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dc.contributor.authorBhattacharya, Prosenjit
dc.date.accessioned2018-05-23T20:18:39Z
dc.date.available2018-05-23T20:18:39Z
dc.date.issued2017
dc.identifier.isbn9780355310238
dc.identifier.other1981333571
dc.identifier.urihttp://hdl.handle.net/10477/77336
dc.description.abstractOrganic electronics (OE) has the potential to revolutionize next generation electronic devices as it exhibits flexibility, softness and compatibility with biological systems – features traditionally missing in silicon-based solutions. Almost from its inception, OE has ignited the imagination to build devices covering a whole spectrum of technologies ranging from transistors, solar cells, diode lighting and flexible displays to integrated smart systems such as RFIDs, smart textiles, artificial skin, and implantable medical devices and sensors. Its promise is fueled by an ability for cheap and rapid roll-to-roll fabrication, compatibility with flexible substrates, high optical absorption coefficients, low-temperature processing, and easy tunability by chemical doping. However, many promising OE technologies are still bottlenecked at the process optimization stage – more specifically, at designing processing pathways that result in tailored morphology. Current state-of-art approaches are primarily Edisonian – trial-and-error experimental approaches – that only explore a very narrow slice of the processing space due to resource and time constraints. This is further exacerbated by a glaring lack of comprehensive knowledge about the influence of processing pathways on morphology evolution as well as the link between processing pathways and device characteristics. In this work, we harness a computational framework to predict evolution of morphology during solvent-based fabrication of organic thin films. Solvent-based thin-film deposition technologies (e.g. spin coating, drop casting, doctor blading, roll-to-roll manufacturing) are the most common OE manufacturing techniques. These techniques, especially doctor blading and roll manufacturing, are very attractive, due to their ease of scale-up for large commercial production. All solution-processing techniques usually involve preparing dilute solutions of polymers in a volatile solvent. After some form of coating onto a substrate, the solvent evaporates. An initially homogeneous mixture separates into polymer-rich regions as the solvent evaporates. Depending on the specifics of the polymer blend and processing conditions (e.g. evaporation rate, solvent type, nature of substrates), different morphologies are typically formed. Multitude of process and system variables makes exploration of the corresponding parameter space impractical. In this work, we propose to perform the sensitivity analysis of the computational framework to identify the key process and system variables affecting significantly the final morphology of organic thin films. For each process and system variables, we will execute the computational model to track morphology evolution under given input parameters, and record the morphology evolution path. Next, we will gather multiple structural features (average domain size, height and time at which the phase separation is initiated) to establish the relationship between input parameters and the physically meaningful morphology descriptors. This work has important implications to navigate the exploration of processing space and ultimately to facilitate design of organic electronics devices with improved properties.
dc.languageEnglish
dc.sourceDissertations & Theses @ SUNY Buffalo,ProQuest Dissertations & Theses Global
dc.subjectApplied sciences
dc.subjectComputational model
dc.subjectMorphology evolution
dc.subjectOrganic electronics
dc.subjectSensitivity analysis
dc.subjectSensitivity indices
dc.subjectSobol method
dc.titleDesign of Organic Electronic Device Fabrication: Sensitivity Analysis of Morphology Evolution Model
dc.typeDissertation/Thesis


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