Bimolecular unit cells have recently become a focus for biologically-inspired smart materials. This is largely due their ability to exhibit many of the same properties as the natural cell membrane. In this study, two lipid monolayers formed at a water/oil interface are brought together, creating a lipid bilayer at their interface with each droplet containing a different concentration of ions. This ionic concentration gradient leads to the development of a membrane potential across the bilayer as ions begin to passively diffuse across the membrane at varying rates, providing the proof of concept for energy storage through cellular mechanics. The focus of the study is to determine the influence of osmotic pressure on the lifespan of the lipid bilayer. We hypothesize that the greater osmotic pressure that develops from a greater ionic concentration gradient will prove to have a negative impact on the lifespan of the bilayer membrane, causing it to rupture sooner. This is due to the substantial amount of osmotic swelling that will occur to compensate for the ionic concentration gradient. This study will demonstrate how osmotic pressure will continue to be a limiting factor in the effectiveness and stability of cellularly-inspired energy relevant materials.
A method for studying the coupled electrical-mechanical response of droplet interface bilayers is proposed. This research examines the concept of the biologically-inspired hair cell in greater depth, attempting to determine the source of the sensing current when no external potential is applied across the sensing droplet-interface bilayer element. Historically the mechanosensitive current in these droplet-interface bilayers has been attributed to a combination of capacitive currents and electrode oscillation (experimental error); however the development of a third sensing mechanism through modifying the bilayer properties may enhance the usefulness of the mechanosensitive droplet interface bilayer networks considerably. This would allow for measurable sensing currents without requiring an externally applied electric field by permanently charging the bilayer element through surface modifications. Charging agents are added to the droplet interface bilayer network as the network is oscillated and the electrical response is recorded for analysis. The adsorption of the charged molecules is studied through the intramembrane field compensation (IFC) approach, and the knowledge gained from this is then applied towards the mechanosensitivity analysis. Multiple charging techniques are tested and employed, and the nature of the sensing current is determined by examining the frequency content of the recorded currents. Several properties are derived, including the nature of the sensing current, the charging mechanisms available for boosting the sensing current, and the nature of the sensing current without externally applied potentials.
Novel biologically-inspired energy harvesting devices constructed with lipid bilayer membranes are studied. Recently the research group has proposed the use of biomolecular unit cells consisting of encapsulated droplets with a lipid bilayer formed at their interfaces, stabilized between the two aqueous compartments. This allows for the rapid study and assessment of the characteristics of the individual unit cell, the insertion of various transport proteins and peptides that shape the response of the unit cell, and the construction of complex networks of these biomolecular systems. The goal of this work is to develop and study methods for constructing energy relevant devices through these biomolecular networks. These networks are highly tailorable, and allow the researcher to alter the embedded proteins/peptides in the lipid bilayer, the bilayer dimensions through the application of compressive forces, and the salt concentrations in the droplets. This allows for a high degree of control over their attributes and outputs. These systems also exhibit collective properties through large networks of the unit cells, allowing for complex sensing and actuation behavior not exhibited by single cells. This paper provides an overview of the development of a model for predicting the performance and output of these energy relevant biomolecular networks as well as preliminary experimental results that demonstrate some of the concepts in action.
Computational models are derived for predicting the behavior of artificial cellular networks for engineering applications. The systems simulated involve the use of a biomolecular unit cell, a multiphase material that incorporates a lipid bilayer between two hydrophilic compartments. These unit cells may be considered building blocks that enable the fabrication of complex electrochemical networks. These networks can incorporate a variety of stimuli-responsive biomolecules to enable a diverse range of multifunctional behavior. Through the collective properties of these biomolecules, the system demonstrates abilities that recreate natural cellular phenomena such as mechanotransduction, optoelectronic response, and response to chemical gradients. A crucial step to increase the utility of these biomolecular networks is to develop mathematical models of their stimuli-responsive behavior. While models have been constructed deriving from the classical Hodgkin-Huxley model focusing on describing the system as a combination of traditional electrical components (capacitors and resistors), these electrical elements do not sufficiently describe the phenomena seen in experiment as they are not linked to the molecular scale processes. From this realization an advanced model is proposed that links the traditional unit cell parameters such as conductance and capacitance to the molecular structure of the system. Rather than approaching the membrane as an isolated parallel plate capacitor, the model seeks to link the electrical properties to the underlying chemical characteristics. This model is then applied towards experimental cases in order that a more complete picture of the underlying phenomena responsible for the desired sensing mechanisms may be constructed. In this way the stimuli-responsive characteristics may be understood and optimized.