Brittle-to-Ductile Transitions of Polyelectrolyte Complexes: Humidity, Temperature, and Salt

Polyelectrolyte complexation is an entropically driven, associative phase separation that results in a polymer-rich polyelectrolyte complex (PEC) and a polymer-poor supernatant. PECs show promise as a new class of sustainable materials since they can be processed using aqueous solutions rather than organic solvents. Previous reports have looked at the mechanical properties and glass transitions of PECs as a function of temperature, relative humidity (rH), and salt concentration (CS), but establishing a universal understanding of how these parameters affect PEC mechanics has yet to be achieved. We examined the effects of temperature, rH, and CS on the mechanical properties of PECs formed from poly(methacrylic acid) and poly(trimethyl aminoethyl methacrylate) with a goal of establishing design rules for their mechanical response. Relative humidity was shown to have the most dramatic effect on the mechanical properties, with temperature and salt concentration having far less of an impact. Furthermore, we observed that the glass transition of PECs is tied to both temperature and relative humidity, creating a glass transition rHg/Tg line that can be modulated by added salt. Finally, we looked at the thermodynamics behind the glass transition of PECs, which yielded similar energies as the condensation of water. We propose the use of water and/or salt as a low energy and efficient method of processing PECs for various applications

Feasts Combined With Interferometry. Iii. The Low Column Density H <sc>i</sc> Around M51 And Possibility Of Turbulent-mixing Gas Accretion

With a new joint-deconvolution pipeline, we combine the single-dish and interferometric atomic hydrogen (H i) data of M51 observed by the Five-hundred-meter Aperture Spherical radio Telescope (FAST) (FEASTS program) and the Very Large Array (VLA) (THINGS). The product data cube has a typical line width of 13 km s-1 and a 2 sigma line-of-sight (LOS) sensitivity of H i column density NH i similar to 3.2 x 18 cm-2 at a spatial resolution of similar to 18 '' (similar to 0.7 kpc). Among the H i detected LOSs extending to similar to 50 kpc, similar to 89% consist of diffuse H i only, which is missed by previous VLA observations. The distribution of dense H i is reproduced by previous hydrodynamical simulations of this system, but the diffuse component is not, likely due to unresolved physics related to the interaction between the circumgalactic and interstellar media. With simple models, we find that these low NH i structures could survive the background ultraviolet photoionization, but are susceptible to the thermal evaporation. We find a positive correlation between LOS velocity dispersion (sigma v) and NH i with a logarithmic index of similar to 0.5. Based on existing turbulent mixing layer (TML) theories and simulations, we propose a scenario of hot gas cooling and accreting onto the disk through a TML, which could reproduce the observed power index of similar to 0.5. We estimate the related cooling and accretion rates to be roughly one-third to two-thirds of the star formation rate. A typical column density of diffuse H i (similar to 1019 cm-2) can be accreted within 300 Myr, the interaction timescale previously estimated for the system. Such a gas accretion channel has been overlooked before, and may be important for gas-rich interacting systems and for high-redshift galaxy evolution

EVALUATION OF DEFENSE AND ATTACK STRATEGIES FOR CHIPLET-BASED SYSTEMS

Industry trends are moving toward chiplets as a replacement for monolithic fabrication. A chiplet is a separately-produced silicon die that when packaged together with other chiplets on a silicon interposer creates a system-on-chip (SoC). Chiplets bring about many benefits: they enable IP reuse, allow for heterogeneous integration, and provide the ability to leverage cost-appropriate process nodes, all while decreasing cost and improving manufacturing yield. Yet, creating systems from separately produced components also brings many new security risks to consider, such as the possibility of die-swapping, physical tampering, and probing, which differ greatly from the threat model of a single-die system. As a new technology coming to market, security questions surrounding chiplets still need to be answered. In this dissertation, we evaluate some of the commonly discussed security threats that chiplets face and provide mitigations for them. Initially, we study a new type of FPGA time-to-digital converter (TDC) sensor for voltage monitoring and side-channel analysis. The sensor described relies on dynamic phase shifting of two clocks and repeated sampling to increase its resolution by 560x and achieve a sub-cycle sampling time. We demonstrate its capabilities by reconstructing the sub-cycle fluctuations in the supply voltage of an FPGA caused by a large number of power wasters activating at the same time during a power attack. In the second part of this dissertation, we adapt this new sensor to realize a delay-based physically unclonable function (PUF). The PUF derives its uniqueness from the variations in delays of interposer wires routing signals between neighboring chiplets. We find that the output of the PUF provides a unique and reliable fingerprint that can be used to authenticate systems, verify their integrity, and provide active protection against physical tampering and probing. We test our PUF at scale using Amazon’s Elastic Compute Cloud F1 instances and perform analysis to pinpoint the source of its entropy. Finally, in the last part, we conduct the first documented probing attack against a Xilinx VU9P chiplet-based FPGA. Using the Hamamatsu PHEMOS-X laser probing microscope we employ Electro-Optical Frequency Mapping (EOFM) to locate the interconnect drivers on the FPGA and then follow up with Electro-Optical Probing (EOP) to read out the data they are transmitting. Our findings indicate that probing chiplet interfaces requires significantly less effort than probing internal nodes, thereby highlighting a unique vulnerability of chiplets relative to monolithic integrated circuits (ICs). Furthermore, we deploy two delay sensors, one based on dynamic phase shifting and one that utilizes a TDC, in an attempt to defend against contactless probing. We, however, find that despite being capable of detecting laser probes, delay-based sensors do not offer adequate protection as the change in wire delay is too small to be distinguishable from noise and changes due to localized heating. In lieu, we offer a way of masking bus data to prevent waveform integration and hence data readout.Doctor of Philosophy (Ph.D.)2026-02-0

Institutional Output Analysis

Code used for review of the Institutional Output Analysis project. This project's goal was to parse through and analyze the publishing footprint of our faculty colleagues across the university and to look at what the spread of open access might be for the scholarly output to compare against the University's policies. This script is used to merge our three data sources (Scopus, The Lens, and OpenAlex) and to prepare this larger dataset for further analysis

The Role of Cis-acting Elements, RNA Modification, and Cellular Localization of mRNA Transcripts During KSHV Lytic Infection

Exploitation and control of the cellular environment is the cornerstone of successful viral infection, with struggle for control over gene expression machinery being a driving force for the evolutionary arms race between viruses and their hosts. During infection, different viruses utilize a wide range of strategies to prioritize viral transcripts over host transcripts. In both Coronavirus and Kaposi’s sarcoma associated herpesvirus (KSHV) this is driven by a wide-spread RNA decay event which leads to the destruction of host RNA as driven by viral proteins. In KSHV, this process is driven by multiple factors during the duration of KSHV lytic infection with a viral endoribonuclease, SOX, being the primary trigger and a process known as hyperadenylation, triggered by SOX activity and SOX nuclear relocalization. In Coronavirus, this process is driven by the nsp1 protein, which has variable activity between different Coronaviruses, but specifically targets and degrades host transcripts, primarily through interactions with the ribosome. However, in both conditions, there exists subsets of transcripts that are resistant to these viral decay events, of both host and viral origin. These resistant transcripts are protected from decay through a myriad of different mechanisms related to RNA secondary structure, RNA modifications, and cellular localization. In this dissertation, we will explore the role of these different factors in two separate viral systems to investigate the impact of viral activity on RNA stability, to better our understanding of viral-host interplay in the RNA landscape. In the first chapter, we will explore the impact of four different Coronavirus nsp1 proteins and how the different classifications of viruses utilize their homologs in a variety of ways to impact the host transcriptional landscape. In the second chapter, we will explore work dedicated to understanding our primary escapee, human Interleukin-6 (IL-6), and the impact of RNA secondary structure, nuclear export, and virally induced RNA modification on this transcript. Finally, in the third chapter, we extend exploration into the polyA landscape of transcripts during KSHV lytic reactivation to better understand the impact of hyperadenylation on the host transcriptome. To achieve this we performed PolyA-seq to explore the changes in polyA tail length as well as subcellular localization of transcripts as well as experiments exploring the impact of CRM1 inhibition on the stability and localization of these transcripts.Doctor of Philosophy (Ph.D.

Time Aware Intelligence for Efficient and Resilient Control

Current artificial intelligence (AI) algorithms for control lack temporal awareness: an understanding of time beyond the mere chronology of events. As a result, these systems cannot adapt their outputs to dynamic temporal contexts, which is critical in real-time control where the optimal action may change rapidly with the passage of time. This thesis explores how introducing time-awareness into control algorithms improves exploration, accelerates learning, enhances compute efficiency, and increases robustness to missing data; all while reducing the need for high-frequency decision-making. I identify three time-awareness mechanisms inspired by the brain: (1) diversity in processing speeds, (2) internal oscillators, and (3) action chunking through fast experience replay. Based on these principles, I propose three novel algorithms that advance the state of the art in continuous control. Temporally Layered Architecture (TLA) employs multiple policies operating at different step sizes to discover Pareto-optimal tradeoffs between accuracy and energy. Despite training three policies in parallel, TLA converges faster due to improved exploration from slower layers. The resulting policies require significantly fewer decisions and compute while maintaining state-of-the-art performance. Sensory Layered Architecture (SLA) extends the TLA concept to the sensory input space, using policies with increasing levels of sensory information to discover Pareto-optimal tradeoffs between information and accuracy. The first layer relies solely on internal signals (e.g., previous actions and an oscillator), enabling robust performance even under input occlusion or noise. SLA demonstrates that training with diverse sensory inputs yields emergent robustness not achievable when layers are trained in isolation. Sequence Reinforcement Learning (SRL) introduces a frequency gap between the actor and critic to learn action chunks—solving a key limitation in continuous control: sensitivity to timestep choice. SRL separates decision and actuation frequencies, dramatically reducing the number of decisions needed without compromising performance. On benchmark tasks, SRL achieves state-of-the-art results at human-level decision rates, paving the way for deployment on low-compute, low-frequency hardware. Together, these algorithms support efficient and resilient control. TLA and SRL allow for ultra-low decision rates in stable settings and rapid adaptation in unpredictable environments. SLA adds robustness to sensory disruptions and adversarial noise. Finally, I propose a general framework for building biologically inspired AI under real-world constraints—energy, information, time, damage, and more—as an alternative to the prevailing paradigm of scaling compute, data, and energy. This approach charts a biologically grounded path toward practical, efficient, and adaptive intelligence.Doctor of Philosophy (Ph.D.

Energetic Materials for Biological and Electronic Applications

Conjugated polymeric materials represent a promising next step for electronic applications. As organic semiconductors (OSCs), they offer key advantages over their inorganic counterparts, including solution-processability, mechanical flexibility, and compatibility with low-cost fabrication techniques. To enable efficient charge transport, OSCs typically require doping to generate charge carriers. However, this creates instabilities and changes within the material. In this dissertation, I investigate the doping mechanism of an n-type dopant and in parallel, I explore strategies to improve charge transport in amorphous p-type conjugated polymers, where disordered morphology poses a significant challenge. Additionally, I examine a biological application that employs abiotic triphosphate compounds to modulate muscle function, expanding the potential of synthetic systems in bioelectronic contexts. Collectively, this work advances the understanding of doping mechanisms in n-type polymers and proposes new methods to enhance electrical performance in disordered systems, paving the way toward cost-effective synthesis and broader application of conjugated polymers.Doctor of Philosophy (Ph.D.)2026-09-0

Detecting Frailty Using Wearable Device-Measured 24-Hour Movement Behaviors in Older Adults

Frailty is a common geriatric syndrome associated with an increased risk of adverse health outcomes, yet its assessment in clinical and research settings remains limited due to reliance on subjective measures and time constraints. The goal of this dissertation was to improve frailty assessment in older adults by leveraging wearable device–measured 24-hour movement behaviors through two approaches: (1) detecting frailty using machine learning models based on accelerometer-derived movement behaviors, and (2) refining the Fried Frailty Phenotype (FFP) by replacing the self-reported low physical activity criterion with accelerometer-measured physical activity. Study 1 was a cross-sectional study involving 44 older adults who wore a thigh-worn accelerometer (ActivPAL) for 10 consecutive days. Frailty was assessed using both the FFP and the Comprehensive Geriatric Assessment–Frailty Index (CGA-FI). Machine learning models were developed to classify frailty status, detect individual FFP components, and estimate CGA-FI scores based on 24-hour movement behavior features. Results showed that the Random Forest model had higher AUC for FFP-defined frailty, while the K-Nearest Neighbors model performed best for CGA-FI-defined frailty. The Support Vector Machine showed higher accuracy for predicting CGA-FI continuous scores. Key predictors for detecting frailty included mean steps/day and variability of stepping and standing time, and time in stepping cadence ≥100 steps/min. Study 2 was a prospective cohort study using data from 38,429 UK Biobank participants aged ≥60 years with valid wrist-worn accelerometer data, modified FFP data (FFP-Mod), and mortality follow-up. Two revised FFP definitions were created by substituting the self-reported low physical activity criterion with accelerometer-measured physical activity including the lowest quintile of: 1) overall mean acceleration (FFP-Acc) and 2) time in MVPA (FFP-MVPA). Results revealed that while individuals classified as frail or prefrail by all three FFP definitions had significantly higher all-cause mortality risk compared to robust individuals, these associations were stronger for FFP-Acc and FFP-MVPA than for FFP-Mod. In conclusion, this dissertation supports the feasibility and utility of integrating wearable-derived movement behavior metrics into frailty assessments. Machine learning models using 24-hour movement behaviors can detect frailty, and replacing self-reported physical activity with accelerometer-measured physical activity improves the predictive validity of FFP. This work contributes to the development of objective and clinically relevant frailty assessments for use in both research and public health applications.Study 1 was supported by the National Institute on Aging (P30AG073107) through the MassAITC pilot project. Study 2 was supported by the Mutual Mentoring Grant from the Office of Faculty Development at the University of Massachusetts Amherst.Doctor of Philosophy (Ph.D.

Neural Operators for Rarefied Gas Dynamics: BGK relaxation problem, Polyatomic Shock waves, and Hypersonic Cylinder Flow

This work introduces a suite of targeted methodologies utilizing neural operators—including physicsinformed, constrained, and data-driven approaches—for creating computationally efficient and robust surrogate models of rarefied gas dynamics, a domain where high-fidelity kinetic solvers are often prohibitively expensive. We present three key contributions demonstrating stability, physics-discovery capabilities, and powerful generalization across distinct challenges. First, for the BGK kinetic relaxation problem, we introduce a novel perturbation ansatz that ensures numerical stability. We leverage this stabilized physics-informed neural network (PINN) framework to solve a challenging combined forward and inverse problem, demonstrating its capability as a physics-discovery tool by successfully inferring the unknown, velocity-dependent relaxation time using only the initial conditions and governing equations. Second, for one-dimensional shock waves in polyatomic gases, we develop a physics-constrained Deep Operator Network (DeepONet) that accurately captures complex non-equilibrium structures for unseen viscosity ratios by embedding monotonicity constraints directly into the learning process, eliminating non-physical oscillations. Finally, we demonstrate significant data efficiency and powerful generalization for two-dimensional hypersonic flow over a cylinder. A data-driven DeepONet ensemble, trained on a dataset spanning a wide hypersonic regime from Mach 5 to 15, accurately predicts the flow field for multiple unseen conditions. The resulting surrogate excels at interpolation (e.g., predicting Mach 7, 9, 12, and 14), and the extrapolation case (predicting Mach 15 while trained on Machs 5-14) with quantitative validation confirming high accuracy across the parametric range. This work establishes a powerful, flexible methodology for building neural operator surrogates that ensure physical consistency, perform physics-discovery, and generalize robustly, significantly lowering the computational barrier for design and analysis in high-speed aerodynamics