**5. Ion exchange on strength, reliability, and lifetime of dental porcelains**

In this section, the effects of chemical toughening using ion exchange by the paste method on the strength and lifetime of dental porcelains are shown. In this study, a feldspathic porcelain (Ultropaline Super Transparent, Jen Dental – UST) recommended for metal-ceramic or all ceramic restorations was used. The chemical composition of this porcelain is shown in Table 4.

**Table 4.** Chemical composition (mol%) measured by XRF spectroscopy and calculated glassy matrix composition (in parenthesis) of Ultropaline Super Transparent (UST) dental porcelain (containing 12 vol% leucite particles)

Disc-shaped green specimens were prepared by vibration-condensation method and then sintered at 930°C following the firing schedule recommended by the manufacturer. After firing, the specimens were machined and mirror-polished with diamond suspensions down to 1 μm. For each test condition at least 10 specimens (12.5 mm in diameter and 1 mm in thickness) were prepared.

For the chemical tempering, a paste was prepared by mixing 10 g of KNO3 powder (Merck) with 4 mL of deionized water. Porcelain discs, containing 0.4 g of this paste on the polished surface, were subjected to the ion exchange treatment in an electric furnace (FP-32, Yamato) with a heating rate of 5°C/min at 470°C (or other specified temperature) during 15 min, after an intermediate step at 150°C for 20 min for drying. After the treatment, the paste residue was easily removed with sprayed water.

The porcelain's strength was determined in biaxial flexural mode, which is an adequate loading condition for thin specimens, like the dental restorations. The biaxial flexure strength (σf ) was determined using a piston-on-three-balls loading device in a universal mechanical testing machine (Syntech 5G, MTS) at a constant stress rate of 10 MPa/s (or other specified rate), with the specimen immersed in artificial saliva (Table 5) heated to 37°C (Figure 11). Flexural test performed in artificial saliva at 37°C is more severe (strength tends to be lower) than in usual laboratory environment, but these conditions are more clinically relevant, since they are closer to the oral environmental conditions.

**Table 5.** Composition of artificial saliva [5,54]

**Figure 11.** Images of biaxial flexural device: (a) loading piston and three-ball support; (b) disc specimen positioned in the flexural device and immersed in artificial saliva with heating element; (c) general view of the biaxial flexural load‐ ing device [5]

The biaxial flexural strength (σf ) was calculated using the following equation [5,55]:

$$\sigma\_f = \frac{0.2387 \cdot F}{w^2} \left\{ (1+\nu) \left[ 1 - 2\ln\left(\frac{B}{A}\right) \right] + \left(1-\nu\right) \left[ \left(\frac{A}{C}\right)^2 - \frac{1}{2} \left(\frac{B}{C}\right)^2 \right] \right\} \tag{10}$$

where, *F* is the load at fracture, *w* is the specimen thickness, *A* is the radius of the support circle (4 mm), *B* is the radius of the piston (0.85 mm), *C* is the radius of the specimen, and *ν* is the Poisson's ratio (determined by ultrasonic pulse-echo method [56]).

Figure 12 shows the effects of ion exchange temperature on biaxial flexural strength, *σf* , and fracture toughness, *KIc*, determined by the indentation fracture method. It can be seen that ion exchanged specimens had significantly higher *σf* values (around 130 MPa) compared to the control sample – without chemical tempering (57 MPa). This substantial increase of around 130% in strength was directly related to the increase in material's resistance to crack propa‐ gation, that is, fracture toughness. This property increased from 1.3 MPa.m1/2 in control sample to around 2.8 MPa.m1/2 in chemically tempered samples (relative increase of around 120%) [57]. The ion exchange by the paste method although short in time results in significantly strength‐ ening and toughening of dental porcelains. Other works also showed increases between 20 to 83% in flexural strength by applying this method for feldspathic porcelains [58,59], although there are also reports showing no significant increases in some dental porcelains [60], partic‐ ularly those with high K2O content [61].

No effects of ion exchange temperature on *σf* and *KIc* were observed when this treatment was carried out between 430 and 510°C (Figure 12), which corresponded to a range between 75 and 89% of the glass transition temperature (Tg) of UST porcelain. This temperature (Tg), deter‐ mined by differential thermal analysis, DTA (404S, Netzsch), at a heating rate of 5°C/min in air, was 575°C [57]. At this temperature range, one could expect a significant increase in the ion exchanged K+ ions with the increase in temperature, since the kinetic of ion exchange by interdiffusion with Na+ ions of the porcelain is exponentially dependent on the temperature (Equations 2–4). In fact, it was determined by XRF spectroscopy that the sodium content in the KNO3 paste residue increased with the increase in temperature of ion exchange treatment (Figure 13). The increase in K2O content and reduction of Na2O content in porcelain were also confirmed by EDS analysis in SEM. In this case, it seems that the increase in ion exchange rate was counterbalanced by the stress relaxation with the increase in temperature (Equation 6), inhibiting further increase in residual compressive stress and increases in toughness and strength. Therefore, an appropriate temperature for making ion exchange in dental porcelains with K+ ions exchanged by Na+ ions by paste method seems to be around 80% of glass transition temperature (Tg), or around 100°C lower than Tg (for UST porcelain at 470°C).

**KH2PO4 (2.5 mM)**

ing device [5]

**Na2HPO4 (2.4 mM)**

180 Ion Exchange - Studies and Applications

**Table 5.** Composition of artificial saliva [5,54]

The biaxial flexural strength (σf

2

exchanged specimens had significantly higher *σf*

ularly those with high K2O content [61].

No effects of ion exchange temperature on *σf*

su

**KHCO3 (1.5 mM)**

**NaCl (1.0 mM) MgCl2 (0.15**

**Figure 11.** Images of biaxial flexural device: (a) loading piston and three-ball support; (b) disc specimen positioned in the flexural device and immersed in artificial saliva with heating element; (c) general view of the biaxial flexural load‐

( ) ( ) 2 2

ì ü é ù × ï ï é ù æ ö æ ö æö

 u

*F B AB w A CC*

where, *F* is the load at fracture, *w* is the specimen thickness, *A* is the radius of the support circle (4 mm), *B* is the radius of the piston (0.85 mm), *C* is the radius of the specimen, and *ν* is the

fracture toughness, *KIc*, determined by the indentation fracture method. It can be seen that ion

control sample – without chemical tempering (57 MPa). This substantial increase of around 130% in strength was directly related to the increase in material's resistance to crack propa‐ gation, that is, fracture toughness. This property increased from 1.3 MPa.m1/2 in control sample to around 2.8 MPa.m1/2 in chemically tempered samples (relative increase of around 120%) [57]. The ion exchange by the paste method although short in time results in significantly strength‐ ening and toughening of dental porcelains. Other works also showed increases between 20 to 83% in flexural strength by applying this method for feldspathic porcelains [58,59], although there are also reports showing no significant increases in some dental porcelains [60], partic‐

carried out between 430 and 510°C (Figure 12), which corresponded to a range between 75 and 89% of the glass transition temperature (Tg) of UST porcelain. This temperature (Tg), deter‐ mined by differential thermal analysis, DTA (404S, Netzsch), at a heating rate of 5°C/min in

= + - +- - í ý ê ú ê ú ç ÷ ç ÷ ç÷ ï ï ë û è ø è ø èø ê ú î þ ë û

Figure 12 shows the effects of ion exchange temperature on biaxial flexural strength, *σf*

0.2387 1 1 1 2ln 1 2 *f*

Poisson's ratio (determined by ultrasonic pulse-echo method [56]).

) was calculated using the following equation [5,55]:

100 mL 100 mL 100 mL 100 mL 100 mL 100 mL 6 mL

**mM)**

**CaCl2 (1.5 mM) Citric acid (0.002 mM)**

(10)

, and

values (around 130 MPa) compared to the

and *KIc* were observed when this treatment was

**Figure 12.** Biaxial flexural strength, *σf* , and fracture toughness, *KIc*, of UST dental porcelain before and after ion ex‐ change (IE) at different temperature. In parenthesis is the temperature of IE relative to the glass transition temperature (Tg = 575°C). Data from [57]

**Figure 13.** Sodium (Na) content in the KNO3 paste residue after ion exchange treatment at different temperature for UST porcelain. Data from [57]

1

22 more sensitive to the size of flaws.

28 distribution is given by [14]:

Small variations in flexural strength with varying ion exchange temperatures were also reported in another work. In general no significant differences were observed in strength when ion exchange with K-containing compound was applied to three dental porcelains between 300 and 600°C (for 30 min), although maximum values were observed at 450°C. Besides, small variations in strength with varying ion exchange time (10 to 90 min at 450°C) were also observed [62].

In order to evaluate the effects of the size of surface flaws on the strength of ion exchanged porcelain, the polished surface of specimens was indented with Vickers impression with increasing load from 2 to 49 N. In Figure 14a, it can be seen that the biaxial flexural strength, σf , of UST porcelain, without (control) and with ion exchange treatment (KNO3, 470°C, 15 min), decreased with the increase in indentation load, since this increase causes the increase in radial/ median crack size, *c* (Figure 7b). However, the decrease in σf was more accentuated in the ion exchanged specimens. These results showed that the beneficial effects of ion exchange are more pronounced for small surface cracks, and less effective for larger and deeper flaws. This behavior is related to the gradient of K+ ion content introduced in the surface region, which results in a gradual decrease of residual compressive stress to the interior of porcelain. The positive effects of ion exchange disappear for flaws deeper than the thickness of compressive stress layer.

16 Book Title

2 **Fig. 14.** Biaxial flexural strength, f, after a Vickers indentation at different loads (a) and 3 fracture resistance, KR, as a function of crack extension, Δc (b), for UST dental porcelain with 4 and without ion exchanges. Data from [63,64] **Figure 14.** Biaxial flexural strength, σf , after a Vickers indentation at different loads (a) and fracture resistance, KR, as a function of crack extension, Δc (b), for UST dental porcelain with and without ion exchanges. Data from [63,64]

5 The fracture resistance, *KR*, or *KIc* as a function of crack extension, Δ*c*, can be evaluated using 6 the following equation [64–66]: The fracture resistance, *KR*, or *KIc* as a function of crack extension, Δ*c*, can be evaluated using the following equation [64–66]:

$$\mathbf{K}\_{\mathbb{R}} = \mathbf{k} \cdot \left(\Delta \mathbf{c}\right)^{\circ} \tag{11}$$

9 the results of biaxial flexural strength, f, as a function of indentation load, P, in Figure 14a. 10 The calculated fracture resistance, *KR*, values as a function of crack extension, *Δc*, are shown Where, the parameters *k* and *q* are determined using the data obtained by power law fits on the results of biaxial flexural strength, σf , as a function of indentation load, P, in Figure 14a.

8 Where, the parameters *k* and *q* are determined using the data obtained by power law fits on

11 in Figure 14b. For the non-treated porcelain, *KR* increased with the increase in crack size, the 12 so-called rising R-curve behavior (crack growth resistance curve). This behavior is observed 13 in leucite-containing porcelains and is caused by the friction between rough crack surfaces 14 caused by crack deflection around leucite agglomerates [11]. Since this mechanical grip acts 15 in the crack wake, the shielding effect at the crack tip is intensified with the increase in crack 16 extension [67]. Rising R-curve effect is a desirable material behavior since it is necessary 17 additional energy to propagate the crack, besides that needed at the crack tip [65,66]. For the 18 ion exchanged porcelain, an opposite result was observed, with the decrease in KR as the 19 crack size increased. The decreasing residual compressive stress from the ion exchanged 20 surface annulled the rising R-curve effect of porcelain microstructure and more, resulted in 21 a decreasing R-curve effect. This behavior makes the strength of ion exchanged porcelain

23 The variability of strength in ceramic materials is closely related to its flaw population, since 24 fracture is a probabilistic event due to a random-like distribution of the strength-limiting 25 flaws [68]. The strength variability of ceramic materials can be evaluated using Weibull 26 statistic, which is based on the weakest-link theory, where the more severe flaw results in 27 fracture propagation and determine the strength [69]. The Weibull two-parameter The calculated fracture resistance, *KR*, values as a function of crack extension, *Δc*, are shown in Figure 14b. For the non-treated porcelain, *KR* increased with the increase in crack size, the so-called rising R-curve behavior (crack growth resistance curve). This behavior is observed in leucite-containing porcelains and is caused by the friction between rough crack surfaces caused by crack deflection around leucite agglomerates [11]. Since this mechanical grip acts in the crack wake, the shielding effect at the crack tip is intensified with the increase in crack extension [67]. Rising R-curve effect is a desirable material behavior since it is necessary additional energy to propagate the crack, besides that needed at the crack tip [65,66]. For the ion exchanged porcelain, an opposite result was observed, with the decrease in KR as the crack size increased. The decreasing residual compressive stress from the ion exchanged surface cancelled the rising R-curve effect of porcelain microstructure and in addition, resulted in a decreasing R-curve effect. This behavior makes the strength of ion exchanged porcelain more sensitive to the size of flaws.

Small variations in flexural strength with varying ion exchange temperatures were also reported in another work. In general no significant differences were observed in strength when ion exchange with K-containing compound was applied to three dental porcelains between 300 and 600°C (for 30 min), although maximum values were observed at 450°C. Besides, small variations in strength with varying ion exchange time (10 to 90 min at 450°C) were also

In order to evaluate the effects of the size of surface flaws on the strength of ion exchanged porcelain, the polished surface of specimens was indented with Vickers impression with increasing load from 2 to 49 N. In Figure 14a, it can be seen that the biaxial flexural strength,

, of UST porcelain, without (control) and with ion exchange treatment (KNO3, 470°C, 15 min), decreased with the increase in indentation load, since this increase causes the increase in radial/ median crack size, *c* (Figure 7b). However, the decrease in σf was more accentuated in the ion exchanged specimens. These results showed that the beneficial effects of ion exchange are more pronounced for small surface cracks, and less effective for larger and deeper flaws. This

results in a gradual decrease of residual compressive stress to the interior of porcelain. The positive effects of ion exchange disappear for flaws deeper than the thickness of compressive

16 Book Title

2 **Fig. 14.** Biaxial flexural strength, f, after a Vickers indentation at different loads (a) and 3 fracture resistance, KR, as a function of crack extension, Δc (b), for UST dental porcelain with

function of crack extension, Δc (b), for UST dental porcelain with and without ion exchanges. Data from [63,64]

5 The fracture resistance, *KR*, or *KIc* as a function of crack extension, Δ*c*, can be evaluated using

The fracture resistance, *KR*, or *KIc* as a function of crack extension, Δ*c*, can be evaluated using

*R* 7 *K k c* (11)

( ) *q*

Where, the parameters *k* and *q* are determined using the data obtained by power law fits on

8 Where, the parameters *k* and *q* are determined using the data obtained by power law fits on 9 the results of biaxial flexural strength, f, as a function of indentation load, P, in Figure 14a. 10 The calculated fracture resistance, *KR*, values as a function of crack extension, *Δc*, are shown 11 in Figure 14b. For the non-treated porcelain, *KR* increased with the increase in crack size, the 12 so-called rising R-curve behavior (crack growth resistance curve). This behavior is observed 13 in leucite-containing porcelains and is caused by the friction between rough crack surfaces 14 caused by crack deflection around leucite agglomerates [11]. Since this mechanical grip acts 15 in the crack wake, the shielding effect at the crack tip is intensified with the increase in crack 16 extension [67]. Rising R-curve effect is a desirable material behavior since it is necessary 17 additional energy to propagate the crack, besides that needed at the crack tip [65,66]. For the 18 ion exchanged porcelain, an opposite result was observed, with the decrease in KR as the 19 crack size increased. The decreasing residual compressive stress from the ion exchanged 20 surface annulled the rising R-curve effect of porcelain microstructure and more, resulted in 21 a decreasing R-curve effect. This behavior makes the strength of ion exchanged porcelain

23 The variability of strength in ceramic materials is closely related to its flaw population, since 24 fracture is a probabilistic event due to a random-like distribution of the strength-limiting 25 flaws [68]. The strength variability of ceramic materials can be evaluated using Weibull 26 statistic, which is based on the weakest-link theory, where the more severe flaw results in 27 fracture propagation and determine the strength [69]. The Weibull two-parameter

*q*

, after a Vickers indentation at different loads (a) and fracture resistance, KR, as a

*Kkc R* =×D (11)

, as a function of indentation load, P, in Figure 14a.

ion content introduced in the surface region, which

observed [62].

182 Ion Exchange - Studies and Applications

stress layer.

behavior is related to the gradient of K+

4 and without ion exchanges. Data from [63,64]

the results of biaxial flexural strength, σf

6 the following equation [64–66]:

the following equation [64–66]:

**Figure 14.** Biaxial flexural strength, σf

22 more sensitive to the size of flaws.

28 distribution is given by [14]:

σf

1

The variability of strength in ceramic materials is closely related to its flaw population, since fracture is a probabilistic event due to a random-like distribution of the strength-limiting flaws [68]. The strength variability of ceramic materials can be evaluated using Weibull statistic, which is based on the weakest-link theory, where the more severe flaw results in fracture propagation and determine the strength [69]. The Weibull two-parameter distribution is given by [14]:

$$P\_{f,l} = 1 - \exp\left[-\left(\frac{\sigma\_{f,l}}{\sigma\_0}\right)^w\right] \tag{12}$$

where, *Pf,i* is the probability of fracture of ith specimen, *i* is order number of *σf,i* (fracture stress of ith specimen, ranked in ascending order of values), *σ*0 is the characteristic strength (scale factor, defined for *Pf* = 63.2%), and *m* is the Weibull modulus (shape factor; the lower this value, the higher is variability). For this analysis, 30 specimens of UST porcelain for each condition (without and with ion exchange treatment – KNO3, 470°C, 15 min) were tested in biaxial flexural mode at a stress rate of 1 MPa/s (in artificial saliva at 37°C). The Weibull parameters (*σ*0 and *m*) were calculated based on the maximum likelihood method [70] and the results are shown in Figure 15.

It can be seen in Figure 15 that the ion exchange treatment increased more than 100% the characteristic strength, *σ*0, but it also caused larger variability in fracture stress, reducing around 50% the Weibull modulus, *m*. Higher variability means lower reliability in the strength of porcelain, which was caused by the decreasing R-curve behavior in ion exchanged porcelain (Figure 14b), since shallow cracks were significantly toughened by high residual compressive stress level near the surface, but deeper surface cracks were less shielded by the decreasing resistance to crack growth. Therefore, strongest specimens in non-treated porcelain were strengthened more than the weakest ones. Clinically, it is more relevant to consider the fracture stress in an acceptable fracture probability, for example, at *Pf* = 5%. Although strengthening is not so high as compared to the *σ*0 value (*Pf* = 63.2%), even at this low level of *Pf*,5% a significant

**Figure 15.** Weibull plot of biaxial flexural strength data, *σf* , for UST dental porcelain with and without ion exchange. Dotted lines are 95% confidence interval; *m* is Weibull modulus, *σ*0 is characteristic strength, and σ5% is fracture stress at 5% fracture probability. Data from [71]

strengthening effect (at 95% confidence interval) is observed (Figure 15). In practice, the strengthening effect could be even higher than for *Pf* = 5%, since clinical studies of inlays constructed with feldspathic porcelains have shown high fracture rates, up to 48% in evalua‐ tion periods of up to 3 years [72,73]. Therefore, although ion exchange reduces the mechanical reliability of dental porcelain, the strengthening effect is still significant even for low levels of fracture probability.

Another relevant factor related to the lifetime of a porcelain restoration is the mechanical degradation over time. When a ceramic material is subjected to a stress level lower than the fracture stress, the flaws (cracks) can growth slowly in a stable manner up to the time at which loading comes to a halt, reducing the material's strength due to the increase in crack size, or when a flaw achieves a critical size, given by the Griffith-Irwin fracture criterion (Equation 1), that results in (fast) fracture. This phenomenon is known as slow (or subcritical) crack growth, SGC, and silicate glasses, like porcelains, usually are highly susceptible to this type of degra‐ dation [74–76]. SCG occurs mainly by a stress corrosion mechanism (Figure 16), in which water molecules diffuse and are adsorbed at the crack tip, and then cause bonding rupture of glass network yielding Si–OH groups on each fracture surface, resulting in crack growth, by [76,77]:

$$\text{Si}-\text{O}-\text{Si} + \text{H}\_2\text{O} \rightarrow 2\text{Si}-\text{OH} \tag{13}$$

Since the oral environment is aggressive to porcelain restorations and has many characteristics that favor SCG (water from saliva and dentin tubules, masticatory stresses, temperature and

18 Book Title

1 and then cause bonding rupture of glass network yielding Si–OH groups on each fracture

3 *Si O Si H*2*O* 2*Si OH* (13)

2 surface, resulting in crack growth, by [76,77]:

SiO44- 7 network causing increase in crack size

4

21

5 **Fig. 16.** Schematic molecular images of slow crack growth (SCG) phenomenon in silicate 6 glass: (a) diffusion of water molecule to the crack front (dotted line); (b) bonding breakage of **Figure 16.** Schematic molecular images of slow crack growth (SCG) phenomenon in silicate glass: (a) diffusion of water molecule to the crack front (dotted line); (b) bonding breakage of SiO4 4- network causing increase in crack size

pH variations [67,78]), it is important to understand the response of strengthened porcelain to this phenomenon. The main method used to characterize the material's susceptibility to SCG is the dynamic fatigue method, in which specimens are tested in different stress rates and using the following equation [79–81]: 8 Since the oral environment is aggressive to porcelain restorations and has many 9 characteristics that favor SCG (water from saliva and dentin tubules, masticatory stresses, 10 temperature and pH variations [67,78]), it is important to understand the response of 11 strengthened porcelain to this phenomenon. The main method used to characterize the

12 material's susceptibility to SCG is the dynamic fatigue method, in which specimens are

14 (14)

$$\log \sigma\_{/} = \frac{1}{n+1} \log \dot{\sigma} + \log \sigma\_{/0} \tag{14}$$

where, *σf* is the flexural strength, *σ*˙ is the stress rate, *σf*0 the scaling parameter, and *n* is the slow crack growth, SCG, susceptibility coefficient (the higher is *n* value the lower is the suscepti‐ bility). Figure 17 shows the results of this test for UST porcelain, non-treated and ion exchanged (KNO3, 470°C, 15 min). It can be seen that ion exchanged porcelain had significantly higher *n* value (relative increase of around 50%), showing more resistance to SCG degradation. This effect is substantial and can impact the lifetime of a restoration. 15 where, *f* is the flexural strength, is the stress rate, *f*0 the scaling parameter, and *n* is the 16 slow crack growth, SCG, susceptibility coefficient (the higher is *n* value the lower is the 17 susceptibility). Figure 17 shows the results of this test for UST porcelain, non-treated and ion 18 exchanged (KNO3, 470°C, 15 min). It can be seen that ion exchanged porcelain had 19 significantly higher *n* value (relative increase of around 50%), showing more resistance to 20 SCG degradation. This effect is substantial and can impact the lifetime of a restoration.

0 log log 1

strengthening effect (at 95% confidence interval) is observed (Figure 15). In practice, the

Dotted lines are 95% confidence interval; *m* is Weibull modulus, *σ*0 is characteristic strength, and σ5% is fracture stress

constructed with feldspathic porcelains have shown high fracture rates, up to 48% in evalua‐ tion periods of up to 3 years [72,73]. Therefore, although ion exchange reduces the mechanical reliability of dental porcelain, the strengthening effect is still significant even for low levels of

Another relevant factor related to the lifetime of a porcelain restoration is the mechanical degradation over time. When a ceramic material is subjected to a stress level lower than the fracture stress, the flaws (cracks) can growth slowly in a stable manner up to the time at which loading comes to a halt, reducing the material's strength due to the increase in crack size, or when a flaw achieves a critical size, given by the Griffith-Irwin fracture criterion (Equation 1), that results in (fast) fracture. This phenomenon is known as slow (or subcritical) crack growth, SGC, and silicate glasses, like porcelains, usually are highly susceptible to this type of degra‐ dation [74–76]. SCG occurs mainly by a stress corrosion mechanism (Figure 16), in which water molecules diffuse and are adsorbed at the crack tip, and then cause bonding rupture of glass network yielding Si–OH groups on each fracture surface, resulting in crack growth, by [76,77]:

Since the oral environment is aggressive to porcelain restorations and has many characteristics that favor SCG (water from saliva and dentin tubules, masticatory stresses, temperature and

2 Si O Si H O 2Si OH --+ ® - (13)

= 5%, since clinical studies of inlays

, for UST dental porcelain with and without ion exchange.

strengthening effect could be even higher than for *Pf*

**Figure 15.** Weibull plot of biaxial flexural strength data, *σf*

at 5% fracture probability. Data from [71]

184 Ion Exchange - Studies and Applications

fracture probability.

**Figure 17.** Biaxial flexural strength, *σf* , as a function of stress rate (a) and predicted flexural strength as a function of time to fracture (b) for UST dental porcelain, with and without ion exchange, in artificial saliva at 37°C. In (a),*σ f*0 is the scaling parameter and n is the slow crack growth, SCG, susceptibility coefficient. Inert strength was determined at 100 MPa/s in air with a drop of silicone oil on the tensile surface to inhibit the occurrence of SCG. In (b), the slope of fitted curve is related with *n*. Data from [63,71]

Using the results of the dynamic fatigue test, it is possible to extrapolate the strength decrease after long lifetimes, as shown in Figure 17b. For both conditions, the average strength decreases over time, and after 10 years the expected remaining strength drops to around 30 MPa for the non-treated porcelain, but it still remains high (around 90 MPa) for the ion exchanged porcelain [71]. The increase in the stress corrosion coefficient, *n*, is a significant effect, since the difference in strength between ion exchanged porcelain and non-treated one increases over time. Therefore, besides increasing the strength the compressive layer generated by ion exchange process also decreases the rate of strength degradation by slow crack growth phenomenon.

Using the results of Weibull distribution (Figure 15) and dynamic fatigue test (Figure 17a) it is possible to construct the strength-probability-time (SPT) diagram [79,82,83], as shown in Figure 18. This diagram makes possible the estimation of a fracture stress at any time during the lifetime of a dental restoration at any fracture probability level. For example, it is possible to verify that UST porcelain after ion exchange has at least twice the fracture stress than nontreated porcelain even at a fracture probability as low as 1% during long lifetimes (e.g., 100 years). Note that the difference in fracture stress increases over time, at any level of fracture probability.

**Figure 18.** SPT (strength-probability-time) diagram for 1 day (1 d), 1 year (1 y), and 100 years (100 y) for UST dental porcelain, with and without ion exchange. Data from [63,71]


*2.3. MTT Assay*

Before adding 3-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyl-tetrazolium bromide (MTT), 3D cultures were transferred to clean cell culture wells. MTT 0.5 mg/mL was prepared in a cell medium and added to the 3D cultures. To measure the percentage of cells that did not attach to the scaffold, MTT was also added to the wells where cells were initially seeded (Figure S1). The MTT solution was incubated for 2 h at 37 ◦C, 5% CO2. After removing the MTT solution, DMSO was added and the absorbance of formazan was measured at 595 nm using a microplate spectrophotometer VICTOR3 V Multilabel plate-reader (PerkinElmer, Wellesley USA). The sum of the two respective absorbance values, the first deriving from the primary wells used during the seeding and the second deriving from the scaffolds, were considered as 100%.


*2.1. Reagents*

Alpha-mangostin (αM) was obtained as described in our previous studies [25] from Garcinia mangostana (DNP International Inc., Whittier, CA, USA) with 98% purity. The LLC-PK1 (ATTC® CL-101) epithelial cell line, derived from proximal tubular cells of the porcine kidney, was acquired from American Type Culture Collection (ATCC, Rockville, MD, USA). Cis-dichlorodiammineplatinum II (CDDP), trypan blue, fluorescein diacetate (FDA), tris(hydroxymethyl)aminomethane, sodium deoxycholate, 4-nonylphenyl-polyethylene glycol (NP-40), sodium fluoride (NaF), D-(+)-glucose, sodium pyrophosphate (Na4P2O7), sodium orthovanadate (Na3VO4), Triton X-100, glycerophosphate, 4-(2-hydroxyethyl)piperazine-1-ethanesulfonic acid (HEPES), Tween 20, glycerol, ethylenediaminetetraacetic acid (EDTA), dimethyl sulfoxide (DMSO), antimycin A, ethylene glycol-bis(2-aminoethylether)-N,N,N- ,N- -tetraacetic acid (EGTA), dinitrophenol (DNP), magnesium chloride (MgCl2) tetrahydrate, oligomycin, rotenone, sodium L-ascorbate, sodium azide (NaN3) and tetramethyl-p-phenylene diamine (TMPD), sodium phosphate monobasic (NaH2PO4), sodium phosphate dibasic (Na2HPO4), sodium dodecyl sulfate (SDS), ponceau S, chloroquine diphosphate salt (CQ), wortmannin and antibodies against microtubule-associated protein light chain 3B (LC3, L7543), ubiquitin-binding protein p62 (p62, P0067) and α-tubulin (α-Tub, T9026) were purchased from Sigma Aldrich (St. Louis, MO, USA). Dulbecco's Modified Eagle's Medium (DMEM), fetal bovine serum (FBS), and penicillin/streptomycin were purchased from Biowest (Riverside, MO, USA). Calcium chloride (CaCl2), sodium bicarbonate (NaHCO3), sodium chloride (NaCl), and hydrochloric acid (HCl) were purchased from J.T. Baker (Xalostoc, Edo. Mex, Mexico). Potassium chloride (KCl) was purchased from Mallinckrodt plc. (St. Louis, MO, USA). Protease inhibitor cocktail was purchased from Roche Applied Science (Mannheim, Germany). MitoTrackerTM Green FM was purchased from Thermo Fisher Scientific. Antibodies against malondialdehyde (MDA, ab6463), peroxisome proliferator-activated receptor gamma (PPARγ) coactivator 1α (PGC-1α, ab54481), 4-hydroxynonenal (4HNE, ab5605), parkin RBR E3 ubiquitin protein ligase (Parkin, ab15954) and total oxidative phosphorylation (OXPHOS, ab110413) rodent western blot (WB) antibody cocktail were purchased from Abcam (Cambridge, MA, USA). Antibodies against nuclear respiratory factor 1 (NRF1, sc-33771), voltage dependence anion channel (VDAC, sc-8828), phosphatase and tensin homologue (PTEN) induced kinase 1 (PINK1, sc-33796), transcription factor A, mitochondria (TFAM, sc-19050) were purchased from Santa Cruz Biotechnology (Dallas, TX, USA). Antibody against mitofusin 2 (MFN2, D2D10) was purchased from Cell Signaling Technology (Danvers, MA, USA). All the other reagents were analytical grade.


**3. Nanofabrication using plant sample**

The biological method that uses plant samples is the easiest and cheapest way to use a bottom-up approach to nanoparticle synthesis. In broad aspect, there are large number of research papers representing other plant parts for nanoformulation, which includes callus, seed, fruit, stem, flower, tendril and root, which we termed as green synthesis because the initial as well as the byproduct after nanoparticle synthesis both are ecofriendly and biodegradable. This greener method of plantderived reducing agent is used to reduce silver ions to form stable zero-valent silver called silver nanoparticles. Likewise, other metallic nanoparticles, such as gold, copper, silver, titanium, etc., are used for synthesis of nanoparticles using plant extract. The basic synthesis of nanoparticles using plant extract and silver salts was discussed in detail in the following paragraphs. Nanoparticle synthesis is a single pot reaction in which the preparation of the plant extract, AgNO3, is performed and these solutions are combined to form silver nanoparticles [14]. Silver salt was usually used in this chapter for analysis along with plant extract for the formulation of nanoparticles.


**Gianpaolo Abatecola 1,\* and Alberto Surace 2,\***

Received: 19 November 2020; Accepted: 15 December 2020; Published: 19 December 2020

**Abstract:** What is the state-of-the-art literature regarding the adoption of the complexity theory (CT) in engineering management (EM)? What implications can be derived for future research and practices concerning sustainability issues? In this conceptual article, we critically discuss the current status of complexity research in EM. In this regard, we use *IEEE Transactions on Engineering Management*, because it is currently considered the leading journal in EM, and is as a reliable, heuristic proxy. From this journal, we analyze 38 representative publications on the topic published since 2000, and extrapolated through a rigorous keyword-based article search. In particular, we show that: (1) the adoption of CT has been associated with a wide range of key themes in EM, such as new product development, supply chain, and project management. (2) The adoption of CT has been witnessed in an increasing amount of publications, with a focus on conceptual modeling based on fuzzy logics, stochastic, or agent-based modeling prevailing. (3) Many key features of CT seem to be quite clearly observable in our dataset, with modeling and optimizing decision making, under uncertainty, as the dominant theme. However, only a limited number of studies appear to formally adhere to CT, to explain the different EM issues investigated. Thus, we derive various implications for EM research (concerning the research in and practice on sustainability issues).

**Keywords:** complexity theory; engineering management; management; sustainability; conceptual


**3.1 Risk: The ethical yardstick**

Ethical decision making for pending technologies combines technical and ethical factors. It makes use of multiplex optimization or benchmarking, where only certain outcomes are acceptable. A technically acceptable outcome may be ethically unacceptable, and an ethically acceptable outcome may be technically unacceptable. The tools needed to evaluate the benefits and risks of emerging technologies share aspects of most decision support tools.

The distinguishing characteristic of a professional is what the Ancients referred to as *ethike aretai.* Roughly translated from Greek, it means "skill of character"(Pence 2003). This is a hybrid of both technical competence and ethics; not separate, but integrated throughout the life cycle of an innovation. Thus, the ethical technologist is not only competent and skillful

It comes as little surprise that inventors and innovators are better prepared and more willing to predict the benefits of their ideas and nascent projects than the concomitant risks. However, such bias is little comfort when mistakes, miscues and misdeeds are uncovered. As evidence, many of the case studies used in introductory engineering ethics courses have

The inventor or sponsor of a new medical device is likely to be very optimistic about the benefits, but predicting possible negative outcomes may be more obscure. Better credit card security devices could tread upon privacy. A genetically modified organism may do its job quite well in making medicine or cleaning up wastes, but may have risks, such as adverse effects on biodiversity. What these three seemingly diverse examples have in common is that the benefits are often more obvious and more immediate than the risks, which may be

Of course, hindsight is often 20/20 and is always easier than foresight. Predictions of an emerging technology's risks require a balance between being so overly cautious as to lead to loss of innovation and the introduction of large opportunity costs. Likewise, the prediction must not be so optimistic, or the risk prediction so rife with oversimplifications and

Another common element of the case studies mentioned is that the risks were not completely transparent or even ignored by decision makers (often by people with more power in the decision making process than the engineers or by engineers who had "forgotten" some of the ethical canons of the profession). Sometimes, the only reason the unethical decision making comes to light is a memo or note from a designer that implicates

Applying the philosophical tools of *reductio ad absurdum*, do we blame the Wright brothers for the misuse of an aircraft or drone? Do we blame Louis Pasteur for the use of anthrax in bioterrorism? Of course not. Somewhere along the way, however, the misuse of a technology must be properly considered. In the rapidly changing world of genetics, systems biology, nanotechnology, systems medicine and information technology, we do not have the

Ethical decision making for pending technologies combines technical and ethical factors. It makes use of multiplex optimization or benchmarking, where only certain outcomes are acceptable. A technically acceptable outcome may be ethically unacceptable, and an ethically acceptable outcome may be technically unacceptable. The tools needed to evaluate the benefits and risks of emerging technologies share aspects of most decision support tools.

luxury of waiting a few decades to observe the downsides of emerging technologies.

within a technical discipline, but is equally trustworthy and honorable.

an element of selective bias toward the predicted benefits of an innovation.

assumptions, that the risks are mischaracterized or completely missed.

**3. Predicting benefits and risk**

years or decades in the future.

the decision makers at the higher level.

**3.1 Risk: The ethical yardstick**

However, as technologies become more complicated, the potential impacts become more obscure and increasingly difficult to predict. The "sword of Damocles" is comprised of all potential, but unintended consequences. This means that new decision support tools must be employed to consider risks and costs over the life of the technology and beyond.

One metric of the ethics of a technology is whether it poses or could pose *unacceptable risk.*  Risk is the likelihood of negative outcomes. Too much risk means the new technology has failed society. Societal expectations of acceptable risk are mandated by technological standards and specifications, such as health codes and regulations, zoning and building codes and regulations, principles of professional engineering and medical practice, criteria in design guidebooks, and standards promulgated by international agencies (e.g. ISO, or the International Standards Organization) and national standard-setting bodies (e.g. ASTM, or the American Society for Testing and Materials).

Specific technologies are additionally sanctioned by organizations. For example, genetic modification of microbes, i.e. medical biotechnologies, are sanctioned by institutes of biomedical sciences, such as the American Medical Association and regulatory agencies, whereas food safety and environmental agencies, such as the U.S. Food and Drug Administration, the U.S. Department of Agriculture and the U.S. Environmental Protection Agency, and their respective state counterpart agencies, are responsible for new biotechnologies in their respective areas. Since emerging biotechnologies carry a reasonable potential for intentional misuse, a number of their research and operational practices are regulated and overseen by homeland security and threat reduction agencies, especially related to microbes that have been or could be used as biological agents in warfare and terrorism.

Of course, two terms used in the previous paragraphs beg for clarity. What is *unacceptable*  and what is *reasonable*? And, who decides where to draw the line between unacceptable and acceptable and between unreasonable and reasonable? It is not ethical to expose people to unacceptable risk. The acceptability of a technology has both inherent and use aspects. For example, radiation emitted from a device is inherently hazardous. However, if no one comes near the device it may present little risk, notwithstanding its inherent properties. Thus, the use of the device drives its acceptability. As such, acceptability is value-laden. A device that destroys a tumor may be well worth the exposure to its inherently hazardous properties.

Likewise, deciding whether a risk of a technology is reasonable also depends on its expected uses. One benchmark of technological acceptability is that a risk be "as low as reasonably practical" (ALARP), a concept coined by the United Kingdom Health and Safety Commission (2011). The Commission is responsible for health and safety regulation in Great Britain. The Health and Safety Executive and local government are the enforcing authorities who work in support of the Commission. The range of possibilities fostered by this standard can be envisioned as three domains (see Fig. 5). In the uppermost domain, the risk is clearly unacceptable. The bottom indicates generally acceptable risk. However, the size of these domains varies considerably on perspective. There is seldom consensus and often never unanimity.

Risks in the ALARP region need to be managed scientifically and ethically to produce an acceptable outcome. Thus, the utility of a particular application of a new biotechnology, for example, can be based upon the greatest good that the use of the technology will engender, compared to the potential harm it may cause. For example, consider a genetically

Ethical Decisions in Emergent Science, Engineering and Technologies 33

Therefore ALARP depends on a defensible margin of safety that is both protective and reasonable Hence, reaching ALARP necessitates qualitative and/or quantitative measures of the amount of risk reduced and costs incurred with the design decisions. The ALARP principle assumes that it is possible to compare marginal improvements in safety (marginal risk decreases) with the marginal costs of the increases in reliability (UK Health and Safety

To ascertain possible risks from emerging technologies, the first step is to identify the hazard (a potential threat) and then to develop a scenario of events that could take place to unleash the potential threat and lead to an effect. To assess the importance of a given scenario, the severity of the effect and the likelihood that it will occur in that scenario is calculated. This

The relationship between the severity and probability of a risk follows a general equation

 *R* = *f*(*S*, *P*) (1) Where risk (*R*) is a function (*f*) of the severity (*S*) and the probability (*P*) of harm. The risk

 *R* = *S* × *P* (2) The traditional health risk assessment, for example, begins with the identification of a hazard, which is comprised of a summary of an agent's physicochemical properties and routes and patterns of exposure and a review of toxic effects. (National Academy of Sciences 2002).

The risks associated with emerging technologies are doubly uncertain since the hazards are difficult to predict and the likely exposure can be variable and highly uncertain. Analogies of the risk of the new technology can seldom be directly extrapolated from existing technologies, *and* the emerging technology often takes place at scales much larger or smaller than better documented technologies. For example, if researchers are engineering materials at scales below 100 nanometers (i.e. nanotechnology), even the physical behavior is unknown. Since risk is a function of hazard and the exposure to that hazard, reliable assessment of that risk depends on sound physical characterization of the hazard. However, if even the physics is not well understood due to the scale and complexity of the research,

Indeed, the ethical uncertainty of emerging technologies is propagated in time and space. For example, many research institutions have numerous *nano-scale* projects (within a range of a few angstroms). Nascent areas of research include ways to links protein engineering with cellular and tissue biomedical engineering applications (e.g. drug delivery and new devices); ultra-dense computer memory; nonlinear dynamics and the mechanisms governing emergent phenomena in complex systems; and state of the art nano-scale sensors (including photonic ones). Complicating the potential societal risks, much of this research frequently employs biological materials and self-assembly devices to design and build some strikingly different kinds of devices. Among the worst case scenarios has to do with the replication of the "nanomachines." Advancing the state-of-the-science to improve the quality of life (e.g. treating cancer, Parkinson's disease, Alzheimer's disease, and improving life expectancies, or cleaning

combination of the hazard and exposure particular to that scenario constitutes the risk.

equation can be simplified to be a product of severity and probability:

the expected hazards to living things is even less well understood.

up contaminated hazardous wastes) can introduce different risks (Vallero 2007).

Commission 2011).

(Doblhoff-Dier 1999):

engineered bacterium that breaks downs a highly toxic contaminant that has seeped into the groundwater more efficiently than other available techniques (e.g. pumping out the groundwater and treating it aboveground using air stripping). If the only basis for success were cleaning up the site, this would be a fairly straightforward. That is, if goodness were based solely on this utility, the project is acceptable. However, such single-variable assessments are uncommon and can lead to erroneous predictions of outcome. For example, the engineer must evaluate whether the use of the biotechnology can introduce side effects, such as the production of harmful new substances, or whether the genetically engineered organisms could change the diversity and condition of neighboring microbial populations.

#### **NEGLIGIBLE RISK**

Fig. 5. Three regions of risk tolerance. *Source:* United Kingdom Health and Safety Commission (1998).

engineered bacterium that breaks downs a highly toxic contaminant that has seeped into the groundwater more efficiently than other available techniques (e.g. pumping out the groundwater and treating it aboveground using air stripping). If the only basis for success were cleaning up the site, this would be a fairly straightforward. That is, if goodness were based solely on this utility, the project is acceptable. However, such single-variable assessments are uncommon and can lead to erroneous predictions of outcome. For example, the engineer must evaluate whether the use of the biotechnology can introduce side effects, such as the production of harmful new substances, or whether the genetically engineered organisms could change the diversity and condition of neighboring microbial populations.

> Risk cannot be justified except in extraordinary circumstances

Tolerable only if risk

impracticable or if its cost is grossly disproportionate to improvement gained.

reduction is

Tolerable if cost of reduction would exceed improvement gained.

No need for detailed

working to demonstrate ALARP.

**INTOLERABLE**

**RISK**

**RISK AS LOW**

**REASONABLY POSSIBLE**

**AS**

**BROADLY ACCEPTABLE**

**RISK**

Fig. 5. Three regions of risk tolerance. *Source:* United Kingdom Health and Safety

**NEGLIGIBLE RISK**

Commission (1998).

Therefore ALARP depends on a defensible margin of safety that is both protective and reasonable Hence, reaching ALARP necessitates qualitative and/or quantitative measures of the amount of risk reduced and costs incurred with the design decisions. The ALARP principle assumes that it is possible to compare marginal improvements in safety (marginal risk decreases) with the marginal costs of the increases in reliability (UK Health and Safety Commission 2011).

To ascertain possible risks from emerging technologies, the first step is to identify the hazard (a potential threat) and then to develop a scenario of events that could take place to unleash the potential threat and lead to an effect. To assess the importance of a given scenario, the severity of the effect and the likelihood that it will occur in that scenario is calculated. This combination of the hazard and exposure particular to that scenario constitutes the risk.

The relationship between the severity and probability of a risk follows a general equation (Doblhoff-Dier 1999):

$$\mathcal{R} = f(\mathcal{S}, P) \tag{1}$$

Where risk (*R*) is a function (*f*) of the severity (*S*) and the probability (*P*) of harm. The risk equation can be simplified to be a product of severity and probability:

$$R = S \times P \tag{2}$$

The traditional health risk assessment, for example, begins with the identification of a hazard, which is comprised of a summary of an agent's physicochemical properties and routes and patterns of exposure and a review of toxic effects. (National Academy of Sciences 2002).

The risks associated with emerging technologies are doubly uncertain since the hazards are difficult to predict and the likely exposure can be variable and highly uncertain. Analogies of the risk of the new technology can seldom be directly extrapolated from existing technologies, *and* the emerging technology often takes place at scales much larger or smaller than better documented technologies. For example, if researchers are engineering materials at scales below 100 nanometers (i.e. nanotechnology), even the physical behavior is unknown. Since risk is a function of hazard and the exposure to that hazard, reliable assessment of that risk depends on sound physical characterization of the hazard. However, if even the physics is not well understood due to the scale and complexity of the research, the expected hazards to living things is even less well understood.

Indeed, the ethical uncertainty of emerging technologies is propagated in time and space. For example, many research institutions have numerous *nano-scale* projects (within a range of a few angstroms). Nascent areas of research include ways to links protein engineering with cellular and tissue biomedical engineering applications (e.g. drug delivery and new devices); ultra-dense computer memory; nonlinear dynamics and the mechanisms governing emergent phenomena in complex systems; and state of the art nano-scale sensors (including photonic ones). Complicating the potential societal risks, much of this research frequently employs biological materials and self-assembly devices to design and build some strikingly different kinds of devices. Among the worst case scenarios has to do with the replication of the "nanomachines." Advancing the state-of-the-science to improve the quality of life (e.g. treating cancer, Parkinson's disease, Alzheimer's disease, and improving life expectancies, or cleaning up contaminated hazardous wastes) can introduce different risks (Vallero 2007).

Ethical Decisions in Emergent Science, Engineering and Technologies 35

protected enough?" Managing risks consists of balancing among alternatives. Usually, no single way to prevent potential problems is available. Whether a risk is acceptable is determined by a process of making decisions and implementing actions that flow from these decisions to reduce the adverse outcomes or, at least to lower the chance that negative

Technologists can expect that whatever risk remains after their technologies reach the users, those potentially affected will not necessarily be satisfied with that risk. People want less risk, all other things being equal. Derby and Keeney (1981) have stated that "acceptable risk is the risk associated with the best of the available alternatives, not with the best of the alternatives which we would hope to have available."Calculating the risks associated with

1. The actual values of all important variables cannot be known completely and, thus

2. The physical and biological sciences of the processes leading to the risk can never be fully understood, so the physical, chemical and biological algorithms written into

3. Risk prediction using models depend on probabilistic and highly complex processes

The decision to proceed with most engineering designs or projects is based upon some sort of "risk-reward" paradigm, and should be a balance between benefits and costs (UK Department of Environment 1984). When comparing benefits to costs, values are inaccurate. Given the uncertainty, even a benefit/cost ratio that appears to weigh more heavily toward benefits, i.e.

For those involved in technologies, there are two general paths to ethical decisions, i.e. duty and outcome. Duty is at the heart of Immanuel Kant's (1785) "categorical imperative":

The categorical imperative is at the heart of duty ethics (so called "deontology"), invoking the question as to whether one's action (or inaction) will make for a better world if all others in that same situation were to act in the same way. Thus, the technology itself can be ethically neutral, whereas the individual action's virtue or vice is seen in a comprehensive manner. The unknowns surrounding emerging technologies may cause one to add safeguards or even to abandon a technology or a particular use of the technology. The obligation of the technologist is to consider the effects of universalizing one's new technology, from an all inclusive

Outcome-based ethics (so called "teleology") can be encapsulated in John Stuart Mill's (1863) utilitarianism's axiom of "greatest good for the greatest number of people." Even the most extreme forms of outcome-based ethics are moderated. For example, Mill added a "harm principle" which requires that no one be harmed in the pursuit of a noble outcome. That is, even though an emerging technology is expected to lead to benefits for the majority, it may still be unethical if it causes undue harm to even one person. John Rawls, who can be

*Act only according to that maxim by which you can at the same time will that it should become a*

these alternatives is inherently constrained by three conditions (Morgan 1981):

well above 1, may not provide an ample margin of safety given the risks involved.

perspective, considering all the potential good and all the potential bad.

cannot be projected into the future with complete certainty.

predictive models will propagate errors in the model.

that make it infeasible to predict many outcomes.

**4. Ethical constructs**

*universal law.*

consequences will occur (The Royal Society 1992).

The uncertain, yet looming threat of global climate change can be attributed in part to technological and industrial progress. Emergent technologies can help to assuage these problems by using alternative sources of energy, such as wind and solar, to reduce global demand for fossil fuels. However, these can have side effects, such as the low-probability but highly important outcomes of genetic engineering, e.g. genetically modified organisms (GMOs) used to produce food. GMOs may well help with world food and energy needs, but are not a panacea.

The renowned physicist Martin Rees (2003) has voiced an extreme perspective related to the apprehension about nanotechnology, particularly its current trend toward producing "nanomachines." Biological systems, at the subcellular and molecular levels, could very efficiently produce proteins, as they already do for their own purposes. By tweaking some genetic material at a scale of a few angstroms, parts of the cell (e.g. the ribosome) that synthesize molecules could start producing myriad molecules designed by scientists, such as pharmaceuticals and nanoprocessors for computing. Rees is concerned that such assemblers could start self-replicating (like they always have), but without any "shut-off." Some have called this the "gray goo" scenario, i.e. creating of an "extinction technology" from the cell's unchecked ability to to replicate itself exponentially if part of their design is to be completely "omnivorous," using all matter as food! No other "life" on earth would exist if this "doomsday" scenario were to occur.

Though extreme and (hopefully) unlikely, this scenario calls attention to the problem that ethics usually follows technological advancement. All events that lead to even this extreme outcome are individually possible. Most life systems survive within a fairly narrow range of conditions. Slight modifications can be devastating. So, emerging technologies call for even more vigilance and foresight. Engineers and scientists are expected to push the envelopes of knowledge. We are rewarded for our eagerness and boldness. The Nobel Prize, for example, is not given to the chemist or physicist who has aptly calculated important scientific phenomena, with no new paradigms. It would be rare indeed for engineering societies to bestow awards only to the engineer who for an entire career used only proven technologies to design and build structures. This begins with our general approach to contemporary scientific research. Technologists are often rugged individualists in a quest to add new knowledge. For example, aspirants seeking Ph.D.s must endeavor to add knowledge to their specific scientific discipline. Scientific journals are unlikely to publish articles that do not at least contain some modicum of originality and newly found information.

Innovation is rewarded. Unfortunately, there is not a lot of natural incentive for the innovators to stop what they are doing to "think about" possible ethical dilemmas propagated by their discoveries. However, the engineering profession is beginning to come to grips with to this issue; for example, in emergent "macroethical" areas like nanotechnology, neurotechnology, and even sustainable design approaches (National Academy of Sciences 2004).

Thus, those engaged in emerging technologies are expected to push the envelopes of possible applications and simultaneously to investigate likely scenarios, from the very beneficial to the worst-case ("doomsday") outcomes. This link between fundamental work and outcomes becomes increasingly crucial as such research reaches the marketplace relatively quickly and cannot be confined to the "safety" and rigor of the laboratory and highly controlled scale-ups.

Technological development thrusts the innovator into uncomfortable venues. Rarely is there a simple answer to the questions "How healthy is healthy enough?" And "How protected is protected enough?" Managing risks consists of balancing among alternatives. Usually, no single way to prevent potential problems is available. Whether a risk is acceptable is determined by a process of making decisions and implementing actions that flow from these decisions to reduce the adverse outcomes or, at least to lower the chance that negative consequences will occur (The Royal Society 1992).

Technologists can expect that whatever risk remains after their technologies reach the users, those potentially affected will not necessarily be satisfied with that risk. People want less risk, all other things being equal. Derby and Keeney (1981) have stated that "acceptable risk is the risk associated with the best of the available alternatives, not with the best of the alternatives which we would hope to have available."Calculating the risks associated with these alternatives is inherently constrained by three conditions (Morgan 1981):

The decision to proceed with most engineering designs or projects is based upon some sort of "risk-reward" paradigm, and should be a balance between benefits and costs (UK Department of Environment 1984). When comparing benefits to costs, values are inaccurate. Given the uncertainty, even a benefit/cost ratio that appears to weigh more heavily toward benefits, i.e. well above 1, may not provide an ample margin of safety given the risks involved.


**2. Geriatric problems in India**

#### **2.1 Health problems of elderly**

In India, the elderly people suffer from dual medical problems, i.e. both communicable as well as non-communicable diseases. It is estimated that one out of two elderly in India suffers from at least one chronic disease like diabetes mellitus, dyslipidemia, hypertension, COPD, thyroid disorders, heart diseases which requires life-long medication. This is further complicated by impairment of special sensory functions like vision and hearing. A decline in immunity as well as age-related physiologic changes leads to an increased burden of communicable diseases or infectious diseases like influenza, pneumonia, tuberculosis (TB) in the elderly population. Most common disabilities among the aged were locomotor and visual, almost half of the elderly disabled population was reported to be suffering from these two types of disabilities (2011 census). Cardiovascular disorders account for one third of elderly mortality followed by respiratory disorders mortality (10%) and infections including TB (10%). It has been reported that a geriatric individual takes an average of six prescription drugs concurrently and often suffers from adverse drug reactions [1, 3, 5, 6].

#### *Importance of Geriatric Health Care in India during Covid 19 Pandemic DOI: http://dx.doi.org/10.5772/intechopen.95500*

A key clinical issue in geriatric mental health is the heterogeneity in clinical presentations that confounds diagnosis and treatment of these problems. Patients usually deny the presence of mental health problems and are reluctant to seek help. Geriatric patients have multiple co-morbidities including psychological problems like depression. Few factors contributing to geriatric depression are female sex, widowed status, nuclear families, and stressful life events. Research indicates comparatively higher prevalence of geriatric depression in India, with a median prevalence rate of 21.9%. Symptoms of geriatric depression affect behavioral, physical, and cognitive domains of an individual, while many elderly do not seek help as a stereotype myth prevails that geriatric depression is normal in old age. Dementia is the most common neuropsychiatric illness besides depression as the major contributor to disability in people above 60 years of age, accounting for one quarter of all disability adjusted life years (DALY). It has been shown in different studies that, many environmental factors as well as caregiver approach towards elderly population with dementia are responsible for development of various psychiatric behaviors in elderly like agitation, irritability, restlessness, emotional distress and sleep disturbance. There is urgent need of sufficient number of dementia care homes, rehabilitation centers along with dementia daycare centers to manage the huge number of dementia patients in India [7–12].

## **2.2 Socioeconomic challenges for the elderly**

Elderly or geriatric populations have different socioeconomic problems in their life in the forms of loss of spouse, economic insecurity, social isolation, not getting pension timely etc. Elderly people in India not only work to support themselves but also make economic contributions to their households. Nearly 66% of those over 60 years of age are currently married, 32% are widowed and nearly 3% are separated or divorced (2011 Census). The proportion of those who have lost their spouse is much higher among women compared to men with 48% of older women and only 15% of the older men being widowed. Since women are more likely to be dependent on men for financial security, women face more adversities due to loss of spouse compared to men. Living arrangements among the elderly was not a problem in India till a few years ago because elderly people were given special respect and care in their family. Majority of the elderly are still living with their children in India, about one fifth either live alone or only with the spouse and hence have to manage their material and physical needs on their own. Financial dependency also increases with age. Around 50% of the elderly have some type of personal income after their retirement in the form of social security measures like pension scheme, provident fund, life insurance policies, post office deposits, savings bank interests and non service people of rural as well as urban India get some amount of incentives as part of different national health programs, schemes for elderly population; which will be discussed in subsequent sections. This income earned by the elderly is not sufficient sometimes to fulfill their basic needs and wishes to buy some gifts for their grandchildren on some special occasions. Most of the time, they are seen dependent financially on their offspring. Almost three fourth of the elderly are either fully or partially dependent on others like relatives, friends and neighbors and such dependency is more for elderly women than men. Overall, it appears that elderly still depend greatly on their earnings to support themselves and their family. Presently, lower class and middle class families are facing a great challenge to take care of elderly population due to reduction in economic activities following sudden Covid19 pandemic and subsequent lock-down [1, 6].


**Acknowledgements**

This work was partly supported by JSPS Bilateral Exchange Program – Open Partnership Joint Research Projects with UTP (Malaysia). YUTP Research Grant for CYH stay in Kumamoto University to carry out the experimental works is also being acknowledged.


**6. Shaft-Hub Connection**

The transferability of model-based static COF´s to real components was recently demonstrated for mechanical manufacturing processes [55]. However, in this study a shaft-hub connection was chosen to transfer the advanced tribological performance of laser textured surfaces to a real-life application for the first time. The shaft diameter was 40 mm with H7 press fit for the shaft-hub connection. As a reference, the static COFs were determined of μ20 = 0.20 and 0.24 for this specific shaft-hub connection considering a turned or rather fine-grinded surface quality of the contact area [34]. In this very initial test, the width of the laser textured area was 50 mm, as can be seen in the topographical analysis in Figure 15, left. Three different laser textures were analyzed: (i) the LSFL texture of Figure 10, (ii) the line pattern of Figure 12 and for reference purposes (iii) a dimple-shaped micro texture produced with a nanosecond laser as described in a previous study [14]. The frictional performance was analyzed by using a specially designed test rig for joined real components. The surface pressure varied in the measurements that was due to manufacturing tolerances of the shaft-hub assembly. The recorded slipping curves can be seen for the three different laser textures in Figure 15, right. Therefrom, the static and the maximum COFs μ20 and μmax as well as the COF type were assessed. It is noteworthy, all COF values determined for the laser textured shaft-hub connection are larger than that for the non-laser textured contact surfaces. Table 2 shows the highest static COF for the line-patterned laser texture, μ20 = 0.34 Type A while the maximum COF μmax = 0.4 was obtained with the dimple textured surface. Thus, the maximum COF increase was found of about + 20% for the LSFL, + 70% for the line pattern and + 100% for the dimple-shaped laser texture, respectively.

**Table 2.** Summary of the tribological characteristics of the different laser textures tested in a shaft-hub connection.

**Figure 15. left:** Topographical analysis of the shaft, the laser textured area is on the left shaft end; **right**: slipping curves captured in the frictional tests using a test rig for joined real components.

The processing rates given in Table 2 for the LSFL and line pattern, however, are noticeably lower than that presented above in Table 1 for ultrafast laser beam scanning. Therefore, it should be mentioned the processing rates in Table 2 are derived from the total processing time as spent for this specific shaft laser texturing using a galvanometer scanner at this very initial state. By further optimizing the laser texturing implementing polygon-mirror based and multi-beam strategies here, a substantially increase of the processing rates will also be achieved for rotational parts.


**6. Coefficient of friction**

Frictional force vs. time plot of Ni Surface Alloyed Cu-10Sn alloy is shown in **Figure 17**. The same trend was found for all the other Ni Surface alloyed specimens.

It can be noticed that the frictional force becomes constant after a short period of time and remains as such. The rapid increase in frictional force found initially is due to the uneven contact between the modified specimen and counter face material. The frictional force remains constant once perfect contact is achieved. A typical plot of coefficient of friction vs. time for the surface alloyed Cu-10Sn alloy is shown in **Figure 18**. The plot shows both transient period and single steady-state regime. The reasons for the transient behavior may be the effect of work-hardening and/or accumulation of debris as reported by Singh et al. [9].

The coefficient of friction obtained in this study for the substrate and the surface alloyed Cu-10Sn alloys are reported in **Table 6**. An average value of 0.23 was obtained as frictional coefficient after surface alloying process.

**Figure 17.** *Frictional force vs. time plot.*


**1. Introduction**

A wide range of signal perception and transduction systems in plant cells is responsible for distinguishing and triggering a correct adaptive response [1,2]. Signal transduction pathways that are specific for different stressors include several steps that are shown to be rather universal. One of them is a cytosolic Ca2+ elevation at internal and external signals application [3–5]. Plant cells carefully maintain a low Ca2+ concentration in the cytosol and a significant gradient between the cell wall and a number of organelles. This balance is actively regulated by a variety of membrane transport systems (recently reviewed in [6]). Another well-documented primary signaling event is the accumulation of reactive oxygen species (ROS) [7–10]. The appearance of ROS caused by different stressors triggers a wide spectrum of reactions and is quickly eliminated by the antioxidant machinery. Both the second messengers (Ca2+ and ROS) are closely linked to each other and are involved in the transduction of different environmental stressors and internal signals such as phytohormones, regulatory proteins, RNAs and metabolites [6,7,11]. One important mechanism is the Ca2+-induced activation of NADPH-oxidase activity via its integration in sterol-rich lipid rafts [12]. On the other

hand, ROS activates Ca2+ channels in the plasma membrane [3] and Ca2+-ATPases, which interfere with Ca2+ homeostasis [13]. Initially, weak signals of both Ca2+ and ROS were hypothesized to be amplified through the so-called ROS-Ca2+ hub [6]. The change of Ca2+ and ROS to an inactive state is also a necessity and supposed to be highly regulated. The cytosol Ca2+ elevation triggered by a variety of internal and external signals coincides with cytosolic acidification (reviewed in [14,15]). It was questionable for some time if protons had a signaling role, probably because of its involvement in metabolism. It is well known that Ca2+ elevation in the cytosol affects the pH level. The duration and intensity of Ca2+ increases might vary and thus determine the specificity of response to diverse signals (stressors, hormones, light, etc.). Different systems of H+ transport through the plasma membrane and tonoplast are involved. Proton pumps are supposed to be regulated on transcriptional and posttranslational levels, which make the H+ signature highly specific.

Electrolyte leakage is another process accompanying transient Ca2+, ROS and proton increases under stress conditions. A number of experimental data reveals that electrolyte leakage is mainly defined as K+ efflux from plant cells [16,17].

Potassium is essential for plant cells/organisms in many aspects. It is a well-known macro-nutrient. Deficiency of K+ results in growth arrest especially in seedlings and young organs. This ion is important for plant metabolism due to its facility to activate more than 70 enzymes [17,18]. Besides that, K+ serves as a charge-balancing ion, which plays an important role in the transport through the plasma membrane under the limitation of ATP and the depolarization of the membrane potential. The K+ gradient maintains turgor and serves as a source of energy to stimulate sucrose loading into the phloem [19,20]. Accumulated evidence indicates that the efflux depends on the type and the intensity of the stressor as well as on affected plant species and tissue. The cytosolic K+ concentration in plant cells is about 70–200 mM [21]. Several transporters have been shown to be involved in K+ accumulation: the High Affinity K+ transporter (HAK)–K+ uniporter and the Arabidopsis K+ Transport system 1 (AKT1)–K+/H+ symporter [18,22]. These processes require energy and depend on the external K+ concentration and the K+ vacuolar pool. The priority role in stress-induced K+ leakage is given to another system: Gated Outward Rectifying K+ efflux (GORK) channels [23]. By activation, it decreases the cytosolic potassium concentration to 10–30 mM [21]. The activity of these channels is sensitive to membrane potential depolarization through a clustering mechanism [24]. A number of experimental data combined with a bioinformatics approach suggest a possible ligand regulation of K+ flux through GORK channels [25]. Cyclic nucleotides (CNs), gamma-aminobutyric acid (GABA), G proteins, protein phosphatases, inositol, ROS and ATP are on a list of potential GORK regulating ligands. The presented data even stronger introduced both K+ and GORK in signaling cascades triggered by stress factors and led to the conclusion that potassium fulfills the role of a second messenger [17,25]. A signaling role of potassium is well documented for salt stress [18].

Oxygen deficiency is another stress factor that affects K+ efflux and causes severe damage to plant organisms [26–30]. Surprisingly, plants known as strict aerobic organisms might be affected not only by an external lack of oxygen. Different tissues or even groups of cells experience hypoxia conditions during normal plant development [31]. Thus, it becomes even more important to discover the steps of early hypoxia signal transduction. The mechanism of oxygen sensing in plants and animals are strictly diverse. However, low oxygen regulates the function of various K+ channels in mammals. Recently, plant cell ion channels have been identified as potential candidates for low oxygen sensing in flooded roots [32]. However, additional studies are required to estimate a possible modulation of K+ transport through an ERF-VII-mediated response to a lack of oxygen. Nevertheless, hypoxic/anoxic environment causes K+ efflux from plant cells and GORK channels are supposed to play a crucial role in this process [25]. Under hypoxic stress, cytosolic K+ participates in the regulation of several physiological processes possibly including the formation of aerenchyma [33].

Oxygen deprivation triggers Ca2+ signaling. The alteration in [Ca2+]cyt is a fast and intensive reaction for limitation in oxygen supply and energy deficiency [34–37]. Ca2+ elevation in cytosol coincides with acidification [15]. A probable reason is the hypoxia-induced inhibition of the plasma membrane H+-ATPase and tonoplast H+-ATPase activities due to the lack of ATP.

Signaling via K+ efflux is transient and therefore the timing of this event should be evaluated. It was found that plant species and even plant organs differ in their tolerance to hypoxia. Some data revealed that the intensity of K+ efflux corresponded to plant sensitivity to oxygen deprivation and depended on metabolic activity [30]. It would be of interest to integrate [K+]cyt into the schedule of other intracellular signaling events such as Ca2+ elevation and acidification under a lack of oxygen. Earlier we provided a comparison of calcium signaling during anoxia in two well-known agricultural plants such as rice and wheat, which differ in tolerance to oxygen limitation [35]. Rice cells were shown to be more reactive to oxygen depletion and depended on both external and internal Ca2+ stores. This study focuses on a possible alteration of intracellular potassium concentration, [K+]cyt, and cytosolic pH, pHcyt, during anoxic signal transduction in the protoplasts from wheat and rice.

The mentioned ions have not only a signaling role but also are very important in the regulation of metabolic processes including those at stress conditions. We therefore also estimated the cell-level events at a whole plant level. We investigated the ion changes after reoxygenation as well. It is of special interest how proton and Ca2+ accumulation inside cells and active potassium efflux would reflect processes during long-time stress applications and regulate the ion exchange with an external medium.

#### **2. Results**


**17.4 Dual antagonist of angiotensin II and endothelin A receptors**

Known is the role of Ang II and ET1 in the pathogenesis of essential hypertension. These substances produce vasoconstriction through the activation of its receptors in vascular smooth muscle: the AT1 and ETA, respectively. Ang II promotes the production of endothelin and endothelin in turn increases the synthesis of Ang II.

Moreover, evidence suggests the interrelation between the endocrine and paracrine systems of Ang II and endothelin. Ang II increases the expression of RNA messenger in endothelial cells. Ang II stimulates the release of ET1 by endothelial cells involving AT1 receptors, Ca2+ mobilization and activation of kinase C protein. ARBs produce a significant decrease in BP and reduce endothelial dysfunction and cardiovascular mortality in hypertensive, diabetic and HF patients. Thus, the activation of ETA and/or ETB receptors of ET1 causes contraction of vascular smooth muscle cells and increases BP and an antagonist of ETA/ETB receptors like bosetan decreases BP in patients with essential hypertension. Thus the combination in one same drug of properties AT1/ETA receptors antagonists may have a greater effect than either drug alone and with fewer side effects. Today ETA receptor blockers have been modified to acquire AT1 receptor antagonism.

There are several compounds (MS-248 360, BMS-248360, SB-290 670) that in laboratory animals decreased BP, but these investigations are still in very early stages. (Kowal et al., 2004) But this new class of antihypertensive drug which simultaneously antagonizes the AT1 and ETA receptors promise to be a novel approach in the treatment of hypertension and other cardiovascular diseases.


**2.2 Proposed design**

Given the constraints presented earlier, we propose a general design. A general view of the structure is shown in **Figure 1**. On the left, we choose a DFB cavity for the

**113**

**Table 2.**

*the last three.*

variations.

**Figure 1.**

**2.3 Choice of material**

*Widely Tunable Quantum-Well Laser: OPO Diode Around 2 μm Based on a Coupled Waveguide…*

laser in order to provide longitudinal as well as transversal single-mode operation. The upper waveguide narrows in the transfer region, where the mode moves to the bottom waveguide. On the right, parametric conversion takes place in the bottom waveguide, where distributed Bragg reflectors (DBRs) provide a high reflectivity at the signal and idler wavelengths. We will describe the device step-by-step, starting from the end because the zone of parametric conversion is the most sensitive to geometry

To reach phase matching and a high conversion efficiency, we simulated vari

ous waveguide geometries before settling on high index contrasts and a pump of order 2 in the direction of growth. To maximize index contrast in the vertical direction, the waveguide is surrounded by Al0.8Ga0.2As in the bottom cladding and by air above. However, in the region of transfer, the top cladding will be provided by whatever material separates laser and underlying waveguide core. We set this material to be Al0.3Ga0.7As, since it is already used as cladding in GaAs lasers. This layer structure is summarized in **Table 2**. It is identical to a standard laser data sheet, apart from the modified separation layer and added nonlinear waveguide and cladding. In the region of frequency conversion, all layers are etched, except for the

last three: nonlinear waveguide, bottom cladding, and substrate.

**Layer type Material** Top cladding AlGaAs Optical confinement (laser core) InGaAsP Quantum well InGaAs Optical confinement (laser core) InGaAsP Separation layer Al0.3Ga0.7As Nonlinear waveguide GaAs Bottom cladding Al0.8Ga0.2As Substrate GaAs

*Layer structure proposed for the coupled-cavity design. In the region of conversion, all layers are etched except*

*DOI: http://dx.doi.org/ 10.5772/intechopen.80517*

*General view of the proposed coupled-cavity design.*

*Widely Tunable Quantum-Well Laser: OPO Diode Around 2 μm Based on a Coupled Waveguide… DOI: http://dx.doi.org/ 10.5772/intechopen.80517*


**NONLINEARITY, BIFURCATION AND CHAOS – THEORY AND APPLICATIONS**

Edited by **Jan Awrejcewicz**  and **Peter Hagedorn**

http://dx.doi.org/10.5772/2888 Edited by Jan Awrejcewicz and Peter Hagedorn

### **Contributors**

Tomasz Jan Kopecki, Miroslav Verbič, Alexander Seyranian, Anton Belyakov, Bohdan Podlevskyi, Fabiola Angulo, Gerard Olivar, John Alexander Taborda, Nikolai A. Alexandrovich Magnitskii, N. M. Evstigneev, Ulrich Füllekrug, Jose Manoel Balthazar, Dumitrache Alexandru, Frunzulica Florin, Ionescu Tudor, Olevs'Kyy Victor, Igor Andrianov, Jan Awrejcewicz, Tomasz Kubiak

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Nonlinearity, Bifurcation and Chaos - Theory and Applications Edited by Jan Awrejcewicz and Peter Hagedorn p. cm. ISBN 978-953-51-0816-0 eBook (PDF) ISBN 978-953-51-5013-8