Effects of Ferroelectric Fillers on Composite Dielectric Elastomer Actuator

High-performance electrically responsive artificial muscle materials for soft robot actuation

Traditional robotic devices are often bulky and rigid, making it difficult for them to adapt to the soft and complex shapes of the human body. In stark contrast, soft robots, as a burgeoning class of robotic technology, showcase exceptional flexibility and adaptability, positioning them as compelling contenders for a diverse array of applications. High-performance electrically responsive artificial muscle materials (ERAMMs), as key driving components of soft robots, can achieve efficient motion and deformation, as well as more flexible and precise robot control, attracting widespread attention. This paper reviews the latest advancements in high-performance ERAMMs and their applications in the field of soft robot actuation, using ionic polymer-metal composites and dielectric elastomers as typical cases. Firstly, the definition, characteristics, and electro-driven working principles of high-performance ERAMMs are introduced. Then, the material design and synthesis, fabrication processes and optimization, as well as characterization and testing methods of the ERAMMs are summarized. Furthermore, various applications of two typical ERAMMs in the field of soft robot actuation are discussed in detail. Finally, the challenges and future directions in current research are analyzed and anticipated. This review paper aims to provide researchers with a reference for understanding the latest research progress in high-performance ERAMMs and to guide the development and application of soft robots. STATEMENT OF SIGNIFICANCE.

M. V, Rao PK, Hiremath P, Naik N. Analysis of Polymer-Ceramic Composites Performance on Electrical and Mechanical Properties through Finite Element and Empirical Models

Polymer and ceramic-based composites offer a unique blend of desirable traits for improving dielectric permittivity. This study employs an empirical approach to estimate the dielectric permittivity of composite materials and uses a finite element model to understand the effects of permittivity and filler concentration on mechanical and electrical properties. The empirical model combines the Maxwell-Wagner-Sillars (MWS) and Bruggeman models to estimate the effective permittivity using Barium Titanate (BT) and Calcium Copper Titanate Oxide (CCTO) as ceramic fillers dispersed in a Polydimethylsiloxane (PDMS) polymer matrix. Results indicate that the permittivity of the composite improves with increased filler content, with CCTO/PDMS emerging as the superior combination for capacitive applications. Capacitance and energy storage in the CCTO/PDMS composite material reached 900 nF and 450 nJ, respectively, with increased filler content. Additionally, increased pressure on the capacitive model with varied filler content showed promising effects on mechanical properties. The interaction between BT filler and the polymer matrix significantly altered the electrical properties of the model, primarily depending on the composite's permittivity. This study provides comprehensive insights into the effects of varied filler concentrations on estimating mechanical and electrical properties, aiding in the development of real-world pressure-based capacitive models.

Additively manufactured unimorph dielectric elastomer actuators: Design, materials, and fabrication

Dielectric elastomer actuator (DEA) is a smart material that holds promise for soft robotics due to the material's intrinsic softness, high energy density, fast response, and reversible electromechanical characteristics. Like for most soft robotics materials, additive manufacturing (AM) can significantly benefit DEAs and is mainly applied to the unimorph DEA (UDEA) configuration. While major aspects of UDEA modeling are known, 3D printed UDEAs are subject to specific material and geometrical limitations due to the AM process and require a more thorough analysis of their design and performance. Furthermore, a figure of merit (FOM) is an analytical tool that is frequently used for planar DEA design optimization and material selection but is not yet derived for UDEA. Thus, the objective of the paper is modeling of 3D printed UDEAs, analyzing the effects of their design features on the actuation performance, and deriving FOMs for UDEAs. As a result, the derived analytical model demonstrates dependence of actuation performance on various design parameters typical for 3D printed DEAs, provides a new optimum thickness to Young's modulus ratio of UDEA layers when designing a 3D printed DEA with fixed dielectric elastomer layer thickness, and serves as a base for UDEAs' FOMs. The FOMs have various degrees of complexity depending on considered UDEA design features. The model was numerically verified and experimentally validated through the actuation of a 3D printed UDEA. The fabricated and tested UDEA design was optimized geometrically by controlling the thickness of each layer and from the material perspective by mixing commercially available silicones in non-standard ratios for the passive and dielectric layers. Finally, the prepared non-standard mix ratios of the silicones were characterized for their viscosity dynamics during curing at various conditions to investigate the silicones' manufacturability through AM.

Effect of the Particle Size and Layer Thickness of GNP Fillers on the Dielectric Properties and Actuated Strain of GNP-PDMS Composites

Dielectric elastomer actuators (DEAs), a type of electroactive polymers (EAPs), are smart materials that are used in various fields such as artificial muscles and biomimetic robots. In this study, graphene nanoplatelets (GNPs), which are conductive carbon fillers, were added to a widely used DEA, namely, polydimethylsiloxane (PDMS), to improve its low actuated strain. Four grades of GNPs were used: H5, H25, M5, and M25 (here, the number following the letter indicates the average particle size of the GNPs in μm). The average layer thickness of the H grade is 13−14 nm and that of the M grade is 5−7 nm. PDMS composites were prepared by adding 0.5, 1, 2, and 3 wt% of each GNP, following which the mechanical properties, dielectric properties, and actuated strain of the composites were measured. The mechanical properties were found to increase as the particle size increased. Regarding the dielectric characteristics, it was found that the higher the aspect ratio of the filler, the easier the formation of a micro-capacitor network in the composite—this led to an increase in the dielectric constant. In addition, the higher amounts of GNPs in the composites also led to an increase in the dielectric constant. For the actuated strain analysis, the electromechanical sensitivity was calculated using the ratio of the dielectric constant to the Young’s modulus, which is proportional to the strain. However, it was found that when the loss tangent was high, the performance of the actuated strain decreased owing to the conversion of electric energy into thermal energy and leakage current loss. As a result, the highest actuated strain was exhibited by the M25 composite, with an actuated strain value of 3.01% measured at a low electric field (<4 kV/mm). In conclusion, we proved that the GNP−PDMS composites with a thin layer and large particle size exhibited high deformation.

Liquid Metal Patterned Stretchable and Soft Capacitive Sensor with Enhanced Dielectric Property Enabled by Graphite Nanofiber Fillers

In this work, we introduce liquid metal patterned stretchable and soft capacitive sensor with enhanced dielectric properties enabled by graphite nanofiber (GNF) fillers dispersed in polydimethylsiloxane (PDMS) substrate. We oxidized gallium-based liquid metal that exhibited excellent wetting behavior on the surface of the composites to enable patterning of the electrodes by a facile stencil printing. The fluidic behavior of the liquid metal electrode and modulated dielectric properties of the composite (k = 6.41 ± 0.092@6 wt % at 1 kHz) was utilized to fabricate stretchable and soft capacitive sensor with ability to distinguish various hand motions.