A three-step seed-mediated growth method was used to make gold nanoparticles. Different surfactants, alkyltrimethylammonium bromides (CnTAB, n ) 10, 12, 14, 16, and 18) and cetylpyridinium chloride (C16PC), were chosen as stabilizers. In general, it was found that as the length of the surfactant chain increased, the resulting gold nanoparticles' aspect ratio increased: the aspect ratio was 1 (for C10TAB), 5 ( 2 (C12TAB), 17 ( 3 (C14TAB), and 23 ( 4 (C16TAB). The plasmon absorption maxima for the gold nanoparticles varied as a function of the shape, from 520 nm (spheres) to beyond 2000 nm (high aspect ratio nanorods). We propose that the surfactant binds as a bilayer to the growing nanoparticle and assists in nanoparticle elongation via a "zipping" mechanism.

We report a method to make crystalline silver nanowires in water, in the absence of a surfactant or polymer to direct nanoparticle growth, and without externally added seed crystallites. The reaction is one in which silver salt is reduced to silver metal, at 100 °C, by sodium citrate, in the presence of NaOH. Hydroxide ion concentration is key to producing nanowires, which are up to 12 microns long, instead of nanospheres.

Reverse microemulsions were used to synthesize barium fluoride doped with 0−65 mol % neodymium. Although the products were polydisperse, average particle sizes below 100 nm were achieved. XRD analysis showed that powders with 0−10 mol % Nd were single phase, while samples with dopant levels of 10−50 mol % contained two phases. Products with more than 50 mol % Nd were amorphous by XRD. Fluorescence of Nd:BaF2 showed an unusually high threshold for concentration quenching as well as very short lifetimes compared to those of bulk samples. The use of a cosurfactant and variation in reaction conditions provided control over particle size; smaller particles resulted by limiting the aqueous volume while simultaneously increasing the amount of cosurfactant for a given concentration of reactants.

In this work, we develop a new approach to generative density estimation for exchangeable, non-i.i.d. data. The proposed framework, FlowScan, combines invertible flow transformations with a sorted scan to flexibly model the data while preserving exchangeability. Unlike most existing methods, FlowScan exploits the intradependencies within sets to learn both global and local structure. FlowScan represents the first approach that is able to apply sequential methods to exchangeable density estimation without resorting to averaging over all possible permutations. We achieve new state-of-the-art performance on point cloud and image set modeling.

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