3. Mathematical model and numerical analysis of reinforced beam deformation

Three-point bending flexural test has been one of the standard techniques to determine physical and mechanical characteristics of materials. Figure 1 shows a scheme of physical model of threepoint bending of a beam with the rectangular cross section b � 2h, and the span l between the supports. The left edge of the beam is hinged, while the right one is supported freely. The force P is applied to the center of the beam. The model neglects the shape of the supports and assumes the occurring load P and support reactions RA and RB to be concentrated. In addition, the model neglects the possible heterogeneity of the deformations in the direction normal both to the longitudinal direction and to the load direction.

Figure 1. Three-point bending of a rectangular-sectioned beam.

In this case, the beam's upper part undergoes compression strain in the longitudinal direction, bottom part—tension strain. VSE-1212 polymer matrix and VKU-28 (T-800 carbon yarn plus VSE-1212 epoxy matrix) structured CFRP react differently to tension and compression. VKU-28 has been one of the most promising types of CFRPs that is going to be used in the latest generations of aircrafts. The effect of accounting for this factor on the computational results is essential. Further, these results are compared to acquired ones.

Estimations for G obtained using variational method are also obtained, and it is shown that

Gf þ ωmGm

Gm (4)

, A1212 ¼ G: (5)

Gm þ ωmGf

gives more accurate approximation than (Eq. (2)) does. Hereinafter share moduli of matrix and

Components of effective stiffness tensor for unidirectionally reinforced layer in case of state of

, A1122 ¼ ν21E1

Unwritten expressions can be obtained using symmetry rule or vanish. Hereinafter, we assume

The coefficients in the relations (Eq. (1)) for example are defined by the formulas given in [1, 17, 18].

Three-point bending flexural test has been one of the standard techniques to determine physical and mechanical characteristics of materials. Figure 1 shows a scheme of physical model of threepoint bending of a beam with the rectangular cross section b � 2h, and the span l between the supports. The left edge of the beam is hinged, while the right one is supported freely. The force P is applied to the center of the beam. The model neglects the shape of the supports and assumes the occurring load P and support reactions RA and RB to be concentrated. In addition, the model neglects the possible heterogeneity of the deformations in the direction normal both to the

3. Mathematical model and numerical analysis of reinforced beam

, Gf ¼ Ef

1 � ν12ν21

2 1 þ νf :

G ¼ 1 þ ωf

Gm ¼ Em

1 � ν12ν21

1 þ ωf

2 1ð Þ þ νm

lower boundary

16 Optimum Composite Structures

fibers are

plane stress have the following form:

α, β ¼ 1, 2 and α 6¼ β.

deformation

Aαααα ¼ Eα

longitudinal direction and to the load direction.

Figure 1. Three-point bending of a rectangular-sectioned beam.

Due to very low deformation rates, the classical theory of beam bending can be regarded as satisfactory for description of the equilibrium state. To this end, it is convenient to consider the beam's median surface as a reference one.

The beam's stress-strain state is characterized by the following values determined on the reference surface: the shear force Q xð Þ, the bending moment M xð Þ, the longitudinal force N xð Þ, and by the longitudinal displacement and bend (u xð Þ, w xð Þ respectively). The corresponding equilibrium equations are written as follows:

$$
\frac{dN}{d\mathbf{x}} = \mathbf{0}, \qquad \frac{d\mathbf{Q}}{d\mathbf{x}} = \mathbf{0}, \qquad \frac{dM}{d\mathbf{x}} = \mathbf{Q}.\tag{6}
$$

The reactions RA and RB can be determined by considering force equilibrium RA ¼ RB ¼ P=2. The bending moments at the support points are equal to zero: MA ¼ MB ¼ 0. The solution of the equation system (Eq. (6)) can be expressed as follows:

$$N=0, \quad Q(\mathbf{x}) = \begin{cases} P/2, & 0 \le \mathbf{x} \le \mathbf{l}/2, \\ -P/2, & \mathbf{l}/2 \le \mathbf{x} \le \mathbf{l}, \end{cases} \qquad M(\mathbf{x}) = \begin{cases} P\mathbf{x}/2, & 0 \le \mathbf{x} \le \mathbf{l}/2, \\ -P(\mathbf{x}-\mathbf{l})/2, & \mathbf{l}/2 \le \mathbf{x} \le \mathbf{l}. \end{cases} \tag{7}$$

Strain distribution for the beam's thickness can be obtained from the Kirchhoff-Love kinematic hypotheses:

$$
\varepsilon(\mathbf{x}, z) = e(\mathbf{x}) + z\kappa(\mathbf{x}) \tag{8}
$$

$$
\varepsilon(\mathbf{x}) = \frac{d\mathbf{u}}{d\mathbf{x}'} \qquad \qquad \kappa(\mathbf{x}) = -\frac{d^2 w}{d\mathbf{x}^2} \,. \tag{9}
$$

where εð Þ x; z is the strain in the beam; e xð Þ is the median surface strain; and κð Þx denotes changes in the median surface curvature. As mentioned earlier, the beam undergoes tension and compression strain, whose interface will be marked as z1. In this case, for the section area �h ≤ z ≤ z1, the strain will be negative, and for z1 ≤ z ≤ h positive. At the interface of these two states, the strains ε vanish, so the interface itself is determined as follows:

$$z\_1 = -\frac{e}{\kappa}, \qquad -h \le z\_1 \le h. \tag{10}$$

The constitutive equation can be expressed as:

$$
\sigma^{\pm}(\mathbf{x}, \mathbf{z}) = f\_i^{\pm}(\mathbf{z}), \tag{11}
$$

where the superscript "+" refers to the areas with positive strains and "–" – to the area with negative ones; fð Þε denotes the approximation selected for the stress-strain curve (a linear function, a polynomial, or a combination of linear and power-law functions).

The longitudinal force N and the bending moment M in the beam cross section are determined by the equations:

$$\begin{aligned} N &= b \left( \int\_{-h}^{z\_1} \sigma^- dz + \int\_{z\_1}^h \sigma^+ dz \right), \\ M &= b \left( \int\_{-h}^{z\_1} \sigma^- z dz + \int\_{z\_1}^h \sigma^+ z dz \right). \end{aligned} \tag{12}$$

As the initial approximation at small values of the load P, the solutions obtained for the linear constitutive equations (Eq. (15)) were used. Since the computation is performed with a relatively small increment of P, in case of big values of P, one can use the computation results

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19

Having determined the change of median surface curvature from the equations (Eq. (13)) and

one can write down a differential equation to determine the beam bend. For that purpose, the

w xð Þ¼ w1ð Þx , at x ∈½ Þ 0; l=2 ,

Using the equation (Eq. (9)) and the beam's fixing conditions, a system of equations can be

w2 dx2 ¼ �κ2, w1ð Þ¼ 0 w2ðÞ¼ l 0, w1ð Þ¼ l=2 w2ð Þ l=2 ,

dx :

The solution of these equations can be obtained using the methods of solving boundary-value problems for systems of ordinary differential equations. For that purpose, the modified collo-

Numerical analysis of deformation processes in VSE-1212 polymer matrix and VKU-28 structured carbon fiber is based on approximation of stress-strain curves and the three-point

2. specimen 2—VKU-28 structured carbon fiber (the specimen was cut out along the rein-

3. specimen 3—VKU-28 structured carbon fiber (the specimen was cut out perpendicular to

In Figure 2, one can see the simulation results for beam three-point bending, obtained through different approaches to approximation of the constitutive equations, and their comparison

Applying the linear dependencies to tension and compression has not resulted in adequate approximation even for 30% of curve. Using more complex than quadratic approximation laws

Further, three different specimens with the geometrical sizes l � 2h � b are considered:

κ1ð Þx , at x∈ ½ Þ 0; l=2 , κ2ð Þx , at x∈ ½ � l=2; l ,

w2ð Þx , at x ∈½ � l=2; l :

acquired at a previous step as the initial approximation for current step.

κð Þ¼ x

d2 w1

cation and least-residuals method [19–21] were applied.

forcement), 90 � 3:45 � 9:85 mm;

with the experimental data.

the reinforcement), 90 � 3:40 � 9:95 mm.

dw1ð Þ l=2

1. specimen 1—VSE-1212 polymer matrix, 75 � 4:78 � 10:05 mm,

bend function is expressed as follows:

dx2 ¼ �κ1, d2

dx ¼ dw2ð Þ l=2

(Eq. (14))

derived:

bending model.

Having substituted (Eq. (12)) with the relations (Eq. (8)), (Eq. (10)), (Eq. (11)) into and integrated it over the beam thickness, one obtains a system of equations to determine κ and e:

at 0 ≤ x ≤ l=2

$$\begin{cases} N(\kappa, e, \mathbf{x}) = 0, \\ M(\kappa, e, \mathbf{x}) = P\mathbf{x}/2, \end{cases} \tag{13}$$

at l=2 < x ≤ l

$$\begin{cases} N(\mathbf{x}, e, \mathbf{x}) = 0, \\ M(\mathbf{x}, e, \mathbf{x}) = -P(\mathbf{x} - \mathbf{l})/2. \end{cases} \tag{14}$$

The system of equations (Eq. (13)) and (Eq. (14)) in general case is nonlinear, but in the case of piecewise linear constitutive equations which take into account different strength and stiffness behavior in tension and compression expressed as follows:

$$
\sigma^{\pm}(\mathbf{x}, \mathbf{z}) = \mathbf{E}^{\pm}\mathbf{e},\tag{15}
$$

it can be solved analytically. In the nonlinear case, the Newton method is applied to solve the equations (Eq. (13)) and (Eq. (14)), and then, the linearized system

$$\begin{split} &N(\varepsilon\_{0},\kappa\_{0}) + \frac{\partial N(\varepsilon\_{0},\kappa\_{0})}{\partial \varepsilon}(\varepsilon-\varepsilon\_{0}) + \frac{\partial N(\varepsilon\_{0},\kappa\_{0})}{\partial \kappa}(\kappa-\kappa\_{0}) = 0, \\ &M(\varepsilon\_{0},\kappa\_{0}) + \frac{\partial M(\varepsilon\_{0},\kappa\_{0})}{\partial \varepsilon} \left(\varepsilon-\varepsilon\_{0}\right) + \frac{\partial M(\varepsilon\_{0},\kappa\_{0})}{\partial \kappa}(\kappa-\kappa\_{0}) = M(\mathbf{x}). \end{split}$$

can be solved for unknown values

$$\kappa = F(\varepsilon\_0, \kappa\_0, M(\mathbf{x})), \quad \varepsilon = G(\varepsilon\_0, \kappa\_0), \tag{16}$$

where ε0 and κ0 are the initial approximations, and M xð Þ is determined from (Eq. (7)).

As the initial approximation at small values of the load P, the solutions obtained for the linear constitutive equations (Eq. (15)) were used. Since the computation is performed with a relatively small increment of P, in case of big values of P, one can use the computation results acquired at a previous step as the initial approximation for current step.

where the superscript "+" refers to the areas with positive strains and "–" – to the area with negative ones; fð Þε denotes the approximation selected for the stress-strain curve (a linear function, a polynomial, or a combination of linear and power-law functions).

The longitudinal force N and the bending moment M in the beam cross section are determined

σ�dz þ

σ�zdz þ

Having substituted (Eq. (12)) with the relations (Eq. (8)), (Eq. (10)), (Eq. (11)) into and integrated it over the beam thickness, one obtains a system of equations to determine κ

> Nð Þ¼ κ;e; x 0, Mð Þ¼ κ;e; x Px=2,

Mð Þ¼� κ;e; x P xð Þ � l =2:

The system of equations (Eq. (13)) and (Eq. (14)) in general case is nonlinear, but in the case of piecewise linear constitutive equations which take into account different strength

it can be solved analytically. In the nonlinear case, the Newton method is applied to solve the

∂Nð Þ ε0; κ0

∂Mð Þ ε0; κ0

Nð Þ¼ κ;e; x 0,

ð h

σþdz

σþzdz

1 A,

> 1 A:

σ�ð Þ¼ x; z E�ε, (15)

∂κ ð Þ¼ κ � κ0 0,

κ ¼ Fð Þ ε0; κ0; M xð Þ , ε ¼ Gð Þ ε0; κ0 , (16)

∂κ ð Þ¼ κ � κ0 M xð Þ

(12)

(13)

(14)

z1

ð h

z1

z ð1

0 @

�h

z ð1

0 @

�h

N ¼ b

M ¼ b

�

and stiffness behavior in tension and compression expressed as follows:

∂ε ð Þþ ε � ε0

∂ε ð Þþ ε � ε0

where ε0 and κ0 are the initial approximations, and M xð Þ is determined from (Eq. (7)).

�

equations (Eq. (13)) and (Eq. (14)), and then, the linearized system

∂Nð Þ ε0; κ0

∂Mð Þ ε0; κ0

Nð Þþ ε0; κ0

Mð Þþ ε0; κ0

can be solved for unknown values

by the equations:

18 Optimum Composite Structures

and e:

at 0 ≤ x ≤ l=2

at l=2 < x ≤ l

Having determined the change of median surface curvature from the equations (Eq. (13)) and (Eq. (14))

$$\kappa(\mathbf{x}) = \begin{cases} \kappa\_1(\mathbf{x}), & \text{at } \mathbf{x} \in [0, l/2), \\ \kappa\_2(\mathbf{x}), & \text{at } \mathbf{x} \in [l/2, l]. \end{cases}$$

one can write down a differential equation to determine the beam bend. For that purpose, the bend function is expressed as follows:

$$w(\mathbf{x}) = \begin{cases} w\_1(\mathbf{x}), & \text{at } \mathbf{x} \in [0, l/2), \\ w\_2(\mathbf{x}), & \text{at } \mathbf{x} \in [l/2, l]. \end{cases}$$

Using the equation (Eq. (9)) and the beam's fixing conditions, a system of equations can be derived:

$$\begin{aligned} \frac{d^2 w\_1}{d\mathbf{x}^2} &= -\kappa\_1, \quad \frac{d^2 w\_2}{d\mathbf{x}^2} = -\kappa\_2, \\ w\_1(0) &= w\_2(l) = 0, \quad w\_1(l/2) = w\_2(l/2), \\ \frac{dw\_1(l/2)}{d\mathbf{x}} &= \frac{dw\_2(l/2)}{d\mathbf{x}}. \end{aligned}$$

The solution of these equations can be obtained using the methods of solving boundary-value problems for systems of ordinary differential equations. For that purpose, the modified collocation and least-residuals method [19–21] were applied.

Numerical analysis of deformation processes in VSE-1212 polymer matrix and VKU-28 structured carbon fiber is based on approximation of stress-strain curves and the three-point bending model.

Further, three different specimens with the geometrical sizes l � 2h � b are considered:

In Figure 2, one can see the simulation results for beam three-point bending, obtained through different approaches to approximation of the constitutive equations, and their comparison with the experimental data.

Applying the linear dependencies to tension and compression has not resulted in adequate approximation even for 30% of curve. Using more complex than quadratic approximation laws

the approximations have shown the results close to experiment. At the same time for the test with the maximum load, the best option has still been application of quadratic approximations. Taking different strength and stiffness behavior in tension and compression into account has an essential effect. As it was demonstrated earlier, the tension tests of VKU-28 specimens produced nonlinear stress-strain curves, while the difference of characteristics between tension and compression reached 5–7% for the longitudinal reinforcements and 12–15%—for the

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21

For the polymer matrix this difference exceeded 15% (see Tables 3 and 4).

Approximation type Approximation coefficients MSD a1 � 10�9 a2 � 10�9 a3

A1 160.8 1.4e – 2 A2 144.9 1.44e + 3 5.7e – 4 A3 144.0 1.66e + 3 –1.14e + 13 4.3e – 4 A4 143.0 8.87e + 2 1.87 4.1e – 4

A1 155.4 5.8e – 3 A2 160.2 –3.33e + 3 3.4e – 3 A3 155.9 4.31e + 3 �3.00e + 15 2.9e – 3 A4 157.7 �5.81e + 8 3.98 3.0e – 3

Approximation type Approximation coefficients MSD a1 � 10�9 a2 � 10�9 a3

A1 7.37 1.1e – 2 A2 7.89 �9.22e + 1 6.4e – 4 A3 7.87 �8.97e + 1 2.54e + 11 4.3e – 4 A4 7.82 �2.23e + 2 2.20 4.8e – 4

A1 8.90 6.7e – 3 A2 9.21 �1.20e + 2 4.1e – 3 A3 8.96 1.16e + 2 �5.09e + 13 3.7e – 3 A4 9.04 �5.16e + 6 3.95 3.7e – 3

Table 2. Approximation coefficients for stress-strain curves of carbon fiber specimens reinforced in transverse direction

Table 1. Approximation coefficients for stress-strain curves of VKU-28 carbon fiber specimens reinforced in longitudinal

transverse ones (see Tables 1 and 2).

Tension of VKU-28 CFRP, ε∈½ � 0; 0:015

Compression of VKU-28 CFRP, ε∈½ � 0; 0:0018

direction and mean square deviation (MSD) of f xð Þ.

Tension of VKU-28 CFRP, ε∈½ � 0; 0:0076

Compression of VKU-28 CFRP, ε∈½ � 0; 0:0034

and mean square deviation (MSD) of f xð Þ.

Figure 2. Experimental (solid curves) and dependencies of beam-deflection and load obtained in simulation: linear approximation (1); quadratic approximation by a polynomial of the second degree (2); cubic approximation (3); linear and power-law approximation (4); a–c are specimens 1 3 respectively; d—the solution to a three-point bending problem without account for the different strength and stiffness behavior in tension (curve 1) and compression (curve 2). The solid line shows the results of mechanical tests.

at first led to a significant deviation from the experimental curve and then to divergence of the Newton method iteration process. This is explained by the fact that in tension tests, due to specimens' fragility, the strain range for the polymer matrix specimens was limited to 2%, while in the bending tests, the strains in tension zone reached 4–5%.

Thus, to solve the bending problem, the tension curve was extrapolated into the domain of high strains. The extrapolations obtained using a polynomial of the third degree, and by linear and power-law function reached the maximum too quickly and then started to decrease, which is against the physics behind the deformation process. A similar effect was observed when calculating the bending of the carbon-fiber specimens cut out along direction of reinforcement filler.

The calculations using quadratic approximation and extrapolation of tension curves and approximation of compression curves within a short (up to 6%) segment have turned out to be best for qualitative and quantitative description of the nonlinear character of VSE-1212 polymer matrix bending. In the case of the specimen cut out perpendicular to direction of its reinforcement, all the approximations have shown the results close to experiment. At the same time for the test with the maximum load, the best option has still been application of quadratic approximations.

Taking different strength and stiffness behavior in tension and compression into account has an essential effect. As it was demonstrated earlier, the tension tests of VKU-28 specimens produced nonlinear stress-strain curves, while the difference of characteristics between tension and compression reached 5–7% for the longitudinal reinforcements and 12–15%—for the transverse ones (see Tables 1 and 2).

For the polymer matrix this difference exceeded 15% (see Tables 3 and 4).

Table 1. Approximation coefficients for stress-strain curves of VKU-28 carbon fiber specimens reinforced in longitudinal direction and mean square deviation (MSD) of f xð Þ.

at first led to a significant deviation from the experimental curve and then to divergence of the Newton method iteration process. This is explained by the fact that in tension tests, due to specimens' fragility, the strain range for the polymer matrix specimens was limited to 2%,

Figure 2. Experimental (solid curves) and dependencies of beam-deflection and load obtained in simulation: linear approximation (1); quadratic approximation by a polynomial of the second degree (2); cubic approximation (3); linear and power-law approximation (4); a–c are specimens 1 3 respectively; d—the solution to a three-point bending problem without account for the different strength and stiffness behavior in tension (curve 1) and compression (curve 2). The solid

Thus, to solve the bending problem, the tension curve was extrapolated into the domain of high strains. The extrapolations obtained using a polynomial of the third degree, and by linear and power-law function reached the maximum too quickly and then started to decrease, which is against the physics behind the deformation process. A similar effect was observed when calculating the bending of the carbon-fiber specimens cut out along direction

The calculations using quadratic approximation and extrapolation of tension curves and approximation of compression curves within a short (up to 6%) segment have turned out to be best for qualitative and quantitative description of the nonlinear character of VSE-1212 polymer matrix bending. In the case of the specimen cut out perpendicular to direction of its reinforcement, all

while in the bending tests, the strains in tension zone reached 4–5%.

of reinforcement filler.

line shows the results of mechanical tests.

20 Optimum Composite Structures

Table 2. Approximation coefficients for stress-strain curves of carbon fiber specimens reinforced in transverse direction and mean square deviation (MSD) of f xð Þ.

Tables 1–4 show approximation results for the above-presented stress-strain curves by differ-

Mathematical Modeling and Numerical Optimization of Composite Structures

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23

However, if bending tests have been performed to determine an elasticity module of CFRPs, different strength and stiffness behavior in tension and compression is compensated and one

It is useful to consider the effect of the way for determining and setting of the mechanical characteristics on modeling of three-point bending of the carbon-fiber beam cutout perpendicular to its reinforcements. Figure 2d shows the solutions obtained while using a linear approximation of the constitutive equations with equal elastic moduli for tension and compression: for curve 1 the modulus was obtained from tension experiments, for curve 2—from compres-

As one can see the calculated linear results without account for the different strength and stiffness behavior in tension and compression have differed from the results of mechanical

Most of the real CFRP structures under day-to-day service conditions bear complex loads that result in formation of tension, compression and bending zones as well as their combinations in the structures. Applying the traditional methods for determination of material characteristics in combination with linear deformation models (in particular those that do not account for the different strength and stiffness behavior in tension and compression) for calculation of such structures, one risks to distort the deformation and stress pattern significantly, which, in its turn, results in either underestimation or overestimation of the structure's strength and rigidity. Keeping in mind that carbon fibers are used for manufacturing of high-duty structures, their computation demands different strength and stiffness behavior in tension and compression to be taken into account.

Composite overwrapped pressure vessels (COPV) are used in the rocket and spacecraft making industry due to their high strength and lightweight. Consisting of a thin, nonstructural liner wrapped with a structural fiber composite COPV are produced to hold the inner pressure of tens and hundreds atmospheres. COPV have been one of the most actual and perspective

Designing of a highly reliable and efficient COPV requires a technology for analysis of its deformation behavior and strength assessment. This technology should allow one to obtain

ent functions at different intervals:

obtains some averaged characteristic.

tests (the solid curve) by more than 15%.

sion ones (see Table 2).

1. by the linear approximation σ ¼ a1ε (A1),

2. by the polynomial of the second degree σ ¼ a1ε þ a2ε2 (A2),

3. by the polynomial of the third degree σ ¼ a1ε þ a2ε2 þ a3ε3 (A3),

4. Numerical analysis and design of pressure vessels

directions of research, supported especially by NASA [22, 23].

4. by a combination of linear and power-law functions σ ¼ a1ε þ a2εa3 (A4).

Table 3. Approximation coefficients for tension curves of VSE-1212 polymer matrix, ε∈½ � 0; 0:018 and mean square deviation (MSD) of f xð Þ.

Table 4. Approximation coefficients for compression curves of VSE-1212 polymer matrix and mean square deviation (MSD) of f xð Þ.

Tables 1–4 show approximation results for the above-presented stress-strain curves by different functions at different intervals:

1. by the linear approximation σ ¼ a1ε (A1),

Approximation type Approximation coefficients MSD a1 � 10�9 a2 � 10�9 a3

A1 3.30 — — 2.9e � 2 A2 3.90 �4.38e + 1 — 1.5e � 3 A3 3.83 �3.17e + 1 �4.94e + 11 6.7e � 4 A4 3.80 �1.05e + 2 2.25 6.6e � 4

A1 3.33 2.7e � 2 A2 3.89 �4.02e + 1 1.8e � 3 A3 3.80 �2.48e + 1 �6.30e + 2 4.0e � 4 A4 3.77 �1.40e + 2 2.35 2.7e � 4

Table 3. Approximation coefficients for tension curves of VSE-1212 polymer matrix, ε∈½ � 0; 0:018 and mean square

Approximation type Approximation coefficients MSD

A1 0.77 2.3e � 1 A2 1.60 �3.97 8.2e � 2 A3 2.36 �1.29e + 1 2.37e + 10 1.4e � 2 A4 �5.71 5.49 0.90 3.8e � 2

A1 0.69 3.5e � 1 A2 1.69 �5.22 1.4e � 1 A3 2.71 �1.84e + 1 3.84e + 1 3.6e � 2 A4 �2.07 1.72 0.72 4.8e � 2

A1 2.10 7.2e � 2 A2 3.05 �2.12e + 1 4.2e � 3 A3 3.18 �2.85e + 1 9.13e + 1 1.1e � 3 A4 3.31 �1.24e + 1 1.75 1.9e � 3

Table 4. Approximation coefficients for compression curves of VSE-1212 polymer matrix and mean square deviation

Compression of VSE-1212 polymer matrix ε∈½ � 0; 0:06 (Variable cross section, shortened test area)

a1 � 10�9 a2 � 10�9 a3

Strain of VSE-1212 polymer matrix (Constant cross section) ε∈½ � 0; 0:018

Strain of VSE-1212 polymer matrix (Variable cross section)ε∈½ � 0; 0:018

Compression of VSE-1212 polymer matrix (Constant cross section), ε∈½ � 0; 0:28

Compression of VSE-1212 polymer matrix (Variable cross section), ε∈½ � 0; 0:28

deviation (MSD) of f xð Þ.

22 Optimum Composite Structures

(MSD) of f xð Þ.

However, if bending tests have been performed to determine an elasticity module of CFRPs, different strength and stiffness behavior in tension and compression is compensated and one obtains some averaged characteristic.

It is useful to consider the effect of the way for determining and setting of the mechanical characteristics on modeling of three-point bending of the carbon-fiber beam cutout perpendicular to its reinforcements. Figure 2d shows the solutions obtained while using a linear approximation of the constitutive equations with equal elastic moduli for tension and compression: for curve 1 the modulus was obtained from tension experiments, for curve 2—from compression ones (see Table 2).

As one can see the calculated linear results without account for the different strength and stiffness behavior in tension and compression have differed from the results of mechanical tests (the solid curve) by more than 15%.

Most of the real CFRP structures under day-to-day service conditions bear complex loads that result in formation of tension, compression and bending zones as well as their combinations in the structures. Applying the traditional methods for determination of material characteristics in combination with linear deformation models (in particular those that do not account for the different strength and stiffness behavior in tension and compression) for calculation of such structures, one risks to distort the deformation and stress pattern significantly, which, in its turn, results in either underestimation or overestimation of the structure's strength and rigidity. Keeping in mind that carbon fibers are used for manufacturing of high-duty structures, their computation demands different strength and stiffness behavior in tension and compression to be taken into account.


**Characterization of Enzymes Associated with Degradation of Insoluble Fiber of Soybean Curd Residue by** *Bacillus subtilis*

Makoto Shoda and Shinji Mizumoto

*Chemical Resources Laboratory, Tokyo Institute of Technology, Nagatsuta, Midori-ku, Yokohama, Japan*


**3. Experimental**

#### *3.1. Materials and Instrumentation*

EAF dust samples were kindly supplied by local smelters around Jakarta and Banten Province, Indonesia. A sieving test showed that more than 90% of dust passed through a 270 mesh (53 μm), and this size fraction was used in further leaching tests. All leaching experiment and characterization studies were performed in the Research Unit for Mineral Technology—Indonesian Institute of Sciences, Bandar Lampung, Indonesia, or otherwise stated.

XRF characterization (Panalytical X'Pert 3 Powder with Omnian Standard) was carried out to determine the major chemical components in EAF dust (Table 2), and shows the most dominant constituents to be zinc and aluminum. The mineralogical phases were determined using X-ray powder diffraction (Panalytical, Expert3 Powder). Quantitative determination of metal contents in EAF dust (Table 3) and pregnant leach solution after leaching for recovery and separation factor calculation were carried out using atomic absorption spectrophotometry (AAS, Shimadzu AA7000, Japan) and Inductively Coupled Plasma-Optical Emission Spectrometry (ICP-OES, Analytik Jena, Plasma Quant 9000 Elite, Germany). SEM-EDS characterization to investigate the change of morphology and elemental distribution on the surface of EAF dust grain before and after leaching was performed using a SEM (Hitachi SUM 3500, Japan) at the Research Unit for Natural Product Technology—Indonesian Institute of Sciences, Yogyakarta, Indonesia. The advanced mineral identification and characterization system (AMICS) analysis to determine and map the mineral phases was carried out by Eagle Engineering, Butte, Montana, USA.

Chemicals such as sodium hydroxide, sulfuric acid, nitric acid, hydrochloric acid, and standard solutions were obtained from Merck, Darmstadt all in analytical grade, while monosodium glutamate was produced by PT Ajinomoto Indonesia (99% purity) and used as received. The solution pH was adjusted using dilute sulfuric acid or sodium hydroxide and monitored using a pH meter (Oakton 45, Vernon Hills, IL, USA), while deionized water (MilliQ) was used throughout the experiment.

**Table 3.** Chemical composition of important base metals in EAF dust (after aqua regia digestion and AAS/ICP-OES determination).

#### *3.2. Leaching Procedure*

Leaching studies were carried out using a batch method. In general, 1 g of EAF dust was mixed with 20 mL lixiviant in 250 mL sealed flask. The mixture was homogenized using an orbital shaker at 200 rpm. After the leaching was completed, the supernatant solution was separated using centrifugation and filtration (Whatman 42). The metal concentration was determined using AAS or ICP-OES. Metal recovery (*R*) was calculated using Equation (1). To evaluate the selectivity of the leaching scheme, a separation factor (SF) between Zn or Cu and other metals was evaluated using Equation (2). All leaching data were obtained in duplicates.

$$R = \frac{C\_E \times V}{C\_o \times m} \times 100\% \tag{1}$$

where:

*CE* Zn or Cu concentration in supernatant solution (mg/L) *Co* Zn or Cu content in EAF dust (mg/g) *m* mass of EAF dust used in leaching (g) *V* leaching agent volume (L)

$$SF = \frac{\text{C}\_{\text{Z}}}{\text{C}\_{\text{M}}} \tag{2}$$

where:

*CZ*, Zn or Cu concentration in pregnant leach solution (mg/L). *CM*, other metal concentration in pregnant leach solution (mg/L).

#### **4. Results and Discussion**

#### *4.1. Characterization*

EAF dust characterization using XRD showed the mineral phase composition was dominated by zinc oxide (ZnO). XRD of the dust residue obtained after leaching (leaching condition pH 9, MSG concentration 1 M, pulp density 50 g/L and 12-hour homogenization) revealed the phases were dominated by spinel (Mg-Al oxide), calcium aluminum oxide and zinc oxide phases (Figure 2). These elements such as Al, Mg, Ca and Fe were found to be relatively retained in the solid phase during the leaching process. The XRD characterization results are supported by AMICS analysis results (Supplementary file: Tables S1 and S2 and Figure S2), which revealed the major Zn phase to be ZnO and gahnite (ZnAl2O4) in original material.

**Figure 2.** Phases present in EAF dust (**a**) before and (**b**) after leaching.

The change in elemental distribution of EAF dust before and after leaching was also explored by SEM-EDS characterization (Figure 3). Surface morphology of EAF dust changed from porous and grainy to relatively smooth. Intensity decrease of Zn and Cu peaks on the EDS profile after leaching confirmed the efficacy of MSG as lixiviant in EAF dust leaching. Qualitative mapping on the surface reveals that elements such as Al, Fe and Ca were relatively retained and even enriched after leaching (Table 4), indicating the selective nature of the leaching using MSG.

**Figure 3.** Morphology (**a**) before and (**b**) after leaching, including EDS intensities of elements based on mapping on the respective field (**c**) before and (**d**) after leaching.

**Table 4.** Qualitative mapping of elements by EDS on grain surface before and after leaching.

#### *4.2. E*ff*ect of pH*

The effect of pH on the Zn and Cu leaching efficiency and selective recovery was studied between 6 and 11. The constant variables included MSG concentration 1 M, pulp density 50 g/L and an agitation time of 12 h at room temperature. pH ranges between 6–11 were chosen according to the modeling in Section 2 in order to suppress the solubility of other elements (Mg, Ca, Al and Fe). The leaching efficiency and leaching selectivity are shown in Figures 4 and 5, respectively.

Based on Figure 4, the optimum pH to recover Zn and Cu are 9. Low recovery at lower pH was due to the weaker or repulsive interaction between glutamate and Zn or Cu since the glutamate species was dominated as a protonated species, e.g., H3Glu+, HGlu−. Lower recovery at pH higher than 9 was probably due to the hydrolysis of Zn and Cu, which was lower compared to the modeling results using Visual Minteq (pH > 12 for Zn and pH > 13 for Cu). In the case of other elements, all but Al extraction decreased as pH became alkaline. Increasing recovery of Al in alkaline conditions (0.5% at pH 11) was due to the amphoteric characteristic of Al, which is soluble in excess of alkali as Al(OH)4 −.

Increasing Al recovery at higher pH and decreasing Zn or Cu contributed to the sharp decrease of the selectivity factor of Zn or Cu toward Al (Figure 5). The figure also shows the optimum pH to separate Zn and Cu are 9 and 10, respectively. The highest separation factor is obtained toward iron, which could reach an order of 104 (with Zn) and 400 (with Cu). Lower separation factors were obtained between Zn or Cu and Mg and Ca (reaching an order of 700).

**Figure 4.** The recovery of (**a**) Zn, Cu, Mg and Ca and (**b**) Fe and Al as function of pH.

**Figure 5.** Separation factor of (**a**) Zn: [Zn]/[Fe] (squares), [Zn]/[Al] (diamonds), [Zn]/[Mg] (circles), [Zn]/[Ca] (triangles) and (**b**) Cu: [Cu]/[Fe] (squares), [Cu]/[Al] (diamonds), [Cu]/[Mg] (circles), [Cu]/[Ca] (triangles) in pregnant leach solution as function of pH.

#### *4.3. E*ff*ect of MSG Concentration*

The effects of MSG concentration to metal recovery and Zn and Cu selectivity over other elements were studied between 0.1 M and 2 M, with constant variables pH 9, pulp density 50 g/L and 12 h leaching time at room temperature. The results in Figure 6 show that MSG concentration has positive effects on metal recovery. Based on modeling using Visual Minteq (Supplementary file: Figure S3), the minimum MSG concentration to completely solubilize Zn is 0.7 M, and at lower MSG concentration, Zn speciation is dominated by hydroxide precipitate. In the case of Cu, the model shows that MSG concentration has little effect on speciation, which contradicts the results in Figure 6. This is probably due to competition with Zn, which has a 40 times larger concentration. The leaching process at higher MSG concentration increased the recovery of other elements, e.g., Fe, due to increasing soluble species (FeGlu+ complex) (Supplementary file: Figure S3). The results in Figure 6 show the optimum recovery of Zn and Cu are attained at 1.2 and 0.7 M, respectively, which at higher MSG concentration did not significantly increase the recovery of both metals. At higher MSG concentration, the recovery of other metals, especially Mg and Ca became substantial, decreasing the selectivity (Figure 7). The optimum MSG concentration based on data in Figure 6 is well supported by results in Figure 7.

**Figure 6.** The recovery of (**a**) Zn, Cu, Mg, Ca and Fe and (**b**) Al as function of MSG concentration.

**Figure 7.** Separation factor of (**a**) Zn: [Zn]/[Fe] (squares), [Zn]/[Al] (diamonds), [Zn]/[Mg] (circles), [Zn]/[Ca] (triangles) and (**b**) Cu: [Cu]/[Fe] (squares), [Cu]/[Al] (diamonds), [Cu]/[Mg] (circles), [Cu]/[Ca] (triangles) in pregnant leach solution as function of MSG concentration.

#### *4.4. E*ff*ect of Pulp Density*

To evaluate the effect of pulp density (ratio between lixiviant and EAF dust) on the recovery and selectivity, leaching was carried out at constant variables pH 9, MSG concentration 1 M, 12 h leach at room temperature, while pulp density was varied between 33 and 200 g/L. The results in Figure 8 show the recovery decreased as pulp density increased. Maximum pulp density to obtain optimum recovery of Zn and Cu were 50 and 100 g/L, respectively. In the case of Mg and Ca the optimum pulp density was 66.67 g/L, while in the case of Fe and Al, the recovery was very low (less than 1 percent).

The plot of separation factor value as a function of pulp density (Figure 9) reveals that pulp density has a negative effect on the separation factor. This was probably caused by the increase of solubility of elements especially Mg, Ca, Fe and Al at larger volumes in alkaline conditions. This also indicates the leaching of EAF dust using MSG as lixiviant in alkaline conditions should be performed using continuous methods, e.g., column leaching or heap leaching instead of a batch method, since in the continuous method the volume could be minimized to optimize the selectivity.

**Figure 8.** The recovery of (**a**) Zn, Cu, Mg and Ca and (**b**) Fe and Al as function of pulp density.

**Figure 9.** Separation factor of (**a**) Zn: [Zn]/[Fe] (squares), [Zn]/[Al] (diamonds), [Zn]/[Mg] (circles), [Zn]/[Ca] (triangles) and (**b**) Cu: [Cu]/[Fe] (squares), [Cu]/[Al] (diamonds), [Cu]/[Mg] (circles), [Cu]/[Ca] (triangles) in pregnant leach solution as function of pulp density.

#### *4.5. Kinetic Studies (E*ff*ect of Leaching Time and Temperature)*

Kinetic studies were carried out at a time range up to 12 h at three different temperatures (30, 55 and 80 ◦C), with constant variables pH 9, MSG concentration 1 M and pulp density 50 g/L. The results in Figure 10 show that maximum leaching efficiency was attained within 120 min for Zn and 4 h for Cu at 30 ◦C. At higher leaching temperature, recovery reached saturation in a shorter period. Figure 10b shows the recovery of Cu is delayed about 1 h. Based on Eh monitoring, in the early stage of leaching the aqueous phase was reductive, which inhibited the oxidation and complexation of Cu by lixiviant. As leaching progressed, the aqueous phase became oxidative, favoring the oxidation and complexation of Cu.

To describe the kinetic process of leaching, three kinetic models were tested to model experimental data: shrinking core model (chemical reaction control), shrinking particle model (film diffusion) and interface transfer and diffusion by Dickinson and Heal (1999) [36]. The fitting results (*R2*, coefficient of correlation) of each model are listed in Table 5.

**Figure 10.** The effect of leaching time and temperature on recovery of (**a**) Zn and (**b**) Cu. In b, the change of Eh in aqueous phase during leaching was obtained at 30 ◦C (single measurement).

**Table 5.** Correlation coefficient for experimental data fitting using three kinetic models (SCM, shrinking core model; SPM, shrinking particle model).

*k*, apparent rate constant (min−1); *t*, time (min); *R*, metal recovery.

Based on the coefficient of the correlation value in Table 5, the interface transfer and diffusion model is the best model to describe the experimental data. Although the shrinking core model and shrinking particle model are more popular models to explain kinetic data, both models are generally based on the assumption of constant lixiviant concentration during leaching [37]. In the leaching process, the concentration of glutamate would decrease due to complex formation. In this study, the model developed by Dickinson and Heal (1999) [36] was adequately applied to analyze the dissolution of Zn and Cu in EAF dust using glutamate.

The value of the apparent rate constant (*k*) obtained for each temperature using the interface transfer and diffusion model (Table 6) was used to determine the activation energy (*Ea*, kJ/mol) using the Arrhenius Equation (3), where *A*, *T*, and *R* are the frequency factor, temperature, and gas constant, respectively. Relatively low energy activation for both Zn and Cu indicates that the effect of temperature on the leaching process is minor.

$$k = Ae^{-\frac{E\_a}{RT}} \text{ or } ln = lnA - \frac{E\_a}{RT} \tag{3}$$

**Table 6.** Apparent rate constant (*k*) attained from fitting of experimental data using interface transfer and diffusion model, which was used to obtain the activation energy (*Ea*) using linear regression, including the coefficient of correlation (*R2*).

#### *4.6. Monosodium Glutamate Recovery*

Due to the significant amount of MSG used in leaching and to sustain the leaching scheme, the regeneration of glutamate is required. Glutamate could be recovered from the pregnant leach solution as precipitate by acidifying the solution to the pH between 2–4.5. In this pH range glutamate is precipitated as neutral species glutamic acid, H2Glu (Supplementary file: Figure S4). The addition of more acid (pH less than 1) caused the formation of soluble cationic species H3Glu+. The experiment performed to recover glutamate from the pregnant leach solution showed that the optimum pH was 3, which in this pH more than 90% of the glutamate was recovered as glutamic acid (Figure 11). The recovery efficiency of glutamate was determined gravimetrically.

**Figure 11.** Monosodium glutamate (MSG) recovery efficiency as function of pH.

#### **5. Conclusions**

Monosodium glutamate effectively and selectively recovered Zn and Cu from EAF dust, based on batch leaching studies according to leaching efficiency and separation factor values. The optimum conditions for the leaching scheme are pH 9, lixiviant concentration 1 M and pulp density 50 g/L. Studies on the effect of pulp density on the metal recovery and separation factor showed that pulp density correlated negatively to the metal recovery and positively to the separation factor of Zn and Cu to the other elements. This indicates the leaching is better performed using a continuous method, considering the selective recovery of Zn and Cu from other elements. Kinetic studies showed the leaching efficiency reached a saturation value in less than 2 and 4 h for Zn and Cu, respectively. The activation energy obtained from the experimental data modeling revealed the effect of temperature on the leaching process was minor. Further the use of MSG as lixiviant offers a sustainable leaching scheme since MSG is recoverable from the pregnant leach solution and reusable for the next leaching cycle.

**Supplementary Materials:** The following are available online at http://www.mdpi.com/2075-4701/10/5/644/s1, Figure S1: Species distribution of (a) Al, (b) Fe, (c) Mg and (d) Ca in glutamate-H2O system as function of pH. Glutamate concentration 1 M, Al2+ 350 mM, Fe3+ 10 mM, Mg2+ 10 mM and Ca2+ 20 mM, Table S1: AMICS Mineralogy for Zinc EAF Dust Sample, Figure S2: Identified AMICS Minerals for Zinc EAF Dust Sample, Table S2, AMICS Color Scheme, Figure S3: Species distribution of (a) Zn and (b) Fe in glutamate-H2O system as function of glutamate concentration at pH 9. Zn2+ 300 mM and Fe3+ 10 mM, Figure S4: Species distribution of glutamate in glutamate-H2O system as function of pH. Glutamate concentration 1 M.

**Author Contributions:** Conceptualization, E.P.; data curation, E.P.; formal analysis, E.P. and C.A.; funding acquisition, E.P. and C.A.; investigation, E.P. and C.A.; methodology, E.P.; project administration, F.R.M., E.P.; resources, E.P., C.A., F.N., M.A.M., A.S.H., F.R.M. and F.B.; validation, E.P.; visualization, M.A.M. and F.B.; writing–original draft, E.P.; writing–review & editing, E.P. and C.A. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded and supported by Indonesian Institute of Sciences FY 2019, Fulbright Program through Visiting Scholar Scheme 2019–2020, Indonesian Ministry of Research and Technology Insinas Program FY 2020, PRN (National Priority Research) of Indonesia FY 2020.

**Acknowledgments:** We would like to express our gratitude to the two reviewers for improving this manuscript.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).


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*Introductory Chapter: Food Processing, Preservation, and Packaging – A Brief Overview DOI: http://dx.doi.org/10.5772/intechopen.110229*


**State of the Art and Future Perspectives in Smart and Sustainable Urban Development**

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**4. Student experience: autonomy**

## **4.1 Introduction**

A hallmark of DL has been its reliance on learner autonomy [63] which was the third hypothesised element of TDT [8] and the focus of this section.

## **4.2 Results**

Literature addressing autonomy in DL, unlike structure or dialogue which was relatively straightforward, was complex and multi-faceted [1]. Major reviews were found discussing autonomy in learning [64] and specifically autonomy in DL [4, 10]. In a review of autonomy and learning, literature was investigated over the last two decades, describing various definitions, and highlighting inconsistencies in the literature [64]. The review was divided into topics; however, there was no explanation as to search criteria or strategies. Autonomy was defined in terms of a redistribution of power concerning the construction of knowledge and the roles of participants. Although, DL was not addressed explicitly, the paper claimed autonomy was '…*a departure from education as a social process'* (p. 116). Over 2000 pieces of literature concerning autonomy were reviewed [4]. This visionary work (preinternet!) explained '*The autonomous learner is not to be thought of as an intellectual Robinson Crusoe, castaway and shut-off in self sufficiency'* ([4], p. 669).

In a later review, research on autonomous learning was reviewed [7] and further, explained that there were two dimensions of autonomy in DL: selfmanagement of pedagogy and self-monitoring of cognition, or metacognition. Both cognitive autonomy and taking responsibility for one's learning were essential. Focusing on the meta-cognitive aspects of learner autonomy, strategies were compared in classroom vs. DL [65]. Using questionnaires followed by verbal reports, the relationship was explored between autonomy and the instructional

**75**

*Transactional Distance Theory: A Critical View of the Theoretical and Pedagogical…*

context of distance learners (n = 274) or classroom learners (n = 143) in a language programme. Variant analysis was applied to the questionnaire data to determine the relationship between learning strategies and context. The results showed that mode of study (distance vs. traditional) was the principal influence of the relationship between students and autonomy (more so than age, level etc.). Distance learners made greater use of metacognitive strategies than classroom learners, especially relating to self-management. A further analysis was done using verbal reports (n = 37) and the data was classified from the transcripts by the researcher and an independent rater. A total of 836 instances of strategies relating to autonomous work were identified. The average instance of strategy use from distance learners was 26.6 whilst a traditional student was 10.2. Instances of using metacognitive strategies in classroom learners was on average four, whilst distance learners reported an average of 15. The results suggested distance learners used more metacognitive strategies than classroom learners [65]. Critically, the numbers in the two groups were uneven and the development of the questions was not well described. However, the dual nature of the study, independent raters, transparency of inter-rater reliability and clear analysis suggested rigour. This study suggested that learners either approach DL with, or develop very quickly, metacognitive and

In a later study, metacognitive knowledge was investigated and experiences in distance education [66]. Thirty one students were interviewed focusing on a model of metacognitive knowledge comprising self, task, strategy and goals. Content analysis was used to identify categories of metacognitive experiences. There was an average of 19.7 instances of metacognitive knowledge per student and in descending order, the four dimensions of metacognition were: self-knowledge, strategy knowledge, task knowledge and knowledge of goals. Each student was able to recount at least one instance of a metacognitive experience. Conclusions included: students appeared to have experienced some, often extremely memorable, metacognitive experiences and metacognitive knowledge of distance students appeared to be primarily about self and strategy and less about tasks and goals. However, these dimensions were highly interactive and not distinct. The quantification of a complex concept such as metacognition, and the suggestion that students can identify a 'metacognitive experience' suggested a positivist approach to a subject containing multiple realties. However, the author attempted rigour in that the methods were clearly explained, two raters were used, and transcripts were revisited for further analysis with discussion to resolve differences. Overall, the metacognitive aspect of autonomy seemed to be occurring and seemed to be important in these student's DL experiences [66]. Knowledge about oneself and strategies were more important for successful learning than knowledge about tasks and goals. This perhaps, suggested that self-monitoring is one of the keys to

Another study investigated how DL students conceptualised the three elements in TDT: structure, dialogue and autonomy [67]. Using a pre-tested and piloted questionnaire, 169 distance education students (72% response rate) were surveyed. Learner autonomy was measured by students indicating which of 11 statements described themselves (i.e. able to learn without lots of guidance, able to develop a personal plan, able to find resources, self-directed, prefer learning in a group, need collaborative learning). The results were analysed using factor analysis and suggested a two-factor solution: independence and interdependence. Independence accounted for 29% of the total variance with a Cronbach's alpha of 0.82. Interdependence (interpersonal, interactive aspects) accounted for 26% of total variance with a Cronbach's alpha of 0.77. The results suggested that the concepts of dialogue, structure and autonomy were complex and that students tended to

*DOI: http://dx.doi.org/10.5772/intechopen.81357*

self-management skills.

autonomy in DL.

#### *Transactional Distance Theory: A Critical View of the Theoretical and Pedagogical… DOI: http://dx.doi.org/10.5772/intechopen.81357*

context of distance learners (n = 274) or classroom learners (n = 143) in a language programme. Variant analysis was applied to the questionnaire data to determine the relationship between learning strategies and context. The results showed that mode of study (distance vs. traditional) was the principal influence of the relationship between students and autonomy (more so than age, level etc.). Distance learners made greater use of metacognitive strategies than classroom learners, especially relating to self-management. A further analysis was done using verbal reports (n = 37) and the data was classified from the transcripts by the researcher and an independent rater. A total of 836 instances of strategies relating to autonomous work were identified. The average instance of strategy use from distance learners was 26.6 whilst a traditional student was 10.2. Instances of using metacognitive strategies in classroom learners was on average four, whilst distance learners reported an average of 15. The results suggested distance learners used more metacognitive strategies than classroom learners [65]. Critically, the numbers in the two groups were uneven and the development of the questions was not well described. However, the dual nature of the study, independent raters, transparency of inter-rater reliability and clear analysis suggested rigour. This study suggested that learners either approach DL with, or develop very quickly, metacognitive and self-management skills.

In a later study, metacognitive knowledge was investigated and experiences in distance education [66]. Thirty one students were interviewed focusing on a model of metacognitive knowledge comprising self, task, strategy and goals. Content analysis was used to identify categories of metacognitive experiences. There was an average of 19.7 instances of metacognitive knowledge per student and in descending order, the four dimensions of metacognition were: self-knowledge, strategy knowledge, task knowledge and knowledge of goals. Each student was able to recount at least one instance of a metacognitive experience. Conclusions included: students appeared to have experienced some, often extremely memorable, metacognitive experiences and metacognitive knowledge of distance students appeared to be primarily about self and strategy and less about tasks and goals. However, these dimensions were highly interactive and not distinct. The quantification of a complex concept such as metacognition, and the suggestion that students can identify a 'metacognitive experience' suggested a positivist approach to a subject containing multiple realties. However, the author attempted rigour in that the methods were clearly explained, two raters were used, and transcripts were revisited for further analysis with discussion to resolve differences. Overall, the metacognitive aspect of autonomy seemed to be occurring and seemed to be important in these student's DL experiences [66]. Knowledge about oneself and strategies were more important for successful learning than knowledge about tasks and goals. This perhaps, suggested that self-monitoring is one of the keys to autonomy in DL.

Another study investigated how DL students conceptualised the three elements in TDT: structure, dialogue and autonomy [67]. Using a pre-tested and piloted questionnaire, 169 distance education students (72% response rate) were surveyed. Learner autonomy was measured by students indicating which of 11 statements described themselves (i.e. able to learn without lots of guidance, able to develop a personal plan, able to find resources, self-directed, prefer learning in a group, need collaborative learning). The results were analysed using factor analysis and suggested a two-factor solution: independence and interdependence. Independence accounted for 29% of the total variance with a Cronbach's alpha of 0.82. Interdependence (interpersonal, interactive aspects) accounted for 26% of total variance with a Cronbach's alpha of 0.77. The results suggested that the concepts of dialogue, structure and autonomy were complex and that students tended to

*Interactive Multimedia - Multimedia Production and Digital Storytelling*

and immediacy impacted on student satisfaction.

discussion for it to be adopted successfully.

learning [16, 39, 47–53].

sion of information.

education.

**4.1 Introduction**

**4.2 Results**

**4. Student experience: autonomy**

• Literature overwhelmingly suggested that learner-instructor and learner-

• Online 'community' or collaboration was an important variable in online classes. Without this online discourse online courses became a mere transmis-

• Several frameworks for designing and analysing interaction in DL were found all aimed at student's progression into higher levels of thinking [54–57].

• E-moderators took on multiple roles: they moderated or facilitated discussion, answered emails and managed the flow of content or responses. Their presence

• Students required usefulness, value or relevance in online interaction or

• The roles that interaction and dialogue play in DL is not well understood Moore (1973) warned this area should not be underestimated and argued no other area of study will have a greater impact on the future of distance

A hallmark of DL has been its reliance on learner autonomy [63] which was the

Literature addressing autonomy in DL, unlike structure or dialogue which was relatively straightforward, was complex and multi-faceted [1]. Major reviews were found discussing autonomy in learning [64] and specifically autonomy in DL [4, 10]. In a review of autonomy and learning, literature was investigated over the last two decades, describing various definitions, and highlighting inconsistencies in the literature [64]. The review was divided into topics; however, there was no explanation as to search criteria or strategies. Autonomy was defined in terms of a redistribution of power concerning the construction of knowledge and the roles of participants. Although, DL was not addressed explicitly, the paper claimed autonomy was '…*a departure from education as a social process'* (p. 116). Over 2000 pieces of literature concerning autonomy were reviewed [4]. This visionary work (preinternet!) explained '*The autonomous learner is not to be thought of as an intellectual*

third hypothesised element of TDT [8] and the focus of this section.

*Robinson Crusoe, castaway and shut-off in self sufficiency'* ([4], p. 669).

In a later review, research on autonomous learning was reviewed [7] and further, explained that there were two dimensions of autonomy in DL: selfmanagement of pedagogy and self-monitoring of cognition, or metacognition. Both cognitive autonomy and taking responsibility for one's learning were essential. Focusing on the meta-cognitive aspects of learner autonomy, strategies were compared in classroom vs. DL [65]. Using questionnaires followed by verbal reports, the relationship was explored between autonomy and the instructional

learner interaction was important to student satisfaction and the facilitation of

**74**

describe themselves as both independent and interdependent. The lack of correlation also suggested these features of autonomy were essential, but separate and distinct attributes. Although the attempt to quantify with statistical analysis something as complex as autonomy was fundamentally flawed, this study provided a particularly interesting idea: an individual's autonomy as a distance learner should be understood as including their abilities to work with others, or be interdependent. Autonomy is multi-faceted and interdependence appeared to be essential. These results suggested that there may be an attempt to move beyond the focus of independence in this environment and move towards 'interdependence'. Other) earlier findings support this 'personal control' [68]. It is suggested successful adult learners demonstrated appropriate dependency needs when participating in DL including: help, approval and support, leadership of others and sharing efforts and responsibility.


**1. Introduction**

The Basic Oxygen Furnace (BOF) slag is a left-behind residual waste generated from the steelmaking industry. There are 1.5 million tons of BOF slags produced annually in Taiwan [1]. This high quantity leads to environmental and ecological issues together with health concerns. This waste demonstrates excellent physical and mechanical attributes, such as far-above-the-ground hardness, a higher compressive strength, and a near-to-the-ground abrasion ratio. It is capable of substituting natural aggregates to produce structural materials, road pavements, and more. Unfortunately, the key setback of BOF slags is their expansion behavior because of their gigantic content of free lime, as a consequence, a hindrance in the context of BOF slag recycling and reuse takes place [2–6]. Most of the coarse BOF slags, larger than four mesh, are mixed with asphalt to produce asphalt-concrete

which can be employed as road pavements. However, approximately 0.7 million tons of fine BOF slags, which is less than four mesh, are unable to be utilized e ffectively, especially used in the Portland cement system [7]. For that reason, the way in which a BOF slag can be reutilized as a fine aggregate has become an imperative issue.

Previous studies on BOF slags used as aggregates in concrete and their advantageous properties have been extensively reported [4,5,8–11]. However, the BOF slag contains free-CaO and free-MgO that can result in volumetric instability due to their reaction with water, yielding alkaline earth metal hydroxides. This must be addressed, employing appropriate treatment to prevent expansion. There are several ways to stabilize BOF slags, such as stabilization in the hot or molten stage [12–14], and stabilization by employing a carbonation process [12,15,16]. Singh et al. supplemented oxalic acid into cement mortar to improve the densification and mechanical properties of concrete [17], while Ding et al. utilized scrubbing attrition and chelating reagent treatment to remove free lime from BOF slags [5].

On the other hand, Lin et al. immersed BOF slags into the water to provide su fficient stabilization reactions [18]. According to previous research results, the adding up of silica sand in the molten stage can complete the solution and get rid of free lime crisis. This brings into question whether or not analogous reactions can occur at room temperature. If possible, they will entirely resolve the quandary of BOF slag expansion and achieve the goals of waste recycling following the principles of the Circular Economy.

Nowadays, innovative geopolymer technology is drawing the attention of researchers on account of not only its nine-times lower carbon footprint and six-times lower energy consumption as compared to the OPC (Ordinary Portland Cement) system [19], but also due to the demonstration of excellent properties by geopolymer composites, such as high initial strength, brilliant resistance to chemicals and freeze-thaw as well as thermal and fire calamities, sustainability, and strength and durability. This means simply that novel geopolymer technology can lend a hand in solving the great dilemma of global warming and the saving of natural resources of rocks and minerals. On top of that, it is cost-e ffective, since it can incorporate profusely accessible various wastes in the manufacture of geopolymer composites, which otherwise create problems of landfilling and pollution of the environment, soil, surface and subsurface waters besides health hazards [20,21]. Accordingly, it also extends to an organized solution of the disposal of these wastes. Inorganic geopolymers are analogous to natural zeolite minerals but possess an amorphous microstructure, they are classed as three-dimensionally networked materials synthesized through the reaction of rich aluminosilicate materials, such as precursors and alkaline solutions, as activators [22–24]. During the geopolymerization reaction, Si gel and Al gel were produced on the solid particle surface, and thus, they formed the Si-O-Al framework. SiO4 and AlO4 tetrahedra are linked to each other by sharing all O2 atoms [25,26]. Their unique internal structure is the core reason for why geopolymer has superior chemical and physical properties such as high compressive strength, high durability, favorable structural integrity, and low permeability. These exothermic and complex geopolymer processes contain large amounts of free silicon. This free silicon can react with oxides of free lime and free magnesium in the BOF slag, and hence, the formation of stable compounds takes place, thereby inhibiting the expansion of the BOF slag.

In this original research, BOF slags, granulated blast furnace slag (GGBS) powder, coal fly ash (FA) and alkali activator solutions were employed as raw materials to manufacture BOF slag-based geopolymer mortar. The e ffect on compressive strength and expansion behavior from the GGBS/FA ratio and the SiO2/Na2O ratio in geopolymer mortar are presented in this investigation. For testing the expansion behavior of BOF slag-based geopolymer mortar, the application of the autoclave testing method as the accelerated test was made [27]. Lab-scale and ready-mixed plant pilot-scale recycling processes are also investigated in this study.

#### **2. Assumption of the Mechanism for Stabilization BOF Slags in Geopolymer System**

In general, the expansion phenomenon of the BOF slag is mainly due to the f-CaO, and f-MgO content presented. When the BOF slag makes contact with water, f-CaO will undergo a hydration reaction, causing volume expansion. The f-MgO hydration reaction is relatively slow, and about 30 days later, it will also cause volume expansion. After the BOF slag absorbs water, f-CaO will become calcium hydroxide "Ca(OH)2", the volume will expand by 127% [28], and f-MgO will become magnesium hydroxide "Mg(OH)2". The volume expansion is about 118% [29]. Therefore, this study conceived that utilizing free silicon to react f-CaO into a stable calcium silicate compound would prevent the expansion. The relevant reaction Equations (1) and (2) are as follows:

$$\text{f-CaO} + \text{Si} \rightarrow \text{CaSiO}\_3 \tag{1}$$

$$\text{f-MgO} + \text{Si} \rightarrow \text{MgSiO}\_3 \tag{2}$$

After the geopolymer reaction is completed, the excessively free silicon will remain in the matrix of BOF slag-based geopolymer. If other factors caused the geopolymer matrix and/or BOF slag aggregates to be broken, the f-CaO may be released again. At this time, if water infiltrates from the crack, the free silicon remaining in the geopolymer product will dissolve in the water, and then react with f-CaO to form a stable calcium silicate to prevent expansion. The reaction schematic diagram is shown in Figure 1.

**Figure 1.** Schematic diagram of f-CaO reaction with free silicon in geopolymer matrix.


**2.1 Testicular toxicity in rat**

In a 13-week rat safety study, testicular atrophy was observed in rats at all doses tested (10, 50, and 250 mg/kg/day); however, these findings were not observed in the 2-week study. At the dose of 250 mg/kg/day, testicular atrophy was observed in approximately 50% of all rats. At doses of 10 and 50 mg/kg/day, these findings were observed in only 10% of rats but responsibility of Drug A for this toxicity could not be discounted (i.e., unequivocal). Therefore, no NOAEL could be assigned in this study, which markedly complicated the further development of this drug candidate.

#### **2.2 Role of toxicokinetics in rat testicular toxicity**

From the rat quantitative whole body autoradiography (QWBA) study, preferential distribution of Drug A-derived radioactivity to the testes was observed; furthermore, this radioactivity was retained in the testes markedly longer compared to other tissues (Figure 1). Since distribution of radioactivity included both parent and its metabolites and the dose in the rat QWBA study was based on the lowest dose of the rat safety study (i.e., 10 mg/kg/day), a cold study was initiated where rats were dosed with a single oral dose of Drug A at 50 mg/kg (similar to the mid dose in the rat safety study). After this single oral dose, the plasma, testes, and epididymes of the rats were collected at different time points and analyzed for Drug A and its two known metabolites (M1 and M2). Interestingly, the predominant metabolite in plasma (i.e., M2) was not the predominant metabolite in testes. M1 preferentially distributed to the testes from plasma; whereas, M2 had limited distribution to this tissue (Table 1 and 2). Furthermore, the Tmax of M1 was 48 hours in testes suggesting a large accumulation potential of this metabolite in testes compared to plasma. Indeed after a follow-up study for six months of repeated daily oral dosing, M1 accumulated approximately five-fold in the testes; whereas, the parent did not accumulate (Figure 2). Furthermore, parent and M1 did not accumulate in the plasma during the 6 month rat safety study (data not shown).

surrogate for tissue levels of drug and its metabolites, especially for the assessment of risk

The following case examples will illustrate how focusing on drug and metabolites in these tissues (where toxicity is observed) instead of plasma increases understanding of the nature of the toxicity and in some cases allows the efficient identification of a backup drug that has markedly less potential to cause that specific organ toxicity under investigation. These case examples are categorized by the different organs where toxicity was investigated and are

This case example (described below) will highlight 1) preferential distribution of parent and metabolites to tissue, 2) a predominant metabolite that is different in the tissue versus plasma, and 3) accumulation of parent and metabolite that occurs in tissue and not in plasma. Furthermore, the case example will highlight that focusing on tissue burden of the drug and its metabolites (and not plasma concentrations) may actually ensure that a backup

In a 13-week rat safety study, testicular atrophy was observed in rats at all doses tested (10, 50, and 250 mg/kg/day); however, these findings were not observed in the 2-week study. At the dose of 250 mg/kg/day, testicular atrophy was observed in approximately 50% of all rats. At doses of 10 and 50 mg/kg/day, these findings were observed in only 10% of rats but responsibility of Drug A for this toxicity could not be discounted (i.e., unequivocal). Therefore, no NOAEL could be assigned in this study, which markedly complicated the

From the rat quantitative whole body autoradiography (QWBA) study, preferential distribution of Drug A-derived radioactivity to the testes was observed; furthermore, this radioactivity was retained in the testes markedly longer compared to other tissues (Figure 1). Since distribution of radioactivity included both parent and its metabolites and the dose in the rat QWBA study was based on the lowest dose of the rat safety study (i.e., 10 mg/kg/day), a cold study was initiated where rats were dosed with a single oral dose of Drug A at 50 mg/kg (similar to the mid dose in the rat safety study). After this single oral dose, the plasma, testes, and epididymes of the rats were collected at different time points and analyzed for Drug A and its two known metabolites (M1 and M2). Interestingly, the predominant metabolite in plasma (i.e., M2) was not the predominant metabolite in testes. M1 preferentially distributed to the testes from plasma; whereas, M2 had limited distribution to this tissue (Table 1 and 2). Furthermore, the Tmax of M1 was 48 hours in testes suggesting a large accumulation potential of this metabolite in testes compared to plasma. Indeed after a follow-up study for six months of repeated daily oral dosing, M1 accumulated approximately five-fold in the testes; whereas, the parent did not accumulate (Figure 2). Furthermore, parent and M1 did not accumulate in the plasma during the 6-

generated from the author's personal experience in the pharmaceutical industry.

**2. Case example: Toxicokinetics and testicular toxicity**

for some types of organ-specific toxicity.

does not produce the same toxicity.

further development of this drug candidate.

month rat safety study (data not shown).

**2.2 Role of toxicokinetics in rat testicular toxicity**

**2.1 Testicular toxicity in rat**

Fig. 1. Total Radioactivity (TR) Concentrations versus Time Profile of Drug A-derived Radioactivity in Rat Plasma, Testicle, Liver, and Lung.

Long Evans rats were dosed with a single oral dose of 10 mg/kg [14C]-labeled Drug A. At different times after this dose, rats were sacrificed via exsanguination (cardiac puncture) under isoflurane anesthesia and blood (approximately 2 to 10 mL) was collected into tubes containing K2EDTA immediately prior to collection of carcasses for QWBA. Samples were maintained on wet ice and refrigerated until aliquoted and centrifuged to obtain plasma. Immediately after blood collection the animals were prepared for QWBA. The carcasses were immediately frozen in a hexane/dry ice bath for approximately 8 minutes. Each carcass was drained, blotted dry, placed into an appropriately labelled bag, and placed on dry ice or stored at approximately -70°C for at least 2 hours. Each carcass was then stored at approximately -20°C. The frozen carcasses were embedded in chilled carboxymethylcellulose and frozen into blocks. Embedded carcasses were stored at approximately -20°C in preparation for autoradiographic analysis.

Table 1. Toxicokinetic Profile of Drug A and its Metabolites in Rat Plasma, Testes, and Epididymes.

To aid the identification of this backup, the rat QWBA study of a prior drug candidate for this target (referred to as Drug B) was assessed where Drug B did not induce testes toxicity in rat during long-term safety studies. Interestingly, Drug B-derived radioactivity was approximately equivalent in blood and testes (Table 3), suggesting that the reduced burden of this tissue may have markedly lowered the susceptibility for this toxicity compared to the structurally similar molecule, Drug A. This markedly lowered distribution to the testes was also mirrored in the volume of distribution calculated after an single intravenous administration of Drug A and B, where the volume of distribution was markedly lower for Drug B compared to Drug A in every animal species tested (e.g., rat, dog, and monkey). Therefore to select future backups of Drug A into development, the volume of distribution was calculated from similar studies with administration of potential backup drug candidates via the intravenous route. These studies led to the identification of a potential drug candidate with similar distribution properties of Drug B (i.e, lower volume of distribution in every animal species tested after a single intravenous dose compared to Drug A). This potential backup to Drug A (referred to as Drug C) was then assessed in a rat QWBA study. In this study, Drug C-derived radioactivity was approximately equivalent in blood and testes (Table 4). From these encouraging results, Drug C was advanced into further development where no testicular toxicity has been observed in long-term rat safety studies. These results support the hypothesis that reduced tissue burden of the drug and its

metabolites may actually predict that a backup does not produce the same toxicity.

0.5 2 4 8 12 24 48 72 120

Blood 21.0 17.0 15.5 8.22 3.09 0.153 ND ND ND Testis 2.84 7.54 15.4 9.98 5.26 0.346 0.135 BLQ BLQ

Table 3. Tissue Concentrations (µg equivalents/g) of Drug B-derived Radioactivity in Rat

Long Evans rats were dosed with single oral dose of 30 mg/kg [14C]-labeled Drug B. See

Time (hr) 1 4 8 24 72 168 336

Blood 4530 1280 310 58.3 BLQ ND ND Testis 2240 989 456 105 BLQ BLQ BLQ

Table 4. Tissue Concentrations (µg equivalents/g) of Drug C-derived Radioactivity in Rat

Long Evans rats were dosed with a single oral dose of 10 mg/kg [14C]-labeled Drug C. See

**2.3 Identification of a backup molecule with limited potential for testicular toxicity**  In order to identify a backup to this molecule (e.g., Drug A), screening potential backups in terms of their toxicity potential to rat testicular atrophy was not practical because of the time required for the toxicity to be observed (i.e., more than 2 weeks). Therefore, another method

of screening potential backups needed to be initiated.

Time (hr)

Plasma and Testis.

Plasma and Testis.

description of Figure 1 for experimental details.

description of Figure 1 for experimental details.

Fed Sprague Dawley rats (n=27) were administered a single oral dose of 50 mg/kg Drug A. Testes, epididymes, and plasma were collected at 1, 4, 8, 24, 48, 72, 96, 168, and 336 hours post dose from three rats at each time point. Bioanalysis of plasma, testes, and epididymes for Drug A (Parent) and its metabolites M1 and M2 was performed. Toxicokinetic parameters were determined on plasma, testes, and epididymes.

Table 2. Tissue to Plasma Ratios of Drug A and its metabolites in Rat Plasma, Testes, and Epididymes.

See description of Table 1 for experimental details. After toxicokinetic parameters were determined on testes, epididymes, and plasma, tissue to plasma ratios were calculated.

Fig. 2. Distribution of Drug A and its Metabolite, M1, in the Rat Testes after 6 Months of Repeated Daily Dosing (50 mg/kg/day) Compared to a Single Oral Dose (50 mg/kg)

Fed Sprague Dawley rats (n=4) were administered a single oral dose or repeated daily oral doses of 50 mg/kg/day Drug A for 6 months. Testes were collected at 24 hours post dose. Bioanalysis of testes for Drug A (Parent) and M1 was performed. Concentrations of Drug A and M1 after 6 months of repeated daily oral dosing (50 mg/kg/day) were compared to a single oral dose (50 mg/kg) at 24 hours post dose (see description of Table 1 for experimental details of the single oral dose study).

Fed Sprague Dawley rats (n=27) were administered a single oral dose of 50 mg/kg Drug A. Testes, epididymes, and plasma were collected at 1, 4, 8, 24, 48, 72, 96, 168, and 336 hours post dose from three rats at each time point. Bioanalysis of plasma, testes, and epididymes for Drug A (Parent) and its metabolites M1 and M2 was performed. Toxicokinetic

 Cmax AUClast AUCinf Testes M1 40 391 386 Testes M2 0.07 0.03 0.08 Testes Parent 2 16 16 Epididymes M1 49 65 64 Epididymes M2 0.22 0.10 0.24 Epididymes Parent 2 3 3 Table 2. Tissue to Plasma Ratios of Drug A and its metabolites in Rat Plasma, Testes, and

See description of Table 1 for experimental details. After toxicokinetic parameters were determined on testes, epididymes, and plasma, tissue to plasma ratios were calculated.

Single dose

6 month daily dosing

Fig. 2. Distribution of Drug A and its Metabolite, M1, in the Rat Testes after 6 Months of Repeated Daily Dosing (50 mg/kg/day) Compared to a Single Oral Dose (50 mg/kg)

Parent M1

experimental details of the single oral dose study).

0

**Testes concentration (ng/g)**

Fed Sprague Dawley rats (n=4) were administered a single oral dose or repeated daily oral doses of 50 mg/kg/day Drug A for 6 months. Testes were collected at 24 hours post dose. Bioanalysis of testes for Drug A (Parent) and M1 was performed. Concentrations of Drug A and M1 after 6 months of repeated daily oral dosing (50 mg/kg/day) were compared to a single oral dose (50 mg/kg) at 24 hours post dose (see description of Table 1 for

parameters were determined on plasma, testes, and epididymes.

Epididymes.

#### **2.3 Identification of a backup molecule with limited potential for testicular toxicity**

In order to identify a backup to this molecule (e.g., Drug A), screening potential backups in terms of their toxicity potential to rat testicular atrophy was not practical because of the time required for the toxicity to be observed (i.e., more than 2 weeks). Therefore, another method of screening potential backups needed to be initiated.

To aid the identification of this backup, the rat QWBA study of a prior drug candidate for this target (referred to as Drug B) was assessed where Drug B did not induce testes toxicity in rat during long-term safety studies. Interestingly, Drug B-derived radioactivity was approximately equivalent in blood and testes (Table 3), suggesting that the reduced burden of this tissue may have markedly lowered the susceptibility for this toxicity compared to the structurally similar molecule, Drug A. This markedly lowered distribution to the testes was also mirrored in the volume of distribution calculated after an single intravenous administration of Drug A and B, where the volume of distribution was markedly lower for Drug B compared to Drug A in every animal species tested (e.g., rat, dog, and monkey). Therefore to select future backups of Drug A into development, the volume of distribution was calculated from similar studies with administration of potential backup drug candidates via the intravenous route. These studies led to the identification of a potential drug candidate with similar distribution properties of Drug B (i.e, lower volume of distribution in every animal species tested after a single intravenous dose compared to Drug A). This potential backup to Drug A (referred to as Drug C) was then assessed in a rat QWBA study. In this study, Drug C-derived radioactivity was approximately equivalent in blood and testes (Table 4). From these encouraging results, Drug C was advanced into further development where no testicular toxicity has been observed in long-term rat safety studies. These results support the hypothesis that reduced tissue burden of the drug and its metabolites may actually predict that a backup does not produce the same toxicity.

Long Evans rats were dosed with single oral dose of 30 mg/kg [14C]-labeled Drug B. See description of Figure 1 for experimental details.

Table 4. Tissue Concentrations (µg equivalents/g) of Drug C-derived Radioactivity in Rat Plasma and Testis.

Long Evans rats were dosed with a single oral dose of 10 mg/kg [14C]-labeled Drug C. See description of Figure 1 for experimental details.

**Liver Liver +**

Day 1 2 292 373 130 168 209 180 59 Mean

6 286 284 131 96 181 123 65 Mean

24 54 48 46 28 68 34 61 Mean

Day 14 2 295 381 226 140 245 90 73 Mean

6 293 275 284 128 187 94 71 Mean

24 39 37 41 29 52 36 72 Mean

Fed Beagle dogs (n=18) were administered a single oral dose or repeated daily oral doses for 14 days of 10 mg/kg drug. Liver, kidney, fat, and plasma were collected at 2, 6, and 24 hours post dose from three dogs at each time point with and without formic acid (formic acid was added to potentially increase the stability of the acyl glucuronide metabolite). Bioanalysis of

Metabolite Type Day 1 Day 14 Day 1 Day 14 Day 1 Day 14 Day 1 Day 14 Parent - 116,860,326 123,174,663122,389,879122,716,07227,431,25236,110,99528,993,390 29,924,921

M2 Glucuronidation 5,299,597 3,790,520 3,552,836 2,817,400 ND ND ND ND M3 Oxidation ND ND ND ND ND ND ND ND M4 Oxidation 453,680 1,035,337 553,582 1,045,264 ND ND ND ND

Table 6. Peak Area Counts Versus Time Profile of Parent and its Metabolites in Dog Plasma,

Sulfation ND ND ND ND ND ND ND ND

Table 5. Concentration-Time Profile of Parent in Dog Plasma, Liver, Kidney, and Fat.

liver, kidney, fat, and plasma for drug candidate was performed.

**Acid**

35 114 17 24 46 68 11 SD

112 92 35 12 102 57 4 SD

44 37 24 16 37 25 5 SD

141 208 100 27 160 38 12 SD

89 110 72 29 27 35 5 SD

54 53 40 24 61 43 3 SD

Plasma Plasma + Acid Liver Liver + Acid

**Kidney Kidney +**

**Acid**

**Fat**

 **Concentration (**µ**g/mL or g)**

**Acid**

**Plasma Plasma +**

**Day Time (hr)**

M1 Oxidation +

ND = not detected

Liver, Kidney, and Fat.


**10. Valve sparing aortic root replacement**

Sparing the patient's native aortic valve while replacing the aortic root is an alternative option in selected patients [54]. This naturally avoids need for anti-coagulation [9]. After an initial pre-operative trans-esophageal echocardiogram, the valve leaflets should be examined prior to proceeding with valve-sparing approach. Leaflet quality is fundamental to event-free survival in these patients.


**6. Midline abnormalities**

*Holoprosencephaly* is a congenital induction disorder, occurring extremely early in pregnancy (3–6 WA), with failing the segmentation of the neural tube [53–57]. This leads to incomplete separation of the prosencephalon. It has been classified into four subtypes: alobar, semilobar, lobar, and a middle interhemispheric fusion variant (syntelencephaly).

The alobar holoprosencephaly is the most severe type, having a complete lack of separation of the cerebral hemispheres; this lead to a single ventricle, absence of the CC and IEF, and fused thalami. In the semilobar type, the cerebral hemispheres are fused anteriorly. In lobar holoprosencephaly, the fusion of the cerebral hemispheres is present at the frontal lobes. The middle interhemispheric fusion variant results from nonseparation of posterior frontal and parietal lobes.

The *corpus callosum* is the largest commissure of the brain, and its development is accelerated between 8 and 20 WA. Any disturbance of this process may lead to CC agenesis or partial agenesis (hypogenesis or dysgenesis). Many CC abnormalities (in terms of dimensions and shape) are frequently diagnosed during pregnancy although their significance is still debated [58–67]. Abnormal CC has been described among patients assessed for mental retardation.

In a similar way, the absence of fluid in the CSP (with or without intact *septum pellucidum* and corpus callosum) may indicate subtle or severe midline brain abnormalities. All these conditions may have significant implications for postnatal neurological development [58, 61, 63–68] (**Figures 24** and **25**).

**Figure 24.** Different cases of holoprosencephaly in the first trimester (a,b). c. a case which associates proboscis.

**Figure 25.** Second-trimester alobar holoprosencephaly (a and b). Short and thick corpus callosum (c).


*3.4. Objective 3: Acceptability for the Children, Parents and Therapists* 3.4.1. Working Alliance in the Intervention

The parents' ratings of working alliance were high (median score of 4, all scores either 3 or 4), whereas the children showed more variation, but with median scores at the high end of the scale (median 3, range 0.5 to 4). See Table 4. Two of the children rated the working alliance as low (1.5 and 0.5). These were one teenager who expressed that he in general did not want to focus on the brain injury, and also that he was not happy that the therapists and his parents talked about his challenges in his absence. The younger child was reluctant to participate in the last sessions, although the goals were achieved.

The therapists' ratings of working alliance were high with regards to feeling welcome to contact the family and the general tone of communication (median 5 for both), but two questions concerning relation and communication with the child had more variable responses, similar to the children's own ratings (median 3, range 1–5). The therapists rated the collaboration with the schools as good (median 4.5, range 3–5).