BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Northeastern University College of Engineering - ECPv6.17.1//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-ORIGINAL-URL:https://nucoe.madebyvital.com
X-WR-CALDESC:Events for Northeastern University College of Engineering
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:America/New_York
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20240310T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20241103T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20250309T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20251102T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20260308T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20261101T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20270314T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20271107T060000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260729T140000
DTEND;TZID=America/New_York:20260729T150000
DTSTAMP:20260722T135945Z
CREATED:20260722T135945Z
LAST-MODIFIED:20260722T135945Z
UID:57254-1785333600-1785337200@nucoe.madebyvital.com
SUMMARY:ChE PhD Dissertation Defense: Svilen Kolev
DESCRIPTION:Name:\nSvilen Kolev \nTitle:\nSpatiotemporal Imaging and Quantitative Analysis of Early Gut Microbiome Assembly in C. elegans \nDate:\n07/29/2026 \nTime:\n02:00:00 PM \nCommittee Members:\nProf. Rebecca Carrier (Advisor)\nProf. Sara Hashmi\nProf. Erel Levine\nProf. Javier Apfeld \nLocation:\n610 EXP \nAbstract:\nUnderstanding host-associated microbiomes requires explaining not only which microbes are present\, but how community states are created and lost. Entry\, growth\, transport\, aggregation\, dispersal\, and clearance occur within a host environment shaped by immune\, physiological\, and ecological selection. Endpoint measurements collapse these processes into composition or abundance\, leaving the underlying interactions and sources of inter-host heterogeneity unresolved. Caenorhabditis elegans and its model microbiome provide a tractable opportunity to observe these dynamics directly. \nTo observe these processes\, we integrated large-scale microfluidics and programmable environmental control with hardware-synchronized fast microscopy and automated acquisition\, enabling us to image gut bacterial populations in hundreds of individually confined worms for up to 20 h. The resulting heterogeneous dataset created an annotation bottleneck: gut-resident particles had to be distinguished from external bacterial signal\, but exhaustive manual labeling was impractical. A data-centric workflow combining experimentally generated gut pseudolabels\, task decomposition\, and iterative human correction produced a reliable segmentation model for extracting longitudinal measurements of bacterial load\, particle size\, and position. \nAlternating-label experiments followed three bacterial isolates separately across four host backgrounds. Population loads became high and broadly distributed\, while single-worm trajectories fluctuated rapidly around slower trends. Within- and between-worm variation contributed substantially to total variance\, supporting partial individuality rather than fixed highand low-load classes. Label-chase dynamics showed that high load did not imply stable residence: most of the load turned over rapidly\, while a minority of worms retained elevated load. Particle-resolved measurements showed that small objects dominated counts and their signal cleared rapidly\, whereas rare large aggregates carried disproportionate signal and were enriched in the retained tail. Aggregation did not guarantee persistence: aggregate-positive entry and later detection were distinct\, microbe- and host-dependent probabilities\, consistent with stochastic fragmentation\, clearance\, and retention. \nTogether\, these results suggest that early assembly reflects isolate-dependent accumulation coupled to rapid turnover and stochastic aggregate-associated transitions. This process-level single-isolate baseline provides a foundation for asking how host and microbial genotypes\, aging\, environmental change\, and additional community members reshape microbiome assembly. More broadly\, it demonstrates that microbiome heterogeneity is best understood through the dynamics that generate it. \n\nSvilen Kolev is a PhD candidate in Chemical Engineering at Northeastern University. He earned his Bachelor of Science in Chemical Engineering from the University of Massachusetts Amherst in 2019 and will defend his doctoral dissertation in August 2026. His dissertation\, Spatiotemporal Imaging and Quantitative Analysis of Early Gut Microbiome Assembly in C. elegans\, combines microfluidics\, microscopy\, machine learning\, and quantitative analysis to study bacterial population dynamics inside living hosts. His work reflects a broader interest in interdisciplinary engineering and in building experimental and computational tools for quantitative biology. Svilen values collaboration and mentorship and has mentored several undergraduate researchers. Outside of research\, he enjoys basketball\, hiking\, skiing\, board games\, dinner parties\, and spending time with friends and family.
URL:https://nucoe.madebyvital.com/event/che-phd-dissertation-defense-svilen-kolev/
LOCATION:610-A EXP\, 360 Huntington Ave\, 610-A EXP\, Boston\, MA\, 02115\, United States
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250806T120000
DTEND;TZID=America/New_York:20250806T160000
DTSTAMP:20250728T133616Z
CREATED:20250728T133616Z
LAST-MODIFIED:20250728T133616Z
UID:52766-1754481600-1754496000@nucoe.madebyvital.com
SUMMARY:ChE PhD Dissertation Defense: Sabrina Marnoto
DESCRIPTION:Name: Sabrina Marnoto \nTitle: Dynamics of Droplet and Soft Particle Systems in Confined Microflows \nDate: 08/06/2025 \nTime: 12:00:00 PM \nCommittee Members:\nProf. Sara M. Hashmi (Advisor)\nDr. Petia Vlahovska\nProf. Xiaoyu Tang\nProf. Ambika Bajpayee\nProf. Mansoor Amiji \nLocation: EXP-610A \nAbstract: \nDroplets and soft particles are ubiquitous\, exhibiting behavior in nature through rainfall and in biological materials\, and serving industrial needs such as hopper de-cloggers and cell encapsulators. While droplets and particles appear in natural and industrial settings\, their behavior is especially rich when flowing in confined geometries. In confined flow systems\, droplets and soft particles exhibit unique phenomena that cause passive sorting and shape deformation.  Many researchers utilize this distinctive behavior for applications in understanding the micro-vascular system\, creating lab-on-a-chip platforms\, and manipulating droplets and soft particles. In this thesis\, we exploit droplet and soft particle dynamics in confined flow for two primary objectives: to develop a technique for measuring mechanical properties of individual droplets or particles and to gain a deeper understanding of the passive softness-driven separation of droplet and particle suspensions. \nTo begin\, we focus on developing a continuous fluidic tool that combines droplet and particle formation with mechanical measurement in a single device. Current measurement tools are known to be challenging to use and time-consuming. Specifically\, methods measure a single droplet or particle at a time. Furthermore\, the measurement tools are discontinuous\, requiring the creation of droplets and particles\, followed by a separate measurement of mechanical properties. Discontinuous methods can introduce experimental errors in measurements. Confined geometries\, such as microfluidic devices\, offer the opportunity to measure the properties of multiple droplets and soft particles continuously. High-throughput continuous methods have implications in automation\, material design optimization\, and provide precise quality control. In this thesis\, we explore the mechanical properties of two systems: droplets and soft particles. For droplets\, we develop a robust yet simple fluidic tensiometer for surface tension measurements. The tensiometer accurately measures surface tension for a wide range of emulsion systems and is validated by established techniques. \nOur tensiometer not only measures surface tension\, but also can measure the restoring stress of soft particles. Restoring stress is the stress for a particle to reform back to its initial shape in response to an applied viscous shear. A higher restoring stress is indicative of a stiffer particle. We use polyethylene glycol diacrylate (PEGDA) as our model for soft particles. PEGDA is biocompatible\, tunable\, and photocurable\, making it a widely used soft particle in fluidic measurements. In fluidic devices\, PEGDA particles are formed by first pinching off PEGDA-filled droplets using oil\, followed by exposure to UV light downstream\, either on or off-chip. Off-chip curing presents complications\, as droplets coalesce as they exit fluidic channels\, causing polydispersion and negating the monodisperse advantage of fluidic techniques.  On-chip exposure is possible with the rise of transparent fluidic device materials. In devices\, many researchers expose particles to a single UV intensity and assume that particles are fully crosslinked as they flow. However\, intensity and particle curing have a direct relationship. The UV curing mechanisms in flow are also not well understood. There are numerous methods for monitoring particle gelation\, but few researchers combine techniques to comprehensively understand UV curing under precise flow conditions and UV control. We utilize our fluidic measurement tool\, combined with a multitude of other analysis techniques\, to fully grasp crosslinking kinetics both in and out of flow. We find that crosslinking particles in flow introduces additional complexities because UV intensity and exposure time depend on velocity and trajectory and can result in nonuniform curing. \nIn confined shear flows\, wall effects cause individual droplets and particles to migrate towards the center of the channel and even deform. Shifting from individual droplet and particle measurements\, we explore both emulsion and particle suspensions in flow. Suspensions of droplets and particles experience additional hydrodynamic forces due to pair-wise interactions with each other. In many computational and theoretical analyses\, researchers assume monodisperse or bidisperse suspensions and simple shear flow for simplicity. However\, real-world systems often deviate from these assumptions\, particularly in polydispersed systems and Poiseuille flow. We employ a scaling behavior analysis on a polydispersed emulsion\, considering both simple shear and Poiseuille phenomena. We investigate the validity of the scaling theory applied to varied shear rates\, volume fractions\, and viscosity ratios. \nMulticomponent suspension flows through confined spaces are ubiquitous in nature. One of the most commonly investigated confined particle suspension systems is blood. In blood vessels in the microcirculation\, red blood cells migrate towards the center of the channel\, creating a cell-free layer\, and other particulates\, such as white blood cells\, platelets\, and leukocytes\, partition towards the walls of the channels. Among the particulates circulating with red blood cells are drug delivery vehicles. Understanding carrier migration in blood vessels is crucial for improving drug efficacy and reducing adverse side effects. Both particle and flow properties control carrier migration. We provide a comprehensive review explaining how various properties affect particle migration in flow. Further\, we suggest a method for quantifying said migration. \nThe thesis explores the individual and collective behavior of both particles and droplets in confined flow. By developing novel measurement tools and gaining a better fundamental understanding of droplet and particle behavior in confined flows\, we present opportunities for broader industrial and academic applications\, including automation\, cell mechanics\, and more.
URL:https://nucoe.madebyvital.com/event/che-phd-dissertation-defense-sabrina-marnoto/
LOCATION:610-A EXP\, 360 Huntington Ave\, 610-A EXP\, Boston\, MA\, 02115\, United States
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250115T120000
DTEND;TZID=America/New_York:20250115T140000
DTSTAMP:20250107T143812Z
CREATED:20250107T143812Z
LAST-MODIFIED:20250107T143812Z
UID:48546-1736942400-1736949600@nucoe.madebyvital.com
SUMMARY:ChE PhD Dissertation Defense: Chao Xu
DESCRIPTION:Name:\nChao Xu \nTitle:\nTowards Automated Heterogeneous Catalysis Design: Integrating Activation Energy Estimation\, Uncertainty Quantification\, and Coverage-dependent Thermodynamics in Microkinetic Modeling \nDate:\n1/15/2025 \nTime:\n12:00:00 PM \nCommittee Members:\nProf. Richard West (Advisor)\nProf. Qing Zhao\nDr. Franklin Goldsmith\nDr. Zack Ulissi \nLocation:\nEXP 610-A \nAbstract:\nIn the context of climate change\, reducing CO2 emission and advancing sustainable development are global priorities. Developing efficient “green” fuel processes requires overcoming the low productivity of industrial catalysts\, necessitating new catalyst designs. Traditional trial-and-error approaches are costly and time-intensive\, but multi-scale modeling provides a low-cost\, efficient alternative by exploring catalyst design across atomic to reactor scales. \nThis dissertation enhances scientific software such as Reaction Mechanism Generator (RMG) and Cantera etc. for automating heterogeneous catalysis modeling to accelerate catalyst design. An active-learning workflow for calculating coverage-dependent thermodynamics with EquiformerV2\, a graph neural network\, was developed\, complementing the existing RMG coverage-dependent kinetics functionality. The estimated coverage-dependent thermodynamics were validated through a CO/H2 methanation model on Ni. Because reaction activation energies are also affected by species’ coverage\, the dissertation also addresses rapid reaction barrier estimation by implementing the Blowers-Masel Approximation (BMA)\, which relates a reaction barrier to enthalpy using minimal data. Integrated into Cantera\, this method supports catalyst screening with linear scaling relationships (LSRs) and BMA kinetics. A methane partial oxidation study on 81 hypothetical metals demonstrated how BMA affects rate-limiting species and high-selectivity catalysts\, while sensitive reactions remain unaffected. \nLastly\, thermodynamic properties of surface species in catalytic methane partial oxidation models on Rh were estimated using DFT\, LSRs\, and a graph neural network named GEMNET\, combined with Bayesian parameter estimation for process optimization. The three methods provided close thermodynamic data with different prior uncertainties\, validating the feasibility of combining machine learning potentials with RMG for thermodynamic estimation on all kinds of binding facets. Bayesian parameter estimation improved simulation accuracy of the three estimation methods while uncovering active site information inaccessible through conventional means. The workflow effectively integrates experimental and computational uncertainties\, enabling data-informed catalyst design and optimization.
URL:https://nucoe.madebyvital.com/event/che-phd-dissertation-defense-chao-xu/
LOCATION:610-A EXP\, 360 Huntington Ave\, 610-A EXP\, Boston\, MA\, 02115\, United States
END:VEVENT
END:VCALENDAR