Nondestructive, Label-Free Characterization of Mechanical

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Tissue Engineering and Regenerative Medicine

Non-destructive, Label-free Characterization of Mechanical Micro-heterogeneity in Biomimetic Materials Devina Jaiswal, Min D. Tang-Schomer, Disha Sood, David L Kaplan, and Kazunori Hoshino ACS Biomater. Sci. Eng., Just Accepted Manuscript • DOI: 10.1021/acsbiomaterials.8b00286 • Publication Date (Web): 27 Jul 2018 Downloaded from http://pubs.acs.org on August 6, 2018

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ACS Biomaterials Science & Engineering

Non-destructive, Label-free Characterization of Mechanical Micro-heterogeneity in Biomimetic Materials Devina Jaiswal1, Min D. Tang-Schomer2, Disha Sood3, David L. Kaplan3, and *Kazunori Hoshino1 1

Department of Biomedical Engineering, University of Connecticut, 260 Glenbrook Road, Storrs, CT 06269 2

3

The Jackson Laboratory for Genomic Medicine, 10 Discovery Drive, Farmington, CT 06032

Department of Biomedical Engineering, Tufts University, 4 Colby Street, Medford, MA 02155

* Author to whom correspondence should be addressed. Email: [email protected]

Keywords: Composite biomaterials, Mechanical characterization, Finite element method, Optical tracking.

Abstract We propose a novel non-destructive, label-free, mechanical characterization method for composite biomimetic materials. The method combines microscale force measurement, bright field microscopy-based deformation analysis, and finite element methods (FEM) to study the heterogeneity in bioengineered composite materials. The method was used to study silk fibroin protein-based, donutshaped scaffolds consisting of a shell (dia. 5 mm) and a core (dia. 2 mm) with a stiff-core or a soft-core configuration. The samples were based on our previously reported bioengineered brain tissue model. Step-wise images of sample deformation were recorded as the automated mechanical stage compressed the sample. The force-compression curves were also recorded with a load cell. A MATLAB program was used to compare and match optically measured strain distribution with that found from the FEMsimulations. Iterative processes are used to determine the values that best represent the elastic moduli of the shell and core regions.

The calculated moduli found from the composite models were not

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significantly different from the values measured separately for each material, demonstrating the efficacy of this new approach. In addition, the method successfully measured multiple distinct regions embedded in a polydimethylsiloxane (PDMS) block. These results demonstrated the feasibility of our method in micro-heterogeneity characterization of biomimetic composite structures.

Introduction Biological tissues are heterogeneous materials with diverse and gradient structures, including stratified epithelial and mesenchymal layers, tubular vessels and spindle nerve fibers. Their mechanical stiffness ranges from 0.1- 50 kPa found in soft brain tissue to 15-30 GPa in rigid bones.1

The

development of tissue heterogeneity is an indication of all biological processes including cell differentiation and migration, and tissue morphogenesis.2 Although there have been many studies of the mechanical properties of a specific organ, tissue and individual cells

1,3-7

, few studies have examined

micro-heterogeneity of composite structures. Quantification of structural-mechanical integrity in tissues is important in order to understand the composition and function of normal as well as diseased tissues. To avoid artifacts, non-destructive, label-free mechanical analysis of these heterogeneous biological or biomimetic structures is desired. Such a process should measure forces resulting from the endogenous tension in the tissue 8, while maintaining the biological integrity of the structure.9 Conventional mechanical analysis and measurements of tensile or compression have been most often designed for centimeter-scale test pieces prepared from materials such as polymers metals.13,14 Micro- or nano-indentation methods

15-17

10,11

, ceramics

12

and

have been developed to help with the measure of

elastic moduli of soft materials such as hydrogels 18,19 and tissues.

8,20

These conventional mechanical

analysis tools are useful for homogeneous materials and do not allow for quantification of heterogeneous constructions of biomimetic materials. To map the micro-heterogeneity of a complex tissue, studies have combined AFM measurements of micro-dissected tissue layers and 3D reconstruction, as in the case of whole brain 21 and breast tumor tissue analysis.22 These approaches, however, require the destruction of a biological tissue; and cannot be adapted to live tissue analysis. 2 ACS Paragon Plus Environment

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In this paper, we present an imaging-based analysis method for the non-destructive characterization of mechanical micro-heterogeneity in composite structures. Similar approaches to measure the elasticity of tissues, termed elastography 23, have been applied to specific imaging modalities of ultrasound imaging 24 or optical coherence tomography (OCT).25,26

The main principle is that the strain (deformation) of a

material under a known applied stress (force) indicates the material’s elastic properties. For most cases of ultrasound or OCT elastography, application of a simple, uniform stress is assumed, and the imaged deformation directly shows the elasticity, i.e., softer areas deform more than stiffer areas do.

27,28

We

extend the concept of elastography to mesoscale tissue analysis with the use of a standard bright field camera and a custom-built micro-compression tool. The unique approach of our method is the combination of microscale force analysis, cross correlation-based optical deformation analysis, and finite element method (FEM) to measure elastic moduli within a complex structure. While the construction of ultrasound or OCT elastography systems is largely restricted by the physics of their imaging modality, the use of bright field images and FEM permits the analysis of a variety of mechanical characterization modes (compression, tensile, shear, etc.) and images recorded at different size scales. Bright field imaging assists optical tracking of internal strain distribution under a known applied force. A finite element model is used to calculate stress distribution and estimate local modulus. A MATLAB program was built to compare optically measured strain distribution with that found from the FEM-simulation. Iterative processes were used to adjust internal elastic moduli to find matches that best represent the elastic moduli of different components in the composite. This new method was applied to the study of biomaterial scaffolds, here using silk fibroin protein-based biomaterials. The versatile mechanical properties of silk fibroin-based biomaterials has found extensive applications in biomimetic tissue models, including brain 29, blood vessel 30, and breast tumors.31 Tissue-mimetic scaffolds can be generated from salt-leached solutions extracted from Bombyx mori silkworm cocoons.34

32

or lyophilized

33

silk fibroin

The scaffold microstructure, such as pore

size and fiber composition, is highly tunable with a wide range of fabrication parameters.35 For the present study, we used a donut-shaped scaffold consisting of a shell and core regions of different 3 ACS Paragon Plus Environment


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stiffnesses. A similar composite structure with infused extracellular matrix gel has been studied to generate bioengineered brain tissue models that supported 3D neuronal tissue growth and electrophysiological responses.36 The goal of the current study was to validate our new system of micromechanical analysis with acellular silk composite materials, and to provide a basis for future live tissue model analysis.

Materials and Methods 3D Silk Fibroin-based Scaffolds Silk scaffolds were processed as previously reported.36 Briefly, silk fibroin solution was extracted from B. mori silkworm cocoons (purchased from Tajima Shoji Co., Yokohama, Japan).

34

Type I porous

scaffolds were prepared via salt-leaching with salt grains of ~500-600 µm dia., termed S.I. Type II porous scaffolds were prepared via controlled freezing/lyophilization of 4% (w/v) silk fibroin solutions, termed S.II.

3D Silk Fibroin/Collagen Gel Composite Scaffolds Collagen gels were prepared from rat tail type I collagen I (8-10 mg/mL, termed Gel) (BD Biosciences, San Jose, CA, USA), 10X 199 media (Gibco) and 1M NaOH by mixing at a ratio of 88:10:2 (v/v/v). Liquid collagen gel was infused into the donut-shaped silk scaffolds and solidified at 37ºC for 1-2 hr. All scaffolds were maintained in phosphate-based saline (PBS) until testing.

Silk Scaffold Specimens The sponges and hydrogels were used to prepare two types of specimens, soft-core samples (shell: S.I / core: Gel) and stiff-core samples (shell S.I / core S.II) (Fig. 1). Silk scaffolds were trimmed either into a donut-shape with a 5 mm outer diameter by 2 mm height and a 2 mm diameter center hole, or cylinders of 2 mm diameter by 2 mm height. Composite structures were assembled by fitting 2 mm dia. cylinders into the 5 mm dia. donut-shaped scaffolds.

All scaffolds were maintained in phosphate-based saline (PBS) 4

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until testing. Five samples for each of (S.I/S.II) and (S.I/Gel) were prepared. Moduli for S.I, S.II and Gel were measured separately using the compression test described in the compression analysis section. The surface of the soft-core samples consisting of hydrogel center was impregnated with 10-25 µm glass beads, simulating a presence of cells to create tissue-like features, for imaging purpose.

Figure 1. Composite specimens based on biomimetic tissues made of silk protein sponges as reported in previous studies.34,36

PDMS Specimen The Polydimethylsiloxane (PDMS) specimen was prepared to demonstrate the analysis of multiple distinct regions in a sample as well as a different type of material. It was a 20 mm × 20 mm × 12 mm piece of PDMS prepared using a machine-milled acrylic mold. Fig. 2 shows the design of the specimen, which was comprised of the main body, a soft square region, and a circular stiff region. The main body with square and circular hole was first made from the mold, followed by the filling with soft and stiff PDMS mixtures. The different stiffness of the three areas were generated by different mixture ratios of Sylgard 527 and Sylgard 184. 37 The weight percentage ratio (Sylgard 527: Sylgard 184) of 100:0, 87:13 and 0:100 were used for the soft , middle (main body,Fig.2), and stiff areas. Sylgard 527 and Sylgard 184 were prepared according to the manufacturer's directions (Sylgard 527: part A: Part B = 1:1 (w/w), Sylgard 184, base: curing agent=10:1 (w/w)). The surface of the sample was impregnated with 10-25 µm glass beads for imaging purposes.



Figure 2. Design of the PDMS composite specimen.

Flow chart of the analysis A custom program built in MATLAB was used to conduct the analyses. The method was based on the integration of optical tracking and FEM with a miniature compression test. Fig. 3 shows the flow chart of the analyses and details of each step are described in the following sections. 1. The sample being tested was imaged for each step of compression from i = 0 to i = N - 1. The compression force was measured by the load cell attached to the compression tool to find the forcedisplacement curve. 2. Based on the initial image (i = 0) of the sample, a finite element model was built. The model comprises the inner core and the outer shell. Multiple areas may be considered. 3. Optical tracking based strain analysis: 3.1. Optical tracking was conducted for each node of the model from Image 0 to image N - 1. 3.2. A strain map was calculated for the optical analysis above (3.1). The averages of strain εx (strain in the direction of compression) in the inner core and the outer shell were calculated as εcoreoptical and εshelloptical. 4. FEM analysis was performed to find a simulated strain map: 4.1. Prescribed displacement was applied to the nodes in contact with the compression arm on each side. The coordinates found by the optical analysis in 3.1 were used to define the displacement.


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4.2. The moduli of the outer shell and the inner core were set to Ecore = E0 and Eshell =r E0. The averages of strain εx in the inner core and the outer shell, εcoreFEM and εshellFEM , respectively, could be found as functions of r by the FEM. Note that the value of E0 does not change the strain analysis in this step, because the displacements, rather than the forces, are given as the boundary condition. If there are multiple areas, repeat this step for each parameter rj. 5. Match optical and FEM analyses: 5.1. Sweeps the value of r to find the value that best matched εcoreFEM / εshellFEM with εcoreoptical / εshelloptical. The Value of r was further refined through iterative adjustments. Repeat this step for each parameter rj. 5.2. Optimization in one parameter rj affects the other areas if there are multiple areas. Re-evaluate εcoreFEM / εshellFEM with εcoreoptical / εshelloptical for all areas and if necessary repeat the optimization from r1 until all conditions are satisfied. 6. The value of E0 that best matches the boundary force FFEM with the measured value F was determined.



Figure 3. Process flow chart.

Compression analysis A miniature compression tool was built to measure the mechanical characteristics of small tissue models. The mechanical stage is driven by a stepper motor and used to actuate the compression arm. The arm is attached with a full-bridge strain gauge force sensor calibrated for 0 N - 4×10-2 N to record the force-displacement curve as it compresses the sample. For each compression step of 26 µm, an image of


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the sample was taken by a 1288×964 pixel CCD camera (FLIR blackfly) at a magnification of (127 µm) / (20 pixels). For the PDMS specimen, a larger compression setup was used with a force sensor calibrated for 0 N - 3 N, and compression steps of

45 µm were imaged with the same CCD camera at a

magnification of (533 µm) / (20 pixels). Prior to measurement with the composite materials, the elastic modulus of each material was measured separately using the same indentation tool with minor modifications. Fig. 4(A) shows the testing method for the sponges. Samples were prepared as cylinders with R = 1mm and L = 3 mm. The force F needed to make an indentation depth of d is expressed as: F =

where

π 8(1 − ν 2 )

⋅E ⋅L⋅d ,

(1)

E and ν are elastic modulus and Poisson’s ratio of the material, respectively. In this study, we

usedν = 0.4 , which is a typical value for tissues.

38,39

Note that in this model, indentation occurs both at

the top and the bottom interfaces. Hydrogel and PDMS measurement is based on the model as shown in Fig. 4(B) and 4(C), where, the applied force F and indentation d satisfies: F = πR 2 ⋅ E ⋅ d / L

(2)

F = a2 ⋅ E ⋅ d / L ,

(3)

respectively. The results of these measurements are referred to as ‘separate’ in 3. Results and Discussion.

Figure 4. Reference compression analysis of sponges, hydrogels and PDMS. The results were compared with the values found from the composite materials.



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Finite Element Model The finite element model was composed of four-noded rectangular elements. The same elements and nodes were used for both the optical tracking and the finite element analysis. The model was built based on the initial image (i = 0) to contain the two areas: the inner core and the outer shell (Fig. 5(a)). The image was first binarized to show the outlines of the inner core and the outer shell, which were then modified manually to remove noise and better fit the model to the image. Each rectangular element before compression corresponds to a panel of 20 × 20 pixels of the initial image, which covers 127 µm × 127 µm of the silk scaffold specimens and 533 µm × 533 µm of the PDMS specimen. The total number of elements was typically ~1,000, with ~150 of these belonging to the inner cores or regions. The displacement of each node in an element (j) is given as (ui, vi) (i = 1,2,3,4) as shown in Fig. 5(b). The displacement vector {qj} of element (j) is defined as an 8-by-1 vector. {qj} = {u1; u2; u3; u4; v1; v2; v3; v4}

(4)

The displacement vector {qj} is considered for both optical tracking and FEM analysis to describe the deformation of the sample. Coordinates (X,Y) are defined for each element as shown in Fig. 5(c). Within an element, displacement (u, v) at a point (X, Y) is given as the linear combination of displacements at the four nodes (ui, vi) as follows: 4

u( X , Y ) =

∑N ⋅u i

(5)

i

i =1 4

v( X , Y ) =

∑N ⋅v , i

(6)

i

i =1

where Ni (i =1,2,3,4) are the shape functions defined in the following way:  N 1 = (1 + X )(1 − Y ) / 4   N 2 = (1 + X )(1 + Y ) / 4   N 3 = (1 − X )(1 + Y ) / 4  N 4 = (1 − X )(1 − Y ) / 4

(7)


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Strain vector {εj}= {εx; εy; γxy} is found by

 du dv du dv   dX ∂ dY ∂ dY ∂ dX ∂  u; v; v . ; ; + = ⋅ ⋅ ⋅ u+ ⋅ dy ∂Y dy ∂Y dx ∂X   dx dy dy dx   dx ∂X

{εj} = 

Here,

(8)

dX and d Y are scaling factors given by dx dy dX dY 2 ∆X ∆Y = = = = ∆x ∆ y 127 µ m dx dy

(9)

dX dY ∆X ∆Y 2 = = = = dx dy ∆x ∆y 533 µ m

(10)

and

For silk scaffolds and PDMS specimen, respectively. Partial derivatives

∂ ∂ ∂ ∂ u, u, v and v are found from eqs (5) and (6) as: ∂X ∂Y ∂X ∂Y

4  ∂ ∂ u ( X ,Y ) = N i ⋅ ui  X X ∂ ∂ 1 i =  4  ∂ ∂  u ( X ,Y ) = N i ⋅ ui  ∂Y i =1 ∂Y  4 ∂  ∂ v( X ,Y ) = N i ⋅ vi  ∂X ∂ i =1 X  4  ∂ ∂ v( X , Y ) = N i ⋅ vi  i =1 ∂Y  ∂Y

∑

∑

(11)

∑

∑

Partial derivatives of the shape functions N i (i =1,2,3,4) were calculated from eq (7) as:

 ∂  ∂X   ∂  ∂X   ∂  ∂X  ∂   ∂X

N1 = (1 − Y ) / 4 N 2 = (1 + Y ) / 4 N3 = −(1 + Y ) / 4 N 4 = −(1 − Y ) / 4

 ∂  ∂Y   ∂  ∂Y   ∂  ∂Y  ∂   ∂Y

N1 = −(1 + X ) / 4 N 2 = (1 + X ) / 4 (12)

N3 = (1 − X ) / 4 N 4 = −(1 − X ) / 4

The strain vector {εj} and the stress vector {σj} = {σx; σy; σxy} for each element are correlated using the stiffness matrix [ D ] in the following way: 11 ACS Paragon Plus Environment


{σ } = [ D]{ε } . j

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(13)

j

Here we assume plane strain and the stress/strain matrix [ D ] given as:  1 − ν Ej  [D ]=  ν (1 + ν )(1 − 2ν )   0 

ν 1 −ν 0

    1 − 2ν   2  0 0

(14)

Note that [ D ] is proportional to the Young’s modulus E j of the element.

Figure 5. Four-noded elements used in the study. (a) Nodes and elements for the core and shell regions are defined from an image. (b) Displacement at each node is considered to describe the sample deformation. (c) Coordinates (X,Y) are defined for each element.

Optical tracking A small panel (20 pixel × 20 pixel) around the node n (xn, yn) in image i was defined as the original pattern. Normalized cross correlation (NCC) was calculated for the areas around n (xn, yn) in image i + 1 to find the best matched pattern n’ (xn’, yn’) and the displacement vector {qj} = (uj, vj). Although images used were pixelated, the displacement vector was found with a sub-pixel resolution using the seconddegree polynomial interpolation in x and y directions. The accuracy and repeatability of optical tracking were evaluated by tracking 10 ×10 = 100 nodes on different materials, namely, silk fibroin-based scaffolds (S.I and S. II), hydrogel (Gel) with live cells, and glass beads on a plain glass substrate. Synovium derived mesenchymal stem cells (MSCs) 12 ACS Paragon Plus Environment

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(ATCC,Virginia) were seeded at a concentrations of 10×106 , 5×106, 2.5×106 and 12×105 cells/ml of hydrogel, labeled as C1, C2, C3 and C4 in the study, respectively. The concentration of C1were chosen to match the cell seeding concentration of 10×106 cells/ml hydrogels reported in literature. 40,41 Samples C2, C3, and D4 were prepared to test cases with less features. Glass beads (Corpuscular, 10-25 µm) were prepared by drop-drying a 10 µL of water suspension onto a glass slide. The concentrations of 30, 15, 7.5 and 3.8 mg/ml, labeled as B1, B2, B3 and B4, respectively, were chosen to visually match with cell density in cell seeded hydrogels. The sample was displaced using a sub-micrometer resolution mechanical stage (Newport Picomotor Stage 9062) by different distances of 5, 10, 15, 20, 25, and 30 µm. The standard deviations in the displacement measured through optical tracking of all 100 nodes were calculated for evaluation. Optical deformation analysis During the compression analysis, the shape of the specimen changes image by image. The closest pattern to the pattern in image i was searched in image i + 1. The best-matched pattern was then used to find the displacement from images i + 1 to i + 2. The process is illustrated in Fig. 6.

Figure 6. Optical tracking at each node to find the deformation of the element.



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FEM The FEM analysis was conducted by assembling the elemental equations into a global equation system. The governing equation for each element is the relationship among the load vector {fj}, displacement {qj}, and the elasticity, which can be found from the principle of virtual work:

∫ {ε } {σ }dV = {q } {f }, T

T

j

j

j

(15)

j

V

where {εj}, {σj}, {qj} and {fj} are the stress vector, the strain vector, the displacement vector, and the load vector, respectively. The load vector {fj} includes forces (pi, qi) (i =1,2,3,4) acting on the four nodes. {fj} = {p1; p2; p3; p4; q1; q2; q3; q4}

(16)

Plugging eqs(8-14) to eq(15) and numerically calculating the integral, we obtained the relationship between displacement vector {qj} and the load vector {fJ} in the following way:

{f } = [k ]{q }, j

j

(17)

j

where kj  is the stiffness matrix of the element. By assembling elemental equations (17) for all elements, the global equation system was found as:

{F } = [K ]{Q} ,

(18)

where {F}, [K], and {Q} are global load vector, global stiffness matrix, and global displacement vector, respectively. Boundary condition is plugged to eq(16) at this point. In this analysis, displacements of the nodes on both sides of compression are prescribed based on the optical analysis. The displacement {Q} was found by solving the equation.

{Q } = [K ]− 1 {F }

(19)

Details for building the global equation system are described in many textbooks. 42,43


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Results Optical tracking Fig. 7 A, B, C, and D show examples of nodes tracked for S.I, S.II, Gel seeded with MSCs, and glass beads. Standard deviations in measured distance among the 100 nodes for the different samples are summarized in Table 1. When we consider ±3 standard derivation (SD) as the repeatability of the optical tracking method, all samples provided ~0.5 µm or better repeatability. Our method can be applied to analyze many practical biosamples and tissues.

Figure 7. Samples and the nodes used to evaluate the repeatability of optical tracking. Scale bar 250 µm.

Sample SD (µm)

Table 1. Repeatability of optical tracking Cell-added Gel Scaffold Scaffold (S.I) (S.II) C1 C2 C3 C4 B1 0.13 0.19 0.19 0.11 0.12 0.22 0.09

Beads B2 B3 0.10 0.11

B4 0.15

Optical deformation analysis Fig. 8(A and B) show the optical tracking analysis of a stiff-core sample and a soft-core sample. The two samples show opposite deformation characteristics represented by the color bar. The red area represents maximum deformation and blue represents minimum deformation. The stiff-core in Fig. 8(A) shows smaller strain, while the soft-core in Fig. 8(B) shows larger deformation in the center. The areas with relatively large strain around the stiff core in Fig. 8(A) are thought to be induced by parts of the shell material that cover part of the core around the interface as indicated in the left panel. In Fig. 8(b), small local areas with larger deformation were observed in the shell, which correspond to the presence of larger pores as indicated in the right panel. 15 ACS Paragon Plus Environment


Figure 8. Optical analysis of (A) Stiff-core sample and (B) Soft-core sample. Red and blue squares represent maximum and minimum deformation, respectively (Supplementary videos S1 and S2 are available). The panel in (A) shows overhanging parts of the shell material indicated by dotted lines. The panel in (B) shows the pores that are thought to have induced large deformation on the surface. Scale bar = 1 mm.

FEM analysis Fig. 9(A) and (B) show the results of FEM analysis for the same samples shown in Fig. 8. As described in the flow chart, the ratio of core-shell average strains, εcoreFEM / εshellFEM, was matched with that found from optical analysis, εcoreoptical / εshelloptical. The stiff-core sample has a lower average strain in the core, and the soft-core sample has a higher average strain in the core. Since the core and the shell are assumed to be a homogeneous material in the FEM analysis, the results tend to look more ‘flat’ or noiseless compared to the optical tracking results.


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Figure 9. FEM analysis of (A) Stiff-core sample and (B) Soft-core sample. Red and blue squares represent areas of maximum and minimum deformation, respectively.

Calculation of elastic modulus Five specimens were tested for each type of sample. For the stiff-core samples, measured elastic moduli for the shell and core were 11.9 ± 2.4 kPa and 43.7 ± 20.7 kPa, respectively. Separately measured moduli for S.I and S.II were 14.7 ± 1.6 kPa and 58.8 ± 11.6 kPa, respectively. We used the student t-test to evaluate the statistical significance of the data. The P-value was >0.05 for each case of S.I. (0.07) and S.II (0.21), showing no significant difference between the value measured in the composite material and those measured separately (Fig. 10). Similarly for soft-core samples, the measured elastic moduli of the shell and core were 10.7 ± 4.6 kPa and 2.1 ± 1.3 kPa, respectively. Separately measured moduli for S.I and gel were 14.7 ± 1.6 kPa and 1.5 ± 0.4 kPa, respectively. The elastic moduli of S.I. and gel measured as a composite and as separate materials were not significantly different from each other by student t-test (P-value > 0.05, S.I: 0.13 and Gel: 0.66). The measured values of S.I elastic modulus measured in the stiff core and soft core samples (S.I was used for the shell in both samples) were not significantly different from each other.



S.I/S.II (Stiff Core) 80

S.I

S.II

Elastic modulus (kPa)

Elastic modulus (kPa)

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

60 40 20 0

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S.I/Gel (Soft Core)

20

S.I.

Gel

16 12 8 4 0

Combined

Separate

Combined

Separate

Figure 10. The elastic moduli directly measured from composite specimens and separately were compared. The average elastic moduli of materials measured as a composite and separately and were not significantly different as given by student t-test (P-value > 0.05).

Analysis of multi core specimen Fig. 11 shows the analysis of the PDMS specimen. Based on the multi parameter optimization algorithm, we were able to find elastic moduli of the two inside regions.

Figure 11. Optical and FEM analyses of the PDMS specimen which contains soft and stiff regions.


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The elastic moduli measured from the analysis and the separately measured values are summarized in Table 2. The result shows that the method may be applied to the study of tissues or biomaterials which contain multiple mechanically distinct regions using our deformation analysis.

Areas Combined measurement Separate measurement (N=4)

Table 2. Analysis of elastic moduli Main body Stiff area (circle) 93 kPa 0.80 MPa (104 ± 9.2) kPa

(0.98 ± 0.17) MPa

Soft area (square) 7.6 kPa (7.5 ± 2.4) kPa.

Discussion Normalized cross correlation (NCC) is a common statistical tool used for pattern or template matching. This technique has been applied to track similarity in images or imaging tiles.

44

Specific features of the

image can be used to track the feature in consecutively taken images. This algorithm has been successfully used by our group previously for the mechanical characterization of 3D cancer spheroids.45 Similarly, our study used sample features captured by CCD camera to track image tiles, and their position is used to calculate the deformation map. This method relies on minute sample features that can be captured by the camera. In order to intensify the sample features, the sample illumination angles were adjusted. Since the softcore sample consisted of collagen hydrogel which depicted no features on the surface of the sample, the sparse distribution of glass spheres (10-25 µm) on the surface of the hydrogel helped to create identifiable features that could be tracked. The size of the spheres was within the range of a normal cell size (10-30µm). The inclusion of glass spheres did not affect the mechanical properties of the hydrogel since the spheres were added to the surface of the core and not included in the bulk. The size of the spheres was within the range of a normal cell size (10-30µm). The inclusion of glass spheres did not affect the mechanical properties of the hydrogel since the spheres were added to the surface of the core and not included in the bulk. Here, the spheres simulated tissue like features that could be tracked. This was confirmed by our optical tracking analysis of hydrogels with MSCs showing that samples with embedded cells of a wide concentration range can be reliably tracked without the need for labeling. Similarly, many biological samples have sufficient optically-trackable surface features without adding 19 ACS Paragon Plus Environment


beads. However, there are cases with samples such as acellular tissues or biomaterials, where use of beads or microparticles is needed to assist optical tracking. Even in such scenarios, one may use certain biocompatible microparticles that can be easily washed and removed after imaging. Gelatin microparticles

(50-100 µm), used as porogens for making tissue engineered scaffolds, dissolve at 37ºC within 12 – 48 hrs and can be easily washed away.46 Likewise, if biocompatible magnetic microparticles (~10 µm) are used on a tissue surface, they can be retrieved by applying an external magnet field.47 The optical deformation analysis was very sensitive to the existence of small surface features including dents, cracks, lumps and bumps. Although this sensitivity may be advantageous for many types of tissue analysis, when quantifying the average elastic moduli of relatively large areas, such as the cores and the shell studied here, small surface features act as noise sources. Using the simple FEM model allows for filtering out information unnecessary for this specific purpose. Assuming a Poisson’s ratio of 0.4 and sample thickness of 2 mm, there may be deformation in the perpendicular direction up to ~40 µm at a maximum compression of ~250 µm. The focal depth of the camera was sufficiently large to capture sample deformation images without any defocusing. Since the camera only records deformation in the xy plane, we assumed a plane strain model for the FEM analysis and only considered deformation in the xyplane. The Young’s modulus of the hydrogels, when calculated separately, were not significantly different from soft core samples. The comparative study shown in Fig. 10 also indicated the efficacy of the method developed in this study for composite biomaterials. The deformation map (Fig. 8) and FEM (Fig. 9) analysis show local mechanical properties of the sample, whereas conventional methods such as Instron™ devices only give bulk mechanical properties for composite biomaterial scaffolds 48,49 and heterogeneous tissues such as the brain.50 The image analysis methodology developed in this study can be used with tensile analysis or other types of mechanical characterization in different size scales. The initial experiments with the PDMS specimen demonstrated the feasibility of applying the method to more complex biosamples. Using a multi-parameter optimization algorithm, an FEM model that contains multiple materials may be matched to the optical deformation analysis.


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The resolution of our structural analysis is mainly defined by the images used. In our process, strain

∂u ∆u is approximated by , where ∆u is the difference of displacement at two nodes and ∆x is ∂x ∆x

the size of an element. Cross correlation analysis finds a displacement

u

at a resolution sufficiently better

than a half-pixel.51 If we consider strain of a few percent, as is often the case with tissue deformation analysis, an element size of 20 pixels is a practical choice. When the use of sub-micrometer resolution (~0.5 µm) microscopy is assumed, our analysis is potentially capable of single cellular resolution (~10 µm) analysis. This is much better than ultrasound elastography, which demonstrates resolutions of typically hundreds of micrometers.28 Although comparable resolution has been discussed for OCT elastography27, it requires a laser light source and specifically designed optics for interferometry, limiting general use for biomaterials characterization. Therefore, this method can be applied for the mechanical characterization of complex tissues, organoids as well as composite biomaterials for tissue engineering applications. On the other hand, it should be noted that the image-based analysis is not suitable for the measurement of ‘rigid’ materials, such as metals or ceramics, where strains in the range of microstrain (10-6) are typically the focus.

Conclusions The elastic moduli of materials composing composite sponge scaffolds were determined. Local mechanical property differences were studied using donut shaped scaffolds with soft and stiff cores. Calculated moduli directly found from the composite models were not significantly different from the values measured separately for each material. The proposed image analysis method can be used along with most types of mechanical characterization, including both prepared biomaterial scaffold systems, as well as native tissue samples.

Acknowledgement



The authors acknowledge the National Science Foundation (IDBR1555986 and CCSS1809047), National Institute of Health (R01NS092847 and U01EB014976) for financial support. The authors thank Dr. W. V. Berg-Foels of University of Connecticut for providing us with synovium derived mesenchymal stem cells used in the image analysis.

Supporting Information Supplementary videos S1 and S2, which correspond to Fig. 8 (A) and (B), respectively, are available. Video S1: Optical analysis of stiff-core sample. Red and blue squares represent maximum and minimum deformation, respectively Video S2: Optical analysis of soft-core sample. Red and blue squares represent maximum and minimum deformation, respectively

References

1. Kim, S. E.; Park, J. H.; Cho, Y. W.; Chung, H.; Jeong, S. Y.; Lee, E. B.; Kwon, I. C. Porous chitosan scaffold containing microspheres loaded with transforming growth factor-β1: implications for cartilage tissue engineering. J. Controlled Release 2003, 91, 365-374, doi.org/10.1016/S01683659(03)00274-8. 2. Fukuda, K. Development of regenerative cardiomyocytes from mesenchymal stem cells for cardiovascular tissue engineering. Artif. Organs 2001, 25, 187-193, doi.org/10.1046/j.15251594.2001.025003187.x. 3. Tan, H.; Chu, C. R.; Payne, K. A.; Marra, K. G. Injectable in situ forming biodegradable chitosan– hyaluronic acid based hydrogels for cartilage tissue engineering. Biomaterials 2009, 30, 2499-2506, doi.org/10.1016/j.biomaterials.2008.12.080.


Page 22 of 38

Page 23 of 38 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


4. Rafat, M.; Li, F.; Fagerholm, P.; Lagali, N. S.; Watsky, M. A.; Munger, R.; Matsuura, T.; Griffith, M. PEG-stabilized carbodiimide crosslinked collagen–chitosan hydrogels for corneal tissue engineering. Biomaterials 2008, 29, 3960-3972, doi.org/10.1016/j.biomaterials.2008.06.017. 5. Xu, W.; Wang, Y.; Liu, E.; Sun, Y.; Luo, Z.; Xu, Z.; Liu, W.; Zhong, L.; Lv, Y.; Wang, A. Human iPSC-derived neural crest stem cells promote tendon repair in a rat patellar tendon window defect model. Tissue Engineering Part A 2013, 19, 2439-2451, doi.org/10.1089/ten.tea.2012.0453. 6. Li, C.; Vepari, C.; Jin, H.; Kim, H. J.; Kaplan, D. L. Electrospun silk-BMP-2 scaffolds for bone tissue engineering. Biomaterials 2006, 27, 3115-3124, doi.org/10.1016/j.biomaterials.2006.01.022. 7. Wiria, F.; Leong, K.; Chua, C.; Liu, Y. Poly-ε-caprolactone/hydroxyapatite for tissue engineering scaffold fabrication via selective laser sintering. Acta biomaterialia 2007, 3, 1-12, doi.org/10.1016/j.actbio.2006.07.008. 8. McKee, C. T.; Last, J. A.; Russell, P.; Murphy, C. J. Indentation versus tensile measurements of Young's modulus for soft biological tissues. Tissue Engineering Part B: Reviews 2011, 17, 155-164, doi.org/10.1089/ten.teb.2010.0520. 9. Plodinec, M.; Loparic, M.; Monnier, C. A.; Obermann, E. C.; Zanetti-Dallenbach, R.; Oertle, P.; Hyotyla, J. T.; Aebi, U.; Bentires-Alj, M.; Lim, R. Y. The nanomechanical signature of breast cancer. Nature nanotechnology 2012, 7, 757-765, doi.org/10.1038/nnano.2012.167. 10. Tranchida, D.; Kiflie, Z.; Acierno, S.; Piccarolo, S. Nanoscale mechanical characterization of polymers by atomic force microscopy (AFM) nanoindentations: viscoelastic characterization of a model material. Measurement Science and Technology 2009, 20, 095702, doi.org/10.1088/09570233/20/9/095702. 11. Tranchida, D.; Piccarolo, S.; Soliman, M. Nanoscale mechanical characterization of polymers by AFM nanoindentations: critical approach to the elastic characterization. Macromolecules 2006, 39, 4547-4556, doi.org/10.1021/ma052727j 12. Tarafder, S.; Balla, V. K.; Davies, N. M.; Bandyopadhyay, A.; Bose, S. Microwave‐sintered 3D printed tricalcium phosphate scaffolds for bone tissue engineering. Journal of tissue engineering and regenerative medicine 2013, 7, 631-641, doi.org/10.1002/term.555. 13. Warnke, P. H.; Douglas, T.; Wollny, P.; Sherry, E.; Steiner, M.; Galonska, S.; Becker, S. T.; Springer, I. N.; Wiltfang, J.; Sivananthan, S. Rapid prototyping: porous titanium alloy scaffolds produced by selective laser melting for bone tissue engineering. Tissue engineering part c: Methods 2008, 15, 115-124, doi.org/10.1089/ten.tec.2008.0288. 14. Xue, W.; Krishna, B. V.; Bandyopadhyay, A.; Bose, S. Processing and biocompatibility evaluation of laser processed porous titanium. Acta biomaterialia 2007, 3, 1007-1018, doi.org/10.1016/j.actbio.2007.05.009. 15. Johnson, B.; Bauer, J.; Niedermaier, D. J.; Crone, W.; Beebe, D. Experimental techniques for mechanical characterization of hydrogels at the microscale. Exp. Mech. 2004, 44, 21-28, doi.org/10.1007/BF02427971.



16. Kaufman, J. D.; Miller, G. J.; Morgan, E. F.; Klapperich, C. M. Time-dependent mechanical characterization of poly (2-hydroxyethyl methacrylate) hydrogels using nanoindentation and unconfined compression. J. Mater. Res. 2008, 23, 1472-1481, doi.org/10.1557/JMR.2008.0185. 17. Ahearne, M.; Yang, Y.; El Haj, A. J.; Then, K. Y.; Liu, K. K. Characterizing the viscoelastic properties of thin hydrogel-based constructs for tissue engineering applications. J. R. Soc. Interface 2005, 2, 455-463, doi.org/10.1098/rsif.2005.0065. 18. Kuo, C. K.; Ma, P. X. Ionically crosslinked alginate hydrogels as scaffolds for tissue engineering: part 1. Structure, gelation rate and mechanical properties. Biomaterials 2001, 22, 511-521, doi.org/10.1016/S0142-9612(00)00201-5. 19. Schmedlen, R. H.; Masters, K. S.; West, J. L. Photocrosslinkable polyvinyl alcohol hydrogels that can be modified with cell adhesion peptides for use in tissue engineering. Biomaterials 2002, 23, 43254332, doi.org/10.1016/S0142-9612(02)00177-1. 20. Fung, Y. Biomechanics: mechanical properties of living tissues; Springer Science & Business Media: 2013, doi.org/10.1007/978-1-4757-2257-4. 21. Iwashita, M.; Kataoka, N.; Toida, K.; Kosodo, Y. Systematic profiling of spatiotemporal tissue and cellular stiffness in the developing brain. Development 2014, 141, 3793-3798, doi.org/10.1242/dev.109637. 22. Nia, H.; Liu, H.; Seano, G.; Datta, M.; Jones, D.; Rahbari, N.; Incio, J.; Chauhan, V.; Jung, K.; Martin, J. Solid stress and elastic energy as measures of tumor mechanopathology. Nature biomedical engineering 2017, 1, 0004, doi.org/ 10.1038/s41551-016-0004. 23. Ophir, J.; Alam, S.; Garra, B.; Kallel, F.; Konofagou, E.; Krouskop, T.; Varghese, T. Elastography: ultrasonic estimation and imaging of the elastic properties of tissues. Proc. Inst. Mech. Eng. Part H J. Eng. Med. 1999, 213, 203-233, doi.org/10.1243/0954411991534933. 24. Ophir, J.; Alam, S. K.; Garra, B. S.; Kallel, F.; Konofagou, E. E.; Krouskop, T.; Merritt, C. R.; Righetti, R.; Souchon, R.; Srinivasan, S. Elastography: imaging the elastic properties of soft tissues with ultrasound. Journal of Medical Ultrasonics 2002, 29, 155-171, doi.org/10.1007/BF02480847. 25. Schmitt, J. M. OCT elastography: imaging microscopic deformation and strain of tissue. Optics express 1998, 3, 199-211, doi.org/10.1364/OE.3.000199. 26. Wang, R. K.; Ma, Z.; Kirkpatrick, S. J. Tissue Doppler optical coherence elastography for real time strain rate and strain mapping of soft tissue. Appl. Phys. Lett. 2006, 89, 144103, doi.org/10.1063/1.2357854. 27. Kennedy, B. F.; Kennedy, K. M.; Sampson, D. D. A review of optical coherence elastography: fundamentals, techniques and prospects. IEEE Journal of Selected Topics in Quantum Electronics 2014, 20, 272-288, doi.org/10.1109/JSTQE.2013.2291445. 28. Li, G.; Cao, Y. In In Mechanics of ultrasound elastography; Proc. R. Soc. A; The Royal Society: 2017; Vol. 473, pp 20160841, doi.org/10.1098/rspa.2016.0841.


Page 24 of 38

Page 25 of 38 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


29. Yang, Y.; Ding, F.; Wu, J.; Hu, W.; Liu, W.; Liu, J.; Gu, X. Development and evaluation of silk fibroin-based nerve grafts used for peripheral nerve regeneration. Biomaterials 2007, 28, 5526-5535, doi.org/10.1016/j.biomaterials.2007.09.001. 30. Lovett, M.; Cannizzaro, C.; Daheron, L.; Messmer, B.; Vunjak-Novakovic, G.; Kaplan, D. L. Silk fibroin microtubes for blood vessel engineering. Biomaterials 2007, 28, 5271-5279, doi.org/10.1016/j.biomaterials.2007.08.008. 31. Talukdar, S.; Mandal, M.; Hutmacher, D. W.; Russell, P. J.; Soekmadji, C.; Kundu, S. C. Engineered silk fibroin protein 3D matrices for in vitro tumor model. Biomaterials 2011, 32, 2149-2159, doi.org/10.1016/j.biomaterials.2010.11.052. 32. Kim, U.; Park, J.; Kim, H. J.; Wada, M.; Kaplan, D. L. Three-dimensional aqueous-derived biomaterial scaffolds from silk fibroin. Biomaterials 2005, 26, 2775-2785, doi.org/10.1016/j.biomaterials.2004.07.044. 33. Mandal, B. B.; Kundu, S. C. Non‐Bioengineered Silk Fibroin Protein 3D Scaffolds for Potential Biotechnological and Tissue Engineering Applications. Macromolecular bioscience 2008, 8, 807818, doi.org/10.1002/mabi.200800113. 34. Rockwood, D. N.; Preda, R. C.; Yücel, T.; Wang, X.; Lovett, M. L.; Kaplan, D. L. Materials fabrication from Bombyx mori silk fibroin. Nature protocols 2011, 6, 1612-1631, doi.org/10.1038/nprot.2011.379. 35. Nazarov, R.; Jin, H.; Kaplan, D. L. Porous 3-D scaffolds from regenerated silk fibroin. Biomacromolecules 2004, 5, 718-726, doi.org/10.1021/bm034327e 36. Tang-Schomer, M. D.; White, J. D.; Tien, L. W.; Schmitt, L. I.; Valentin, T. M.; Graziano, D. J.; Hopkins, A. M.; Omenetto, F. G.; Haydon, P. G.; Kaplan, D. L. Bioengineered functional brain-like cortical tissue. Proc. Natl. Acad. Sci. U. S. A. 2014, 111, 13811-13816, doi.org/10.1073/pnas.1324214111. 37. Palchesko, R. N.; Zhang, L.; Sun, Y.; Feinberg, A. W. Development of polydimethylsiloxane substrates with tunable elastic modulus to study cell mechanobiology in muscle and nerve. PloS one 2012, 7, e51499, doi.org/10.1371/journal.pone.0051499. 38. Choi, A.; Zheng, Y. Estimation of Young's modulus and Poisson's ratio of soft tissue from indentation using two different-sized indentors: finite element analysis of the finite deformation effect. Medical and Biological Engineering and Computing 2005, 43, 258-264, doi.org/10.1007/BF02345964. 39. Hayes, W.; Keer, L.; Herrmann, G.; Mockros, L. A mathematical analysis for indentation tests of articular cartilage. J. Biomech. 1972, 5, 541-551, doi.org/10.1016/0021-9290(72)90010-3. 40. Kim, J.; Kim, I. S.; Cho, T. H.; Lee, K. B.; Hwang, S. J.; Tae, G.; Noh, I.; Lee, S. H.; Park, Y.; Sun, K. Bone regeneration using hyaluronic acid-based hydrogel with bone morphogenic protein-2 and human mesenchymal stem cells. Biomaterials 2007, 28, 1830-1837, doi.org/10.1016/j.biomaterials.2006.11.050.



41. Schneider, R. K.; Puellen, A.; Kramann, R.; Raupach, K.; Bornemann, J.; Knuechel, R.; Pérez-Bouza, A.; Neuss, S. The osteogenic differentiation of adult bone marrow and perinatal umbilical mesenchymal stem cells and matrix remodelling in three-dimensional collagen scaffolds. Biomaterials 2010, 31, 467-480, doi.org/10.1016/j.biomaterials.2009.09.059. 42. Hughes, T. J. The finite element method: linear static and dynamic finite element analysis; Courier Corporation: 2012. 43. Logan, D. L. A first course in the finite element method using Algor; Brooks/Cole Publishing Co.: 2000. 44. Chambon, S.; Crouzil, A. Similarity measures for image matching despite occlusions in stereo vision. Pattern Recognit 2011, 44, 2063-2075, doi.org/10.1016/j.patcog.2011.02.001. 45. Jaiswal, D.; Cowley, N.; Bian, Z.; Zheng, G.; Claffey, K. P.; Hoshino, K. Stiffness analysis of 3D spheroids using microtweezers. PLoS ONE 2017, 12, doi.org/10.1371/journal.pone.0188346. 46. Leong, W.; Lau, T. T.; Wang, D. A temperature-cured dissolvable gelatin microsphere-based cell carrier for chondrocyte delivery in a hydrogel scaffolding system. Acta biomaterialia 2013, 9, 64596467, doi.org/10.1016/j.actbio.2012.10.047. 47. Liu, Z.; Wang, H.; Li, B.; Liu, C.; Jiang, Y.; Yu, G.; Mu, X. Biocompatible magnetic cellulose– chitosan hybrid gel microspheres reconstituted from ionic liquids for enzyme immobilization. Journal of Materials Chemistry 2012, 22, 15085-15091, doi.org/10.1039/C2JM33033D. 48. Li, Z.; Ramay, H. R.; Hauch, K. D.; Xiao, D.; Zhang, M. Chitosan–alginate hybrid scaffolds for bone tissue engineering. Biomaterials 2005, 26, 3919-3928, doi.org/10.1016/j.biomaterials.2004.09.062. 49. Shor, L.; Güçeri, S.; Wen, X.; Gandhi, M.; Sun, W. Fabrication of three-dimensional polycaprolactone/hydroxyapatite tissue scaffolds and osteoblast-scaffold interactions in vitro. Biomaterials 2007, 28, 5291-5297, doi.org/10.1016/j.biomaterials.2007.08.018. 50. Jin, X.; Zhu, F.; Mao, H.; Shen, M.; Yang, K. H. A comprehensive experimental study on material properties of human brain tissue. J. Biomech. 2013, 46, 2795-2801, doi.org/10.1016/j.jbiomech.2013.09.001. 51. Bing, P.; Hui-Min, X.; Bo-Qin, X.; Fu-Long, D. Performance of sub-pixel registration algorithms in digital image correlation. Measurement Science and Technology 2006, 17, 1615, doi.org/10.1088/0957-0233/17/6/045.


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Table of Contents Graphic

Non-destructive, Label-free Characterization of Mechanical Micro-heterogeneity in Biomimetic Materials Devina Jaiswal, Min D. Tang-Schomer, Disha Sood, David L. Kaplan, and Kazunori Hoshino



Figure 1. Composite specimens based on biomimetic tissues made of silk protein sponges as reported in previous studies. 105x43mm (300 x 300 DPI)


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Figure 2. Design of the PDMS composite specimen. 94x38mm (300 x 300 DPI)



Figure 3. Process flow chart. 151x212mm (300 x 300 DPI)


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Figure 4. Reference compression analysis of sponges, hydrogels and PDMS. The results were compared with the values found from the composite materials. 154x35mm (300 x 300 DPI)



Figure 5. Four-noded elements used in the study. (a) Nodes and elements for the core and shell regions are defined from an image. (b) Displacement at each node is considered to describe the sample deformation. (c) Coordinates (X,Y) are defined for each element. 152x37mm (300 x 300 DPI)


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Figure 6. Optical tracking at each node to find the deformation of the element. 59x33mm (300 x 300 DPI)



Figure 7. Samples and the nodes used to evaluate the repeatability of optical tracking. Scale bar 250 µm. 129x36mm (300 x 300 DPI)


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Figure 8. Optical analysis of (A) Stiff-core sample and (B) Soft-core sample. Red and blue squares represent maximum and minimum deformation, respectively (Supplementary videos 1 and 2 are available). The panel in (A) shows overhanging parts of the shell material indicated by dotted lines. The panel in (B) shows the pores that are thought to have induced large deformation on the surface. Scale bar = 1 mm. 164x54mm (300 x 300 DPI)



Figure 9. FEM analysis of (A) Stiff-core sample and (B) Soft-core sample. Red and blue squares represent areas of maximum and minimum deformation, respectively. 118x53mm (300 x 300 DPI)


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Figure 10. The elastic moduli directly measured from composite specimens and separately were compared. The average elastic moduli of materials measured as a composite and separately and were not significantly different as given by student t-test (P-value > 0.05). 384x140mm (96 x 96 DPI)



Figure 11. Optical and FEM analyses of the PDMS specimen which contains soft and stiff regions. 173x68mm (300 x 300 DPI)


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Nondestructive, Label-Free Characterization of Mechanical

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