Filtered Mechanosensing Using Snapping Composites with

Subscriber access provided by UNIV AUTONOMA DE COAHUILA UADEC

Article

Filtered Mechanosensing Using Snapping Composites with Embedded Mechano-Electrical Transduction Hortense Le Ferrand, André R. Studart, and Andres F Arrieta ACS Nano, Just Accepted Manuscript • DOI: 10.1021/acsnano.9b01095 • Publication Date (Web): 29 Mar 2019 Downloaded from http://pubs.acs.org on March 29, 2019

Just Accepted “Just Accepted” manuscripts have been peer-reviewed and accepted for publication. They are posted online prior to technical editing, formatting for publication and author proofing. The American Chemical Society provides “Just Accepted” as a service to the research community to expedite the dissemination of scientific material as soon as possible after acceptance. “Just Accepted” manuscripts appear in full in PDF format accompanied by an HTML abstract. “Just Accepted” manuscripts have been fully peer reviewed, but should not be considered the official version of record. They are citable by the Digital Object Identifier (DOI®). “Just Accepted” is an optional service offered to authors. Therefore, the “Just Accepted” Web site may not include all articles that will be published in the journal. After a manuscript is technically edited and formatted, it will be removed from the “Just Accepted” Web site and published as an ASAP article. Note that technical editing may introduce minor changes to the manuscript text and/or graphics which could affect content, and all legal disclaimers and ethical guidelines that apply to the journal pertain. ACS cannot be held responsible for errors or consequences arising from the use of information contained in these “Just Accepted” manuscripts.

is published by the American Chemical Society. 1155 Sixteenth Street N.W., Washington, DC 20036 Published by American Chemical Society. Copyright © American Chemical Society. However, no copyright claim is made to original U.S. Government works, or works produced by employees of any Commonwealth realm Crown government in the course of their duties.

Page 1 of 30 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

ACS Nano

Filtered Mechanosensing Using Snapping Composites with Embedded Mechano-Electrical Transduction Hortense Le Ferrand1,2†, André R. Studart1,*, Andres F. Arrieta1,* 1

Complex Materials, Department of Materials, ETH Zürich, Vladimir-Prelog-Weg 5, 8093

Zürich, Switzerland 2

School of Mechanical Engineering, Purdue University, 585 Purdue Mall, West Lafayette, IN,

USA Corresponding Authors *corresponding

authors: [email protected], [email protected]

ABSTRACT Mechanosensing is ubiquitous in natural systems. From the skin ridges of our finger tips to the microscopic ion channels in cells, mechanosensors allow organisms to probe their environment and gather information needed for processing, decision making and actuation. Despite technological advances in synthetic mechanosensing, it remains challenging to achieve this functionality at the scale of large stiff structures where both the amount of data to sense locally at a large scale and the diversity of input stresses that the sensors have to withstand require highly tunable systems. Filtered sensing using mechanical displacement is an effective strategy developed

ACS Paragon Plus Environment

1

ACS Nano 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 2 of 30

by organisms to cope with large sets of stimuli. Inspired by this biological strategy, we fabricate bistable elements that can passively filter mechanical inputs, translate them into electrical signals and be reset to their original sensing state using an external magnetic field. These multiple functionalities are achieved using hierarchically structured composites that can be arranged in large-area arrays. The filtering capability and fast passive response of our mechanosensors is experimentally demonstrated using simple electrical circuits and magnets. Thanks to their scalability and applicability to a wide range of material systems, these low-power sensors are avenues for the fabrication of load-bearing structures that are able to sense, compute, communicate and autonomously adapt in response to external magneto-mechanical stimuli.

KEYWORDS: mechanosensing, bio-inspired composite, hierarchical microstructure; electrical conductivity, anisotropy. Sensing of external mechanical stimuli is a key feature used by living organisms and synthetic devices to probe, respond and adapt to their environment.1,2 The demand for advanced sensing systems in structural applications has increased significantly in recent years with the development of, for example, soft robots,3-6 morphing wings for airplanes7-9 and reconfigurable buildings.10 During their lifetime, these structures sustain mechanical loads that vary in time and location at their interacting surface. Monitoring of these local mechanical inputs is important to determine the status of the structure, to detect flaws or damage, provide information about the source of the mechanical stimuli and permit these structures to respond in dynamic environments. Such interactivity and dynamic functionalities will become increasingly important for future digital technologies involving real-time monitoring of physical interactions of adaptive structures with the surrounding environment and with living systems. Enticing perspectives for the


2

Page 3 of 30 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

ACS Nano

forthcoming digital era are however accompanied by major technical challenges. Given the large dimensions, the amount of data generated by state-of-the-art sensors positioned at the surface of such systems is enormous, posing a real big data challenge along with a high energy cost.11 To address this challenge, it is crucial to develop passive and selective mechanosensing strategies to filter the meaningful part of the mechanical perturbation of interest using fast and scalable sensing technologies.12 Real-time filtering of background noise is possible by using, for example, dynamic wireless systems whose resonance frequency depends on the variation of strain.13,14 The signals obtained are then filtered by wireless readers through intensive digital data processing.15,16 Despite the effective filtering effect, such systems are very power-intensive and thus difficult to implement in large area, mobile applications. Analog circuits have been utilized to decrease power consumption and increase the throughput of analog to digital interfaces. As an example, distributed self-powered body sensor networks have been developed to monitor the health status of a patient with minimum power and to even harvest energy from daily movements.17,18 In contrast to synthetic approaches, dynamic living systems have evolved strategies to achieve low-power mechanosensing. Instead of electrical filtering, living organisms in nature often employ distributed mechanical filters intimately integrated within their material architecture. Programming the sensing functionality at the material level reduces significantly the amount of information that needs to be handled by a centralized signal processing unit. The resulting mechano-neuromorphic system is able to simultaneously sense and actuate, therefore minimizing the time and energy required to perform dynamic biological functions.19–21 In a typical example of biological sensing, geometrical features in the form of hairs or cilia are positioned on the legs of spiders, on the wings of bats and on the skin of fish, to mechanically sense the flow conditions of the surrounding environment.22–25 Another illustrative example are the covert receptors associated


3

ACS Nano 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 4 of 30

to contour feathers in the dorsal wings of birds.26 When the airflow increases the elevation angle of the feathers above a threshold of 30°, the mechanoreceptor neural cells located at the base of the feathers are mechanically actuated and emit a burst of electrical signal at a frequency that depends on the elevation angle. After this electrical discharge, the neuron regains its initial state, until another change in elevation angle above the threshold is recorded. Acting as a high-pass filter, this sensory mechanism presumably enables the bird to measure the airspeed and to adapt its wing shape and direction to maximize flight efficiency.26 Many attempts have been made to fabricate bio-inspired passive mechanosensors that rely on the filtering effect of hair-like structures to obtain flow data relevant for flight.27–33 Flow sensitivity comparable to that of the mechanosensory biological systems (i.e. fish) combined with minimum detectable velocities as low as 0.008 mm/s has been achieved.29 Using photolithography, these micro- and nano-electromechanical systems (MEMS and NEMS) can be organized into arrays of a few millimetres to a couple of centimetres to provide accurate sensing and high signal-to-noise ratios.29 However, the small and delicate sizes of these MEMS and NEMS makes it challenging to apply such mechanosensors on the highly populated sensing networks envisioned for example in large-area airplane wings. The presence of hydrogels or soft polymers in these sensors also restricts the mechanical loads to which they can be exposed to. Typically, the maximum flow that soft hydrogel hair-like sensors can withstand is in the range of 100 mm/s,29 which is about three orders of magnitude lower than the airflow around an aircraft. To enable passive and selective sensing for airflow monitoring and haptic interactions over large areas, such microstructured mechanosensors should be easily scalable and exhibit sufficient strength to sustain the high mechanical forces it may be submitted to during application.


4

Page 5 of 30 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

ACS Nano

In this paper, we propose a general method for fabricating stiff composites with mechanosensing capabilities that are programmed within the hierarchical architecture of the material. To achieve fast and selective mechanosensing, the hierarchical material is designed to showcase three main stress-dependent functionalities. First, the composite exhibits a bilayer design that gives rise to mechanical bistability. By setting the minimum load required to snap the composite to the “onstate”, this bistable behaviour is used to program the sensitivity threshold to an external mechanical trigger. Second, one of the layers contains metallic particles that form an electrically-conductive path only when the composite snaps into the “on-state”. Analogous to the synaptic connections between neurons, the reduction of the distance between metallic particles within the composite enables the transmission of electrical signals to the main processing unit for further computation and decision-making. Third, ferromagnetic particles are also added to the composite to enable resetting of the bilayer to the “off-state” using an external magnetic field. The analogies of this composite architecture with the mechanosensorial system in the wing of birds is schematically shown in Figure 1. In the following, we describe the manufacturing and hierarchical structure of the proposed composite and discuss the multifunctional properties and macroscopic behaviour of the material. A proof-of-concept example of successive actuation and re-setting operations is then shown to illustrate the three main functionalities programmed within the material’s architecture. Finally, we characterize the dynamical response and demonstrate the suitability of these sensors for large-area and fast sensing with stress selectivity and low power consumption.


5

ACS Nano 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 6 of 30

Figure 1. Schematics describing the sensing and actuating principles of a bird wing (A) and the bioinspired concept of bistable mechanosensing developed in this study (B). (A) Under airflow, the deflection of covert feathers of birds above a threshold of ~30° triggers a neuronal electrical discharge, which is further processed by the neuronal network to actuate the shape of the wing. The signal is reset after the input air flow ceases. (B) In the envisioned bioinspired analogue, a bistable mechanosensor could be used to monitor the airflow across the wing of an aircraft. When the air flow is sufficient to snap the bistable sensor, a change in electrical conductivity is detected. This electrical signal can be further processed through a central unit to provide the relevant actuation of the wing of the plane. Resetting to the initial condition is possible by applying a magnetic field. Results and discussion Hierarchical design and fabrication process


6

Page 7 of 30 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

ACS Nano

Figure 2. (A) Hierarchical architecture of a bistable mechanosensor illustrated by electron and optical micrographs (top) and explanatory schematics (bottom). (B) Relationships between the composition and the properties of a single layer of composite material: (i) coefficient of thermal expansion 𝛼, parallel (𝛼 ∥ ) or perpendicular (𝛼 ⊥ ) to the alignment direction, as a function of the volume fraction 𝑓𝐴𝑙2𝑂3 of stiff alumina platelets. The lines correspond to theoretical calculations (see equations S1 and S2 in supporting information); (ii) electrical conductivity 𝜎 of non-aligned composites as a function of the volume fraction 𝑓𝑁𝑖 in nickel flakes. The blue line corresponds to a fit using a percolation model (equation S3) with a percolation threshold (𝑓𝑁𝑖,𝑐) of 4; (iii) magnetic remanence B as a function of 𝑓𝑁𝑖 fitted by a linear regression.


7

ACS Nano 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 8 of 30

To achieve the multiple coupled functionalities proposed (Figure 1), our mechanosensing composite is organized in a hierarchical structure (Figure 2) comprising: micron-sized platelets (level 1), aligned platelets in a polymer matrix (level 2), bilayer architecture (level 3) and macroscopic geometry (level 4). Such hierarchy from microelements to macroscopic form is reminiscent of natural materials.34 Below, we describe how this hierarchical architecture is designed and manufactured to achieve the three stress-dependent functionalities: (i) mechanical bistability, (ii) filtered sensing via snapping-induced electrical percolation, (iii) and magnetic restoring of the initial configuration. The structure-property relationships resulting from this hierarchical architecture are then discussed and quantified (Figure 2A). The mechanical bistability of our composites arises from a bilayer design in which two orthotropic plies are orthogonally combined. In such orthogonal arrangement, the direction of highest stiffness and coefficients of thermal expansion of each individual layer lies perpendicular to one another. This structure recalls the layered and anisotropic microstructures found in morphing natural composites such as the bilayers in Bauhinia seedpods or in pine cones.32 Orthotropy is achieved here by aligning microplatelets in deliberate directions within the polymer matrix of each composite layer. In addition to mechanical bistability, our composite is designed to be electrically conductive in one of the bistable configurations. This is implemented by the incorporation of aligned nickel flakes within the individual composite layers. By tuning the concentration of nickel flakes to be close to a percolation threshold, the compressive strain induced within the composite layer in one of the bistable states enables physical contact between adjacent flakes, thus forming an electrically conductive network. Finally, the magnetic properties of the composite are directly enabled by the presence of the ferromagnetic nickel flakes within the layers. We found that a nickel flake concentration of 4 vol% is enough to make composites with a magnetization level of ~ 0.03


8

Page 9 of 30 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

ACS Nano

emu.mg-1, which is sufficient for magnetic actuation using a handheld external magnet (see supporting information Figure S1). Composites with this hierarchical design were manufactured using a magnetic alignment technology that provides control over the orientation of platelets and flakes in a fluid, which is later converted into a polymer matrix by simple curing.35-38 To demonstrate that our strategy can be applied for actual applications where structural demands can be high, we use stiff alumina platelets that are easily available in large quantities and are already used in many structural applications. Such microplatelets are in average 10 µm in diameter and 180 nm in thickness. In contrast to the long carbon fibres used in conventional composite technology,39,40 these platelets allow for controlled microstructuring of the composite at finer length scales below 10 m. Magnetic alignment of alumina platelets in an epoxy matrix has already been shown to be suitable for the fabrication of mechanically bistable shells.36 To make them responsive to magnetic fields, the alumina microplatelets were decorated with iron oxide nanoparticles of 12 nanometre diameter by simple van der Waals attraction in deionized water. Along with the alumina platelets, nickel flakes of average diameter 20 µm and 500 nanometre in thickness were also incorporated into the composite to provide the magnetic and electrical properties required in our hierarchical design. These two anisotropic microparticles exhibit the ultra-high magnetic response necessary for biaxially alignment throughout the composite using low rotating magnetic fields.37 Indeed, low magnetic field strengths ranging from 10 to 100 mT were sufficient to align the microparticles during composite fabrication.36 Composite bilayers are obtained by sequentially casting, aligning and curing the mixture of particles in the epoxy matrix and turning the direction of alignment by 90° between the two layers. We have demonstrated previously that the sequential casting with a final curing of the bilayer at 100 °C lead to an intimate bonding between the two layers.36


9

ACS Nano 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 10 of 30

Therefore, the second layer was directly casted and cured on top of the first and already consolidated layer, enabling the partial infiltration of the liquid uncured material at its surface and the formation of strong chemical bonds between the layers. In the final material, the interface between the epoxy matrix of the two layers could barely be distinguished (see Figure S2). Bistability in the sheet-like bilayer composite arises during cooling from the setting temperature of the matrix to room temperature. Since the composite is constrained and unable to deform during the setting processes, internal stresses are accumulated and released only upon unmoulding.36 The release of internal stresses eventually leads to a macroscopic deformation of the bilayer into one of the bistable states. Properties of individual composite layers The implementation of mechanical bistability, filtered sensing and re-storing functionalities within the composite bilayer requires tuning of three specific properties of individual layers: thermal expansion coefficient, electrical conductivity and magnetic susceptibility. These properties can be tuned by modifying the chemical composition and structural features at each length scale of the composite (Figure 2B). For a given composite geometry and processing conditions, the coefficients of thermal expansion (CTE) of the individual layers determine the shape change that the orthogonal bilayer undergoes during cooling and thus the mechanical bistability of the system. The CTE of individual layers can be controlled by the concentration and orientation of the reinforcing alumina particles used in the composite. Earlier work has shown that the CTE parallel to the platelet orientation (𝛼 ∥ ) decreases with the concentration of reinforcing particles (Figure 2B(i)). For the perpendicular direction, the CTE value (𝛼 ⊥ ) first increases with the platelet content before it steadily drops for particle concentrations higher than approximately 5 vol%. These trends follow the behaviour expected


10

Page 11 of 30 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

ACS Nano

from a simple rule of mixtures (see supporting information).36 Overall, the difference between the CTE values in the two orthogonal directions (𝛼 ⊥ -𝛼 ∥ ) remains constant at about 410-5 K-1 for platelet concentrations above 5 vol% (see Supporting information). Similarly, each layer presents stiffness anisotropy that increases with the concentration in platelets. At 10 vol% of alumina platelets, the Young’s modulus parallel to the direction of alignment reaches 6 GPa, whereas it is only of 1.2 GPa in the perpendicular direction.36 To ensure that switching between bistable states results in an electrical signal that can be used for passive sensing, the composite layers are designed to change significantly their electrical conductivity depending on the curvature of the bilayer. This is possible by incorporating electrically-conductive nickel flakes at concentrations close to the threshold needed to form a percolating network of contacting flakes. We explored this idea by measuring the effect of the concentration of nickel flakes on the electrical conductivity of the epoxy matrix (Figure 2B(ii)). As predicted by percolation theory, the electrical conductivity  in one ply changes with the concentration of electrically conductive elements (fNi) according to the power law: 𝜎 = 𝐶 ∙ (𝑓𝑁𝑖 ― 𝑓𝑁𝑖,𝑐)𝑡, where 𝐶 is a constant, 𝑓𝑁𝑖,𝑐 is the volume fraction of flakes at the percolation threshold and 𝑡 is a system-dependent exponent. Because of this power law dependence, small variations in flake concentration around the percolation threshold lead to a dramatic increase in the global conductivity of the composite.42 In our composites, the nickel flakes were found to percolate above 4 vol% with a critical exponent t of 4 (Figure S3). This very high exponent, twice higher than a typically observed t of 2, probably results from the anisotropy of the flakes.42 In addition to the flake concentration, the global conductivity of the composite close to the percolation threshold also depends on the state of local internal stresses and strains within the material. Compressive stresses should decrease the distance between conducting particles and thus


11

ACS Nano 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 12 of 30

promote the formation of an electrically conductive percolating network. Conversely, tensile stresses should have the opposite effect. Since switching between two mechanical bistable states changes the internal stresses within the composite layer from tensile to compressive and viceversa, the binary mechanical states of our composite bilayers can be directly translated into binary electrical states. Finally, the addition of ferromagnetic nickel flakes also empowers the composite with the magnetic properties needed to restore the sensor to its active state after snapping events triggered by the environment. To quantify the magnetic properties of the composites, we measured the remanent magnetic field B of cured composite plies as a function of the concentration of nickel flakes (fNi). Our results show that the remanent magnetization of the composites varies linearly with the volume fraction fNi (Figure 2B, (iii)), as expected from theory (see Supporting Information). Based on these experimental and theoretical analyses, a volume fraction of 6 vol% of alumina platelets and 4 vol% of nickel flakes was selected for the fabrication of bistable composites featuring mechanosensing capabilities. Indeed, hierarchical composite bilayers with this composition were found to develop a natural curvature (0) of 4 ± 1 m-1 after fabrication, if shell-shaped square specimens with individual layer thicknesses and widths of, respectively, 200 m and 10 cm are used (Figure 2A).

Filtered sensing and restoring To demonstrate the principle of our bistable mechanosensor in action, we performed cycles of sensing and restoring using the square composite shell with optimized composition and


12

Page 13 of 30 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

ACS Nano

geometry (Figure 3). The sensing and restoring functionalities are illustrated by tracking the actual shell curvature, the force exerted on (or by) the shell and the electrical resistance of one of the composite layers as a function of the displacement () imposed during sensing and the magnetic field (H) applied during restoring.

Figure 3. Sensing-restoring cycle of a bistable mechanosensor consisting of a 10 cm square composite shell: (A) Schematics of the sensor in the Off-state displaying a curvature 𝜅0 and high electrical resistance parallel (𝑅 ∥ ) and perpendicular (𝑅 ⊥ ) to the direction of alignment of flakes in the top layer. (B) Sensing of an external force F applied as depicted in the picture (top): curvature 𝜅 (black dots), force applied (white dots) and resistances 𝑅 ∥ (bold) and 𝑅 ⊥ (thin) as a function of


13

ACS Nano 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 14 of 30

the displacement 𝜀 of the point at the center of the shell. The snap-through transition at the displacement threshold 𝜀𝑡ℎ is highlighted in red. (C) Sensor in the On-state featuring a curvature 𝜅1 and a low electrical resistance. (D) Restoring of the shell’s initial curvature by applying a magnetic field H as shown in the picture (bottom): curvature 𝜅 (black dots), reaction force (white dots) and resistances 𝑅 ∥ (bold) and 𝑅 ⊥ (thin) as a function of the magnetic field strength H. The snap-through transition at the magnetic field threshold 𝐻𝑡ℎ is highlighted in red. In its initial convex state (Figure 3A), the top layer of the sensor is under tensile strain, which keeps a large distance between the nickel flakes and ensures a high electrical resistance (R). Applying a mechanical displacement through a force localized at the centre of the shell, the initial curvature 𝜅0 decreases continuously (Figure 3B). When the displacement of the central point of the shell reaches a threshold displacement (𝜀𝑡ℎ) of 5.5 mm the sensor snaps into its concave geometry (state 1). For a given loading distribution, this threshold displacement can be tuned by varying the geometry and materials properties of the composite shell. We note that the applied force (𝐹𝑎𝑝𝑝) reaches a maximum before the snapping event. This effect is commonly observed in bistable shells and is usually associated with the development of frictional forces between the shell and the rod used to impose the mechanical displacement.43 After the transition, the top ply of the shell, initially under tension, becomes compressed. This brings the nickel flakes into contact, thereby decreasing the electrical resistance of the top composite layer. Because the conductive flakes are biaxially aligned in a specific orientation, the drop in electrical resistance depends on the direction along which the shell is electrically probed. The larger contact areas between flakes established along the thickness of the particles (smaller dimension) results in three orders of magnitude drop in resistance when the measurement is conducted perpendicular to the area of the flakes. This contrasts with the 10-fold reduction if the measurement is performed in the other


14

Page 15 of 30 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

ACS Nano

orthogonal direction. The lower electrical resistance of this concave state indicates that the filtering sensor was subjected to a displacement higher than its threshold value 𝜀𝑡ℎ (Figure 3C). Once the sensor has transmitted the electrical signal to a peripheral or main processing unit, the initial convex state of the composite shell can be restored by applying a similar deformation in the opposite direction. Instead of a mechanical input, the threshold displacement is imposed in this case by remotely applying an external magnetic field. In our experiment, the field was generated by simply approaching a strong rare-earth magnet close to the shell while one edge was maintained clamped (Figure 3D). The attracting force developed between the permanent magnet and the sensor deforms the shell towards its original convex configuration (state 0) if the applied magnetic field strength exceeds a threshold value 𝐻𝑡ℎ of 280 mT. By snapping back to the initial position 0, the sensor recovers the initial electrical resistance. After restoring, the bistable mechanosensor generates a reaction force 𝐹𝑟𝑒𝑎𝑐𝑡 of 0.6 N, which is in the same order of magnitude of the maximum force 𝐹𝑎𝑝𝑝 required to trigger the transition from state 0 to state 1. The bistable shells can be operated through several cycles, as long as the internal stresses responsible for the snapping events are present within the structures. Although stress relaxation has been observed in the epoxy matrix utilized in our composites (see Supporting Information), this can be inhibited by using liquid crystalline epoxy resins as matrix44 or through the addition of small quantities of carbon nanotubes.45

Dynamic response and integration The ability to snap only when subjected to a threshold displacement allows the proposed bistable composites to filter mechanical input data from a noisy dynamic environment, thus saving


15

ACS Nano 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 16 of 30

the power needed for sensing. We illustrate this filtering effect by connecting the bistable composite to an external electrical circuit that visually indicates the binary state of the shell using a commercial LED (Figure 4). The mechanosensing composite was integrated into the electrical circuit via a 1-mm-wide silver electrode deposited on the surface of the shell and perpendicular to the long-axis of the platelets. The sensor was connected in series with the LED using a simple electrical set-up (Figure 4A). In this configuration, the LED lights up if the shell is in the concave state and turns off if it snaps back into the convex geometry. Upon the application of arbitrary mechanical noise below the critical displacement of the shell (𝜀𝑡ℎ), no snap-through event was triggered, keeping the electrical resistance low and the LED turned on (Figure 4B). Once the snapthrough was triggered above the threshold value (𝜀𝑡ℎ = 2.5 mm in this case), the electrical resistance increased, switching off the LED. For the shell used in this specific example, the percolating network of nickel flakes is enough to turn the LED off but an insulating network (R=infinite) is only formed for a short period of time. This transient behaviour resembles the spike of the action potential developed between neurons in biological systems and is a mechanism to further reduce the energy required for sensing. Approaching the other side of the shell with a high strength magnet triggers the snap-through of the shell to its initial convex configuration, restoring the sensing capability of the system.


16

Page 17 of 30 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

ACS Nano

Figure 4: (A) Electrical circuit with integrated bistable mechanosensor used to demonstrate the filtered sensing capabilities of the hierarchical composite shell (left). (B) Displacement 𝜀 of the center of the shell (black) and its electrical resistance (blue) as a function of time t, showing the filtering of noise signal below the displacement threshold 𝜀𝑡ℎ (highlighted in red) and the restoring of the initial state using an external magnetic field H. (C) Dynamics of the snapping event characterized by digital image correlation (DIC) and electrical resistance measurements. The displacement 𝜀 (black, left) and the electrical resistance Δ𝑅 (blue, right) are shown as a function of time t during the snap-through transition (grey area highlighted in 4B). (D) Pictures and corresponding schematics of a large-area sensing array comprising four bistable mechanosensing elements integrated onto an airplane wing.


17

ACS Nano 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 18 of 30

In addition to energy consumption, the timescale required for sensing is another important parameter for the performance of the mechanosensor in dynamic environments and in potential computing applications. To assess this timescale, we tracked the motion of the snapping shell using a digital image correlation (DIC) set up (Figure 4C). Measurements of the shell displacement over time show the oscillatory motion typically observed after a snapping event. The amplitude of the oscillation decays over a time period of approximately 200 ms, which is in agreement with previous experiments with composite shells of comparable dimensions and composition.36 Because of the fast nature of the snapping event, the period of the oscillatory motion is as small as 10 ms. This is in line with the duration of the spike in electrical resistance measured on the same shell during snapping. If compared with biological systems, the operating timescale of our mechanosensor lies between those observed for the action potential in cardiac (100 ms) and nerve cells (1 ms).46 The size dependence of the oscillations after snap-through allow for reaching faster responses with smaller specimens if needed. Finally, we demonstrate the potential of these mechanosensors for large structure monitoring by assembling a four-element sensing array on an aircraft skin (Figure 4D). In this example, the sensors were connected by a thin layer of flexible silicone to enable the overall structure to conform to the curved surface of an airplane wing. Each element could be activated independently of each other, allowing for the generation of sixteen distinct possible mechanical states. By extending this concept to larger arrays and networks, it should be possible to build structures that can mechanically sense the environment and perform logic operations that are later translated into specific actuation responses (further details in Figure S5 and Supplementary text). Since the bistable mechanosensing elements can be restored independently, such actuation may even emerge from macroscopic morphing effects induced by the internal stresses generated upon snapping.


18

Page 19 of 30 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

ACS Nano

Conclusion In summary, our concept of bistable mechanosensing offers a promising route towards lowpower adaptive structures with sensing and possibly computing capabilities directly programmed within the hierarchical design of the material. With the help of machine learning algorithms, such intelligent structures can potentially be trained to sense, communicate, compute and actuate in an autonomous fashion with minimum energy consumption in a range of applications in robotics, transportation and medicine.47-53 Experimental Section Fabrication of microstructured composite plates: Alumina platelets (Ronaflair, WhiteSapphire, diameter 8.9 ± 2.9 µm and thickness 0.30 ± 0.13 µm) were magnetized according to a procedure described elsewhere using superparamagnetic iron oxide nanoparticles (EMG-705, Ferrotec, Germany).37 Magnetized alumina platelets and nickel flakes (Novamet, USA, diameter 29 ± 5.6 µm and thickness 0.58 ± 0.24 µm) were co-mixed using a mechanical mixer in a liquid epoxy in pre-determined ratios. Typically, the epoxy composite containing 6 vol% of magnetized alumina and 4 vol% of nickel flakes was composed of 0.916 g resin (Araldite GY250, Hunstmann, Belgium), 0.783 g hardener (Aradur 917 CH, Hunstmann, Belgium), 0.0412 g polyether diamine (Jeffamine D-230, Hunstmann, Belgium), 0.014 catalyst (DY070, Hunstmann, Belgium), 0.472 g magnetized alumina and 0.684 g nickel flakes. The mixture was homogenised at 60°C using a mechanical mixer before being cast into a mould pre-warmed at 100°C. The mould consisted of an aluminium plate covered with a release plastic film and spacers of 100 µm thickness made from aluminium tape (Griffon, Germany). The suspension was deposited into the mould, covered and


19

ACS Nano 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 20 of 30

pressed by another aluminium plate covered with release film, and placed under a rotating magnetic field on a heating plate. The aforementioned field was generated by a permanent neodymium magnet (300 mT, Supermagnete, Switzerland) rotating at 400 rpm. The film was finally cured for 10 hours at 100°C. Characterization of the physical parameters of single microstructured plates: Thermal expansion coefficients (CTE) were measured using an optical dilatometer (DIL806, TA instruments, Waters, Germany) by heating the composite specimen from 12 to 100°C at a rate of 5 °C/min. The glass transition temperature (Tg) was measured by Differential Scanning Calorimetry (DSC822, Mettler Toledo, Switzerland) using a heating rate of 10 °C/min. Saturated magnetization of the samples was measured using a Physical Property Measurement System (PPMS, Quantum Design, USA) at room temperature. Electrical conductivity was measured using a portable multimeter (VC230, Voltcraft, Germany) and 2 points probes. Characterization of the microstructure of the composites: The samples of interest were fractured, coated with Pt (Safematic, Switzerland), and observed with a Scanning Electron Microscope (Leo 1530, Zeiss, Germany). Characterization of the functional response of bistable shells: The curvature of the shells was measured from optical micrographs by fitting the sample contour with a circle using the software Image J. The mechanical force required for snapping was measured in a compression machine (Shimadzu, Japan) with a 8 mm diameter screw acting as the loading part and a 100 N load cell measuring the applied force. The strain rate applied was fixed at 1 mm/min. A video was recorded while the specimen was mechanically loaded. The magnetic restoring effect was performed by approaching the bistable plate close to a 300 mT magnet. A video was recorded to measure the


20

Page 21 of 30 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

ACS Nano

curvature of the plate as a function of the distance to the magnet. Three sensing-restoring experiments were performed to ensure reproducibility of the presented data. The videos obtained were analysed using iMovie and Image J. The mechanical force generated during the snap-through event was measured by manually snapping the composite shell inside a plate-plate compression device and correlating the force measured with the curvature. The electrical resistance variation during snapping was measured using 2-points probe electrodes fixed in the centre of the shell. The composites were snapped manually while recording the electrical resistance. Integration of the shape-changing sensors into electrical circuits: Copper electrical cables where taped at the surface of the composite shells over a length of 5 mm. The shell was then connected via an insulating board (Elenco precision model 9440, USA) to a red LED, a resistor of 333 Ohms and a power supply (Nobatron, Sorensen). The dynamic displacement of the shell was recorded using Digital Image Correlation (DIC) with the help of two cameras (Fastcam MiniUX) and a dedicated software (Vic 3D). Images were acquired simultaneously with the recording of the electrical resistance with an oscilloscope (Agilent L1251A, USA). To track the dynamics of the snapping event using DIC, the mechanosensor was covered with a white and opaque thin tape layer, which was further covered by a layer of black paint. For the final demonstrator, bistable shells were connected using a membrane of PDMS (Sylgard 184) to form a 2x2 array. The shells were first heated at 100°C above the glass transition temperature of the epoxy to drive them flat. Then, a 5 cm width aluminium square was placed at the centre of each shell to maintain them in position, while 1 mm of liquid PDMS was deposited on top and cured for 30 minutes at 100 °C. After curing, the four connected shells were turned upside down and the process repeated on the other side to create a symmetrical coating of PDMS. After final curing, the aluminium plates were removed and electrical cables were connected to the shell by simple contact.


21

ACS Nano 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 22 of 30

ASSOCIATED CONTENT Supporting Information. The supporting information is available free of charge on ACS Publications website. It contains theoretical estimations of the coefficients of thermal expansion in the two orthogonal directions, the theoretical fit of the electrical conductivity using the percolation theory, the theoretical magnetization of the composite layer, details on composite relaxation and cyclic response as well as an example of bistable mechanosensing for robotic applications. Additional figures comprise magnetization curve, electron micrograph of the cross-section of the composite, fit of the percolation, curvature and resistance in along multiple cycles and schematics of a practical set-up for large-scale mechanosensing. The supplementary material also provides three additional references.

AUTHOR INFORMATION Corresponding Authors *corresponding

authors: [email protected], [email protected]

Present Addresses 1

Complex Materials, Department of Materials, ETH Zürich, Vladimir-Prelog-Weg 5, 8093

Zürich, Switzerland 2

School of Mechanical Engineering, Purdue University, 585 Purdue Mall, West Lafayette, IN,

USA Present address for author: † School of Mechanical & Aerospace Engineering, Nanyang Technological University, 50 faculty avenue, Singapore 639798.


22

Page 23 of 30 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

ACS Nano

Author Contributions The manuscript was written through contributions of all authors. All authors have given approval to the final version of the manuscript. ACKNOWLEDGMENTS We thank D. Koulialias for the measurement of the magnetic field of the composite. We acknowledge funding from ETH Zurich and Purdue University; H. Le Ferrand and A. R. Studart were partially funded by the European Office of Aerospace Research and Development (EOARD, grant FA9550-16-1-0007).

REFERENCES (1)

Stieve, H. Sensors of Biological Organisms- Biological Transducers, Sens. Actuators 1983, 4, 689-704.

(2)

Beccai, L.; Lucarotti, C.; Totaro, M.; Taghavi, M. Soft robotics: Trends, Applications and Challenges, in Biosystems & Biorobotics, vol 17, Eds: C. Laschi, J. Rossiter, F. Iida, M. Cianchetti, L. Margheri, Springer International Publishing AG, Switzerland 2011, pp 1134.

(3)

Maheshwari, V.; Saraf, R.F. High-Resolution Thin-Film Device to Sense Texture by Touch, Science 2006, 312, 1501–1505.

(4)

Sasagawa, G.; Zumberge, M.A. A Self-Calibrating Pressure Recorder for Detecting Seafloor Height Change, IEEE J. Oceanic Eng. 2013, 38, 447–454.

(5)

Boutry, C.M.; Nguyen, A.; Lawal, Q.O.; Chortos, A.; Rondeau-Gagné, S. ; Bao, Z. A


23

ACS Nano 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 24 of 30

Sensitive and Biodegradable Pressure Sensor Array for Cardiovascular Monitoring, Adv. Mater. 2015, 27, 6954–6961. (6)

Polygerinos, P.; Correll, N.; Morin, S.A.; Mosadegh, B.; Onal, C.D.; Petersen, K.; Cianchetti, M.; Tolley, M.T.; Shepherd, R.F. Soft Robotics: Review of Fluid-Driven Intrinsically Soft Devices: Manufacturing, Sensing, Control, and Applications in HumanRobot Interaction, Adv. Eng. Mater. 2017, 19, 1700016.

(7)

Lentink, D.; Müller, U.K.; Stamhuis, E.J.; De Kat, R.: Van Gestel, W.; Veldhuis, L.L.M.; Henningsson, P.; Hedenström, A.; Videler, J.J.; van Leeuwen, J.L. How Swifts Control Their Glide Performance with Morphing Wings, Nature 2007, 446, 1082–1085.

(8)

Kuder, I.K.; Fasel, U.; Ermanni, P.; Arrieta, A.F. Concurrent Design of a Morphing Aerofoil with Variable Stiffness Bistable Laminates, Smart Mater. Struct. 2016, 25, 1-8.

(9)

Barbarino, S.; Bilgen, O.; Ajaj, R.M.; Friswell, M.I.; Inman, D.J. A Review of Morphing Aircraft, J. Intell. Mater. Syst. Struct. 2011, 22, 823-877.

(10)

Boadi-Danquah, E.; Robertson, B.; Fadden, M.; Sutley, E.J.; Colistra, J. Cyclic Performance of a Lightweight Rapidly Constructible and Reconfigurable Modular Steel Floor Diaphragm, Key Eng. Mater. 2018, 763, 541–548.

(11)

Chi, M.; Plaza, A.; Benediktsson, J.A.; Sun, Z.; Shen, J.; Zhu, Y. Big Data for Remote Sensing: Challenges and Opportunities, Proc. IEEE 2016, 104, 2207- 2220.

(12)

Saudabayev, A.; Varol, H.A. Sensors for Robotic Hands: a Survey of State of the Art, IEEE Access 2015, 3, 1765–1782.

(13)

Tata, U.; Deshmukh, S.; Chiao, J.C.; Carter, R. Bio-Inspired Sensor Skins for Structural


24

Page 25 of 30 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

ACS Nano

Health Monitoring, Smart Mater. Struct. 2009, 18, 104026. (14)

Yi, X.; Cho, C.; Cooper, J.; Wang, Y.; Tentzeris, M.M.; Leon, R.T. Passive Wireless Antenna Sensor for Strain and Crack Sensing – Electromagnetic Modeling, Simulation and Testing, Smart Mater. Struct. 2013, 22, 085009.

(15) Ozbey, B.; Erturk, V.B.; Dan, H.V.; Altinta, A.; Kurc, O. A Wireless Passive Sensing System for Displacement/Strain Measurement in Reinforced Concrete Members, Sensors 2016, 16, 496-513. (16)

Yao, J.; Tjuatja, S.; Huang, H. Dynamic Interrogation of Wireless Antenna Sensor, IEEE Sens. J. 2015, 15, 4338–4345.

(17)

Murmann, B. Digitally Assisted Analog Circuits, IEEE Micro 2006, 26, 38–47.

(18)

Poon, C.C.Y.; Lo, B.P.L.; Yuce, M.R.; Alomainy, A., Hao, Y. Body Sensor Networks: in the Era of Big Data and Beyond, IEEE Rev. Biomed. Eng. 2015, 8, 4–16.

(19)

Carruthers, A.C.; Thomas, A.L.R.; Taylor, G.K. Automatic Aeroelastic Devices in the Wings of a Steppe Eagle Aquila Nipalensis, J. Exp. Biol. 2007, 210, 4136–4149.

(20)

Barth, F.G. Learning from Animal Sensors: the Clever “Design” of Spider Mechanoreceptors, In Proceedings SPIE Volume 8339, Bioinspiration, Biomimetics, and Bioreplication 2012; 833904 2012, 8339, 1–11.

(21)

Studart, A.R. Biologically Inspired Dynamic Material Systems, Angewandte Chemie Int. Ed. 2015, 54, 3400–3416.

(22)

Fratzl, P.; Barth, F.G. Biomaterial Systems for Mechanosensing and Actuation, Nature


25

ACS Nano 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 26 of 30

2009, 462, 442-449. (23)

Yang, Y.; Nguyen, N.; Chen, N.; Lockwood, M.; Tucker, C.; Hu, H.; Bleckmann, H.; Liu, C.; Jones, D.L. Artificial Lateral Line with Biomimetic Neuromasts to Emulate Fish Sensing, Bioinspiration Biomimetics 2010, 5, 016001.

(24)

Giri, A.; Kottapalli, P.; Asadnia, M.; Miao, J.; Venkatraman, S.S.; Triantafyllou, M.S. Nanofibril Scaffold Assisted MEMS Artificial Hydrogel Neuromasts for Enhanced Sensitivity Flow Sensing, Sci. Rep. 2016, 6, 19336.

(25)

Sterbing-D’Angelo, S.; Chadha, M.; Chiu, C.; Falk, B.; Xian, W.; Barcelo, J.; Zook, J.M.; Moss, C.F. From the Cover: Bat Wing Sensors Support Flight Control, Proc. Natl. Acad. Sci. 2011, 108, 11291–11296.

(26)

Brown, R.E.; Fedde, M.R. Airflow Sensors in the Avian Wing, J. Exp. Biol. 1993, 30, 13– 30.

(27)

Suh, J.W.; Darling, R.B.; Donald, B.R.; Baltes, H.; Kovacs, G.T.A. CMOS Integrated Siliary Actuator Array as a General-Purpose Micromanipulation Tool for Small Objects, J. Microelectromech. Syst. 1999, 8, 483–496.

(28)

Evans, B.A.; Shields, A.R.; Carroll, R.L.; Washburn, S.; Falvo, M.R.; Superfine, R. Magnetically Actuated Nanorod Arrays as Biomimetic Cilia, Nano Lett. 2007, 7, 14281434.

(29)

Bora, M.; Kottapalli, A.G.P.; Miao, J.; Triantafyllou, M. MEMS Biomimetic Superficial and Canal Neuromasts for Flow Sensing, Bioinspiration Biomimetics 2018, 13, 025002.

(30)

Herzog, H.; Klein, A.; Bleckmann, H.; Holik, P.; Schmitz, S.; Siebke, G.; Tätzner, S.;


26

Page 27 of 30 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

ACS Nano

Lacher, M.; Steltenkamp, S. µ-Niomimetic Flow-Sensors – Introducing Light-Guiding PDMS Structures into MEMS, Bioinspiration Biomimetics 2015, 10, 036001. (31)

Kottapalli, A.G.P.; Bora, M.; Asadnia, M.; Miao, J.; Venkatraman, S.S.; Triantafyllou, M. From Biological Cilia to Artificial Flow Sensors: Biomimetic Soft Polymer Nanosensors with High Sensing Performance, Sci. Rep. 2016, 6, 1–12.

(32)

Kottapalli, A.G.P.; Asadnia, M.; Miao, J.; Triantafyllou, M. Touch at a Distance Sensing: Lateral-Line Inspired MEMS Flow Sensors, Bioinspiration Biomimetics 2014, 9, 46011.

(33)

Sen, S.; Chatterjee, S.; Har, C. Design and Impedance Estimation of a Biologically Inspired Flexible Mechanical Transmission with Exponential Elastic Characteristic, IEES/RSJ International Conference on Intelligent Robots and Systems 2013, 5425–5430.

(34) Fratzl, P.; Weinkamer, R. Nature’s Hierarchical Materials, Prog. Mater. Sci. 2007, 52, 12631334. (35)

Erb, R.M.; Sander, J.S.; Grisch, R.; Studart, A.R. Self-Shaping Composites with Programmable Bioinspired Microstructures, Nat. Commun. 2013, 4, 1712.

(36)

Schmied, J.; Le Ferrand, H. ; Ermanni, P.; Studart, A.R.; Arrieta, A.F. Programmable Snapping Composites with Bio-Inspired Architecture, Bioinspiration Biomimetics 2017, 12, 1–26.

(37)

Erb, R.M.; Segmehl, J.; Charilaou, M.; Loffler, J.F.; Studart, A.R. Non-Linear Alignment Dynamics in Suspensions of Platelets under Rotating Magnetic Fields, Soft Matter 2012, 8, 7604–7609.

(38)

Riley, K.S.; Le Ferrand, H. ; Arrieta, A.F. Modeling of Snapping Composite Shells with


27

ACS Nano 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 28 of 30

Magnetically Aligned Bio-Inspired Reinforcements, Smart Mater. Struct. 2018, 27, 114003. (39)

Arrieta, A.F.; Kuder, I.K.; Waeber, T.; Ermanni, P. Variable Stiffness Characteristics of Embeddable Multi-Stable Composites, Compos. Sci. Technol. 2014, 97, 12-18.

(40)

Hyer, M.W. Calculations of the Room-Temperature Shapes of Unsymmetric Laminates, J. Compos. Mater. 1981, 15, 296-310.

(41)

Celzard, A.; Marêché, J.F. Non-Universal Conductivity Critical Exponents in Anisotropic Percolating Media: a New Intepretation, Phys. A (Amsterdam, Neth.) 2003, 317, 305–312.

(42)

Le Ferrand, H.; Bolisetty, S.; Demirors, A.F.; Libanori, R.; Studart, A.R.; Mezzenga, R.; Magnetic Assembly of Transparent and Conducting Graphene-Based Functional Composites, Nat. Commun. 2016, 7, 12078.

(43)

Zhang, Z.; Wu, H.; He, X.; Wu, H.; Bao, Y.; Chai, G. The Bistable Behaviors of CarbonFiber/Epoxy Anti-Symmetric Composite Shells, Composites, Part B 2013, 47, 190–199.

(44)

Yi, L.; Kessler, M. R., Creep-Resistant Behavior of Self-Reinforcing Liquid Crystalline Epoxy Resins, Polymer 2014, 55, 2021-2027.

(45)

Starkova, O.; Buschhorn, S. T.; Mannov, E., Schulte, K.; Aniskevitch, A. Creep and Recovery of Epoxy/MWCNT Nanocomposites, Composites, Part A 2012, 43, 1212-1218.

(46)

Handwerker, H. O. General Sensory. In Human Physiology; Schmidt, R.F. ; Thews, G., Eds.; Springer-Verlag: Berlin Heidelberg, 1983; pp 176-195.

(47)

Lum, G.Z.; Ye, Z.; Dong, X.; Marvi, H.; Erin, O.; Hu, W.; Sitti, M. Shape-Programmable Magnetic Soft Matter, Proc. Natl. Acad. Sci. 2016, 41, 7-15.


28

Page 29 of 30 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

ACS Nano

(48)

Menges, B.A., Material Computation: Higher Integration in Morphogenic Design, Architectural design 2012, 13-21.

(49)

Tibbits, S.; Cheung, K. Programmable Materials for Architectural Assembly and Automation, Assembly Automation 2012, 32, 216-225.

(50)

Fang, Y.; Yashin, V.V.; Levitan, S.P.; Balazs, A.C. Pattern Recognition with “Materials that Compute”, Sci. Adv. 2016, 1, 1–11.

(51)

McEvoy, M.A.; Correll, N. Materials that Couple Sensing, Actuation, Computation and Communication, Science 2015, 347, 1327-1336.

(52)

Jiang, Y.; Korpas, L. M. ; Raney, J.R. Bifurcation-Based Embodied Logic and Autonomous Actuation, Nat. Comm. 2019, 10, 1-10.

(53)

Treml, B.; Gillman, A.; Buskohl, P.; Vaia, R. Origami Mechanologic, Proc. Natl. Acad. Sci. 2018, 115, 6916-1921.


29

ACS Nano 1 2 3 4 5 6

Page 30 of 30


Filtered Mechanosensing Using Snapping Composites with

Recommend Documents