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dc.contributor.advisorT. Alan Hatton.en_US
dc.contributor.authorKwon, Seok Joonen_US
dc.contributor.otherMassachusetts Institute of Technology. Department of Chemical Engineering.en_US
dc.date.accessioned2014-05-07T17:10:42Z
dc.date.available2014-05-07T17:10:42Z
dc.date.copyright2013en_US
dc.date.issued2013en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/86862
dc.descriptionThesis: Ph. D., Massachusetts Institute of Technology, Department of Chemical Engineering, 2013.en_US
dc.descriptionCataloged from PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references.en_US
dc.description.abstractColloidal dispersion of nanoparticles (CNPs) has interesting properties both in terms of fundamental studies and industrials applications. Particular focus on the phase equilibrium and separation dynamics of CNPs has been necessary for understanding how exactly and fast CNPs are assembled and for controlling the assembly structure and dynamic properties. For understanding and controlling assembly structure and dynamics of CNPs, theoretical analysis in conjunction with computational approaches supported by experimental validation is necessary. In this thesis, studies on the phase-equilibrium-mediated assembly of CNPs are performed by using various computational tools accompanied by theoretical modeling to cover wide range of spatio and temporal dimensions of the desired system containing CNPs. To address the phase separation of CNPs, we studied on two main mechanisms; (1) cluster formation and (2) spinodal decomposition. In each mechanism, we developed novel, effective, and efficient computational algorithms to elucidate phase-equilibrium assembly structure and formation dynamics of CNPs: (1) a kinetic Monte Carlo (KMC) algorithm for cluster formation in microscopic dimensions and spinodal decomposition of homogeneous mixture of CNPs in mesoscopic scale, (2) a self-consistent mean-field (SCMF) model for surface-directed separation of a binary mixture of CNPs in mesoscopic-macroscopic scale, and (3) the spectral method for spinodal decomposition of a binary or ternary mixture of CNPs in macroscopic scale. All the algorithms and results from the simulations were verified by either mathematical proofs or comparisons to other computational methods. In particular, proof-of-concept experimental results of the fabricition of a functional thin film in which a binary mixture of CNPs form the controlled gradient concentrations profile across the thickness direction were presented. On the basis of the experimental demonstration, we showed the validity of the computational model and possible future applications of the fabricated thin film as an optically-functional material. The computational algorithms and numerical tools developed in this thesis supported by theoretical analysis and experimental demonstration can be applicable to various dynamic problems regarding CNPs, especially, for the complicated cases including multi-component, multi-phase systems. We expect that the work performed in this thesis can provide a substantial advantage for future research, such as controlled cluster formation of CNPs by polymer gel mesh, cluster formation of Janus CNPs, and physically controlled spinodal decomposition of CNPs in thin films, as well as progressive application to preparation of novel devices.en_US
dc.description.statementofresponsibilityby Seok Joon Kwon.en_US
dc.format.extent263 pagesen_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsM.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectChemical Engineering.en_US
dc.titlePhase-equilibrium-mediated assembly of colloidal nanoparticlesen_US
dc.typeThesisen_US
dc.description.degreePh. D.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Chemical Engineering
dc.identifier.oclc877965389en_US


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