Designing self-powered materials systems that perform pattern recognition.

Inspired by the advances in both materials and computer science, we describe efforts to design "materials that compute" where the material and the computer are the same entity. Using theory and simulation, we devise systems that integrate the behavior of self-oscillating gels and fundamental concepts from oscillator-based computing. We specifically focus on gels that undergo the Belousov-Zhabotinsky (BZ) reaction and thus exhibit self-sustained oscillations. In our models, we couple the BZ gel to an overlaying piezoelectric (PZ) cantilever to create a BZ-PZ unit. By connecting the BZ-PZ units by electrical wires, we design networks that autonomously transduce chemical, mechanical, and electrical energy to propagate a signal across the device and achieve synchronization of the oscillating gels in the network. This synchronization allows the device to perform pattern recognition in a self-organized manner, without the need for external electrical power sources. In particular, we imposed a collection of input patterns onto different BZ-PZ networks, where each network encompassed a distinct stored pattern. The network encompassing the stored pattern closest to the input pattern exhibited the fastest convergence time to the stable synchronization behavior, and could be identified as the "winner". In this way, the networks of coupled BZ-PZ oscillators achieved pattern recognition. We demonstrated that the convergence time to the stable synchronization provides a robust measure of the degree of match between the input and stored patterns. Through these studies, we laid out fundamental and experimentally realizable design rules for creating "materials that compute".

Pattern recognition with "materials that compute".

Driven by advances in materials and computer science, researchers are attempting to design systems where the computer and material are one and the same entity. Using theoretical and computational modeling, we design a hybrid material system that can autonomously transduce chemical, mechanical, and electrical energy to perform a computational task in a self-organized manner, without the need for external electrical power sources. Each unit in this system integrates a self-oscillating gel, which undergoes the Belousov-Zhabotinsky (BZ) reaction, with an overlaying piezoelectric (PZ) cantilever. The chemomechanical oscillations of the BZ gels deflect the PZ layer, which consequently generates a voltage across the material. When these BZ-PZ units are connected in series by electrical wires, the oscillations of these units become synchronized across the network, where the mode of synchronization depends on the polarity of the PZ. We show that the network of coupled, synchronizing BZ-PZ oscillators can perform pattern recognition. The "stored" patterns are set of polarities of the individual BZ-PZ units, and the "input" patterns are coded through the initial phase of the oscillations imposed on these units. The results of the modeling show that the input pattern closest to the stored pattern exhibits the fastest convergence time to stable synchronization behavior. In this way, networks of coupled BZ-PZ oscillators achieve pattern recognition. Further, we show that the convergence time to stable synchronization provides a robust measure of the degree of match between the input and stored patterns. Through these studies, we establish experimentally realizable design rules for creating "materials that compute."

Achieving synchronization with active hybrid materials: Coupling self-oscillating gels and piezoelectric films.

Lightweight, deformable materials that can sense and respond to human touch and motion can be the basis of future wearable computers, where the material itself will be capable of performing computations. To facilitate the creation of "materials that compute", we draw from two emerging modalities for computation: chemical computing, which relies on reaction-diffusion mechanisms to perform operations, and oscillatory computing, which performs pattern recognition through synchronization of coupled oscillators. Chemical computing systems, however, suffer from the fact that the reacting species are coupled only locally; the coupling is limited by diffusion as the chemical waves propagate throughout the system. Additionally, oscillatory computing systems have not utilized a potentially wearable material. To address both these limitations, we develop the first model for coupling self-oscillating polymer gels to a piezoelectric (PZ) micro-electro-mechanical system (MEMS). The resulting transduction between chemo-mechanical and electrical energy creates signals that can be propagated quickly over long distances and thus, permits remote, non-diffusively coupled oscillators to communicate and synchronize. Moreover, the oscillators can be organized into arbitrary topologies because the electrical connections lift the limitations of diffusive coupling. Using our model, we predict the synchronization behavior that can be used for computational tasks, ultimately enabling "materials that compute".

Modeling the entrainment of self-oscillating gels to periodic mechanical deformation.

Polymer gels undergoing the oscillatory Belousov-Zhabotinsky (BZ) reaction are one of the few synthetic materials that exhibit biomimetic mechano-chemical transduction, converting mechanical input into chemical energy. Here, we consider self-oscillating BZ gels that are subjected to periodic mechanical forcing, and model the entrainment of the oscillatory gel dynamics to this external stimulus. The gel size is assumed to be sufficiently small that the chemo-mechanical oscillations are spatially uniform. The behavior of the system is captured by equations describing the kinetics of the oscillatory BZ reaction in the gel coupled to equations for the variations in gel size due to the inherent reaction and imposed force. We employ the phase dynamics approach for analyzing the entrainment of the BZ gel to force- and strain-controlled compressive deformations. The phase response curves are obtained using Malkin's method, and time-averaging is applied to extract the slow phase dynamics caused by the periodic forcing. We demonstrate that the entrainment of the self-oscillating BZ gel is sensitive to the chemo-mechanical coupling in gel, the mode of deformation, and the level of static compression. Kuramoto's model of phase oscillators is shown to be applicable if the external forcing is purely harmonic.

Dielectrophoresis-based classification of cells using multi-target multiple-hypothesis tracking.

In this paper we present a novel methodology for classifying cells by using a combination of dielectrophoresis, image tracking and classification algorithms. We use dielectrophoresis to induce unique motion patterns in cells of interest. Motion is extracted via multi-target multiple-hypothesis tracking. Trajectories are then used to classify cells based on a generalized likelihood ratio test. We present results of a simulation study and of our prototype tracking the dielectrophoretic velocities of cells.

Designing communicating colonies of biomimetic microcapsules.

Using computational modeling, we design colonies of biomimetic microcapsules that exploit chemical mechanisms to communicate and alter their local environment. As a result, these synthetic objects can self-organize into various autonomously moving structures and exhibit ant-like tracking behavior. In the simulations, signaling microcapsules release agonist particles, whereas target microcapsules release antagonist particles and the permeabilities of both capsule types depend on the local particle concentration in the surrounding solution. Additionally, the released nanoscopic particles can bind to the underlying substrate and thereby create adhesion gradients that propel the microcapsules to move. Hydrodynamic interactions and the feedback mechanism provided by the dissolved particles are both necessary to achieve the collective dynamics exhibited by these colonies. Our model provides a platform for integrating both the spatial and temporal behavior of assemblies of "artificial cells," and allows us to design a rich variety of structures capable of exhibiting complex, cooperative behavior. Due to the cell-like attributes of polymeric microcapsules and polymersomes, material systems are available for realizing our predictions.

Sensitivity enhancement of fiber Bragg gratings to transverse stress by using microstructural fibers.

We present simulation and experimental results of fiber Bragg grating responses to transverse stress in microstructure fibers. The grating wavelength shifts and peak splits are studied as a function of external load and fiber orientation. Both simulation and measurement results indicate that the sensitivity of grating sensors to the transverse stress can be enhanced by a factor of eight in a two-hole fiber over that in a standard fiber.