This research addresses the critical challenge of sustaining advancements in computational performance and energy efficiency in the wake of Moore’s law reaching its physical limits.
Since computer systems face increasing demands driven by data-intensive applications such as artificial intelligence workloads, alternative nanoscale design paradigms are essential. This study discusses Quantum-dot Cellular Automata (QCA) as a potential post-CMOS technology, offering a different approach to circuit design based on quantum dot arrangements and electron interactions governed by Coulomb repulsion. The investigation begins with a foundational understanding of QCA’s building blocks, including cell architecture, binary encoding mechanisms, and majority gate logic. To illustrate QCA’s practical potential, three to several core circuit designs— explored and analyzed. These designs serve to demonstrate how QCA-based systems may replicate or surpass the functionality of traditional silicon-based architectures. The study focuses on evaluating QCA through key performance metrics: speed, power consumption, and spatial density. By comparing these metrics against the limitations imposed by classical CMOS scaling, this research aims to assess the viability of QCA in addressing critical bottlenecks in modern computing systems. Ultimately, this work contributes to the exploration of novel design paradigms capable of enabling future generations of high-performance, low-power computing architectures beyond the capabilities of conventional silicon technologies.

Dimethyl nicotinamide is a molecule that contains an amide C-N bond, which exhibits a barrier to internal rotation about the C-N bond. As a result, the hydrogen atoms on the amine group appear chemically non-equivalent at room temperature. This non-equivalence is demonstrated by NMR spectroscopy as the two types of hydrogen atoms are displayed as two distinct peaks in the NMR spectrum. Our lab has previously demonstrated that the barrier to internal rotation of dimethyl nicotinamide is affected by the nature of the solvent it is dissolved in and the concentration of the compounds within the solution. The purpose of this study is to continue the work of examining how the chemical environment in which dimethyl nicotinamide is dissolved affects the barrier to internal rotation. Specifically, these experiments will focus on examining how the solvent and concentrations affect the magnitude of the barrier to internal rotation about the C-N bond. This will be accomplished by collecting variable temperature NMR spectra which will lead to an analysis of the spectral peaks as a function of temperature. The analysis will yield a value of the barrier to internal rotation that will be compared to the barriers obtained for different solvent/concentration samples.

Activated Protein Kinase (AMPK) is a key enzyme that regulates cellular energy homeostasis. In this way, it is activated under metabolic stress, such as low glucose. This study aims to explore how caffeine, a stimulant that activates muscle contraction responses, can modulate and/or regulate AMPK activity and how this effect is influenced by glucose availability. It is predicted that caffeine will increase AMPK activity in C2C12 cells with a greater response to low glucose conditions. To test this, C2C12 myoblasts will be grown for 4-5 days in either high- or low-glucose media. Cells will then go under a 0.05mM caffeine treatment for 24 hours. Buffers will be prepared to perform an AMPK enzyme assay. AMPK enzyme will be measured using the SAMS peptide-based colorimetric kinase assay. Malachite green will be used as a phosphate detection agent, and read at 620-660nm. Additionally, protein concentrations within the samples will be determined using the Bradford assay protocol. Results from this study will provide insight into metabolic regulation relating to cellular physiology and environmental stressors like glucose availability and caffeine-influenced metabolic enzyme activity in skeletal muscles.

Neurodegenerative diseases, including Multiple Sclerosis, Alzheimer’s Disease, Parkinson’s Disease, and Dementia, disproportionately affect older adults and represent a growing public health challenge amid a globally aging population. Early intervention remains challenging, as clinically relevant symptoms often appear only after significant neurological deterioration. This study seeks to identify early-stage biomarkers associated with cognitive decline and neurodegenerative disease using macaque monkeys as a traditional aging model. We hypothesize that measurable cognitive and physiological changes occur before the onset of clinical symptoms and can serve as early indicators of age-related neurodegenerative conditions. To test this, I supported efforts to engineer a high-throughput cognitive testing device that allows for macaques to complete behavioral tasks from within their home enclosures. These devices serve both as enrichment tools and as a tool to quantify cognitive performance across various domains. Simultaneously, blood and cerebrospinal fluid samples were collected to assess biomarkers of inflammation and immune activation. Alongside other lab members, I gathered extensive cognitive performance data alongside physiological samples from a diverse macaque population. Preliminary observations reveal age-related differences in cognitive outcomes and immune markers, suggesting emerging patterns of decline that warrant further analysis. By linking cognitive outcomes with biological data, with the goal in mind to identify biomarkers predictive of early cognitive decline, these insights could advance early detection methods for neurodegenerative diseases and inform research in human-centered aging and neurological research.