We describe the spontaneous incorporation of amphiphilic gold nanoparticles (Au NPs) into the walls of surfactant vesicles. Au NPs were functionalized with mixed monolayers of hydrophilic (deprotonated mercaptoundecanoic acid, MUA) and hydrophobic (octadecanethiol, ODT) ligands, which are known to redistribute dynamically on the NP surface in response to changes in the local environment. When Au NPs are mixed with preformed surfactant vesicles, the hydrophobic ODT ligands on the NP surface interact favorably with the hydrophobic core of the bilayer structure and guide the incorporation of NPs into the vesicle walls. Unlike previous strategies based on small hydrophobic NPs, the present approach allows for the incorporation of water-soluble particles even when the size of the particles greatly exceeds the bilayer thickness. The strategy described here based on inorganic NPs functionalized with two labile ligands should in principle be applicable to other nanoparticle materials and bilayer structures.

We investigate the self-assembly of amphiphilic nanoparticles (NPs) functionalized with mixed monolayers of hydrophobic and hydrophilic ligands in water. Unlike typical amphiphilic particles with "fixed" surface chemistries, the ligands used here are not bound irreversibly but can rearrange dynamically on the particles' surface during their assembly from solution. Depending on the assembly conditions, these adaptive amphiphiles form compact micellar clusters or extended chain-like assemblies in aqueous solution. By controlling the amount of hydrophobic ligands on the particles' surface, the average number of nearest neighbors--that is, the preferred coordination number--can be varied systematically from ∼ 1 (dimers) to ∼ 2 (linear chains) to ∼ 3 (extended clusters). To explain these experimental findings, we present an assembly mechanism in which hydrophobic ligands organize dynamically to form discrete patches between proximal NPs to minimize contact with their aqueous surroundings. Monte Carlo simulations incorporating these adaptive hydrophobic interactions reproduce the three-dimensional assemblies observed in experiment. These results suggest a general strategy based on reconfigurable "sticky" patches that may allow for tunable control over particle coordination number within self-assembled structures.

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The present paper presents an intelligent automation of the electron beam physical vapour deposition (EBPVD) process to achieve high quality and cost efficient coatings for low volume part production, using realtime feedback control. A computational model of EBPVD for predicting coating thickness is used with an optimisation heuristic for reducing coating thickness variance and feedback control approaches for substrate temperature control and melt pool control. The computational model can be readily generated using a standard computer aided design (CAD) model of the workpiece, which makes the method applicable to workpieces with complex threedimensional geometry. Based on this model, an optimisation heuristic for the EBPVD process is developed to control workpiece motion systematically with the objective of reducing coating thickness variance, i.e. providing a uniform coating. These computational developments are illustrated using a simulation of a turbine blade coating in which the coating thickness variance is reduced significantly. Process level intelligence is incorporated using realtime feedback control for substrate temperature and melt pool control using an open architecture control system. Results using thermocouple based temperature control and realtime vision for melt pool control are presented. Video images of the melt pool are analysed on a block by block basis, using a technique to identify critical regions of the melt pool. Simulation results demonstrate the feasibility of automating the electron gun beam steering sequence. The proposed methods offer the prospect of eliminating dedicated tooling/fixtures and improving the cost effectiveness of the process, especially for low volume production.