3.2.1 Antenna structure and geometry

The designed antenna structure is composed of three connected semicircular arc monopoles with circular patch fed by microstrip transmission line and modified half circular shaped ground plane as shown in Figure 13. The initial design is validated and optimized by simulating the proposed antenna. The proposed antenna is designed on FR4 substrate with height 1.6 mm, dielectric constant εr = 4.6, and loss tangent tan δ = 0.02. The antenna is fed by a 50-Ω transmission line (TL).


**5. Conclusion**

A 3D transient CFD model was developed to illustrate the impacts of wind direction on the cooling of a PV panel installed on the surface of a slanted roof. The simulation includes radiative effects by surface-to-surface radiation model, natural convection generated by the heated PV due to the buoyancy effects and forced convection due to the wind effects. The numerical results showed that the wind direction has a significant impact on the cooling rates of the module. At angles below 90 degrees, due to direct contact between the wind and sides of the PV panel, the cooling rate of the PV is higher resulting in lower PV temperatures. However, at higher angles, the walls and edges of the structure act as a barrier between the wind flow and PV and prevent direct contact between them. Therefore, the convective cooling rates degrade, and PV temperature rises.

It should be noted that the results may depend on other geometrical parameters such as distances from the edges of the PV module to the edges of the roof and building height, which could be investigated in future studies.


**1. Introduction**

156 Advanced Holography – Metrology and Imaging

Zhang, S. H., Gan, Sh., Cao, D. Zh., Xiong, J., Zhang X., & Wang, K., (2009b). Phase-reversal

Zhang, S. H., Gan, Sh., Xiong, J., Zhang, X., & Wang, K., (2010). Illusion optics in chaotic light, *Phys. Rev. A* Vol.82, No.2, (August 2010) 021804(R), ISSN 1050-2947.

031895(R), ISSN 1050-2947.

diffraction in incoherent light, *Phys. Rev. A* Vol.80, No.3, (September 2009)

Long wavelength interferometry has been widely applied in different fields, such as infrared optics, infrared transmitting materials, high-reflective multilayer dielectric coatings for highpower laser systems. In optical metrology, long-wave interferometers are also employed for shape measurement of reflective rough surfaces and for testing optical systems that requires deep aspherics. An advantage of using longer wavelength is that the aspheric departure from the best fit reference-sphere, in unit of the probing wavelength, is reduced at longer wavelength, thus allowing one to obtain an interferogram of the deep aspheric under test. This leads to extension of the unambiguous distance measurement range, a well-known problem in interferometric metrology, where the essential difficulty relates to the interferometric fringe order, which cannot be determined unambiguously from a single measurement of phase interference. This problem is also of particular significance in digital holographic and interferomety based applications where digital processing of the recorded interferogram makes it possible to extract quantitatively amplitude and phase of the numerically reconstructed wavefront (Cuche el al., 1999a, 1999b; Vest, 1979; Yaroslavsky & Eden, 1996). Digital holography based imaging techniques provide real time capabilities to record also three-dimensional objects using interference between an object wave and a reference wave captured by an image sensor such a CCD sensor. Three-dimensional 3D information of the object can be obtained from the numerical reconstruction of a single digitally recorded hologram, since the information about the optically interfering waves is stored in the form of matrices. The numerical reconstruction process offers many more possibilities than conventional optical processing (Goodman & Lawrence, 1967; Stetson & Powell, 1966; Stetson & Brohinsky, 1985). For example, it is possible to numerically focus on any section of the three dimensional volume object without mechanical focusing adjustment (Grilli et al., 2001; Lai et al., 2000), correct optical components defects such as lens aberrations or compensate the limited depth of field of an high magnification microscope objective. Full digital processing of holograms requires high spatial resolution sensor arrays with demanding capabilities for imaging applications and non destructive testing. In this regard, several new recording materials and optoelectronic sensors have been devised for

Infrared Holography for Wavefront Reconstruction and Interferometric Metrology 159

2 2 , , , 2 , , cos , , *o R I xy Rxy Oxy Rxy Oxy xy xy*

which shows explicitly that the recorded hologram contains a term with an amplitude and phase modulated spatial carrier. For the numerical reconstruction of the recorded hologram, the interference pattern *I x* , *y* is illuminated by the reference wave *R x* ,*y* , i.e , we have

2 22 2 \* *R x*,, , , , , , , , , *y I x y R x y R x y R x y O x y R x y O x y R x y O x y*

a)

b)

Fig. 1. Optical configuration for recording (a) and for reconstruction (b) of off-axis

*R y* is the complex amplitude of the reference wave with real

(1a)

(2)

*R x*,*y* . Eq. (1) can be modified to the form

*R x* , , exp , *y R x y i x*

holograms.

amplitude *R x* , *y* and phase

recording holograms beyond the visible wavelengths, in the infrared. Coherent imaging based on digital processing of infrared holograms presents the great advantage of providing quantitative data from the recorded interference pattern (Lei et al., 2001a, 2001b; Leith & Upatnieks, 1965; Nilsson & Carlsson, 2000; Seebacker et al., 2001) but, the lower spatial resolution of the infrared sensor arrays compared to those working in the visible region, appears to be a major limiting factor to accurate numerical wavefront reconstruction for the digitized interferogram.

In this chapter we will describe potential applications of digital processing methods for whole optical wavefront reconstruction of infrared recorded holograms. The basic principle of numerical reconstruction of digitized holograms will be reviewed and we will discuss numerical methods to compensate the loss of spatial resolution at longer wavelength and the limited spatial resolution of the recording array We will present several examples to show that infrared digital holography can be exploited as a high accuracy technique for testing both reflective and transmissive objects. In order to demonstrate the feasibility of digital holography in the infrared region we will describe a two beam interferometric technique for recording holograms, both in reflection and transmission type geometry which employs a pyroelectric sensor array. The availability of pyroelectric sensor arrays for recording digital infrared holograms offers the advantage of a compact and efficient interferometric set-up for recording infrared hologram. We will also present application of digital holographic processing methods for numerical reconstruction of wavefronts of optical beams, such as Laguerre-Gauss beams possessing wavefront singularities in the infrared region. In fact, this technique can be usefully employed for characterizing the vorticity of infrared beams of potential use in optical telecommunication applications, where the preservation of the purity of the mode along the propagation direction is a problem of crucial importance. We will present examples of characterization of the vortex signature of infrared Laguerre-Gauss beam through the numerical reconstruction of the singular phase of the beam from the digitized hologram.


*3.2. Criteria*

This study compares various independent and dependent software reliability models and the proposed model introduced Table 1 using 11 criteria. Based on the difference between the actual observed value and the estimated value, we would like to find a better model by comparing it with criteria reflecting the number of parameters used in each model.

First, the mean squared error (MSE) is defined as the sum of squares of the distance between the estimated value and the actual value when considering the number of parameters and the number of observations [37].

$$\text{MSE} = \frac{\sum\_{i=1}^{n} (\hat{m}(t\_i) - y\_i)^2}{n - m} \tag{11}$$

where *m*6(*ti*) is the estimated value of the model *m*(*t*), *yi* is the actual observed value, *n* is the number of observations, and *m* is the number of parameters in each model.

Second, the mean absolute error (MAE) defines the difference between the estimated number of failures and the actual value considering the number of parameters and the number of observations as the sum of the absolute values [38].

$$\text{MAE} = \frac{\sum\_{i=1}^{n} |\hat{m}(t\_i) - y\_i|}{n - m} \tag{12}$$

Third, Adj\_R2 is the modified coefficient of determination of the regression equation and determines how much explanatory power it has in consideration of the number of parameters [39].

$$\mathbf{R}^2 = 1 - \frac{\sum\_{i=1}^n \left(\hat{m}(t\_i) - y\_i\right)^2}{\sum\_{i=1}^n \left(y\_i - \overline{y\_i}\right)^2}, \text{Adj}\\_\mathbf{R}^2 = 1 - \frac{\left(1 - R^2\right)(n - 1)}{n - m - 1} \tag{13}$$

Fourth, the predictive ratio risk (PRR) is obtained by dividing the distance from the actual value to the estimated value by the estimated value in relation to the model estimation [40].

$$\text{PRR} = \sum\_{i=1}^{n} \left( \frac{\hat{m}(t\_i) - y\_i}{\hat{m}(t\_i)} \right)^2 \tag{14}$$

Fifth, the predictive power (PP) is obtained by dividing the distance from the actual value to the estimated value by the actual value [41].

$$\text{PP} = \sum\_{i=1}^{n} \left( \frac{\hat{m}(t\_i) - y\_i}{y\_i} \right)^2 \tag{15}$$

Sixth, Akaike's information criterion (AIC) was used to compare likelihood function maximization. This is applied to maximize the Kullback–Leibler level between the probability distribution of the model and the data [42].

$$\begin{aligned} \text{AIC} &= -2\log L + 2m\\ L &= \prod\_{i=1}^{n} \frac{(m(t\_i) - m(t\_{i-1}))^{y\_i - y\_{i-1}}}{(y\_i - y\_{i-1})!} e^{-(m(t\_i) - m(t\_{i-1}))}\\ \log L &= \sum\_{i=1}^{n} \left\{ (y\_i - y\_{i-1})\ln(m(t\_i) - m(t\_{i-1})) - (m(t\_i) - m(t\_{i-1})) \right. \\ &\qquad - \ln((y\_i - y\_{i-1})!) \right\} \end{aligned} \tag{16}$$

Seventh, the predicted relative variation (PRV) is the standard deviation of the prediction bias and is defined as [43]

$$\text{PRV} = \sqrt{\frac{\sum\_{i=1}^{n} (y\_i - \hat{m}(t\_i) - Bias)^2}{n-1}} \tag{17}$$

Here, the bias is *n* ∑ *i*=1 *m*6(*ti*)−*yi n* .

The root mean square prediction error (RMSPE) can estimate the closeness with which the model predicts the observation [44]:

$$\text{RMSPE} = \sqrt{Variance^2 + Bias^2} \tag{18}$$

Ninth, the mean error of prediction (MEOP) sums the absolute value of the deviation between the actual data and the estimated curve and is defined as [38]

$$\text{MEOP} = \frac{\sum\_{i=1}^{n} |\widehat{m}(t\_i) - y\_i|}{n - m + 1} \tag{19}$$

Tenth, the Theil statistic (TS) is the average percentage of deviation over all periods with regard to the actual values. The closer the Theil statistic is to zero, the better the prediction capability of the model. This is defined as [45]

$$\text{TS} = 100 \ast \sqrt{\frac{\sum\_{i=1}^{n} (y\_i - \hat{m}(t\_i))^2}{\sum\_{i=1}^{n} y\_i^2}} \% \tag{20}$$

Eleventh, it takes into account the tradeoff between the uncertainty in the model and the number of parameters in the model by slightly increasing the penalty each time parameters are added to the model when the sample is considerably small [46].

$$\text{PC} = \left(\frac{n-m}{2}\right) \log\left(\frac{\sum\_{i=1}^{n} (\widehat{m}(t\_i) - y\_i)^2}{n}\right) + m \left(\frac{n-1}{n-m}\right) \tag{21}$$

Based on the above criteria, we compared the proposed model with the existing NHPP software reliability model. When Adj\_R2 is closer to 1, and the other 10 criteria are closer to 0, it indicates a better fit. Using R and MATLAB, the parameters of each model were estimated through the LSE method, and the goodness of fit is calculated to compare the superiority. This is a method of estimating parameters through the difference between the model in Table 1 and the actual number of failures in Table 2, and follows LSE = ∑*nt*=1(*yt* − *m*(*t*))2 [47].

#### *3.3. Results of Dataset 1*

Table 3 shows the estimated values for the parameters of each model obtained using dataset 1. Each parameter of the proposed model is represented by *a*ˆ = 80.0907, ˆ *b* = 0.07231, *c* ˆ = 15.9288, and ˆ *h* = 9.8182. Figure 2 shows the result of calculating the estimated value of *m*(*t*) at each time point based on the cumulative number of failures at each time point in dataset 1 and each model equation. The black dotted line represents the actual data, and the dark red solid line represents the predicted failure value at each time point of the proposed model. Compared with other models, it shows the predicted value closest to the actual value.

*c*

ˆ

> =

ˆ

*h* = 9.8182

15.9288,

**Table 3.** Parameter estimation of model from dataset 1.

 Proposed model

Table 4 shows the results of calculating the criteria of each model using the parameters obtained through dataset 1. As a result, the values of MSE, MAE, PRR, PP, PRV, RMSPE, MEOP, TS, PC of the proposed model show the smallest values of 9.9274, 3.2140, 0.0647, 0.0577, 2.6866, 2.6870, 2.8569, 4.6682, and 13.0594, respectively. The AIC shows the second smallest value at 73.4605. In addition, Adj\_R2 is 0.9821, which is the closest to 1. The DPF model shows the highest value with AIC = 73.2850, and the second highest result for the other criteria. The model with the third highest criterion is the Vtub model.

**Table 4.** Comparison of all criteria from dataset 1.

#### *3.4. Results of Dataset 2*

Table 5 shows the estimated values for the parameters of each model obtained using dataset 2. Each parameter of the proposed model is represented as *a*ˆ = 79.1444, ˆ *b* = 0.2001, *c*ˆ = 72.3208, and ˆ *h* = 9.3327. Figure 3 shows the results of calculating the estimated value of *m*(*t*) for each point in time based on the cumulative number of failures at each time point in dataset 2 and each model equation. Here, the black dotted line represents the actual data, whereas the dark red solid line represents the predicted failure value at each time point of the proposed model. Compared with the other models, the predicted value is closest to the actual value.

Table 6 shows the results of calculating the criteria of each model using the parameters obtained through dataset 2. As a result, the values of MSE, MAE, PRR, PP, AIC, PRV, RMSPE, MEOP, TS, and PC of the proposed model are 18.9722, 4.3544, 0.1615, 0.1482, 92.2155, 3.7139, 3.7145, 3.8706, 6.3751, and 15.6500, respectively, which show the smallest criteria. Adj\_R2 is 0.9723, which is the closest to 1. The model with the second highest criterion is DPF, and Vtub is the third best-fitting model.

**Figure 3.** Prediction of all models for dataset 2.

**Table 6.** Comparison of all criteria from dataset 2.

*3.5. Results of Dataset 3*

Table 7 shows the estimated values for the parameters of each model obtained using dataset 3. Each parameter of the proposed model is represented through *a*ˆ = 194.7684, ˆ *b* = 0.3062, *c*ˆ = 307.0805, and ˆ *h* = 135.5641. Figure 4 shows the results of calculating the

estimated value of *m*(*t*) at each point in time based on the number of cumulative failures at each time point in dataset 3 and for each model equation. The black dotted line indicates the actual data, and the dark red solid line is the predicted failure value at each time point for the proposed model. Compared with other models, the proposed model shows the predicted value closest to the actual value.

Table 8 shows the results of calculating the criteria of each model using the parameters obtained through dataset 3. As a result, MSE and PC of the proposed model show the smallest values of 26.8047 and 24.5551, respectively, and Adj\_R2 shows the closest value to 1 at 0.9765. In addition, MAE, PRR, PP, PRV, RMSPE, MEOP, and TS are 4.9209, 0.0096, 0.0092, 4.6668, 4.6668, 4.5694, and 2.5484, respectively, showing the second smallest values. Figure 4 shows the estimated failure values at each time point using the developed models. The Vtub model shows the most suitable criteria of 0.0094, 0.0090, 4.6356, 4.6357, and 2.5315 in PRR, PP, PRV, RMSPE, and TS, and DPF shows the most suitable criteria of 4.9195 and 4.5682 with MAE and MEOP. However, in calculating the AIC of the Vtub model, DPF, KSRGM, and the newly proposed model, a value indicating *t* = 14 is shown, indicating that the calculation is no longer being applied. In the process of calculating the AIC value, if there is no difference between the value at a specific point in time and the next point in time, the denominator is 0, so the AIC calculation can not be performed.

**Table 7.** Parameter estimation of model from dataset 3.

**Figure 4.** Prediction of all models for dataset 3.

#### **4. Optimal Release Time**

When releasing software, it is very important that find the optimal release time. In order to find that, we need to find a time that minimizes the cost. We apply *m*(*t*) proposed in Section 2 to the cost model to find the optimal time point between time to market and the minimum cost. The optimal time is suggested based on the cost model that reflects the software installation cost, software test cost, operation cost, software removal cost, and risk cost when the software failure occurs. Figure 5 describes the software field environment from the software installation of the software cost model. The expected software cost model follows Equation (22) [30,31].

$$\mathbb{C}(T) = \mathbb{C}\_0 + \mathbb{C}\_1 T + \mathbb{C}\_2 m(T) + \mathbb{C}\_3 (1 - R(\mathbf{x}|T)) \tag{22}$$

where *C*0 is the installation cost for system testing, *C*1 is the system test cost per unit time, *C*2 is the error removal cost per unit time during the test phase, and *C*3 is the penalty cost owing to a system failure. In addition, *x* represents the time the software was used. In addition, in the cost model equation, *R*(*x*|*T*) follows (23) [32,33].

$$R(\mathbf{x}|T) = \mathbf{e}^{-\left[m(t+\mathbf{x}) - m(t)\right]} \tag{23}$$

**Figure 5.** System cost model structure.

In this section, we propose a cost model using dataset 1 based on the proposed software reliability model and find the optimal time point between time to market and the minimum cost by changing the cost coefficients from *C*0 to *C*3.

#### *4.1. Results of the Optimal Release Time*

For the parameters of the cost model, *a*, *b*, *c*, and *h* calculated through numerical examples described in Section 3 were used. The cost coefficient of the cost model aims to find the optimal release time with the lowest cost by finding the optimal value through the changes in several values. The baseline value of the cost coefficient is as follows:

$$C\_0 = 500, \ C\_1 = 20, \ C\_2 = 50, \ C\_3 = 5000, \ x = 6$$

Here, baseline denotes to the reference value for confirming the change of the cost coefficient. The total cost value obtains as a reference value is 4888.856, and the optimal release time *T* at this time is 18.3. Table 9 changes the cost coefficient of each reference value, checks the minimum cost *C*(*T*) and optimal release time *T*∗, and then checks the changing trend to find the most optimal release time *T*∗. When *x* = 2, the smallest total cost value obtains 4886.985 at *T*∗ = 18.2. When *x* = 4, the smallest total cost value shows 4888.735 at *T*∗ = 18.3. When *x* = 6, the smallest total cost value shows 4888.856 at *T*∗ = 18.3. When *x* is 8 and 10, the smallest total cost value shows 4888.863 at *T*∗ = 18.3.

**Table 9.** Optimal release time of expected total cost according to baseline.

Here, *C*0 is the setup cost, and as the value increases, the cost, which is directly proportional, increases as well; thus, the lower the setup cost is, the lower the cost. Table 10 compares the changes when the coefficients of are 300, 500, and 700. It is found that the higher the value is, the higher the total cost value, whereas the optimal time does not change. Therefore, it appears that *C*0 does not help determine the optimal release point. However, because the setup cost for a system stabilization is required, the appropriate *C*0 cost coefficient is set to 500. Figure 6 shows a graph of the results according to the change in *C*0.

**Table 10.** Optimal release time of expected total cost according to *C*0.

**Figure 6.** Optimal release time of total cost according to *C*0.

Table 11 compares the changes when the coefficients of *C*1 are 10, 20, and 30. The results show that when *C*1 is 10, the total cost is the minimum at approximately 18.9 to 19.0, and when *C*1 is 20, the minimum value is at 18.2 to 18.3, and when it is 30, the total cost shows the minimum value at approximately 17.8 to 17.9. As the cost coefficient *C*1 increases, the optimal release time is gradually pushed back. Figure 7 shows a graph of the results according to the changes in *C*1.

**Table 11.** Optimal release time of expected total cost according to *C*1.

Table 12 compares the changes when the coefficients of *C*2 are 30, 40, 50, and 60. It can be seen that the cost coefficient *C*2 does not change from 18.2 to 18.3 at the optimal release time as the value changes. Figure 8 shows a graph of the results according to the change in *C*2.

Table 13 compares the changes when the coefficients of *C*3 are 5000, 7000, 10, 000, and 15, 000. The results show that when *C*3 is 5000, the total cost is the minimum at approximately 18.2 to 18.3; when it is 7000, it shows the minimum value at 18.5 to 18.6; when it is 10, 000, the total cost shows the minimum value at approximately 18.9 to 19.0; and when it is 15, 000, the total cost shows the minimum value at approximately 19.2 to 19.3. This indicates that the optimal release time gradually increases as the cost coefficient *C*3 increases. Figure 9 shows a graph of the results according to the changes in *C*3.

**Table 12.** Optimal release time of expected total cost according to *C*2.

**Figure 8.** Optimal release time of the total cost according to *C*2.

**Figure 9.** Optimal release time of the total cost according to *C*3.

#### *4.2. Results of Variation in Cost Model for Changes in Parameter*

In this section, we check whether the optimal release time is affected by the change in the cost model according to the change in the parameters of the proposed model. The parameters *a*, *b*, *c*, and *h* of the proposed model are set at −20%, −10%, 0%, 10%, and 20%, respectively, in 10% increments, and the coefficient of the cost model is fixed at the baseline value in Section 4.1. Thus, the minimum cost value is calculated depending on changes in the parameters, and it derives appropriate release time. In Table 14, 0% is the same as the value suggested in Table 9 by substituting the parameter estimates described in Section 3 and the coefficient values of the cost model proposed in Section 4.

**Table 14.** Optimal release time of cost according to parameter change.

From Table 14 and Figures 10–13, the value of the cost model *C*(*T*) increases as the change in parameter *a* increases, whereas the optimal release time *T*∗ decreases. As the values of parameters *b* and *h* increase, the cost model *C*(*T*) increases, and the release time *T*∗ is shown to decrease; in addition, it is found that the change in parameter *h* had a very slight effect on the optimal release time compared to parameter *b*. As the value of parameter *c* increases, the cost model *C*(*T*) and release time *T*\* increase together. Based on this, it is found that parameter *a* had a very large minimum width of the cost model compared with the changes of the other parameters, and parameter *b* had the greatest influence on determining the optimal release time.

**Figure 10.** Optimal release time of cost according to *a*.

**Figure 11.** Optimal release time of cost according to *b*.

**Figure 12.** Optimal release time of cost according to *c*.

**Figure 13.** Optimal release time of cost according to *h*.


D. Third case of partial shading

**Figure 13.** PV characteristics under partial shading: (200 W/m2, 300 W/m2, 700 W/m2, 1000 W/m2).

Table 5 presents a comparative study between the two MPPT algorithms for different types of shading distribution on the panels. The essential criteria for a judicious comparison between the two algorithms are:

As can be observed, the simulation results from Figure 10 to Figure 13 show the evolution of the power, current, voltage and duty cycle of the PV system for the four types of shading distribution we have tested on the panels. From these results, it can be observed that the two solutions ensure a good MPP tracking. The advantage of the PSO MPPT over the GWO MPPT is related to two issues: (i) the amplitude of the oscillations at the transient state and (ii) the accuracy to track the point of maximum power. A high oscillation exists for the case of GWO, which can be one the weaknesses of this algorithm. There is also a small oscillation when executing the PSO at the beginning, when the radiation form changes. However, this will not cause a problem as in the real situation, the modification of the radiation comes very slow. So, we a look a better performance in a real situation.

**Table 5.** Comparative study between PSO-MPPT and GWO-MPPT.

The application of these algorithms in real time requires the use of the high-speed processor given the large number of operations to be carried out in one second (processing and control measurement). Therefore, the time needed to converge towards the best response depends on the speed of the algorithm used and the material available (essentially the speed of the processor). In addition, the presence of high amplitude oscillations during the transient phase is a harmful phenomenon for electrical systems and can cause a variety of problems. According to the simulation results and the criteria indicated above, the PSO MPPT algorithm shows itself well for the real-time application.

We have also studied how these algorithms impact on the battery State-of-Charge (SoC). In particular, we have studied the effects on a representative lithium-ion battery characterized by the parameters in Table 6. Figure 14 shows the efficiency of PSO MPPT versus GWO MPPT in terms of storage charge in the battery under uniform irradiation. A gain of about 0.0014% SoC for 1 s corresponds to almost 5.04% of battery charge for one hour.

**Table 6.** Parameters of lithium-ion battery.

**Figure 14.** SoC using the PSO MPPT and GWO MPPT.


**3. Results**

### *3.1. Characterization of Surface Morphology, Surface Chemistry and Porous Structure*

### 3.1.1. Analysis of the Surface Morphology by SEM Imaging

Figure 2a (low magnification) shows the typical morphology of a yarn in the knitted AC fabric. Each yarn is a twisted bundle of loose carbon fibers of about 10 μm in average diameter. A higher magnification image (Figure 2b) reveals that each fiber has a ridge surface with grooves parallel to the fiber axis direction. This shape is typical of wet spun viscose fibers [35] used as precursor material in the activated carbon fabric manufacture. At an electropolymerization time of 10 min, PAni can already be discerned on the surface of ACC (Figure 2c–d). The polymer appears to be distributed over the carbon fabric in a scattered fashion and in the form of smooth and compact deposits on the surface of the fibers. These deposits are preferentially localized around the grooves in a carbon fiber. The number of such PAni deposits increases slightly with the electropolymerization time, their size seems also to grow along the fiber grooves, and some degree of roughening appears (Figure 2e–f). In all these examples, the polymeric material was only distinguished on the ACC face in contact with the stainless steel anode inside the filter-press cell, whereas no evidence of PAni was found on the opposite face. However, the surface N/C quotient (as measured by XPS, see Section 3.1.2) increases with the increasing time of treatment at both sides of the ACC. The evolution of this ratio is a diagnostic signal that the polymer grows throughout the whole fabric surface, but whenever it does as a very smooth and thin film, it may appear morphologically featureless and barely discernable by SEM at the magnification reached in Figure 2 [36].

**Figure 2.** Low- and high-magnification Scanning Electron microscopy (SEM) micrographs of (**a**), (**b**) untreated activated carbon cloth (ACC) and PAni-ACC composites synthesized after (**c**), (**d**) 20 min and (**e**), (**f**) 40 min of electropolymerization.

After 120 min of galvanostatic treatment, a remarkable increase in the amount of electrodeposited conducting polymer is observed (Figure 3). At this stage, a thick PAni layer grows on the ACC face in contact with the anode surface, fully covering most of the carbon fibers and even filling most of the void space among fiber bundles in a yarn (Figure 3a). This thick PAni coating appears brittle and easily peels o ff upon slight fabric bending. Also, some polymer material detached from the fabric during manipulation for withdrawal from the cell and during subsequent rinsing. High magnification micrographs show that the PAni layer is rather porous (Figure 3b) and can be properly described as a nanofibrous mat (see inset to Figure 3b). Localized open networks of either nanofibrous polyaniline or aggregates of short and randomly aligned nanowires can also be discerned on the face exposed to the electrolyte (Figure 3c–d). These supramolecular structures of polymeric material are unevenly distributed among the individual carbon fibers forming a yarn and their proportion to the carbon fabric is much lower than that in the opposite face of the cloth. Accordingly, the XPS N/C atomic ratio is also lower than that for the side fully coated by a thick polymer layer (see below).

**Figure 3.** Low- and high-magnification SEM micrographs of PAni-ACC composites synthesized after 120 min of electropolymerization, (**a**), (**b**) electrode side; (**c**), (**d**) electrolyte side.

### 3.1.2. Surface Chemical Composition by XPS.


**Author details**

Milica M. Vasić1 , Dušan M. Minić2 and Dragica M. Minić1 \*

1 Faculty of Physical Chemistry, University of Belgrade, Belgrade, Serbia

2 Military Technical Institute, Belgrade, Serbia

\*Address all correspondence to<EMAIL_ADDRESS>
© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

**41**

*Thermal Stability and Phase Transformations of Multicomponent Iron-Based Amorphous Alloys*

bulk glassy alloys with high saturation magnetization above 1.5 T. Materials Transactions. 2002;**43**:2350-2353

[10] Inoue A. High strength bulk amorphous alloys with low critical cooling rates. Materials Transactions.

[11] Greer AL. Confusion by design.

Crystallization and magnetic properties of Fe40Ni38B18Mo4 amorphous alloy. Journal of Non-Crystalline Solids.

materials. Progress in Materials Science.

[14] Kulik T. Nanocrystallization of metallic glasses. Journal of Non-Crystalline Solids. 2001;**287**:145-161

[15] Blagojević VA, Minić DM, Vasić M,

[16] Vasić MM, Minić DM, Blagojević VA, Žák T, Pizúrová N, David B, et al. Thermal stability and mechanism of thermally induced crystallization of Fe73.5Cu1Nb3Si15.5B7 amorphous alloy. Acta Physica Polonica, A.

[17] Blagojević VA, Vasić M, David B, Minić DM, Pizúrová N, Žák T, et al. Thermally induced crystallization of Fe73.5Cu1Nb3Si15.5B7 amorphous alloy.

[18] Vasić MM, Roupcová P, Pizúrová N, Stevanović S, Blagojević VA, Žák T, et al. Thermally induced structural

Intermetallics. 2014;**45**:53-59

Minić DM. Thermally induced structural transformations and their effect on functional properties of Fe 89.8Ni1.5Si5.2B3C0.5 amorphous alloy. Materials Chemistry and Physics.

Nature. 1993;**366**:303-304

[12] Du SW, Ramanujan RV.

[13] Gleiter H. Nanocrystalline

1995;**36**:866-875

2005;**351**:3105-3113

1989;**33**:223-315

2013;**142**:207-212

2015;**128**:657-660

*DOI: http://dx.doi.org/10.5772/intechopen.88260*

[2] Flohrer S, Herzer G. Random and uniform anisotropy in soft magnetic nanocrystalline alloys. Journal of Magnetism and Magnetic Materials.

[1] Takayama S. Amorphous structures and their formation and stability. Journal of Materials Science.

1976;**11**:164-185

**References**

2010;**322**:1511-1514

2013;**58**:131-166

[3] Suryanarayana C, Inoue A. Iron-based bulk metallic glasses. International Materials Review.

[5] Pang S, Zhang T, Asami K, Inoue A. Effects of chromium on the glass formation and corrosion behavior of bulk glassy Fe-Cr-Mo-C-B alloys. Materials Transactions.

[6] Gavrilović A, Rafailović LD, Minić DM, Wosik J, Angerer P, Minić DM. Influence of thermal treatment on structure development and mechanical properties of

amorphous Fe73.5Cu1Nb3Si15.5B7 ribbon. Journal of Alloys and Compounds.

[7] McHenry ME, Willard MA, Laughlin DE. Amorphous and nanocrystalline materials for applications as soft magnets. Progress in Materials Science.

2002;**43**:2137-2142

2011;**509S**:S119-S122

1999;**44**:291-433

2007;**22**:344-351

[8] Gu XJ, Poon SJ. Mechanical

properties of iron-based bulk metallic glasses. Journal of Materials Research.

[9] Inoue A, Shen B. Formation and soft magnetic properties of Fe-B-Si-Zr

[4] Shen B, Inoue A. Superhigh strength and good soft-magnetic properties of (Fe,Co)-B-Si-Nb bulk glassy alloys with high glass-forming ability. Applied Physics Letters. 2004;**85**:4911-4913

*Thermal Stability and Phase Transformations of Multicomponent Iron-Based Amorphous Alloys DOI: http://dx.doi.org/10.5772/intechopen.88260*


**1. Introduction to Tissue Engineering Sca**ff**olds and Bottom-Up Fabrication**

Traumas, diseases and population ageing are major reasons for damage and failure of human body tissues and organs, which require medical treatments for their restoration or replacement. Despite the intrinsic body capability of repairing small injuries given sufficient time, to date, tissue growth in large (centimeter-size) defects requires complex, expensive and patient-painful autografts, allografts or xenografts [1,2]. In the case of bone, autograft and allograft implantation produces the best clinical results, but it requires secondary surgery and has a limited supply. The main advantage of a xenograft is its abundant supply and no need for secondary surgery, but poor implantation results and problems of infection from donors are critical issues [2]. Besides, the neo-tissue generated within the interstitial spaces of these grafts is often different from the native tissue and requires large remodeling time for the complete biological and biomechanical integration with surrounding tissues. For these reasons, the

development of novel solutions for tissues and organs bioengineering is extremely demanding in the medical field.

Tissue engineering (TE), an important biomedical engineering field, aims to solve this important challenge by combining scaffolds and bioactive molecules for the artificial reconstruction of functional, three-dimensional (3D) tissues and organs [3]. Biomedical scaffolds are porous, implantable biomaterials, shaped to promptly restore the natural tissue anatomy and mechanical functions. The scaffolds must also be capable of controlling foreign body reaction and new-tissue formation by targeted presentation and delivery of key molecules, e.g., anti-inflammatory, growth factors, and proteins. Indeed, these molecules help scaffolds to reduce inflammation, recruit and direct differentiation of stem cells from surrounding tissues and ultimately, promote functional tissue integration in situ [4–6].

Scaffolds design and fabrication have evolved greatly in the past twenty years due to the large knowledge accumulated on materials design, processing and characterization of cell/scaffold interactions. In the natural tissues, cells and extracellular matrix (ECM) organize into 3D structures from sub-cellular to tissue level. Consequently, to engineer functional tissues and organs successfully, scaffolds must capture the essence of this cells/ECM organization and must provide a porous structure able to facilitate cells distribution and guide 3D tissue regeneration [7–9]. Scaffolds pore size and shape, pore wall morphology, porosity, surface area and pore interconnectivity, are probably the most important architectural parameters, as they have been shown to directly impact cells migration and colonization, new ECM biosynthesis and organization, oxygen and nutrients transport to cells, as well as metabolic wastes removal in the whole cell/scaffold construct [10–16]. The scaffold material must be selected and/or designed with a degradation and resorption rate such that scaffold strength is retained until the tissue-engineered transplant is fully remodeled by the host tissue and can assume its own structural role [17,18]. More importantly, controlling mechanical properties at cellular and sub-cellular levels is important to emulate as closely as possible the in vivo cell behavior and tissue growth [19,20]. Nevertheless, controlling the morphological and biomechanical properties of porous scaffolds is not enough for the success of scaffolds-based therapies, as there is also the need to load matricellular and soluble molecules inside scaffolds' matrix as biochemical regulators of cells behavior [21]. Indeed, it is reported that porous scaffolds releasing biochemical signals following a precise dose and time intervals to target sites stimulate cells' functions (e.g., adhesion, proliferation and migration) [22–25], promote the biosynthesis of new ECM [26] and, ultimately, guide tissue growth, morphogenesis [27] and vascularization [28–30].

Increasing scaffolds' design complexity is therefore extremely demanding and scientists face some important challenges, such as: (1) engineering of scaffold microarchitecture to mimic the ECM structure, (2) imprinting topological and biochemical patterns inside scaffolds pores to guide cells' growth and tissue morphogenesis, and (3) developing automated processes for the precise and reliable control of scaffolds' features and geometry.

Porous bioactive scaffolds can be fabricated by combining biomaterials and growth factors through different processing techniques. This review focuses the attention on modular approaches where samples are built by the "bottom-up" assembly of smaller units or "modules", each one specifically designed for distinct tasks [31–33]. Bottom up approaches have the potential to build scaffolds mimicking the complex molecular and structural microenvironment of the native ECM of every kind of tissues by the proper assembly of micro- and nano-structured modules with well-defined morphological and biochemical properties [34].

Several processing techniques are available for modules fabrication, including fluidic emulsion [35], electrofluidodynamic processes [36], and advanced computer aided manufacturing [37,38]. All of these approaches offer, nowadays, a wide library of materials, each one characterized by different composition, shape, nano- and micro-topography, and porous architecture. The assembly of individual modules, such as microparticles (μPs) or patterned layers, by packing, stacking, and printing, allows for achieving multifunctional scaffolds for tissue and organ bioengineering. As will be discussed in the next sections, cell-free or cell-laden μPs can be packed together in a mold giving rise to a sintered

matrix by contact points union [39]. Sintering can be obtained by heat or proper plasticizers, in the case of cell-free samples, and by promoting cells/cells and cells/ECM interlocking to obtain hybrid structures [34]. μPs can also be used as cell and/or drug carriers to be loaded inside hydrogel pastes for printing more ordered and complex structures [40]. Layer-by-layer scaffolds' fabrication uses medical imaging combined with computer-aided design (CAD) and automated scaffolds' manufacturing processes to produce customized cell-free or cell-laden scaffolds characterized by a highly controlled structure and reliable properties. This broad category of fabrication techniques includes discontinuous processes, based on the assembly (stacking/sintering) of layered structures obtained by mold replication methods [37]. Alternatively, continuous processes, named additive manufacturing (AM), are used to construct scaffolds by joining/printing biomaterials and cells [38].

The focus of this review is to describe and discuss the advancement of current bottom-up techniques for creating adaptive scaffolds built from μPs or prepared using layer-by-layer assembly techniques, focusing on cell-free and cell-laden strategies. The advantages of each approach to controlling scaffolds' microstructural properties and drug release capability will be discussed, outlining some of the most promising results achieved for regenerating different tissues and organs, such as bone and cartilage, blood vessels, and derma.

#### **2. Microparticles (**μ**Ps) as Building Blocks for Modular Tissue Engineering Sca**ff**olds**

Nowadays, μPs are essential elements of clinical and regenerative medicine applications such as cell culture μ-scaffolds for in vivo cell delivery and in vitro tissue biofabrication, and drug delivery carriers for biosensing and diagnostic purposes [41–43]. Both synthetic and natural polymeric biomaterials have been investigated for μPs design and engineering. Indeed, chemical and physical polymers properties can be easily manipulated to design and fabricate μPs with tailored morphological properties, size-shape distribution, and degradation rate. Furthermore, scaffolds prepared from synthetic polymeric μPs offer better chemical stability and mechanical properties than those prepared by using natural polymers, especially for load bearing applications. Common examples of the main synthetic polymers for μPs' fabrication which can be mentioned are PCL, poly-lactic acid (PLA), polylactic-co-glycolic acid (PLGA), poly-ethylene glycol (PEG), and their composites with ceramic fillers like calcium phosphate, alumina, and hydroxyapatite [4,44–48]. Natural polymers, conversely, have a chemical composition and structure resembling that of native biological tissues. This aspect is extremely fascinating for μPs' fabrication as it makes it possible to achieve materials faithfully replicating the ECM microenvironment functions. Natural polymeric μPs can be classified into two main groups: protein-based, such as silk, collagen, and fibrin, and polysaccharide-based, like agarose, chitosan, and hyaluronic acid. These kinds of μPs have advantages like excellent biocompatibility, immunogenicity, and degradation rate that can be tuned by varying μPs' materials composition, molecular weight, and crosslinking degree. In the next section, an overview will be provided about μPs' fabrication, highlighting the most advanced techniques to control μPs' composition, structure, and size-shape distribution. Furthermore, the use of μPs as building blocks for cell-free and cell-laden scaffolds fabrication and their use as μ-scaffolds for in vitro cell culture and tissue production will be described in detail.