Multi-point Holographic Optical micro-VelocimetryMicron-sized particles can be hold stuck by light in a fluidflow. If the lights are turned off for a short time, particleswill be set free to flow for a short <strong>di</strong>stance with the localfluid velocity. This is the core idea of Holographic Opticalmicro-Velocimetry [1], a newly developed technique whichis able to probe flow fields at those micro-scales which areso relevant for biology and micro-flui<strong>di</strong>cs. The idea ofprobing fluid flows following freely flowing tracers has beenlargely applied by Particle Image Velocimetry, formacroscopic flows, and by its more recent miniaturizedversion for micro-flui<strong>di</strong>cs. The main drawback of thisapproach consists in the need of continuously injecting ahigh concentration of tracers in the flowing fluid. This isrequired in order to cover all the interesting points inspace and to be able to average over the thermal noisealways affecting the trajectories of micron sized probes.This con<strong>di</strong>tion can be very restrictive in densely packedgeometries and heavily alter the function of theinvestigated micro-device. Holographic Optical micro-Velocimetry offers the possibility of choosing an arbitraryset of interesting points in the flow geometry and only usea small number of tracers, one for each measuring point.The tracers are held in place by the forces exerted by lighttraps. Light traps are submicron regions of high lightintensity whose <strong>di</strong>stribution in space is dynamicallygenerated by a computer controlled hologram [2]. Micronsized <strong>di</strong>electric particles are alternatively trapped andreleased from the trapping sites by chopping the trapbeam. Using <strong>di</strong>gital video microscopy we can detect theprobe particles' <strong>di</strong>splacements after a fixed delay timefrom release instant. The velocity field is thus mapped<strong>di</strong>rectly and simultaneously on the trap sites allowing toreconstruct the two <strong>di</strong>mensional projection of flow field onthe imaging plane in a three <strong>di</strong>mensional domain. Mostimportantly, the technique is independent of the trapcharacteristics, viscosity or temperature of the fluid.The vali<strong>di</strong>ty of the technique has been demonstrated byFig. 2:. Azimuthal components of measured flowvelocities versus the correspon<strong>di</strong>ng ra<strong>di</strong>al <strong>di</strong>stancefrom the centre of a spinning sphere. Solid linesrepresent the range of the expected velocityvalues for a sphere of ra<strong>di</strong>us R=3.7 µm spinning at5.2 Hz.Fig. 3: Measured flow field (arrows) at the outlet ofa 15 µm wide PDMS micro-channel.mapping out the fluid flow around a five microns spinningsphere (Fig.1,2) and at the outlet of a micro-channel(Fig.3). In the future tracers could be permanentlyembedded inside micro-flui<strong>di</strong>cs devices or biologicalsystems and used to probe micro-flows during normaldevice operation.Fig. 1: Measured flow field (arrows) around a 4 µmra<strong>di</strong>us vaterite particle spinning at 5.2 Hz(a) CRS SOFT-INFM-CNR, Roma, Italy.(b) <strong>Dipartimento</strong> <strong>di</strong> <strong>Fisica</strong>, Universita’ <strong>di</strong> Roma, Italy.References[1] R. Di Leonardo, J. Leach, H. Mushfique, J. Cooper, G.Ruocco, M. Padgett, Phys. Rev. Lett., 96, 134502,(2006).[2]D.G. Grier, Nature, 424, 810, (2003).AuthorsR. Di Leonardo (a), G. Ruocco (b,a)69SOFT Scientific <strong>Report</strong> 2004-06
Scientific <strong>Report</strong> – Non Equilibrium Dynamics and ComplexityPolymeric Elements For Adaptive NetworksWe consider an adaptive network to be a system ofinput and output electrodes, connected through acomplex net of nonlinear elements and “intelligent”wires, provi<strong>di</strong>ng numerous possible pathways forsignal propagation from each input to each output.Characteristic feature of the network is the possibilityof the establishing preferential pathways accor<strong>di</strong>ngto the previous experience of the system (“learning”)and to the training procedure performed by externalaction. Thus, a specific output signal will appear incorrespondence with a given input signal or asequence of input signals. However, to be reallyadaptive, the network must have temporal evolutionof these connections, and must allow the variation ofthe input-output relationships accor<strong>di</strong>ng to the“learning” or if the new information from the externaltraining appears (“teaching”). A final feature of thenetwork is its topological complexity: such parametershould influence strongly the efficiency of theaforementioned processes.The only known perfectly working adaptive system,namely, the neuron network of biological cognitivesystems, is constructed from organic molecules anduses electrochemical principles for most of itsfunctioning. Furthermore, the adaptive mechanismswe wish to consider are conceptually similar to theso-called Hebbian rule [1] which is at the basis ofmodern neuroscience. Thus it is of interest to searchfor an organic complex adaptive network inspired byneurosystems.Within this general research program, we haveobtained the following results:1. We have fabricated and characterized anelectrochemically controlled non linear polymericheterostructure [2] Such structure should mimic inCurrent (A)7.0E-086.0E-085.0E-080.0E+00 2.0E+03 4.0E+03 6.0E+03 8.0E+03Time (s)Fig. 2: Temporal behaviour of the current of thepolymeric analog of generating neuron.our network the functional behavior of synapses inbiological cognitive systems. In particular, we havefound an asymmetric behavior in the time relaxationof the electrochemically controlled current, which isessential for the adaptive behavior we seek.2. We have shown that our device, with some smallbut essential mo<strong>di</strong>fications, can have self-sustainingcurrent oscillations for constant applied bias. Thismakes our device analogous to the so-calledgenerating neurons. In the case of the pond snail,Lymnaea stagnalis, which we chose as our biologicalbenchmark, this corresponds to the N1M neuron [3].Furthermore, we have some preliminary evidencethat such oscillations may be similar to the wellknown Bielousov-Zhavatinski reaction [4].3. We have also verified the possibility of usingstatistical assembly to create mixed networks of ourbasic polymeric components, namely doped PEO anddoped PANI fibers, in which due to sufficiently highcomplexity the probability of the occurrence ofjunctions with the same characteristics of ourfabricated PEO-PANI heterojunction is high [5].bReferences[1] D.O. Hebb, The organization of behavior: Aneuropsychological Theory, Wiley Sons, NY (1961).[2] V. Erokhin, Y. Berzina, and M.P. Fontana, J. Appl.Phys., 97, 064501 (2005).[3] V.A. Straub, K. Staras, G. Kemenes and P.R.Benjamin, J. Neurophysiol., 88, 1569 (2002).[4] V. Erokhin, T. Berzina, M.P. Fontana, Crystallogr.Rep., in press (2006).[5] V. Erokhin, T. Berzina, P. Camorani, M.P.Fontana, manuscript submittedAuthorsV. Erokhin, T. Berzina, and M.P. FontanaUniversity of ParmaCRS SOFT CNR-INFMFig. 1: Temporal behaviour of the current of thepolymeric synapses analog for positive (a) andnegative (b) bias.SOFT Scientific <strong>Report</strong> 2004-0670
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