Centimeter scale

To find crack patterns, one hardly needs to go as far as Mercury or Mars. From paint cracks to craquelure, many examples of crack patterns exist from the centimetre to the meter

scale. The focus here on crack in paintings, where environmental effects on the substrate determines if a painting cracks or not, and memory effects in pastes where periodic driving forces determine the structure of the crack patterns.

Craquelure is defined as a dense crack pattern. It is commonly found in paintings and in glaze. The study of craquelure has received much attention because of its applications to conservation of paintings[2, 19, 20, 9].Figure 1.7 (a) shows an image of a painting where the right part of the painting, glued to a wooden piece whereas the left side of the canvas is left hanging ([10],[9]). Fixing the canvas prevents cracks from occurring.

Karpowickz [46] measured the strain in a gel under uniaxial stress, and postulated that cracks in a painting could be due to viscoelastic recovery after drying. He also showed how exposing brittle thin films to high humidity causes contraction of the film leading to a ”typical craquelure pattern” [47].

Berger and Russell [11] measured the stress change in a canvas due to varying environ-mental conditions. In figure 1.7 (b), the top plot shows the changing temperature and relative humidity, and the bottom plot shows the change in stress due to large scale changes in humid-ity. Berger and Russell suggest that in order to conserve an oil painting and prevent cracks, a canvas must be stretched so that it remains stiff. The tension in the canvas can be com-promised due the change in the environmental conditions, mainly humidity. Cyclic changes in humidity or temperature can either overstretch or contract the canvas. They suggest that one of the best ways to prevent cracks is to attach a rigid support to the canvas in order to prevent loss of tension.

Nakahara et al.[68, 62] studied the memory effects in pastes. They found that pastes had

“remembered” the direction of vibration, and when dried and formed cracks in a direction opposite to the direction vibration. In other cases, pastes cracked along the direction of flow.

Figure 1.8 shows a dried magnesium carbonate hydroxide paste that has cracked in a direction perpendicular to vibration direction, and parallel to the direction of flow. They conducted experiments with colloidal particles and showed that in a paste, decreasing particle size leads to a stronger memory effect due to vibration.

The driving force of fracture for the two systems presented here are the same. In both cases, drying causes stress in the cracking materials and crack patterns form. Unique to each system is an external factor that affects the cracking process. In the paint cracks, the structure of the substrate gets altered due to change in humidity. By studying the crack pattern is it possible to determine how the substrate changes? In order to understand this, crack patterns must be generated using substrates of varying stiffness in order to determine conclusively if a stiffer substrate will necessarily prevent paint cracks. Cracks in pastes with memory are a unique system. There is no variation in the substrate however a driving force alter the cracking medium and induces internal stresses within the material. Quantifying the crack pattern would allow a comparison between the crack pattern and the driving force that generated the crack pattern. This in turn could assist in predicting what type of crack pattern will be generated based on the magnitude and direction of shaking. The next set of examples will deal with micro-scale cracking.

(a) (b)

Figure 1.7: Role of substrate in formation of craquelure: Experiments to study the effect of changing environmental conditions on the canvas .(a) On the left side of the painting, crack patterns are observed. On the right side of the painting, no crack are observed due to the present of a wooden support. Figure adapted from [10] . (b) Top panel shows the changing environmental conditions with respect to time to which the canvas is exposed. The line with pluses represents the change in relative humidity. The solid line represents the change in temperature which is generally allowed to vary between 22^◦-25^◦ C. (b) The bottom panel represents the change in stress due to the change in environmental conditions. Notice that the maxima in stress occur at the same time points as the maxima of the relative humidity [11]. Figure adopted from [11]

. 1.4.3 Microscale and below

Three types of crack patterns are presented here - cracks in a gallium nitride film, cracks in blood droplets and finally cracks in an Au/PDMS bilayer. The cracks in the gallium nitrite films and the Au/PDMS bilayer occur due to misfit strains that occur between the deposited material and the substrate. These misfit strains occur due to difference in elasticity and are common in epitaxial growth processes as well since, in process of deposition or growth of the material, any defects cause strains to build up in the the crystal structure and this in turn causes fractures.

Fracture patterns at the micron scale are shown in figure 1.9. In figure 1.9 (a), a gallium nitrite film is deposited on a silicon substrate [78]. The film is approximately 5µm in thickness.

Thin films of such size are routinely used in industry, especially in building circuits for micro

Figure 1.8: Memory effects in pastes: (a) memory of vibration- A water poor paste of magnesium carbonate hydroxide (volume fraction ρ = 12.5%) is shaken at an amplitude a= 15mm and frequencyf = 2Hz [68] . The arrow shows the direction of shaking. Primary cracks are perpendicular to the direction of vibration; secondary cracks are parallel to the direction of vibration [68]. (b) A water rich paste of magnesium carbonate hydroxide (volume fraction ρ = 6.7%) is shaken in the direction of the arrow. Here, the primary cracks are perpendicular to the direction of shaking [68]. What do the crack patterns tell us about the stress distribution inside the medium?

mechanical electronics machines (MEMS) [69, 3]. In figure 1.9 (a) the thicker cracks are the primary cracks, and thinner cracks are the secondary cracks. Cracking in microfilms at such length scales can be disastrous. Numerous attempts have been made to better understand how cracking occurs in thin films [12, 91, 88, 90] some of these ideas are discussed in the next section.

Figure 1.9 (b) shows a dried and cracked droplet of blood. Blood is a colloid that consists of plasma and celluar matter which include red blood cells, white blood cells and platelets.

Sobac and Brutin [80] showed how a drying droplet of blood have two regimes, and how in the second regime, which is defined primarily by diffusion, a radial crack pattern is formed.

In a follow up paper in 2014 [81], they show how as a gelation front reaches the center, cracks follow. The drying mechanism is similar to that of the coffee ring effect. They also showed that the crack spacing, is roughly proportional to the thickness of the drop of blood. As a droplet dries fully, in the center of the dried blood droplet, an isotropic crack pattern forms.

They observed delamination along the edges of the droplet as well.

Figure 1.9 (c) shows a pattern created using controlled cracking in gold, PDMS bilayer.

Here, micro-groves were built into the PDMS substrate then a gold film was deposited and cracked. By controlling the frequency of notches at will and the spacing between the notches, it is possible to create crack patterns [28, 50, 51].

Kim et al.[51] describe methods to use controlled fracture to create nano and micro-structures. When PDMS is strained then exposed to plasma and the strain is released, cracks form [72, 18, 67]. This is because the surface of the PDMS oxidizes which creates a thin stiff layer. The elastic mismatch between the stiff surface and interior of the PDMS causes cracks to occurs [58]. By precisely controlling the stress it is possible to control crack spacing and by controlling the oxidation time of PDMS surface, the crack depth can be controlled. In this paper, Kim et al. refer to various other methods of nano fabrication and suggest potential

application to biological systems [51].

(a) (b) (c)

Figure 1.9: Crack patterns at the mirco-scale: (a) 5µm thick gallium nitrite film. Cracks appear along the cleavage planes. Cracks tend to intersect at 60^◦. Darker cracks are the primary cracks, the second generation of cracks are fainter and are in general parallel to each other [78]. Figure adapted from [78] . (b) Crack pattern of a dried blood droplet. Droplet diameter is 8.6 mm, at room temperature of 22^◦C and relative humidity of RH = 42%.

Initially as the droplet dries, it gels[80] . The region of geletion shrinks and during this shrinkage, crack form along the edges and propagate inwards[81]. Inside the droplet, a finer crack pattern can be seen [80]. Figure adapted from [80]. (c) Crack pattern generated by on a Au/PDMS bi-layer. Gold is deposited onto a layer of PDMS under strain. The PDMS layer contains notches. A detailed method of fabrication is presented in [50]. Figure adapted from [80].

The three crack patterns presented conclude the examples of crack patterns at different length scales. From the micro scale to the macro scale, in all the examples, the substrates play a major role in determining how crack patterns form and propagate. In two of the cases - memory pastes and blood cracks- although there is no substrate, external influences alter how the crack pattern can evolve. In the case of blood cracks, the crack pattern can change based on the temperature, humidity or pH of the environment. This may happen due to change in drying rate or change in the structure of the cells within in the blood droplet.

Some references have been made to quantifying crack patterns. In the next section, previous attempts to quantify crack patterns will be discussed.

1.5 Quantification of crack patterns

There are numerous approaches that have been attempted quantify a crack pattern. There are two approaches used- one approaches involves generating either a purely artificial network or an artificial network based on a real network and developing methods to analyse them. The second approach involves using real crack networks to define measures for a crack pattern.

Andresen et al. study fracture outcrops by representing them as an artificial networks and measuring network parameters [5]. Such an approach has also been used by Valentini et al. to analyse rock fractures networks [86]. Fracture outcrops are lines of cracks that are part

of exposed geological structures. Examples of these geological structures are large boulders or sedimentary rocks. Andresen et al. took various fracture outcrops and generated networks by labelling cracks as nodes. The intersection between the cracks are labeled as edges which connect the nodes. They measured the degree distribution, the clustering coefficient, the efficiency of the network and the characteristic path length. The degree k of a node is the total number of neighboring nodes. If the degree distributionP (k) follows a power law, then the network is considered scale free. The clustering coefficient measures the local connectivity of the network. The clustering coefficient has a value between 0 and 1 where values close to 1 represents a condition where two neighbours of a single node share an edge of the network.

The clustering coefficient is a means to measure the local connectivity of the network. Such local measures would be useful pattern seen in polygonal terrain where local structure exists with a larger crack pattern. The efficiencyE of the network is a measure of how well different parts of the networks are connected to each other, and this is a global measure of the pattern.

E is proportional to the inverse of the distance between two nodes in the network. It falls between 0 and 1 except for the case whereE =∞. This too can be generally applied to many crack patterns. For example, comparing the blood cracks and the memory paste cracks, E may be smaller for the blood cracks since very few radial cracks are connected to each other ( white regions in the figure 1.9 (b)) whereas in figure 1.8 a path between any two vertices of the crack pattern can be found by travelling along the crack pattern. The last parameter they mention is the characterstic path length L which represents the average distance between any two nodes in the network. The average path length is large for fracture networks that have a small E value since the network is not well connected. While the methods discussed by Andresen et al. work well at characterizing networks, they may not be easily applicable to real crack patterns. Firstly, they require a crack pattern with a large number of cracks, in order to plot any meaningful distributions of P (k). With systems like that of Nakahara et al., it would be difficult to get the adequate statistics. Secondly, the measures contain very little information about the substrate of the crack pattern. This is required initially to understand how a crack patterns evolve.

Hafver et al. [42] took a different approach where rather than using existing crack patterns as models they generated artificial crack patterns and made measurements on them. Their cracks were straight lines whose position was chosen according to a probability which was weighted by a distance map of the pattern, and whose the orientation was chosen randomly.

Once a line was placed, it was extended in both directions until it either met another line or hit a boundary. Hafver et al. had two control parameters for their patterns: γ which controls the homogeneity of the pattern andωwhich controls the topology of the pattern. γcould take the values−2≤γ ≤2 whereas 0≤ω≤1. For a value ofω = 0 lines form a tree like structure and forω= 1 the lines generate polygonal structures. A pattern with γ = 2 andω= 0 is an isotropic pattern where most lines have one free end. The free end refers to one end of the line not intersecting with the boundary, or with any other line. On the other extreme where γ =−2 and ω = 0 most of the lines in the pattern lie at the boundary; no lines are present in the center of the image. Increasing the value of ω to 1 with γ = 2, generates a pattern

where the majority of lines lie along the diagonal to the square boundary or perpendicular to the diagonal. Values ofγ = 2 andω = 1 generate an isotropic pattern where there are no free ends for any line, all lines are connected on both ends to either another line or to the boundary. Usingω, Hafver et al. define an order parameterR= (1−ω)/(1 +ω) which is the ratio of the free ends to the number of intersections of lines. Since replacing the lines with cracks does not affect the definition of the parameter, they propose thatω can be used as a measure of crack patterns as well. Another order parameter they define is the measured value ofγ. They measureγ based on the temporal hierarchy of the pattern. Both the parameters are applied to crack patterns in gelatin confined to a Hele-Shaw cell, ice fractures on Mars and weathering cracks on the surface roads. Similar to the parameters of Anderesen et al.

such a parameter does not characterise the symmetry of the pattern. A radial crack pattern can either have cracks extending radially outwards or cracks that lie parallel to boundary, the two parameters can be tuned such that in both those cases the values of ω and γ are the same. Furthermore, the parameterR is then dependent on the number of free ends in the crack pattern, hence if a crack pattern were allowed to evolve for long enough, R will drastically change. This is a benefit for time lapse imaging of a crack pattern but if the crack pattern has an overall directionality, which can be imposed by the substrate, thenR would not be able to capture the influence of the substrate.

Bohn et al. [16] took an experimental approach to defining an order parameter. They studied the temporal and hierarchical evolution of glaze in ceramics by analysing a crack pat-terns generated by drying starch slurries on a rectangular polymethylmethacrylate (PMMA) substrate. They show that orientation of the first fracture is non-deterministic for low layer heights and with increasing layer height, the orientation and structure of the first crack becomes deterministic. They quantify this using two order parameters, these are δ = |d₁ +d₂|/√

A and ∆ = p

d₁²+d₂²/√

A where d₁ is the distance along the rectan-gle, between one end of the crack and the center of the left edge of the rectanrectan-gle, d2 is the same, however for the right edge. The definitions of these are shown in figure 1.10. Both parameters δ and ∆ yield 0 if a crack divides rectangular region into two equal halves. For a curved crack δ > 0 and ∆ >0. These parameters approach close to zero with increasing layer heights. The large spread in values ofδ and ∆ at low layer heights is what signifies the disorder whereas at large layer heights the standard deviation of both order parameters is small. Both these order parameters can be written according to the symmetry of the region bounding the crack pattern, however, since they pertain to only a single crack, they cannot be used to analyse current experiments because the information about the first crack is lost in a mature crack pattern.

The various methods to quantify crack patterns that have been presented above have a certain realm of applicability. The δ and ∆ parameters serve to quantify a crack in a rectangular domain, the network parameter presented by Andersen et al. are suited for crack patterns with large number of intersections, the parametersR andγ are best suited to study an evolving crack pattern. None of these parameters contain any information about the orientation or the symmetry of the substrate, hence it is difficult to apply them to the

(a) (b)

Figure 1.10: Definitions ofd1 and d2. Figure reproduced from [16]

current problem.

1.6 Scope of the thesis

The thesis is divided into 6 chapters. This section concludes the end of chapter 1 which aims to present introductory ideas about the research problem. The second chapter contains the experimental details and information about the image processing methods used to pre-pare the images for analysis. Two types of uneven substrates are used in generating cracks patterns- sinusoidal plates and radially sinusoidal plates, details about the number of plates and variation in amplitudes and wavelengths of the plates are discussed. The procedure of preparation of the slurry is also discussed. Some preliminary raw images of crack patterns are shown. Once images of crack patterns have been acquired, they must be processed, such that metrics and measure can be applied. The image processing of crack patterns is also discussed in chapter 2 - Materials and methods.

Chapter 3 - Analysis of crack patterns - defines new measures that are used to classify crack patterns. These measures employ the symmetry and orientation of the substrate to quantify crack patterns generated in chapter 2. The algorithm of each measure is described in this chapter and a few crack patterns are analysed as examples. Along with the measurement parameters, the method to measure the crack spacing is also presented.

In chapter 4 - Results, the data for different measurement parameters are presented. The chapter begins with the data for time evolution of a measurement parameter. This is done to

In chapter 4 - Results, the data for different measurement parameters are presented. The chapter begins with the data for time evolution of a measurement parameter. This is done to