We describe the formation of artificial bilayer lipid membranes (BLMs) by the controlled, electrical manipulation of aqueous droplets immersed in a lipid-alkane solution. Droplet movement was generated using dielectrophoresis on planar microelectrodes covered in a thin insulator. Droplets, surrounded by lipid monolayers, were brought into contact and spontaneously formed a BLM. The method produced BLMs suitable for single-channel recording of membrane protein activity and the technique can be extended to create programmable BLM arrays and networks.
"The effect of nanoparticles on the structural integrity of the lipid bilayer can be studied by placing a droplet of nanoparticle dispersion on the chip and subsequently bringing it into close contact with a second droplet so that a suspended lipid bilayer is formed that is exposed to the nanoparticles (see Figure 1). It is now possible to insert AgAgCl electrodes into each droplet, voltage-clamp the bilayer and record the current over the bilayer, as a function of time, with pA sensitivity . Since the droplets are optically accessible it is also possible to visualize the distribution of the nanoparticles within the droplet, although this requires the use of fluorescently labeled nanoparticles. "
"In our case, the medium is compartmentalized into small droplets   that form, when the medium is dripped into oil. The compartments are stabilized against merging through lipid molecules that self-assemble at the border between the aqueous and the oil phase. "
[Show abstract][Hide abstract] ABSTRACT: Here we present a model and a simulator for a computing architecture that uses excitable chemical droplets, which can be interconnected to form a cal-culating network. The model uses discrete internal states to represent the excitation phase of each droplet, which can influence the states of neighboring droplets if they are connected. Networks can be analyzed in this framework through simulation and by algebraic probability calculus. Furthermore, it is an abstract, phenomenological model and thus applicable to a variety of implementations for compartmentalized excitable chemical media, such as the Belousov-Zhabotinsky reaction with the right parameterization. By staying abstract in respect to the chemical implementation, we are focusing on the signal propagation behavior and its implications for possible signal encoding schemes and computing paradigms.
[Show abstract][Hide abstract] ABSTRACT: We investigate several evolutionary computation approaches as a mechanism to “program” networks of excitable chemical droplets. For this kind of systems, we assigned a specific task and concentrated on the characteristics of signals representing symbols. Given a Boolean function as target functionality, 2D networks composed of 10 × 10 droplets were considered in our simulations. Three different set-ups were tested: Evolving network structures with fixed on/off rate coding signals, co-evolution of networks and signals, and network evolution with fixed but pre-evolved signals. Evolutionary computation served in this work not only for designing droplet networks and input signals but also to estimate the quality of a symbol representation: we assume that a signal leading to faster evolution of a successful network for a given task is better suited for the droplet computing infrastructure. Results show that complicated functions like XOR can evolve using only rate coding and simple droplet types, while other functions involving negations like the NAND or the XNOR function evolved slower using rate coding. Furthermore we discovered symbol representations that performed better than the straight forward on/off rate coding signals for the XNOR and AND Boolean functions. We conclude that our approach is suitable for the exploration of signal encoding in networks of excitable droplets.
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