds to glaciation. A novel multi-proxy model was developed leveraging field observations. This model was used to constrain weathering flux changes over the past 10 ka in two Icelandic watersheds with different glacial histories. The results show a synchronous increase in weathering fluxes with the expansion of glaciers. This positive effect of glaciation on weathering my allow for rapid transitions between Earth’s glacial and interglacial (i.e. bistable) climate states.
Chapter three examines the glacial control on secondary phase formation during silicate weathering. The extent of secondary clay formation in a recently deglaciated and a currently glaciated catchment was constrained by chemical, isotopic, and mineralogical compositions of river and suspended sediments. The observed spatial heterogeneity, modulated by landscape type, suggests that secondary clay formation depends on the history of glaciation and that the influence of glaciers on environmental processes persists beyond deglaciation.
Topological Photonic Devices in the UV-visible Spectrum Based on the III-N Wide Bandgap Semiconductor Platform
(2024-04-19) Li, Tao; Zhao, Yuji; Huang, Shengxi; Chen, Songtao
Topological photonics, renowned for the edge/interface states resistant to local defects and back-scattering, can be a promising solution for ensuring the stability in integrated photonic platforms and has already found applications in lasers and quantum photonic circuits. However, existing topological photonic demonstrations have primarily operated in the microwave or near-infrared spectrum due to material and nanofabrication limitations. In this thesis, we break through this wavelength barrier and extend the limit into UV-visible spectrum by implementing topological photonics on the III-N wide bandgap semiconductor platform.
In the first part of the thesis, we devise a 1D topological photonic cavity fabricated from a gallium nitride on silicon (GaN-on-Si) wafer. The designed cavity has a single resonance mode around the wavelength of 800 nm and shows a simulated quality factor (Q) around 1600. Based on the non-zero second-order susceptibility of the GaN, we further demonstrate the second harmonic generation (SHG) from the 1D topological photonic cavity and reveal the power dependence and polarization dependence of the cavity-based SHG.
The second part of the thesis focuses on the design of topological photonic routing devices in the visible spectrum based on 2D photonic crystals (PC) made of hexagonal boron nitride (h-BN). Interfacing 2D h-BN PCs with distinct topological phases gives rise to topological edge states supporting polarization-resolved unidirectional propagation. Through meticulous design of the interfaces’ shape, we demonstrate ultra-compact topological photonic routers. These routers feature 6 input/output ports within a 10 µm × 10 µm footprint and showcase a simulated crosstalk extinction ratio exceeding 15 dB.
The results from this thesis underpin the UV-visible topological photonics based on the III-N wide bandgap semiconductor platform and can potentially benefit the design of high-performance integrated photonic devices in the UV-visible spectrum by leveraging the unique properties of photonic topology.
An enhanced approach to signal analysis in the XENONnT dark matter experiment
(2024-01-17) Farrell, Sophia Judith; Higuera, Aaron
The XENONnT experiment is a dark matter detector that strives to perform the most sensitive search for particle dark matter to date. Functioning as a dual-phase time projection chamber, XENONnT relies upon unprecedentedly low background rates, which are achieved through a combination of hardware purification technology and software and analysis techniques. In particular, solar neutrinos pose an irreducible background source to XENONnT, as they interact with the xenon atoms and cannot be physically shielded or vetoed due to their weak interaction strength with matter. This background, however, also presents a unique opportunity for XENONnT to search for neutrino-induced physics beyond the standard model.
The sensitivity of XENONnT is limited by the degree to which background sources dominate signal production in the detector. Analyses aim to reduce background rates through event reconstruction and selection. Background reduction methods can introduce a number of undesirable consequences, including reducing the efficiency of identifying true dark matter or neutrino signals and introducing selection bias into the final data used in analysis. This thesis presents a novel analysis of the first science data obtained with the XENONnT dark matter experiment by employing a waveform-based Bayesian network to perform signal quality selection. This technique was applied to the search for new electronic-recoil interactions in XENONnT and, by extension, was used to quantify a limit on the value of the neutrino magnetic moment.
A Bayesian network was trained on simulated and observed experimental data for classifying scintillation and ionization detector signals. The network showed improved performance for signal classification over the current software method and a baseline neural network method. The Bayesian network outputs were then used in a continuous way to perform signal characterization, identifying those detector signals that were most alike to true physics interactions. This characterization reduced events outside of the analysis region of interest independently of the methods developed in the standard analysis of XENONnT. Compared to the traditional analysis method, this method of data selection improved XENONnT's efficiency of true signals by 3%. The analysis confirmed the background-only hypothesis of electronic recoil data from XENONnT's first science run. A test of a neutrino magnetic moment component in the data yielded a 90% confidence-level upper limit of 1.29 × 10^(−11)μ_B.
The Bayesian network method of signal characterization offers several advantages for the analysis of a dark matter detection experiment. First, the method provides a valid cross-check to an important experimental result. This improves the confidence of the finding reported by the XENONnT experiment and can be utilized in many applications to verify experimental results and check for biases in data analysis. Second, the method reduces potential systematic uncertainties due to the sequential development of highly optimized data selection criteria, where each introduces the possibility of a systematic effect in the analysis dimension(s). The traditional data analysis method relies on an approach of calculating efficiencies and selection criteria definitions that are degenerate (one cannot be used independently of the other). By contrast, in this work, the definition of the Bayesian network-based signal characterization boundary is independent of the calculation of efficiency applied in the analysis. Finally, the result of using fewer cuts optimized on efficiency results in an improved signal acceptance.
Bayesian networks are widely applicable to dark matter and neutrino physics experiments, where signal classification and characterization are central to sensitive measurements. In the future, analysis methods such as this can be used to perform valuable verification of experimental results and for the reduction of backgrounds in cases where waveform-based analysis is beneficial.