dc.contributor.advisor | Manoharan, Vinothan | |
dc.contributor.author | Klein, Ellen Doyle | |
dc.date.accessioned | 2019-12-11T09:38:27Z | |
dc.date.created | 2019-11 | |
dc.date.issued | 2019-09-10 | |
dc.date.submitted | 2019 | |
dc.identifier.citation | Klein, Ellen Doyle. 2019. Structure and Dynamics of Colloidal Clusters. Doctoral dissertation, Harvard University, Graduate School of Arts & Sciences. | |
dc.identifier.uri | http://nrs.harvard.edu/urn-3:HUL.InstRepos:42013053 | * |
dc.description.abstract | We study the structure and dynamics of colloidal clusters as a model system to understand fundamental physical processes such as phase transitions and nucleation. Colloidal clusters consist of small numbers of spherical, colloidal particles bound by weak, short-range attractions. We can identify and locate individual colloidal particles within these clusters, determine which particles are bound, and thus determine the cluster structure. By studying the cluster structure as a function of the number of particles in a cluster, N, we aim to understand how and when dense, lattice-like structures become favored in equilibrium. We can also identify and track colloidal particles in time, allowing us to study the dynamics of how colloidal clusters form. By characterizing both the structure and the dynamics of these small colloidal clusters, we seek to better understand the statistical physical principles that govern the self-assembly of more complex systems.
We begin by deriving a statistical mechanical model for equilibrium colloidal clusters. This model was originally developed for molecular systems, but it describes our classical experimental systems well. We discuss, from a purely classical perspective, how and why cluster characteristics such as the symmetry number, moments of inertia, and vibrational frequencies affect the equilibrium probabilities.
We then present two approaches to studying the equilibrium structure of colloidal clusters as a function of $N$. In the first approach, we synthesize, assemble, and image dense clusters of colloidal particles. In the second, we use a convolutional neural network to extract structural information from a single image, or hologram, of a cluster.
Finally, we turn our attention to dynamics. We study a two-dimensional cluster of seven colloidal particles as it relaxes from a linear configuration to a compact cluster. We classify the first minimal-energy structure formed. We find that the system rarely reaches the expected global free-energy minimum. | |
dc.description.sponsorship | Physics | |
dc.format.mimetype | application/pdf | |
dc.language.iso | en | |
dash.license | LAA | |
dc.subject | self-assembly, colloids, colloidal-particles | |
dc.title | Structure and Dynamics of Colloidal Clusters | |
dc.type | Thesis or Dissertation | |
dash.depositing.author | Klein, Ellen Doyle | |
dc.date.available | 2019-12-11T09:38:27Z | |
thesis.degree.date | 2019 | |
thesis.degree.grantor | Graduate School of Arts & Sciences | |
thesis.degree.grantor | Graduate School of Arts & Sciences | |
thesis.degree.level | Doctoral | |
thesis.degree.level | Doctoral | |
thesis.degree.name | Doctor of Philosophy | |
thesis.degree.name | Doctor of Philosophy | |
dc.contributor.committeeMember | Brenner, Michael | |
dc.contributor.committeeMember | Spaepen, Frans | |
dc.type.material | text | |
thesis.degree.department | Physics | |
thesis.degree.department | Physics | |
dash.identifier.vireo | | |
dash.author.email | ellen.klein@aya.yale.edu | |