150813 research outputs found
Sort by
Recycling and Regeneration of Spent Perfusion Media via Ion Concentration Polarization
No full textThe widespread adoption of monoclonal antibody therapies is often constrained by their high prices, which can limit accessibility, particularly for patients in low- and middle-income countries. Addressing this economic barrier is crucial to ensure that life-saving treatments can reach all who need them. We present a series of bioprocessing innovations designed to reduce the cost of monoclonal antibody manufacturing and improve global access to these critical therapeutics. The work focuses on developing technologies for media regeneration and recycling, with the goal of reducing the economic and environmental impact of cell culture media in perfusion mammalian cell culture.
We demonstrate a microfluidic separation device engineered to selectively remove metabolic waste products—specifically ammonia and lactate—from spent media using ion concentration polarization. Building on this foundation, a scalable millifluidic system was developed to enable higher-throughput waste removal. We characterized the impact of media recycling upon batch and perfusion cell cultures. We devised a nutrient supplementation strategy to create ‘regenerated’ media that minimized any effect on cell growth and productivity compared to fresh media.
To support continuous manufacturing, a perfusion culture system incorporating a microfluidic spiral cell retention device and continuous cell bleed was established, and stable performance was maintained over extended durations. A further innovation introduced a multi-stage waste recovery system that increased media regeneration yield to 87.5%. This recovery rate enabled a self-recycling perfusion bioreactor in which 75% of the media feed was regenerated, without significant impact on cell growth, productivity, or product quality.
Together, these advances establish a novel biomanufacturing platform that combines electrokinetic waste removal with media regeneration and recycling. The approach is broadly adaptable to mammalian cell culture processes and offers a promising path toward more sustainable, cost-effective, and environmentally responsible production of monoclonal antibodies and other biologics.Ph.D
Time-Domain Astrophysics with the Transiting Exoplanet Survey Satellite
No full textThe Transiting Exoplanet Survey Satellite (TESS) is conducting an all-sky survey with the primary aim of detecting planets orbiting nearby stars. However, its large field of view and 200 s imaging cadence are useful for other science cases, ranging from stellar astrophysics to transient science. This thesis focuses on using TESS to study both the circumstellar environment and stellar interiors, as well as using the satellite to detect and characterize optical emission from gamma-ray bursts (GRBs). Chapter 2 focuses on the discovery of HD 135348, a "rigidly rotating magnetospheric" star–wherein the stellar magnetic field traps dust in a co-rotating orbit and leads to complex periodic photometric modulations–using solely photometric data. Chapter 3 focuses on the discovery of a long-period subdwarf B (sdB) star using 20 s cadence TESS data and proposes a novel formation mechanism for long-period sdB stars that relies upon stable, nonconservative mass transfer. Chapters 4 and 5 focus on pulsating stars in close binaries, and the evolutionary insights that these "tidally tilted" pulsations enable. In particular, we focus on developing models to track the amplitude and phase of these pulsations as a function of orbital phase, as well as tools to perform physically-motivated modeling of the binary components. Chapters 6-7 focus on the optical signatures of gamma-ray bursts in TESS, and analyze the prompt optical flash that is often observed contemporaneously with the high-energy emission from these bursts. Chapter 7, in particular, aims to connect the prompt optical flash to the high-energy spectral energy distribution (SED), and explains the suppression of the optical flash (compared to the extrapolation of the high-energy SED) by invoking dust extinction in the host galaxy. This thesis represents a significant step forward in both stellar and transient astrophysics; throughout this work, we emphasize the use of an unconventional tool–TESS–to pursue timely scientific questions.Ph.D
A Career in Catalysis: Mark E. Davis
No full textMark E. Davis led an independent research program from 1981 to 2023, beginning at the Virginia Polytechnic Institute and State University (VPI) and then transitioning to the California Institute of Technology (Caltech). His research program was marked by exceptional creativity, breadth, and depth. With classical training in reaction engineering, Davis developed expertise in experimental heterogeneous catalysis and led work in this discipline for more than 40 years. His name is synonymous with zeolites, and today, he is one of the most widely recognized experts in zeolite synthesis, characterization, and catalysis in the world. Early work at the VPI focused on zeolites and catalysis with supported metal coordination complexes. His creativity was evident at the earliest stages of his career, with the development of supported aqueous phase catalysts and the world’s first crystalline, extra-large pore molecular sieve, both reported in the late 1980s. A move to Caltech saw a significant expansion of his zeolite synthesis program and the rapid acceleration of a multidecade collaboration with Dr. Stacey I. Zones of Chevron. At Caltech, his work expanded to include studies of molecular recognition and catalysis with organic/inorganic hybrid materials, and he developed a large, parallel program in drug delivery. His work on catalysis heavily emphasized zeolite catalysis, including major thrusts on the conversion of sugars in the liquid phase and methanol in the gas phase. Numerous new zeolites and molecular sieves were discovered throughout the four decades of the Davis laboratory, highlighted by a successful, multidecade quest to prepare a chiral zeolite with enantioselective catalytic properties. Davis is one of the most decorated researchers of the last four decades. He is one of only 21 living people currently elected to all of the US National Academies (Engineering, Science, Medicine) and elected as a Fellow of the National Academy of Inventors. He was the first engineer to win the NSF’s Alan T. Waterman Award and is one of only two researchers (to date) to win the International Zeolite Association’s Donald Breck Award twice (1989, 2019). Awards from the ACS (Ipatieff, Murphree, and Somorjai Awards), AIChE (Colburn, Professional Progress Awards), and North American Catalysis Society (Emmett Award) are among his accolades
Expanding the Phase Space of Photons in Matter: From High-Throughput Screening to Atom-by-Atom Engineering
No full textFocusing on the topological band properties of photonic crystals and the plasmonic properties of two-dimensional metals, we seek to answer the question: what is the phase space of photons in matter? For topology, what are the physical parameters that determine whether a given photonic crystal band hosts Dirac points, a non-zero Chern number, or topologically protected corner states? And for plasmons, what are the experimentally addressable ranges of plasmonic dispersions, phase velocities, confinements, and losses? In particular, is it possible to engineer the elusive lossless plasmon? Using high-throughput screening, artificial intelligence, and atom-by-atom engineering through density functional theory, we determine the topological prevalence of photonic bands, propose two systems that evade plasmonic losses through the electron-phonon interaction, and (re)discover general physical laws that govern the geometries of photonic eigenstates.Ph.D
The Effect of the Solar Cycle on Satellite Orbital Lifetime
No full textThe lifetime of a satellite in Low Earth Orbit (LEO) is affected by the 11-year solar cycle. At a fixed altitude, increasing solar activity increases atmospheric density which leads to an increase in drag, and a decrease in mission lifetime without using propulsion to recover altitude. Satellites may have longer orbital lifetimes if more of their mission is operational during a solar minimum due to lower solar activity and lower atmospheric drag. Satellites with larger area-to-mass ratios generally have shorter orbital lifetimes than satellites with small area-to-mass ratios. Missions that get delayed and have more of their operations during solar maximum than planned originally may have too short of a mission lifetime or, conversely, may be at risk of increasing their orbital lifetime past regulatory limits (five years for satellites in LEO according to the FCC) if they launch closer to solar minimum. For example, a satellite with an area-to-mass ratio of 0.014 m2/kg – such as a 1U CubeSat – and a one-year mission that is launched in 2021 without onboard propulsion, would have an orbital lifetime of 1.051 years. However, if that mission were delayed a year, a common occurrence in the industry, it would no longer be able to achieve its mission as its orbital lifetime with a deployment in 2022 is 0.44 years. Conversely, if the same 1U CubeSat is launched during solar max in January 2025, it would have an orbital lifetime of 2.2 years, and would re-enter in February of 2027. However, if that mission were delayed a year, the satellite would launch in January 2026 and instead be in orbit for 6.4 years before re-entering. They could be fined for violating the FCC deorbit limit of five years. This thesis quantifies the effect of launch or processing delays on satellite orbital lifetime based on their orbit altitude and vehicle parameters such as mass, cross sectional area, altitude, and bus size. In general, it is found that four-year and six-year delays have the greatest effect on a satellite’s orbital lifetime because the satellite will be deorbiting almost half a solar cycle (5.5 years) from its intended deployment year. However, two-year delays can still affect satellite operators, as they can increase the orbital lifetime, even by up to 1.5 years for low area-to-mass ratio satellites in 400 km orbits and almost five years for satellites in orbits higher than 500 km. Two-year delays can also decrease the orbital lifetime of a satellite by up to 1.7 years for low area-to-mass ratio satellites in 400 km orbits and almost two years at altitudes higher than 500 km.S.M
Implementation of Sub‐Grid Scale Temperature Perturbations Induced by Non‐Orographic Gravity Waves in WACCM6
No full textAtmospheric gravity waves can play a significant role on atmospheric chemistry throughtemperature fluctuations. A recent modeling study introduced a method to implement subgrid‐scale orographicgravity‐wave‐induced temperature perturbations in the Whole Atmosphere Community Climate Model(WACCM). The model with a wave‐induced temperature parameterization was able to reproduce for example,the influence of mountain wave events on atmospheric chemistry, as highlighted in previous literature. Here weextend the subgrid‐scale wave‐induced temperature parameterization to also include non‐orographic gravitywaves arising from frontal activity and convection. We explore the impact of these waves on middle atmospherechemistry, particularly focusing on reactions that are strongly sensitive to temperature. The non‐orographicgravity waves increase the variability of chemical reaction rates, especially in the lower mesosphere. As anexample, we show that this, in turn, leads to increases in the daytime ozone variability. To demonstrate anotherimpact, we briefly investigate the role of non‐orographic gravity waves in cirrus cloud formation in this model.Consistent with findings from the previous study focusing on orographic gravity waves, non‐orographic wavesalso enhance homogeneous nucleation and increase cirrus clouds. The updated method used enables the globalchemistry‐climate model to account for both orographic and non‐orographic gravity‐wave‐induced subgrid‐scale dynamical perturbations in a consistent manner
From Campus to Commerce: Examining MIT Alumni Roles in Startup Ecosystems
No full textThe Massachusetts Institute of Technology (MIT) has a strong tradition of fostering entrepreneurship among its alumni, with more than 20% having founded for-profit ventures; significant portion of these enterprises are based in Massachusetts and California [1]. To gain deeper insights into this economic engagement, we conducted a comprehensive survey to assess MIT alumni involvement in startup ventures. From the survey data, we estimated company formation rates, sectors of operation, geographic distribution, and the influence of MIT’s educational environment on these types of pursuits. The analysis of survey responses identifies patterns and factors that influence alumni’ decisions to join startups and the results of their activities. The findings provide insights into the effectiveness of MIT’s support systems for new technologies and businesses, and inform strategies to enhance alumni contributions to innovation ecosystems. In addition, the study explores demographic variables such as gender, graduation year, and academic discipline to understand their correlation with entrepreneurial involvement. The ultimate goal is to offer an understanding of how MIT alumni contribute to the startup landscape, guiding future initiatives to support their economic impact.MN
Understanding the Milky Way with Stars
No full text"How do galaxies form?" is one of the most important questions in modern astrophysics. Hierarchical growth, the most plausible theory behind galaxy formation, suggests that galaxies, including the Milky Way, assemble through the accretion of smaller systems, over a scaffolding of the invisible Dark Matter. Such growth is evidenced by the differences in stellar structures found in the Galaxy over the last few decades, accelerated most recently by the Gaia space mission. Yet, we still lack a full picture of the formation of the Milky Way and its stellar components, and we are even further in understanding its underlying Dark Matter distribution. For the latter, discrepancies between observations and predictions from CDM model at galactic scales have sparked debate about how well this model accounts for the evolution of the Milky Way. Stellar tracers provide a powerful tool for examining these discrepancies, helping us explore the hierarchical assembly of galaxies in the Local Group and test different models for dark matter. At the same time, cosmological simulations and machine learning techniques offer a bridge between the theory and observations.
In this thesis, I combine observation of stellar kinematics and chemistry with cosmological simulations to understand the formation and evolution of the Milky Way and its satellite dwarf galaxies. I map the dark matter distributions in the Milky Way and one of its ultra-faint dwarf galaxies using stellar dynamics, combining simulations of tidal disruption with observational data to study ongoing merger events and how hierarchical assembly shaped the Milky Way today. I conduct robust machine learning searches of kinematic substructures from disrupted dwarf galaxy debris in the Milky Way and utilize stellar heavy element abundances to probe the galaxies that merged with the Milky Way in the past. Lastly, I develop synthetic surveys from simulations to bridge gaps between theory and observation, testing the robustness of current and future methodologies in understanding how the Milky Way came to be.Ph.D
Systematic Development of Healthcare AI: From Data Curation, Algorithm Optimization, Benchmark Design and Clinical Applications
No full textArtificial intelligence (AI) has brought transformative changes to healthcare industry in the recent years from various aspects, such as patient care, disease diagnosis and medical research. As healthcare systems worldwide face increasing pressure from aging populations and rising chronic disease rates, there is an urgent need for systematic approaches to develop reliable and safe AI solutions. This thesis advances the systematic development of healthcare AI through four interconnected components: data curation, algorithm optimization, benchmark design, and clinical applications. The primary contribution of this thesis focuses on establishing a comprehensive pipeline for healthcare large language models (LLMs), spanning from data curation to clinical deployment. At the data level, a rule-based filtering framework was developed to select high-quality subsets from the large pre-training corpora, significantly improving both continue pre-training and fine-tuning performance of LLMs. For safety alignment, an automated pipeline was developed for preference learning that includes preference dataset synthesis, rule-based and data-adaptive annotation, and reward model training. Additionally, two novel benchmarks were created to ensure reliability and safety of LLMs in healthcare tasks: one assessing demographic biases of LLMs across common diseases, while another assessing models’ ability to reject illogical requests from users in drug-related scenarios. Finally, LLMs were used to generating patient-friendly educational content for clinical trials, demonstrating their role in improving patient education and engagement in clinical trials. This systematic progression from data to deployment establishes a blueprint for developing safe and effective language models in healthcare settings. Beyond language models, machine learning techniques were applied on an additional healthcare task. In this project, a novel approach combining normalized cross-correlation and attention graph convolutional recurrent networks was developed to realize contactless, continuous and reliable radar-based vital signs monitoring in dynamic home environments. Through systematic data collection and algorithm optimization, the accurate heart rate can be obtained across varying radar-subject distances (2-2.5m) and subject orientations, demonstrating robust performance in real-world conditions through extensive validation in four test houses with six subjects. Collectively, these contributions advance healthcare AI development across 2 fronts: establishing frameworks for safe and effective deployment of language models in healthcare settings and enabling reliable and continuous health monitoring at-home without wearable devices.Ph.D
Supernotes: Driving Consensus in Crowd-Sourced Fact-Checking
No full textWWW ’25, Sydney, NSW, AustraliaX's Community Notes, a crowd-sourced fact-checking system, allows users to annotate potentially misleading posts. Notes rated as helpful by a diverse set of users are prominently displayed below the original post. While demonstrably effective at reducing misinformation's impact when notes are displayed, there is an opportunity for notes to appear on many more posts: for 91% of posts where at least one note is proposed, no notes ultimately achieve sufficient support from diverse users to be shown on the platform. This motivates the development of Supernotes: AI-generated notes that synthesize information from several existing community notes and are written to foster consensus among a diverse set of users. Our framework uses an LLM to generate many diverse Supernote candidates from existing proposed notes. These candidates are then evaluated by a novel scoring model, trained on millions of historical Community Notes ratings, selecting candidates that are most likely to be rated helpful by a diverse set of users. To test our framework, we ran a human subjects experiment in which we asked participants to compare the Supernotes generated by our framework to the best existing community notes for 100 sample posts. We found that participants rated the Supernotes as significantly more helpful, and when asked to choose between the two, preferred the Supernotes 75.2% of the time. Participants also rated the Supernotes more favorably than the best existing notes on quality, clarity, coverage, context, and argumentativeness. Finally, in a follow-up experiment, we asked participants to compare the Supernotes against LLM-generated summaries and found that the participants rated the Supernotes significantly more helpful, demonstrating that both the LLM-based candidate generation and the consensus-driven scoring play crucial roles in creating notes that effectively build consensus among diverse users