Liquid MetalâElastomer Soft Composites with Independently

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Liquid Metal-Elastomer Soft Composites with Independently Controllable and Highly Tunable Droplet Size and Volume Loading Ravi Tutika, Steven Kmiec, A B M Tahidul Haque, Steve W Martin, and Michael D. Bartlett ACS Appl. Mater. Interfaces, Just Accepted Manuscript • DOI: 10.1021/acsami.9b04569 • Publication Date (Web): 22 Apr 2019 Downloaded from http://pubs.acs.org on April 22, 2019

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ACS Applied Materials & Interfaces

Liquid Metal-Elastomer Soft Composites with Independently Controllable and Highly Tunable Droplet Size and Volume Loading Ravi Tutika,† Steven Kmiec,‡ A B M Tahidul Haque,† Steve W. Martin,‡ and Michael D. Bartlett∗,† †Department of Materials Science and Engineering, Soft Materials and Structures Lab, Iowa State University, 528 Bissell Rd, Ames, Iowa 50011, United States ‡Department of Materials Science and Engineering, Iowa State University, 528 Bissell Rd, Ames, Iowa 50011, United States E-mail: [email protected] Keywords: liquid metal, microstructure, permittivity, elastomers, particle size effects.

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Abstract Soft composites are critical for soft and flexible materials in energy harvesting, actuators, and multifunctional devices. One emerging approach to create multifunctional composites is through the incorporation of liquid metal (LM) droplets such as eutectic gallium indium (EGaIn) in highly deformable elastomers. The microstructure of such systems is critical to their performance, however, current materials lack control of particle size at diverse volume loadings. Here, we present a fabrication approach to create liquid metal-elastomer composites with independently controllable and highly tunable droplet size (100 nm ≤ D ≤ 80 µm) and volume loading (0 ≤ φ ≤ 80%). This is achieved through a combination of shear mixing and sonication of concentrated LM/elastomer emulsions to control droplet size and subsequent dilution and homogenization to tune LM volume loading. These materials are characterized utilizing dielectric spectroscopy supported by analytical modeling which shows a high relative permittivity of 60 (16x the unfilled elastomer) in a composite with φ = 80%, a low tan δ of 0.02, and a significant dependence on φ and minor dependence on droplet size. Temperature response and stability are determined using dielectric spectroscopy through temperature and frequency sweeps and with DSC. These results demonstrate a wide temperature stability of the liquid metal phase (crystallizing < -85 ◦ C for D < 20 µm). Additionally, all composites are electrically insulating across a wide frequency (0.1 Hz - 10 MHz) and temperature (-70◦ C to 100◦ C) range even up to φ = 80%. We highlight the benefit of LM microstructure control by creating all soft matter stretchable capacitive sensors with tunable sensitivity. These sensors are further integrated into a wearable sensing glove where we identify different objects during grasping motions. This work enables programmable LM composites for soft robotics and stretchable electronics where flexibility and tunable functional response are critical.

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1.

Introduction

Stretchable electronics and soft robotics require functional materials with high extensibility and flexibility. As a result, soft functional materials with innovative properties such as stretchability, 1,2 thermal conductivity, 3 dielectric behavior, 4 electrical conductivity, 5 and toughness 6 are being increasingly investigated. Development of these soft materials has led to advanced functionality in applications such as soft electronics, 7 soft robotics, 8 stretchable sensors, 9 biomedical devices, 10 and energy storage instruments. 11 Recently, the use of liquid metal (LM) in soft materials has provided a significant contribution in these fields. For instance, gallium based LM alloys such as eutectic gallium-indium (EGaIn) and galliumindium-tin (Galinstan) which are non-toxic and liquid at room temperature, demonstrate desirable combinations of electrical, thermal, and mechanical properties. 12 LM is used in various formfactors, including microfluidic channels, 13–15 patterned sheets and films, 16–18 and particles/droplets. 19,20 For the case of LM droplets, these materials have primarily been used as either films of isolated LM droplets supported on substrates, or as droplets dispersed in elastomers. Isolated LM droplets or LM films have been utilized to pattern electronic circuits and antennas, 21,22 create microfluidic pumps 14 and cooling devices, 23 and create conductive surfaces. 24 For LM droplets dispersed into elastomers, these can be used to create all-soft matter composites to enable bulk materials with diverse functionalities such as electrical, 25–29 thermal, 30–33 and tunable mechanical properties 6,34 while maintaining a soft material response. The fluidic nature of the LM in these soft particle composites can overcome the inherent stiffness mismatch that can significantly increase the rigidity often observed in solid particle soft composites, such as those based on metallic, 35 ceramic, 36 and polymeric 37 fillers. Despite the prospect of LM based soft elastomer composites, the fabrication approaches capable of producing particles of different sizes, morphologies, and concentrations/loadings require further development to allow for enhanced control and tunability of mechanical and functional properties.

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To date, various fabrication techniques have been developed to create LM droplets of different shapes and sizes for soft functional devices. To prepare isolated particles or particles in solutions researchers have used sonication, 24,38,39 acoustic wave, 19,40 microfluidic flow focusing, 41–43 and mold casting. 44 By following intricate approaches in sonication 24 and microfluidic flow focusing, 41,43 high concentrations of different sized LM droplets can be obtained. Although, neither of these methods have reported the suitability for both nano and micro level particles. One challenge in LM fabrication methods arise from the solid gallium oxide (Ga2 O3 ) layer that encloses each LM droplet which governs the scission and coalescence of particles. 45 In order to obtain small particle sizes, researchers have employed various surfactants for controlling surface tension of the oxide layer. For instance, surfactants such as thiol 24,38 and acetic acid 46 are extensively used to stabilize the oxide layers during fabrication of LM droplets. Compared to isolated droplets, LM dispersed in elastomers have primarily focused on shear mixing of LM with uncured elastomers through mortar and pestle or mechanical mixers. 26–28,30–33 For example, Koh et al. demonstrated a shear mixing approach with an overhead mixer to create different sized particles dispersed in silicone elastomers. However, the particle size and volume loading were not independently controllable, likely attributed to the viscosity of the mixture changing with different LM loadings. 28 Therefore, approaches to create elastomeric composites with systematic control over LM particle size and loading are still needed for robust and controllable soft composite fabrication. Here, we demonstrate a fabrication approach to disperse LM droplets with independently controllable and highly tunable droplet size and volume loadings into soft elastomers. Specifically, we create four distinct EGaIn particle sizes (D) ranging between 100 nm - 80 µm and disperse these LM droplets into Ecoflex silicone elastomer with four controlled volume loadings (φ) up to 80%. As seen in Figure 1, this is achieved through a combination of shear mixing and sonication of concentrated LM/elastomer emulsions to control particle size and subsequent dilution and homogenization to tune LM volume loading. The micrographs and SEM images in Figure 2 present a grid of LM composites for different combinations of D

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and φ. We explore the functionality of LM particle soft composites by characterizing the dielectric properties of the composites over broad temperature and frequency ranges for each combination of particle size and volume loading. Our investigation demonstrates the dielectric character of the soft composites where a high permittivity of 60 and low dissipation factor (tan δ < 0.5) is attained even at high frequency and over a broad temperature range. Importantly, the dielectric constant is found to strongly depend on volume loading with minimal effect of particle size and temperature. Differential scanning calorimetry (DSC) tests are further performed to determine temperature stability. The liquid character of the LM is maintained down to -85 ◦ C for droplets with D < 20 µm, and the crystallization and the melting temperatures are found to be dependent on particle size but largely independent of volume loading. These results also show that melting and crystallization temperature of LM droplets are suppressed compared to bulk LM, therefore being more favorable than bulk LM for low temperature applications. We further demonstrate the benefit of controlling LM droplet size in LM embedded composites by creating all soft matter capacitive sensors. Using composites with φ = 60% of different droplet sizes as dielectrics we create deformation sensors with tunable sensitivity and extensibility. These soft sensors are integrated into a wearable sensing glove which can identify the grasping of different sized balls over multiple cycles, showing repeatability and high sensitivity in the sensor signal. The systematic fabrication of LM embedded elastomers presented in this study enables independent tuning of LM droplet size D and loading φ. The presented techniques and materials will provide tools to advance LM based soft composites for a variety of soft electronic and robotic applications, thermal interface devices, and energy harvesting systems.

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2.

Results and Discussion

2.1.

Soft Composite Fabrication

Composites with four different particle sizes over four different LM volume loadings are created to demonstrate the tunability in fabrication and to investigate the particle size and volume loading effects on the composite dielectric and thermal behavior. In all cases, the matrix is a two-part Pt-cure elastomer, Ecoflex 00-30 and the filler is a LM composed of a eutectic alloy of gallium and indium (EGaIn). Two procedures are utilized either separately or sequentially to achieve the desired combination of particle size and volume loading 1) shear mixing with a dual asymmetric centrifugal (DAC) mixer, and 2) probe sonication, which inputs high energy into the materials through vibration. The particle and subsequent composite fabrication process can be described in three major steps as illustrated in Figure 1; 1) Combine elastomer and LM at high concentration and place them in a mixing cup/sonication vial, 2) Employ shear mixing/sonication to set the desired particle size, and 3) Add elastomer to dilute the emulsion and reach a desired LM volume loading and then use the shear mixer to homogenize and de-gas. The resulting final composition of the uncured composite is then ready to be cast/molded and cured. The first step in the process is to form an emulsion of LM droplets in the uncured, liquid elastomer. This has similarities to other emulsions such as water-in-oil emulsions. Mackay and co-workers hypothesized that emulsion stability is due to the formation of a film at the water droplet-oil interface that resists water droplet coalescence in water-inoil type emulsions. 47 In a similar manner, we hypothesize that the thin oxide layer which results from compounds of gallium and oxygen 13 help the formation of stable LM/elastomer emulsions. One approach to create this emulsion is through shear mixing. Under laminar flow conditions that are applicable for highly viscous systems, mixed shear and elongation flow fields act most frequently. 48 The droplets are deformed, broken up (dispersed) and

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Figure 1: Fabrication scheme for LM soft composite fabrication. Combine elastomer and LM at high concentration and then 1) High shear mixing for larger micron sized particles (D = 80 µm, 20 µm) and 2) Sonication for smaller micron and sub-micron sized particles (D = 1 µm, 100 nm). Each emulsion is made at φ = 80% and then diluted to a desired volume loading (φ = 60, 40, and 20%) by adding uncured elastomer through low shear mixing that homogenizes and de-gasses the final composition. shape-fixed (encapsulated). Deformation and breakup behavior of the dispersed drops are strongly dependent on the rheology of the emulsion as well as the interface properties. Koh et al., showed that the dispersion viscosity is more dependent on LM loading than particle size or polydispersity in a galinstan/polydimethylsiloxane (PDMS) system, where a 50% volume loading of galinstan increases viscosity by approximately five orders of magnitude. 28 To create micron sized LM particles we use a dual asymmetric centrifuge (DAC), where the shear mixing takes place in a cylindrical, mixing cup that is rotated in one direction at rotations as high as 2500 rpm, while the basket containing the cup is simultaneously rotated in the opposite direction in a tilted plane at approximately one third the rotational speed. To keep the mixing parameters consistent for all the samples, we maintain the same volume of initial materials (Ecoflex and LM) in the same size mixing cup. For the case of shear mixing the parameters of interest are the mixing speed, viscosities, and presence of surfactants. 49 This enables us to vary the speed of mixing (rpm) to control the particle size. A mixing speed of 1000 rpm is utilized to obtain a major axis (D) of 80 µm and 1600 rpm for 20 7



µm. First, a sample with φ = 80% LM is made to set the particle size and then, a lower speed (800 rpm) is used to dilute the concentration of LM as required by adding a calculated amount of Ecoflex. The result is a highly homogeneous mixture of LM and elastomer, in a total mixing time of 5 minutes. All samples are cast into 1 mm thick sheets and cured at 80 ◦

C for 12 hr.

Figure 2: Micrographs of LM composites. The columns are the different volume loadings (φ = 20, 40, 60, 80%) and the rows are different particle sizes (D = 80 µm, 20 µm, 1 µm, 100 nm) at each volume loading (the measured volume loadings and particles sizes are presented next to the images). For 100 nm, SEM image of particles is presented

To achieve the LM/elastomer emulsions with particle size in the micron to sub-micron regime (< 5 µm), we use a probe sonicator in combination with the shear mixer. In a liquid, the rapid vibration of the probe tip causes cavitation, resulting in the formation and vio-

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lent collapse of microscopic bubbles which releases large amounts of energy in the cavitation field. This leads to desired effects in the liquid such as homogenization, dispersion, deagglomeration, emulsification, etc., The sonication technique has widely been used in effective dispersion of nanoparticles and carbon nanotubes in various carrier solutions. 50–52 In the case of LM, it is actively being used to make LM particles and disperse them in solvents to prepare colloidal solutions which are further useful in applications ranging from 3D printing, 53 patterning 54 to drug delivery. 55 The size of the particles is controlled by the amplitude, time of the sonication and the volume of the materials. Centrifugation can also be performed after particle fabrication to purify/rinse LM nanoparticle solutions. 24,39,56 Researchers have sonicated LM in various types of solvents including chlorobenze, 56 ethanol, 38 and propanol, 57 in some cases surfactants are added to achieve stability and prevent coalescence of LM micro or nano particles in the resulting suspension. We choose to sonicate LM in hexanes due to its comparable performance to other solvents and good miscibility in silicone which helps to incorporate the as-fabricated LM particles into the Ecoflex matrix. For particles with D = 1 µm, 0.6 ml of Ecoflex is mixed with 6 ml hexanes in the shear mixer to homogenize, then added to 2.4 ml of LM in a 20 ml scintillation vial, and sonicated at 30% amplitude for 20 min with a 1/800 diameter probe. For D = 100 nm particles, a 0.1 ml LM is first sonicated in hexanes with 5% acetic acid as surfactant. Once the particles are fabricated, multiple samples are mixed to achieve the desired volume loading of LM, and the hexanes are removed by keeping the sample in a fume hood for ∼2 hours. Once the majority of the hexanes are removed, we add the already mixed and uncured 2-part elastomer and employ the shear mixer to simultaneously blend the materials and remove any residual solvent. The final emulsion is then cast into a 1 mm thick sheet and cured at 80 ◦ C for 12 hours similar to the micro regime approach. Following similar fabrication conditions (same mixing speeds and sonication procedures), we are also able to independently control particle size and particle loading in PDMS (Dow Corning Sylgard 184), as observed in the micrographs (Figure S1) and particle analysis (Figure S2, Table S1).

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The optical and SEM images of the final as-cured composites are presented in Figure 2. All of our final samples have some degree of polydispersity. In the case of the sub-micron sized LM particle composites, the huge surface area from numerous nanoparticles at any given φ plays a dominant role in forming the emulsions. Owing to this, we could fabricate upto φ = 20% LM for D = 100 nm, and test upto φ = 60% LM for D = 1 µm composites. In these cases, the large filler loadings combined with the small particle size results in a brittle, or grainy material. This is attributed to the large surface to volume ratio of the smaller particles, resulting in an inability to completely coat/wet all of the LM particles.

2.2.

Particle Size Analysis

Through the techniques outlined in section 2.1, we create LM/elastomer composites at different volume loadings and particle sizes of LM. We perform 2D image analysis on the optical and SEM micrographs using Fiji software. The micrographs obtained are adjusted for brightness and contrast to differentiate the filler from the matrix. The image is then thresholded resulting in a binary image with dark regions for filler and bright regions for the matrix. This is followed by the ‘watershed’ feature that separates the connected dark regions. A built-in particle analysis macro is utilized to calculate these separated areas and fit ellipses. The major axis (D) of the resulting ellipses are plotted as histograms. Mean and standard deviation of the particle size distributions are calculated using Gaussian and log-normal fits on the histogram plot and found to be similar in both cases Figure S3. The representative images analyzed for each particle size are presented in Figure 3a-d and the distribution plots for different LM particle size can be visualized in Figure 3e-h. The histograms of D show that the mean and standard deviation for different volume loading remains similar for a given particle size (Figure 3i). In our process, we set the particle size in a φ = 80% LM emulsion and then add elastomer to dilute to a desired volume loading, then this is shear mixed to homogenize. It is found that the particle size is not significantly changed during this homogenization step, and the particles maintain their original size. For 10


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Figure 3: Particle size analysis. a-d) Analyzed images of LM composite with fit ellipses at φ = 60% and D = 80 µm, 20 µm, 1 µm, 100 nm respectively. e-h) Histograms of particle distribution with varying φ = 20, 40, 60, 80% for corresponding mean D. i) Mean and standard deviation for each group of histograms. Note: For 100 nm, SEM image of particles is analyzed. example, for the case of nominally 80 µm particles, at 80% loading they are 72.3 ± 16.1 µm, upon diluting down to 20% they are D = 75.3 ± 12.3 µm. For a table of all particle sizes, refer to Table S1 for Gaussian fit values of all distributions. The particles tend to be slightly elliptical, with aspect ratios < 1.2. This demonstrates that we are able to maintain the size of the LM particles while varying the loading in our materials. The significance of this result is that the droplet size and volume loading can be tuned independently, allowing for the creation of model systems to study composite properties as well as routes to create high performance soft composite materials. In this study, we characterize the dielectric properties as well as the melting and crystallization dependence of the LM particle composites as a function of particle size and volumetric loading.

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2.3.

Dielectric Spectroscopy

The dielectric properties of the soft composites are characterized at frequencies from 0.1 Hz to 10 MHz at room temperature. The dielectric behavior is plotted for samples with combinations of volume loading and particle size in Figure 4a. The dielectric response of these composites exhibits a strong dependence on the volume loading of liquid metal, and to a lesser extent the particle size. Specifically, the relative permittivity (εr ) increases with the volume loading of the LM and maintains a similar value as the particle size is changed, depicted by the bands of data in Figure 4a. The unfilled elastomer exhibits a relative permittivity of 3.6 at 25 ◦ C, similar to the reported value of ≈3-4 in literature. 27 Furthermore, we varied the curing conditions of the composite and found subtle differences (generally within 10%) in dielectric constant (see Figure S4), while changes in the electrical conductivity in LM-solid particle composites have been shown to show a stronger dependence on curing conditions. 58 With the addition of LM particles, the relative permittivity increases with the volume fraction reaching a value as high as εr = 60 for φ = 80% at 90 kHz. The loss behavior is captured by the dissipation factor (tan δ) and is plotted in Figure 4b. We find higher losses at lower and higher frequencies with a minimum loss around 0.01 to 1 MHz. We do not observe any abrupt increase in the tan δ values, which indicates the absence

Figure 4: Dielectric behavior - Frequency dependence. a) Relative permittivity and b) Dissipation factor, both as a function of frequency at room temperature for different φ and D. c) Relative permittivity as a function of φ at 90 kHz for different D.

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of percolation in these composites. Previous research efforts have reported very high εr in traditional polymer-matrix composites with ceramic fillers, these generally suffer from percolation, which leads to rapid drops in εr once the critical point is triggered and very lossy behavior with the value of tan δ changing from 0.01 to 1000. 4 In contrast, the tan δ value is always < 0.5 from 0.1 Hz to 10 MHz in our LM soft composites. We also notice a decrease in the relative permittivity (εr ) for the φ = 80%, D = 80 µm composite at higher frequencies, which may be observed in composite dielectrics and is attributed to an increase in tan δ at that frequency. 59 To predict the dielectric behavior in these composites and aid in design, we use an analytic theory by Nan et al., which is based on effective medium theory (EMT) approach. 60 The enhancement in the relative permittivity can be predicted as a function of φ and aspect ratio p of the filler material. The effective relative permittivity ε∗r can be calculated as: ε∗r

= εrm

11 (1− < cos2 θ >) + 1 + [ 1−L L11

1−L33 (< L33

cos2 θ >)]φ

1−φ

(1)

L11 and L33 are geometrical factors dependent on the particle shape and are given by:

L11 =

p2 p − cosh−1 p, f or p > 1 2 2 2(p − 1) 2(p − 1)3/2

L33 = 1 − 2L11

(2)

(3)

where εrm is the matrix relative permittivity at φ = 0% and θ is the angle between the axis along which permittivity is being calculated and the principal axis corresponding to the measuring dimension. For our materials, the average aspect ratio of the LM inclusions measured through particle analysis is p = 1.2 and < cos2 θ > = 1/3 for randomly orientated ellipsoids. Using these parameters, the effective relative permittivity as a function of φ as predicted by Eq. 1 is plotted as a solid line in Figure 4c, with experimental εr values at each 13



volume loading and particle size at 90 kHz. The theoretical prediction is in good agreement with the measured values even at high volume loading of 80%. The EMT approach is based on multiple-scattering theory which considers shape, distribution, filler volume, and interface effects to arrive at a general effective medium approximation that can be widely applicable. 61 This implies that there is only a slight particle size dependence on the dielectric properties, which is supported by the relatively insignificant effect of particle size in our data.

Figure 5: Dielectric behavior- Temperature dependence. a) Relative permittivity and b) Dissipation factor as a function of temperature at 90 kHz for different φ and D combinations. c) Relative permittivity for D = 20 µm as a function of φ at 90 kHz for two temperatures. d) Relative permittivity and e) Dissipation factor as a function of frequency in the temperature range -100 ◦ C to 100 ◦ C for φ = 60% and D = 1 µm. To understand the temperature dependent dielectric properties of the soft composites we perform dielectric spectroscopy in the temperature range of -70 ◦ C to 100 ◦ C and a frequency range of 0.1 Hz to 10 MHz. The εr and tan δ values of the composites from -70 ◦ C to 100 ◦

C at 90 kHz are plotted in Figure 5a and Figure 5b respectively. In general, the εr of the

composites drops steadily as the temperature is increased and εr increases with increasing φ. Similar to the frequency dependence, εr is weakly dependent on particle size. The tan δ 14


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value generally remains below 0.01 at 90 kHz over the temperature range. The εr values at two temperatures (-60 ◦ C, 60 ◦ C) for a composite with D = 20 µm at different φ are plotted in Figure 5c, where εr is slightly higher at lower temperatures for a given composition. To show the behavior of the composites over the measured frequency range and temperature range, the εr and tan δ values of a φ = 60% and D = 1 µm sample are plotted in Figure 5d,e respectively. At 100 ◦ C, the εr value drops by ∼14 when the frequency is increased from 0.1 Hz to 10 MHz. This relaxation behavior gradually stabilizes as the temperature is decreased to -100 ◦ C owing to lower kinetic energy in the molecules when an electric field is applied. One interesting observation here is that the εr drops abruptly at -100 ◦ C, which might be due to the freezing of the LM particles at temperatures < -85 ◦ C. For tan δ, the composites show a decrease of two orders of magnitude at 100 ◦ C as frequency increases and this behavior becomes more stable as the temperature is lowered to -100 ◦ C. All these observations show that these composites have a wide working range of frequencies and temperatures and can be tailored for specific dielectric properties.

2.4.

Temperature transitions and thermal stability

We perform DSC analysis on the composites to identify the particle size dependence on thermal transitions in the composites. Supercooling phenomenon is commonly observed in liquid metals, meaning that the liquid can be cooled below the crystallization temperature while still remaining liquid. 46,62 However, the initiation of crystallization occurs rapidly if there are any defects that cause heterogeneous nucleation. To keep the liquid metal in a supercooled state, one approach is to cover the surface with a polymer layer. 46 This supercooling effect has been known to happen particularly in Ga-rich alloys such as EGaIn. 63–65 To understand if the LM particle size has any effect on its supercooling behavior inside the elastomer matrix we use DSC to identify phenomenon such as melting (Tm ) and crystallization (Tc ) in the LM particles and the elastomer matrix. All the samples are tested at a temperature ramp of 20 ◦ C min-1 . We vary particle size at constant φ and vice-versa to study the effect of 15



Figure 6: Differential scanning calorimetry (DSC). a) Heating and cooling curves for a representative LM composite (φ = 60% and D = 1 µm) and an unfilled silicone/Ecoflex ( φ = 0%). The influence of φ and D on b) LM crystallization temperature (Tc ) c) LM melting temperature (Tm ). DSC curves as d) φ is varied at constant D and as e) D is varied at constant φ. these parameters on the LM crystallization and melting points in the composites. For the bulk liquid metal, we measure a melting temperature of Tm = 18.8 ◦ C and a crystallization temperature of Tc = -21.2 ◦ C, with a slight variation due to a change in heating/cooling rates (see Figure S5). Figure 6a shows representative DSC heating and cooling curves for these composites marked with the different critical points. Upon cooling, we see two peaks corresponding to LM crystallization and a broad distribution of exothermic events, which might indicate an overlap of the LM and the Ecoflex peaks. Heating the samples, we see the melting peak of the elastomer and two peaks corresponding to the melting of the LM particles. We believe that the dual peaks for LM are due to the slight polydispersity of particles in the sample 66

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(See Figure S6 for different heating/cooling rates). In Figure 6b and 6c we show the effect of particle size and φ on the Tc and Tm of the LM respectively by plotting the temperature of the highest peak in the cooling and heating cycles. For the particle sizes and volume fractions examined in this work they all show significant undercooling compared to bulk LM. Within the different composites it can be observed that the Tc drops by ≈20◦ as the LM particle size decreases at constant φ, but Tc is relatively invariant at a constant particle size of D = 20 µm for different φ. In a similar fashion, the melting temperature Tm drops by ≈15◦ as the LM particle size decreases at constant φ, and relatively invariant at a constant particle size for different φ. To show how the DSC curves evolve as the φ is increased, we plot the curves of D = 20 µm composites for different φ in Figure 6d. It can be seen that the elastomer peaks get less prominent and LM peaks get more pronounced as φ is increased. Specifically, upon cooling the composite, three exothermic peaks are observed in Figure 6d between 70◦ C and -90◦ C. The peak assigned to the crystallization of Ecoflex (∼ -75◦ C) decreases in magnitude and shifts to higher temperatures with increased loading of LM. The two lower temperature peaks correspond to the crystallization of the LM. The shape and distribution of these two crystallization peaks can be related to the particle analysis histogram in Figure S3. The broad crystallization peak centered at ∼ -80◦ C can be attributed to the particles with major axis > 20 µm while the sharper peak centered at -90◦ C has been assigned to the crystallization of particles with major axis < 20 µm. This suggests that even with the the relatively narrow distribution of particles sizes in the 20 µm sample series, the small variability can still be observed in the crystallization behavior. This is consistent with the behavior observed in Figure 6e, which shows how Tc varies with particle size for φ = 60% composites. We further note that the observed LM transitions are unique compared to many other particle filled composites, where a solid filler phase does not typically melt or crystallize. The DSC characterization of the LM composites helps in understanding the contrasting behavior of LM particles in comparison with bulk LM. The particle-form makes the LM more

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thermally stable, especially at low temperatures where the high degree of supercooling allows for smaller LM droplets to be used well below the bulk LM crystallization temperature (Tc = -21.2 ◦ C). These results exhibit the potential of LM composite systems for low temperature applications.

3.

All soft matter wearable capacitive sensor To show a direct application of these composites we develop all soft matter stretchable

capacitive sensors. We leverage the electro-mechanical coupling of deformable parallel plate capacitors to create sensors consisting of the soft LM embedded composite as a dielectric layer coated by EGaIn electrodes. This capacitor is then entirely encapsulated by silicone elastomer (Figure 7a). The capacitance of a parallel plate capacitor is linearly proportional to the planar area and relative permittivity (εr ) and inversely proportional to the dielectric thickness. As the capacitor deforms as shown in Figure 7c, assuming incompressibility of the soft composite, 67 area increases and the thickness decreases, resulting in a rise in capacitance. We characterize the response of the capacitive sensors through uniaxial stretching with LM volume loading of φ = 60% for three different droplet sizes (D = 80, 20, 1 µm). Figure 7b presents results of the normalized capacitance C/C0 as a function of tensile strain, where C0 is the capacitance in the unstrained condition and C is the instantaneous capacitance. We obtain a linear response where the change in sensor signal (C/C0 ) per change in strain () is 0.8 for larger (D = 80, 20 µm) size droplets, however, for smaller (D = 1 µm) droplets the change is 1.5, an increase in sensitivity of 190%. We also find that the sensor composed of the smaller droplets electrically fails at a decreased stretchability (∼ 300% strain) compared to the larger droplets (∼ 400% strain). These results demonstrate that the sensitivity and extensibility of the stretchable sensors can be tuned by changing the LM droplet size. Additionally, the incorporation of the LM droplets at φ = 60% increases the dielectric constant by over 6.5× compared to an unfilled elastomer. This increase allows for

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smaller capacitor areas while maintaining a larger overall capacitance, which is desirable to decrease signal-to-noise ratios for common measurement systems and can help improve the susceptibility to parasitic capacitance. 68 The sensors exhibit a linear capacitance-stretch relationship which is desirable for various wearable electronics and soft robotics applications. 69 We exploit this electro-mechanical functionality by integrating the capacitive sensors with a glove to quantify the gesture of the proximal interphalangeal (PIP) joint as shown in Figure 7d. To demonstrate the sensitive motion detection ability of the sensors, we perform finger grip tests for two sizes of styrofoam

Figure 7: All soft matter capacitive sensors. a) Schematic of the dogbone samples used to evaluate the change in capacitance as a function of strain, side-view shows thickness of individual layers. b) Plot of the ratio of Capacitance (C) to initial capacitance (C0 ) versus strain for composites with φ = 60% and D = 80, 20, 1 µm. c) Images of the sensors before and after the application of 270% strain. d) Wearable sensing glove realized by attaching the sensors at finger joints to detect hand motion, specifically the grasping of objects. e) Capacitance signal as a function of time for different sized balls over 3 cycles, showing the distinct and repeatable signal when grasping.

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balls with diameters of 47 mm and 19 mm. Due to a difference in the ball size, the PIP joints bend at different angles to grip a ball leading to distinct capacitive response. Specifically, we alternatively hold and release the big and small balls for 10 seconds for three repeated cycles. The capacitance of the unbent sensor is 48 pF . The response for gripping the balls is plotted in Figure 7e which shows that the capacitance rises to a stable value of 65 pF and 72 pF for the big and small ball respectively. The distinct signals corresponding to different sized balls are shown in Figure 7e, where the dotted horizontal lines indicate the strain sensitivity and repeatability of the sensors.

4.

Conclusion

In this work we present versatile and robust fabrication techniques to create soft composites of micron and sub-micron LM particles with major axis ranging from D = 100 nm to 80 µm and independent control in D and φ. For the smallest particle size of D = 100 nm, it is found that incorporation into elastomers at loadings higher than φ = 20% is a significant challenge. A detailed and thorough imaging and particle analysis on the composites shows the microstructure and size distribution where the particle size is maintained across a wide range of LM loadings. Dielectric spectroscopy across a range of D, φ, frequency, and temperature shows that these composites are electrically insulating even up to φ = 80%. Furthermore, the value of εr increases with increasing φ however is relatively insensitive to particle size, as predicted by EMT theories. Specifically, at 90 kHz, a composite with φ = 80% shows an εr of 60 and a tan δ of 0.02. Thermal characterization of the composites using DSC shows a wide range of thermal stability and significant undercooling where the LM droplets stay liquid down to temperatures of ∼-85 ◦ C. We observe a drop in Tc and Tm as the particle size reduces, while there is minimal variation in either Tc or Tm as φ is changed. Although we demonstrate this fabrication approach in silicone elastomers (Ecoflex and PDMS), we anticipate that this method could be adapted to a variety of elastomer

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systems including polyurethanes, other silicone elastomers, and thermoplastic elastomers. Furthermore, independent tunability of LM droplet size and volume loading can be adapted to various applications. One opportunity could be for highly porous sponges 70 , where the precise control over droplet microstructure could enable porous elastomeric sponges for the storage and release of aqueous solutions. Based on the insight from the thorough dielectric characterization, we created all soft matter capacitive sensors which can be directly integrated in soft electronic designs. We utilize these sensors by fabricating wearable sensing gloves and successfully tracked distinct human gestures to illustrate their potential in human machine interfaces and sensing skins. In conclusion, the materials developed and characterized in this work will provide independent control on LM particle size and φ with highly tunable D to enable programmable LM composites in emerging applications of soft robotic systems and stretchable electronics where flexible and tunable functional response is critical.

5. 5.1.

Experimental section Fabrication

The composites are fabricated by dispersing LM microdroplets in a two-component Pt-cure silicone elastomer (Ecoflex 00-30; Smooth-On Inc.). LM is a eutectic alloy of gallium and indium and is prepared by mixing Ga:In (Rotometals Inc.) at a 3:1 ratio by mass and then heating and homogenizing at 200 ◦ C overnight on a hot-plate followed by cooling to room temperature. First, the two-part silicone prepolymer is prepared by combining part A and part B at a 1:1 ratio by mass and then thoroughly mixing and degassing in a planetary centrifugal mixer (Flaktek SpeedmixerTM ). Second, the LM droplets are incorporated into the prepolymer to get a homogeneous mixture. This final mixture is degassed and cast on an Ease release 200 (Smooth-On Inc.) coated glass slide with a Universal applicator (ZUA 2000; Zehntner Testing Instruments), creating a layer ∼ 1 mm thick. These films are cured in a convection oven at 80 ◦ C for 12 hours. The LM composites with PDMS (Dow Corning 21



Sylgard 184) are mixed at a prepolymer to curing agent ratio of 10:1 and are prepared with the same mixing speeds (1000 and 1600 rpm for the two largest particles) and sonication procedures (for the two smallest particles). We note that the Ecoflex particles mixed at 1000 rpm are larger than the PDMS particles under the same mixing conditions (see Table S1). Sensor fabrication: A 400 µm thick composite layer is cast on a glass slide coated with ease release. After curing, an EGaIn electrode (10 mm X 8 mm) is deposited using a N2 assisted spray coating technique on a template mask and copper tape is attached to one end. This is encapsulated using a 400 µm ecoflex layer. Once this layer is cured, the sample is flipped and same procedure is followed resulting in a final sample thickness of 1200 µm. A schematic of the final sample is presented in Figure 7a. For the glove sensors, the EGaIn electrode has an area of 8 mm X 8 mm.

5.2.

Characterization

Microscopy: Optical micrographs are obtained using a Leica DMi8 inverted microscope in bright and dark field modes. Scanning electron Microscopy (SEM) images are obtained using FEI Inspect F50 with a Back-scattered Electron (BSE) detector at a spot size of 3 and an accelerating voltage of 10 kV. Dielectric Spectroscopy: The frequency dependent permittivity (ε) and tan delta (tan δ) are determined for different particle size and volume fraction using a Novocontrol Broadband Dielectric Spectrometer equipped with Alpha-A High Performance Frequency Analyzer. Samples are tested using 15.5 mm stainless steel electrodes and measured using a frequency range of 107 Hz to 10-1 Hz. Temperature measurements are made using a Novocontrol Quatro cryostat system to measure the frequency dependent dielectric response at temperatures ranging from -70 ◦ C to 100 ◦ C. Differential Scanning Calorimetery: Samples are cooled from 25 ◦ C to -150 ◦ C at a rate of 20 ◦ C min-1 using a LN2 Perkin Elmer Cryo DSC to determine the effect of particle size and the volume loading on crystallization temperature of the liquid metal particles. 22


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Sensor Characterization: Dogbone samples with EGaIn electrodes are stretched on an Instron 5944 mechanical testing machine at an extension rate of 1 mm/sec. Simultaneously, capacitance is measured with a BK Precision 880 LCR meter at 1 V and 100 kHz frequency. Capacitance data is smoothed with an adjacent averaging filter.

Supporting Information The Supporting Information is available free of charge on the ACS Publications website. (Figure S1–S6, and Table S1)

Acknowledgements MDB, AH, and RT acknowledge support through Defense Advanced Research Projects Agency Young Faculty Award (DARPA YFA) (D18AP00041). SWM, and SK acknowledge the combined support of the NSF through grant DMR 1304977 and the Advanced Research Projects Agency-Energy through grants DE-AR-0000654 and DE-AR-0000774.

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