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X-WR-CALDESC:Events for Department of Chemical Engineering
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DTSTART;TZID=America/New_York:20260727T110000
DTEND;TZID=America/New_York:20260727T120000
DTSTAMP:20260715T205639Z
CREATED:20260715T205639Z
LAST-MODIFIED:20260715T205639Z
UID:6131-1785150000-1785153600@che.northeastern.edu
SUMMARY:ChE PhD Dissertation Defense: Sevy Harris
DESCRIPTION:Name:\nSevy Harris \nTitle:\nAutomated Methods for Improving Microkinetic Models through Uncertainty Quantification and Sensitivity Analysis \nDate:\n07/27/2026 \nTime:\n11:00:00 AM \nCommittee Members:\nProf. Richard West (Advisor)\nProf. Steve Lustig\nProf. Qing Zhao\nProf. Franklin Goldsmith \nLocation:\n222 Hayden Hall \nAbstract:\nMicrokinetic modeling is an invaluable technique for investigating new fuels in pursuit of cleaner combustion devices. These models\, specifying a network of reacting species and elementary reactions\, use physically meaningful parameters to predict how systems will react over a wide range of conditions. Automated mechanism generators\, like Reaction Mechanism Generator (RMG) can systematically build microkinetic models by reacting species together according to predefined templates. These are necessary to handle the complexity of combustion mechanisms\, which can have hundreds of intermediate species and thousands of reactions\, but the first result is rarely accurate enough to be useful. Human intervention is generally required to analyze the model\, identify important parameters\, and then improve their values with experimental results or quantum chemistry calculations. This work introduces a highly automated workflow for automatically building and improving mechanisms. It uses automated uncertainty and sensitivity analysis to rank the most important parameters to improve upon\, and then uses quantum chemistry calculations to compute the most important thermokinetic parameters. The workflow is applied to propane and butane models for ignition delay\, demonstrating the strengths and weaknesses of this method of automated model improvement. \nThe automated uncertainty analysis portion of the improvement workflow was implemented by earlier work of Gao\, Liu\, and Green. They assigned uncertainties to every input parameter by looking up the source RMG used to estimate the value (e.g. a trusted library entry from the literature\, or a less trusted rate rule estimation)\, and then applying a default uncertainty for that source. They accounted for certain parameter correlations by keeping track of parameters estimated with the same source. However\, the previous implementation did not allow for the underlying sources themselves to be correlated\, and some new additions to the RMG database include correlation data. The previous implementation also applied the same default uncertainty to all the underlying data in the RMG database\, even though certain values are known to be higher quality than others. This work expands and improves upon the existing RMG uncertainty framework to incorporate correlated source data and to apply more specific uncertainty assignments based on information already available in the database. It also extends the uncertainty suite to handle surface-phase mechanisms and enables the easy export of uncertainty covariance matrices so that RMG users can conduct global uncertainty analysis entirely outside the RMG framework. A case study of propane in a jet-stirred reactor shows how these extensions enable global Monte Carlo and Sobol uncertainty analyses\, with error bars that are an improvement on previous estimates. \nAmmonia is being considered as a possible alternate fuel to traditional hydrocarbons\, but when it reacts inside a stainless steel reactor\, the nitrogen will\, under certain conditions\, form nitrides that degrade the steel and shorten its lifespan. A multiscale model of the nitridation of stainless steel was built in collaboration with Mitsubishi Heavy Industries (MHI) to predict the conditions under which nitridation occurs. In the first stage of the multiscale model\, the thermodynamics of the reacting surface species are computed using machine learning models. In the second stage\, those thermodynamic parameters are used to build a microkinetic model of species reacting on the steel surface. In the third stage\, surface concentrations from the second stage inform a transport model that computes how nitrogen and oxygen diffuse into the steel bulk. The end result is a model which translates microscale phenomena into macroscale observables that can be compared to experiments by MHI. \n\nSevy Harris is a PhD candidate in Chemical Engineering at Northeastern University. She received her BS in Electrical Engineering in 2014 from Ohio State and her MS in Electrical Engineering from Stanford in 2016. She worked at Microsoft for three years\, designing flexible printed circuits and later writing test software to characterize a VR depth camera. She joined the Computational Modeling group at Northeastern in 2020 to study kinetic modeling and how these models can be used to investigate alternate fuels for cleaner combustion.
URL:https://che.northeastern.edu/event/che-phd-dissertation-defense-sevy-harris/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260729T140000
DTEND;TZID=America/New_York:20260729T150000
DTSTAMP:20260722T135945Z
CREATED:20260722T135945Z
LAST-MODIFIED:20260722T135945Z
UID:6142-1785333600-1785337200@che.northeastern.edu
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://che.northeastern.edu/event/che-phd-dissertation-defense-svilen-kolev/
LOCATION:610-A EXP\, 360 Huntington Ave\, 610-A EXP\, Boston\, MA\, 02115\, United States
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DTSTART;TZID=America/New_York:20260806T130000
DTEND;TZID=America/New_York:20260806T140000
DTSTAMP:20260729T133907Z
CREATED:20260729T133907Z
LAST-MODIFIED:20260729T133907Z
UID:6145-1786021200-1786024800@che.northeastern.edu
SUMMARY:ChE PhD Dissertation Defense: Alexander Kaltashov
DESCRIPTION:Name:\nAlexander Kaltashov \nTitle:\nTemporal and Spatial Control of Structure and Rheology in Binary Colloidal Gels \nDate:\n08/06/2026 \nTime:\n01:00:00 PM \nCommittee Members:\nProf. Safa Jamali (Advisor)\nProf. Sara Hashmi\nProf. Craig Maloney\nProf. Steven Lustig \nLocation:\nHayden Hall 424 \nAbstract:\nColloidal gels are soft materials formed when attractive interactions between nanometer-to-micrometer scale particles dispersed in a fluid phase drive their self-assembly into a percolated network. Traditionally\, the structure and mechanical properties of single-component colloidal gels are tuned through control of inter-particle interactions and particle concentration. The introduction of additional components into a system expands the range of accessible gel architectures\, which can be further tailored through multiple coexisting inter-particle interaction types\, as well as size disparity and component stoichiometry. Beyond these conventional control parameters\, the design space can be expanded further by treating the self-assembly pathway itself as an independent variable. Specifically\, temporal control through sequential gelation protocols and spatial control through prescribed composition gradients enable binary colloidal gel architectures with structural characteristics beyond those achievable through conventional controls alone. Yet\, despite their promise as routes to engineering novel materials\, these concepts remain underexplored in colloidal gels. This dissertation presents a series of computational studies that explore pathway-dependent design strategies for binary colloidal gels. Sequential gelation is first examined as a temporal control mechanism for programming network formation and structural evolution\, followed by an investigation of composition gradients as a spatial control strategy for creating heterogeneous gel architectures. Collectively\, these studies establish the self-assembly pathway as an independent design parameter for engineering multicomponent colloidal materials with tailored morphologies and properties. \n\nAlexander I. Kaltashov is a doctoral candidate in Chemical Engineering at Northeastern University\, where he conducts computational studies of the structure\, dynamics\, and rheology of multicomponent colloidal gels. His doctoral research focuses on understanding how temporal and spatial controls over self-assembly pathways can be used to control the architecture and mechanical properties of binary colloidal networks. His work combines particle dynamics simulation\, rheological modeling\, and structural analysis to investigate the relationship between colloidal interactions\, self-assembly processes\, and structural/material properties. Prior to his doctoral studies\, Alexander received a Bachelor of Chemical Engineering from McGill University in Montreal\, Canada\, in 2020. During his undergraduate studies\, he conducted research across a variety of fields\, including the development of a low-cost\, large-area UV photolithography system\, investigation of protein phase separation\, and oxygen mass transfer studies in laboratory-scale bioreactors. His current research interests broadly include soft materials\, colloidal assembly\, rheology\, and computational modeling.
URL:https://che.northeastern.edu/event/che-phd-dissertation-defense-alexander-kaltashov/
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DTSTART;TZID=America/New_York:20260818T100000
DTEND;TZID=America/New_York:20260818T110000
DTSTAMP:20260805T152715Z
CREATED:20260805T152715Z
LAST-MODIFIED:20260805T152715Z
UID:6148-1787047200-1787050800@che.northeastern.edu
SUMMARY:ChE PhD Dissertation Defense: Erin Heeschen
DESCRIPTION:Name:\nErin Heeschen \nTitle:\nTemporal and Spatial Control of Structure and Rheology in Binary Colloidal Gels \nDate:\n08/18/2026 \nTime:\n10:00:00 AM \nCommittee Members:\nProf. Magda Barecka (Advisor)\nProf. Marion Börnhorst\nProf. Damilola Daramola\nProf. Richard West \nLocation:\nBehrakis Health Sciences Center 424 \nAbstract:\nChemical production plants contribute to a significant portion of global carbon emissions\, making them a key target for decarbonization efforts. Carbon dioxide (CO₂) electrolysis is a promising pathway towards decarbonization that utilizes renewable energy to convert emitted CO₂ into valueadded chemicals. While the environmental and industrial incentives to support this technology are apparent\, further progress toward large-scale implementation remains constrained by inconsistent performance. \nElectrolyte flow within CO₂ electrolyzers influences crucial parameters such as local velocity and pressure\, interfacial pH gradients\, and gas bubble accumulation\, which can be controlled through flow-field design. Neighboring fields of electrochemistry\, including fuel cells\, redox flow batteries\, and water electrolyzers\, have identified uniform catholyte flow as a vital parameter to enhance electrochemical stability and energy efficiency. Despite the importance of catholyte flow fields\, flow field geometries are rarely reported in the CO₂ electrolysis literature\, and it is unknown how uniform catholyte flow influences cell performance. \nThe hypothesis investigated in this dissertation is that understanding the relationship between catholyte flow uniformity\, reproducibility\, and the electrochemical performance of CO₂ electrolyzers can allow us to design flow fields that allow for better flow control\, better selectivity\, and more predictable outcomes of electrochemical reactions. To address this hypothesis\, this dissertation includes a workflow to design\, simulate\, 3D print\, validate\, and experimentally test four catholyte flow field geometries for use in flow-based CO₂ electrolyzers. Liquid flow is simulated through each catholyte flow field at four inlet flow rates using ANSYS Fluent to analyze electrolyte uniformity distribution and pseudo-boundary layer thicknesses. Each design was 3D printed and the simulations validated using a novel instance segmentation program for use in volumetrically small cells. Finally\, two of the investigated catholyte flow field geometries are deployed in a liquid-liquid CO₂ electrolyzer. Results indicate that uniform catholyte distribution strongly enhances experimental reproducibility. However\, it is challenging to achieve the highest single-product selectivity without losing flow uniformity. Based on these findings\, general design rules are established to support the further development of electrolyzers amenable to scale-up and industrial application. \n\nErin Heeschen is a 4th year Chemical Engineering PhD Candidate in the College of Engineering at Northeastern University dedicated to sustainability focused technologies. In August 2026\, she will be defending her doctoral thesis in the field of Chemical Engineering with a specialization in CO2 electrolysis and reactor design. Erin was nominated to Sigma Xi\, the Scientific Research Honor Society\, and is an active member of the New England section of the Electrochemical Society. In addition to recognized excellence in research\, Erin is an established science communicator for cutting edge research in field of sustainability and electrochemistry. She designed and headed a booth for the Barecka Lab’s Airthanol project at the 2026 ARPA-E Energy Summit in San Diego\, CA (winning honorable mention for best booth) and 2026 Sustainability Innovation Week Expo at Northeastern University; she placed 2nd in the Graduate Student Research Presentations at the Northeastern University Poster Showcase Presentation Competition (2023) and was nominated best elevator pitch by her cohort (2023); she also presented her research at numerous conferences such as MRS (Material Research Society)\, ACS-GCI (American Chemical Society Green Chemistry Institute) Pharmaceutical Roundtable\, and AIChE (American Institute for Chemical Engineers) in Boston. Outside of her research\, Erin is a longtime boardgame enthusiast (even going so far as to open a board game store when she was 17!) who will never turn down a long hike through the woods.
URL:https://che.northeastern.edu/event/che-phd-dissertation-defense-erin-heeschen/
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260930T120000
DTEND;TZID=America/New_York:20260930T130000
DTSTAMP:20260903T144707Z
CREATED:20260903T144707Z
LAST-MODIFIED:20260903T144707Z
UID:6164-1790769600-1790773200@che.northeastern.edu
SUMMARY:Chemical Engineering Fall Seminar Series: Trevor Sherwood
DESCRIPTION:Discovery of BMS-986526\, an EP4 agonist for the treatment of IBD using a direct-to-biology platform \nLocation: 108 Snell Engineering Center \nAbstract: This seminar will cover a brief overview of medicinal chemistry and the discovery of an EP4 agonist for IBD. The EP4 receptor is a GPCR expressed in multiple tissues. In the intestines\, the activation of EP4 is linked to restitution of the intestinal epithelial barrier and anti-inflammatory effects on immune cells\, making it a target of interest for IBD. However\, EP4 expression in other tissues complicates the utility of EP4 agonists. For instance\, EP4 agonism in systemic circulation has been shown to result in changes in heart rate and blood pressure. We set out to identify selective EP4 agonists with minimal systemic exposure outside of the gastrointestinal tract. Our campaign began with a high throughput screen which identified a triazine chemotype that was elaborated into a lead compound which demonstrated high circulating exposure. To identify compounds with lower exposure\, a direct-to-biology campaign leveraging a nanosynthesis platform was undertaken\, enabling us to rapidly explore novel chemical space and identify BMS-986526\, a lead that was subsequently nominated as a development candidate. This presentation will describe our lead chemotype\, the execution of our nanosynthesis and direct-to-biology campaign\, and the in vivo profile of BMS-986526 and other related lead compounds. \n\nTrevor Sherwood is a Scientific Associate Director in the Bristol Myers Squibb Discovery Chemistry group in Princeton\, NJ. He earned his B.S. in chemistry at Rensselaer Polytechnic Institute in 2008 and then earned his Ph.D. in 2013 with Prof. Scott Snyder at Columbia University where he completed multiple total syntheses of alkaloid and polyphenolic natural products. In 2013\, Trevor joined BMS where he has performed research in immunology\, oncology\, and neuroscience and has led multiple drug discovery programs. He is passionate about LGBTQ representation in chemistry and co-organized a session at the 2024 Spring ACS National Meeting featuring presentations from LGBTQ medicinal chemists.
URL:https://che.northeastern.edu/event/chemical-engineering-fall-seminar-series-trevor-sherwood/
LOCATION:108 SN
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