Design and Modeling of a New Biomimetic Soft Robotic Jellyfish Using IPMC-Based Electroactive Polymers

Smart materials and soft robotics have been seen to be particularly well-suited for developing biomimetic devices and are active fields of research. In this study, the design and modeling of a new biomimetic soft robot is described. Initial work was made in the modeling of a biomimetic robot based on the locomotion and kinematics of jellyfish. Modifications were made to the governing equations for jellyfish locomotion that accounted for geometric differences between biology and the robotic design. In particular, the capability of the model to account for the mass and geometry of the robot design has been added for better flexibility in the model setup. A simple geometrically defined model is developed and used to show the feasibility of a proposed biomimetic robot under a prescribed geometric deformation to the robot structure. A more robust mechanics model is then developed which uses linear beam theory is coupled to an equivalent circuit model to simulate actuation of the robot with ionic polymer-metal composite (IPMC) actuators. The mechanics model of the soft robot is compared to that of the geometric model as well as biological jellyfish swimming to highlight its improved efficiency. The design models are characterized against a biological jellyfish model in terms of propulsive efficiency. Using the mechanics model, the locomotive energetics as modeled in literature on biological jellyfish are explored. Locomotive efficiency and cost as a function of swimming cycles are examined for various swimming modes developed, followed by an analysis of the initial transient and steady-state swimming velocities. Applications for fluid pumping or thrust vectoring utilizing the same basic robot design are also proposed. The new design shows a clear advantage over its purely biological counterpart for a soft-robot, with the newly proposed biomimetic swimming mode offering enhanced swimming efficiency and steady-state velocities for a given size and volume exchange

A Hyperelastic Porous Media Framework for Ionic Polymer-Metal Composites and Characterization of Transduction Phenomena via Dimensional Analysis and Nonlinear Regression

Ionic polymer-metal composites (IPMC) are smart materials that exhibit large deformation in response to small applied voltages, and conversely generate detectable electrical signals in response to mechanical deformations. The study of IPMC materials is a rich field of research, and an interesting intersection of material science, electrochemistry, continuum mechanics, and thermodynamics. Due to their electromechanical and mechanoelectrical transduction capabilities, IPMCs find many applications in robotics, soft robotics, artificial muscles, and biomimetics. This study aims to investigate the dominating physical phenomena that underly the actuation and sensing behavior of IPMC materials. This analysis is made possible by developing a new, hyperelastic porous media modeling framework for IPMCs. Using the principles of continuum thermodynamics and multiphasic materials, a finite-strain porous media formulation of IPMC materials is developed. The intricate polymer-electrode interface coupling is extended to such a finite-strain model by accounting for charge conservation at deforming material interfaces. Using this new modeling framework, the effects of kinematic nonlinearity are explored, and a partially linearized kinematic model is proposed for capturing rotational deformation in an otherwise linear model. The most comprehensive dimensional analysis of IPMC transduction phenomena is presented, characterizing the IPMC actuator, short-circuit current, and open-circuit voltage response under static and dynamic loading. The information obtained in this analysis is used to construct nonlinear regression models for the transduction response as univariant and multivariant functions. Automatic differentiation techniques are leveraged to linearize the nonlinear regression models in the vicinity of a representative IPMC description and derive the sensitivity of the transduction response with respect to the driving independent variables. Further, the multiphysics model is validated using experimental data collected for the dynamic IPMC actuator and voltage sensor. With data collected from physical samples of IPMC materials in-lab, the regression models developed under the new computational framework are verified. Using these regression models to interpret the experimental data allowed for further material property characterization to occur, demonstrating the capability of using hybrid computational / experimental regression models to extract information regarding material properties that would otherwise be unknown within the data collected. Key values for the mobile concentration and electric potential fields are approximated using order-of-magnitude arguments and the sharpness of the gradients that occur at the polymer-electrode interfaces of IPMC materials. These values allow for approximate reconstruction of the fields themselves, which in turn are leveraged to formulate the internal bending moments and steady-state curvature of the IPMC. Using both an Euler-Bernoulli beam and a constant curvature arc model for the IPMC, the deformation and rotation of the of the order of magnitude model demonstrated impressive performance for being based on rough approximations. The curled shape of IPMCs under large applied potentials with nonlinear deformation are recovered using this simplified model, and the ability to extend the model for dynamic actuation is outlined

Understanding the Thermal Properties of Precursor-Ionomers to Optimize Fabrication Processes for Ionic Polymer-Metal Composites (IPMCs)

Ionic polymer-metal composites (IPMCs) are one of many smart materials and have ionomer bases with a noble metal plated on the surface. The ionomer is usually Nafion, but recently Aquivion has been shown to be a promising alternative. Ionomers are available in the form of precursor pellets. This is an un-activated form that is able to melt, unlike the activated form. However, there is little study on the thermal characteristics of these precursor ionomers. This lack of knowledge causes issues when trying to fabricate ionomer shapes using methods such as extrusion, hot-pressing, and more recently, injection molding and 3D printing. To understand the two precursor-ionomers, a set of tests were conducted to measure the thermal degradation temperature, viscosity, melting temperature, and glass transition. The results have shown that the precursor Aquivion has a higher melting temperature (240 °C) than precursor Nafion (200 °C) and a larger glass transition range (32–65°C compared with 21–45 °C). The two have the same thermal degradation temperature (~400 °C). Precursor Aquivion is more viscous than precursor Nafion as temperature increases. Based on the results gathered, it seems that the precursor Aquivion is more stable as temperature increases, facilitating the manufacturing processes. This paper presents the data collected to assist researchers in thermal-based fabrication processes

Natriuretic peptides and integrated risk assessment for cardiovascular disease: an individual-participant-data meta-analysis

BACKGROUND: Guidelines for primary prevention of cardiovascular diseases focus on prediction of coronary heart disease and stroke. We assessed whether or not measurement of N-terminal-pro-B-type natriuretic peptide (NT-proBNP) concentration could enable a more integrated approach than at present by predicting heart failure and enhancing coronary heart disease and stroke risk assessment. METHODS: In this individual-participant-data meta-analysis, we generated and harmonised individual-participant data from relevant prospective studies via both de-novo NT-proBNP concentration measurement of stored samples and collection of data from studies identified through a systematic search of the literature (PubMed, Scientific Citation Index Expanded, and Embase) for articles published up to Sept 4, 2014, using search terms related to natriuretic peptide family members and the primary outcomes, with no language restrictions. We calculated risk ratios and measures of risk discrimination and reclassification across predicted 10 year risk categories (ie, <5%, 5% to <7·5%, and ≥7·5%), adding assessment of NT-proBNP concentration to that of conventional risk factors (ie, age, sex, smoking status, systolic blood pressure, history of diabetes, and total and HDL cholesterol concentrations). Primary outcomes were the combination of coronary heart disease and stroke, and the combination of coronary heart disease, stroke, and heart failure. FINDINGS: We recorded 5500 coronary heart disease, 4002 stroke, and 2212 heart failure outcomes among 95 617 participants without a history of cardiovascular disease in 40 prospective studies. Risk ratios (for a comparison of the top third vs bottom third of NT-proBNP concentrations, adjusted for conventional risk factors) were 1·76 (95% CI 1·56-1·98) for the combination of coronary heart disease and stroke and 2·00 (1·77-2·26) for the combination of coronary heart disease, stroke, and heart failure. Addition of information about NT-proBNP concentration to a model containing conventional risk factors was associated with a C-index increase of 0·012 (0·010-0·014) and a net reclassification improvement of 0·027 (0·019-0·036) for the combination of coronary heart disease and stroke and a C-index increase of 0·019 (0·016-0·022) and a net reclassification improvement of 0·028 (0·019-0·038) for the combination of coronary heart disease, stroke, and heart failure. INTERPRETATION: In people without baseline cardiovascular disease, NT-proBNP concentration assessment strongly predicted first-onset heart failure and augmented coronary heart disease and stroke prediction, suggesting that NT-proBNP concentration assessment could be used to integrate heart failure into cardiovascular disease primary prevention. FUNDING: British Heart Foundation, Austrian Science Fund, UK Medical Research Council, National Institute for Health Research, European Research Council, and European Commission Framework Programme 7