ALTERNATING CURRENT DIELECTROPHORESIS OF CORE-SHELL NANOPARTICLES: EXPERIMENTS AND COMPARISON WITH THEORY

Nanoparticles are fascinating where physical and optical properties are related to size. Highly controllable synthesis methods and nanoparticle assembly are essential [6] for highly innovative technological applications. Among nanoparticles, nonhomogeneous core-shell nanoparticles (CSnp) have new properties that arise when varying the relative dimensions of the core and the shell. This CSnp structure enables various optical resonances, and engineered energy barriers, in addition to the high charge to surface ratio. Assembly of homogeneous nanoparticles into functional structures has become ubiquitous in biosensors (i.e. optical labeling) [7, 8], nanocoatings [9-13], and electrical circuits [14, 15]. Limited nonhomogenous nanoparticle assembly has only been explored. Many conventional nanoparticle assembly methods exist, but this work explores dielectrophoresis (DEP) as a new method. DEP is particle polarization via non-uniform electric fields while suspended in conductive fluids. Most prior DEP efforts involve microscale particles. Prior work on core-shell nanoparticle assemblies and separately, nanoparticle characterizations with dielectrophoresis and electrorotation [2-5], did not systematically explore particle size, dielectric properties (permittivity and electrical conductivity), shell thickness, particle concentration, medium conductivity, and frequency. This work is the first, to the best of our knowledge, to systematically examine these dielectrophoretic properties for core-shell nanoparticles. Further, we conduct a parametric fitting to traditional core-shell models. These biocompatible core-shell nanoparticles were studied to fill a knowledge gap in the DEP field. Experimental results (chapter 5) first examine medium conductivity, size and shell material dependencies of dielectrophoretic behaviors of spherical CSnp into 2D and 3D particle-assemblies. Chitosan (amino sugar) and poly-L-lysine (amino acid, PLL) CSnp shell materials were custom synthesized around a hollow (gas) core by utilizing a phospholipid micelle around a volatile fluid templating for the shell material; this approach proves to be novel and distinct from conventional core-shell models wherein a conductive core is coated with an insulative shell. Experiments were conducted within a 100 nl chamber housing 100 um wide Ti/Au quadrapole electrodes spaced 25 um apart. Frequencies from 100kHz to 80MHz at fixed local field of 5Vpp were tested with 10-5 and 10-3 S/m medium conductivities for 25 seconds. Dielectrophoretic responses of ~220 and 340(or ~400) nm chitosan or PLL CSnp were compiled as a function of medium conductivity, size and shell material.

Calibrating Wireless Sensor Network Simulation Models with Real-World Experiments

This paper studies the energy-efficiency and service characteristics of a recently developed energy-efficient MAC protocol for wireless sensor networks in simulation and on a real sensor hardware testbed. This opportunity is seized to illustrate how simulation models can be verified by cross-comparing simulation results with real-world experiment results. The paper demonstrates that by careful calibration of simulation model parameters, the inevitable gap between simulation models and real-world conditions can be reduced. It concludes with guidelines for a methodology for model calibration and validation of sensor network simulation models.

Learning Paradigms Exemplified by Virtual Experiments with Honey Bees

Learning is based on rules that can be elucidated by behavioural experiments. This article focuses on virtual experiments, in which non-associative learning (habituation, sensitization) and principles of associative learning (contiguity, inhibitory learning, generalization, overshadowing, positive and negative patterning) can be examined using 'virtual' honey bees in PER (Proboscis Reaction Extension) conditioning experiments. Users can develop experimental designs, simulate and document the experiments and find explanations and suggestions for the analysis of the learning experiments. The virtual experiments are based on video sequences and data from actual learning experiments. The bees' responses are determined by probability-based learning profiles.