2 resultados para Ranking and Selection

em QSpace: Queen's University - Canada


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The main goal of this thesis was to prepare medium-chain-length poly-3-hydroxyalkanoate (mcl-PHA) nanoparticle suspensions at high solids content (≥ 10 % w/v). A two-stage emulsification-solvent evaporation process was employed to produce poly-3-hydroxydecanoate (PHD) suspensions. The formulation and processing conditions including ultrasonication time and amplitude, selection of solvent, and selection of surfactants and their concentrations were investigated to make concentrated suspensions (10 and 30 % (w/v)) of PHD with particles less than 300 nm. Among the ionic surfactants tested to stabilize the suspension, the anionic, sodium dodecyl sulphate (SDS), and the cationic, dodecyltrimethylammonium bromide (DTAB) surfactants produced the smallest particle sizes (~100 nm). However, more stabilized nanoparticles were obtained when the ionic surfactant, SDS, was combined with any of the non-ionic surfactants tested, with polyoxyethylene octyl phenyl ether (Triton X-100) or polyoxyethylene (20) sorbitan monooleate (Tween 80) resulting in a slight increase in zeta potential over 30 days while the zeta potential with other non-ionic surfactants decreased. Mcl-PHA containing 11 and 18 % of carboxyl groups was synthesized via free radical addition reaction of 11-mercaptoundecanoic acid to the pendant double bonds of unsaturated poly-3-hydroxynonanoate (PHNU). Colloidal suspensions prepared by ultrasonication needed a surfactant to maintain stability, even at 0.4 % solids of mcl-PHA containing 11 % carboxylation (PHNC-1) unlike the stable suspensions prepared without surfactants by the titration method. Similar particle sizes (155.6 ± 8.4 to 163.4 ± 11.3 nm) and polydispersity indices (0.42 ± 0.03 to 0.49 ± 0.04) were obtained when several non-ionic surfactants were tested to minimize particle agglomeration, with the smallest particles obtained with Triton X-100. When Triton X-100 was combined with a variety of ionic surfactants, smaller nanoparticles (97.1 ± 1.1 to 121.7 ± 5.7 nm) with a narrower particle size distribution (0.21 ± 0.001 to 0.25 ± 0.003) were produced. The SDS and Triton X-100 combination was chosen to evaluate other mcl-PHAs at 10 % (w/v) solids content. Slightly smaller nanoparticles were formed with carboxylated mcl-PHAs compared to mcl-PHAs having aliphatic pendant side chains. Mcl-PHA consisting of 18 % carboxylation (PHNC-2) formed a much smaller nanoparticles and higher zeta potential.

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This paper considers the analysis of data from randomized trials which offer a sequence of interventions and suffer from a variety of problems in implementation. In experiments that provide treatment in multiple periods (T>1), subjects have up to 2^{T}-1 counterfactual outcomes to be estimated to determine the full sequence of causal effects from the study. Traditional program evaluation and non-experimental estimators are unable to recover parameters of interest to policy makers in this setting, particularly if there is non-ignorable attrition. We examine these issues in the context of Tennessee's highly influential randomized class size study, Project STAR. We demonstrate how a researcher can estimate the full sequence of dynamic treatment effects using a sequential difference in difference strategy that accounts for attrition due to observables using inverse probability weighting M-estimators. These estimates allow us to recover the structural parameters of the small class effects in the underlying education production function and construct dynamic average treatment effects. We present a complete and different picture of the effectiveness of reduced class size and find that accounting for both attrition due to observables and selection due to unobservable is crucial and necessary with data from Project STAR