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dc.contributor.advisorFikile R. Brushett and Yuriy Rom̀n-Leshkov.en_US
dc.contributor.authorOrella, Michael J.(Michael Julian)en_US
dc.contributor.otherMassachusetts Institute of Technology. Department of Chemical Engineering.en_US
dc.date.accessioned2020-09-15T22:04:44Z
dc.date.available2020-09-15T22:04:44Z
dc.date.copyright2020en_US
dc.date.issued2019en_US
dc.identifier.urihttps://hdl.handle.net/1721.1/127579
dc.descriptionThesis: Ph. D., Massachusetts Institute of Technology, Department of Chemical Engineering, May, 2019en_US
dc.descriptionCataloged from the official PDF of thesis.en_US
dc.descriptionIncludes bibliographical references (pages 205-227).en_US
dc.description.abstractThe development of efficient electrochemical processes, which can utilize electrons from renewable energy sources and incorporate sustainable sources of carbon present in biomass, could enable the decarbonization of the industrial sector and spur technological and scientific innovation. Moreover, electrochemical processing, specifically hydrogenation and hydrodeoxygenation, may allow new molecular transformations at previously unachievable conditions, unlocking what had been inaccessible or unimaginable chemical processing. Accordingly, there is significant room for exploration in organic electrochemistry to identify opportunities within the chemical industry to both replace crude-oil derived feedstocks with biomass and to shift from traditional thermally-driven reactions to those that use electrical energy.en_US
dc.description.abstractAdvancing the science and engineering of these nascent process concepts requires an interdisciplinary approach with key knowledge gaps that traverse distinct research communities and apply to the problem at multiple scales. My thesis work developed modeling toolkits that will be useful across the spectrum of biomass generation in planta to electrochemical processing of liquefied feedstocks, all of which are available open source to reduce the barrier to entry for new researchers interested in the potential for this interdisciplinary topic. Specifically, I developed Lignin-KMC, a model based on kinetic Monte Carlo methods that utilize first-principle-derived kinetic parameters to predict the structure of native lignin biopolymers, as having an accurate molecular model of reactor feeds is necessary to understand any reactivity trends that may be observed.en_US
dc.description.abstractNext, I created DropPy, a Python-based toolkit for automating the analysis of contact angle goniometry data, as the performance of many electrochemical cells can be anticipated from the wettability of the electrode surface. Finally, I established a generalized techno-economic framework which could be used to evaluate the overall cost to the consumer of electrochemically-derived products, and could be used by researchers with various electrolysis interests to better understand the most critical areas of improvement for their devices. Through these three developments, the efficiency of research carried out in this space will be improved, hopefully speeding the eventual development of electrochemical upgrading devices at a lower total research cost and final system price.en_US
dc.description.statementofresponsibilityby Michael J. Orella.en_US
dc.format.extent229 pagesen_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsMIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectChemical Engineering.en_US
dc.titleModels across multiple length scales to advance biomass upgradingen_US
dc.typeThesisen_US
dc.description.degreePh. D.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Chemical Engineeringen_US
dc.identifier.oclc1193321517en_US
dc.description.collectionPh.D. Massachusetts Institute of Technology, Department of Chemical Engineeringen_US
dspace.imported2020-09-15T22:04:43Zen_US
mit.thesis.degreeDoctoralen_US
mit.thesis.departmentChemEngen_US


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