**6. Impact of plastic deformation parameters on microstructure and properties evolution**

Some major metal processing parameters such as temperature, strain rate, and strains that impact steel microstructures and their flow curves are illuminated in detail in the following.

### **6.1 The role of work-hardening rate**

The WH rate enables strengthening and hardening to the materials below the recrystallization temperature. Rapid WH rates are realized in low strain regions due to increases in dislocation density while at later strain increase, the effect starts to decrease due to recrystallization of new strain-free grains [31, 36, 40, 50, 56]. In WH, dislocations are preferably pinned, which will impede crack propagation on the microscale. With increasing temperature, the probability of rearrangement of atoms is higher which assists lower strength but increases the ductility of materials. Samantaray et al. [36] have reported for 316 L stainless steel that the WH rate starts rapidly with increasing temperature and strain rate at a specific value of strain (see **Figure 8**). The WH rate gradually decreased at higher temperature with increasing strain while it falls more rapidly under lower temperature conditions.

Lin et al. [44] have derived the following model for the influence of dynamic recovery during WH (see Eq. (2)) and dynamic recrystallization Eq. (3) under different deformation conditions within TMP.

$$\sigma = \left[ \sigma\_{DRV}^2 + \left( \sigma\_0^2 - \sigma\_{DRV}^2 \right) \exp(-\mathcal{Q} \,\varepsilon) \right]^{0.8} \tag{2}$$

**11**

*Plastic Deformation Behavior in Steels during Metal Forming Processes: A Review*

( ) 1

*The impact of temperature and strain rate on work hardening rate of stainless steel [57].*

is peak strain; εc is critical strain; is coefficient of dynamic recovery.

 σ

σ σ ε

*DRV P DRX d c*

− =σ − −σ − − ≥

Where σ is flow stress; σDRV is steady-state stress due to dynamic recovery; σDRV is steady-state stress due to dynamic recrystallization; σ0 is yield stress; ε is strain; εP

Generally, the critical strain acknowledgeable for the start of DRX can be calculated either by deformed microstructure or flow stress curves [58], In which flow stress curve analysis are simple and easier while microstructural are complicated. This flow curve analysis method was proposed in 1981 by Mecking et al. [59] and later it developed by Ryan et al. [60] and McQueen et al. [61] emphasize the point where DRX occurs on the flow curves. This method allows to find out the critical strain point where the flow curve changes due to the formation of new strain-free

**6.2 On properties derived from flow curves and relation to microstructures**

The flow stress–strain curve reflects the changes in the material through plastic deformation during dynamic loading [25, 32, 62–66]. The flow stress can be influenced by several factors like chemical composition, crystal structure (e.g., steel matrix - bcc, fcc, Mg-base - hcp, and others) [50, 67], different phases and compounds [17, 30, 50, 68–70], grain boundaries [25, 50, 71, 72] as well as imperfections [34, 50, 55, 73, 74]. Other factors such as friction (σf), thermal (σt) and athermal (σa) terms also affect flow stresses, as indicative by relations

> σ εε

Where T is temperature, ἐ is strain rate and ε is strain. σa represents the internal stress which occurs due to long range barriers to dislocation motion in the materials, while σf term reflects the stress needed to overcome the lattice friction depending

 σ=+ + f( , ,, *T T* ) *t a* ( ) (4)

*exp K*

*d n c*

ε ε (3)

*P*

ε

ε ε

*DOI: http://dx.doi.org/10.5772/intechopen.97607*

σ

grains via DRX.

**Figure 8.**

in Eq. (4) [75].

on strain rate and temperature.

*Plastic Deformation Behavior in Steels during Metal Forming Processes: A Review DOI: http://dx.doi.org/10.5772/intechopen.97607*

**Figure 8.** *The impact of temperature and strain rate on work hardening rate of stainless steel [57].*

$$\sigma = \sigma\_{DRV} - \left(\sigma\_P - \sigma\_{DRX}\right) \left\{ \mathbf{l} - \exp\left[ -K\_d \left(\frac{\mathbf{c} - \mathbf{c}\_c}{\mathbf{c}\_p}\right)^{n\_d} \right] \right\} \mathbf{c} \ge \mathbf{c}\_c \tag{3}$$

Where σ is flow stress; σDRV is steady-state stress due to dynamic recovery; σDRV is steady-state stress due to dynamic recrystallization; σ0 is yield stress; ε is strain; εP is peak strain; εc is critical strain; is coefficient of dynamic recovery.

Generally, the critical strain acknowledgeable for the start of DRX can be calculated either by deformed microstructure or flow stress curves [58], In which flow stress curve analysis are simple and easier while microstructural are complicated. This flow curve analysis method was proposed in 1981 by Mecking et al. [59] and later it developed by Ryan et al. [60] and McQueen et al. [61] emphasize the point where DRX occurs on the flow curves. This method allows to find out the critical strain point where the flow curve changes due to the formation of new strain-free grains via DRX.

#### **6.2 On properties derived from flow curves and relation to microstructures**

The flow stress–strain curve reflects the changes in the material through plastic deformation during dynamic loading [25, 32, 62–66]. The flow stress can be influenced by several factors like chemical composition, crystal structure (e.g., steel matrix - bcc, fcc, Mg-base - hcp, and others) [50, 67], different phases and compounds [17, 30, 50, 68–70], grain boundaries [25, 50, 71, 72] as well as imperfections [34, 50, 55, 73, 74]. Other factors such as friction (σf), thermal (σt) and athermal (σa) terms also affect flow stresses, as indicative by relations in Eq. (4) [75].

$$
\sigma = \sigma\_f \left( \pounds, T \right) + \sigma\_\iota \left( \pounds, \pounds, T \right) + \ \sigma\_\iota \tag{4}
$$

Where T is temperature, ἐ is strain rate and ε is strain. σa represents the internal stress which occurs due to long range barriers to dislocation motion in the materials, while σf term reflects the stress needed to overcome the lattice friction depending on strain rate and temperature.

*Material Flow Analysis*

tion sub boundaries.

*800°C with strain rate 0.001 s−1 [52].*

**Figure 7.**

detail in the following.

temperature conditions.

different deformation conditions within TMP.

σ σ

**and properties evolution**

**6.1 The role of work-hardening rate**

bulging can be seen in **Figure 7b**. It is obvious that the dDRX nuclei contain a much lower dislocation density than the deformed region and these nuclei are distinct from highly disturbed substructures with twin boundaries and low angle disloca-

*(a) Schematic correlation between cDRX and dDRX [51], (b) dDRX nuclei in austenitic stainless steel at*

One additional terminology has recently been denoted as post-DRX which

Some major metal processing parameters such as temperature, strain rate, and strains that impact steel microstructures and their flow curves are illuminated in

The WH rate enables strengthening and hardening to the materials below the recrystallization temperature. Rapid WH rates are realized in low strain regions due to increases in dislocation density while at later strain increase, the effect starts to decrease due to recrystallization of new strain-free grains [31, 36, 40, 50, 56]. In WH, dislocations are preferably pinned, which will impede crack propagation on the microscale. With increasing temperature, the probability of rearrangement of atoms is higher which assists lower strength but increases the ductility of materials. Samantaray et al. [36] have reported for 316 L stainless steel that the WH rate starts rapidly with increasing temperature and strain rate at a specific value of strain (see **Figure 8**). The WH rate gradually decreased at higher temperature with increasing strain while it falls more rapidly under lower

Lin et al. [44] have derived the following model for the influence of dynamic recovery during WH (see Eq. (2)) and dynamic recrystallization Eq. (3) under

> σ σ

2 22 0.5 0 [ ( )exp( )]

 Ω = + − −ε *DRV DRV* (2)

**6. Impact of plastic deformation parameters on microstructure**

occurs during the annealing process in deformed materials [55].

**10**

In addition, processing temperatures and strain rates are equally important for the plastic deformation behavior. Therefore, the dynamics of TMP can be understood through the investigation of microstructural changes combined with interpretations of trends of flow stress–strain curves which depend on DRV and DRX, and SRX [76, 77]. I.

It is noticed in most cases that flow stress decreases with increase in temperature and depends upon the applied strain rate [27, 32, 36, 78]. In terms of temperature, strain, and strain rates, the flow curves can be expressed by Eq. (5) [75].

$$
\sigma = \frac{2}{\sqrt{3 \left( 1 - m \right)}} K e^{\
u} \text{ è, } \exp \left( -\beta T \right) \tag{5}
$$

Where m stands for strain rate sensitivity, n represents the strain hardening exponent, and K, β represents material constant.

In the following, some flow curve trends of different steels and underlying phenomena are discussed.

Researchers reported that the series of flow curves are subjected to different temperatures and strain rates for different grades of steels [26, 27, 44, 45, 64]. Lin et al. [44] have reported interesting results for hot deformation of 42CrMo grade high strength steel in which they found that flow stress increased with decreasing temperature (**Figure 9a**) while it increased with strain rates (**Figure 9b**). At the slower strain rate in different ranges of temperatures, the flow stress will decrease with increase in temperature due to increase in the amount of cross slip screw dislocations and climb of edge dislocations, as well as vacancy diffusion. This results in an increase of grain boundary mobility and energy accumulation at boundaries for the nucleation and growth in DRX grains and dislocation annihilation which is responsible for the decrease in flow stress [27, 64].

Kumar et al. [27] have found for hot deformed condition in high strength steel that flow stress increases continuously at lower deformation temperature (750–850°C) due to continuation of work hardening phenomena High temperature showed the higher steady-state condition where DRX was dominant. While both DRV and DRX were dominant at all strain rates with decreasing temperature, the dDRX phenomena was more prominent at slow strain rate (0.001 s-1) at 900°C due to nucleation of unstrained grains that occurs normally in low SFE high strength steels. A flow curve without pronounced peak stress, but which exhibits

**13**

*Plastic Deformation Behavior in Steels during Metal Forming Processes: A Review*

a steady-state, is generally associated with dynamic recovery being the dominant

Zhang et al. [31] have reported that several original grains were broken and recrystallized new grains showed up along the grain boundaries at the deformed condition at 900°C with a high strain rate 10 s−1, which indicates that the deformed morphology is inhomogeneous. In contrast, at the same temperature but with a lower strain rate (1 s−1), DRX was observed along the grain boundaries. This is due to local temperature rise within the samples during deformation. It is interesting to note that at the initial stage of strain, the flow stress increases steeply due to work hardening phenomena in materials having higher carbon content and less austenite

stabilizing alloying elements. It reaches a peak value before going into the

temperatures with different strain rates in austenitic stainless steel. Also, some differences could be seen in the work hardening phenomena; the slopes of the flow stress curves changed. In the initial work hardening region, the increase of dislocation density during deformation is controlled by the competition between storage and annihilation of dislocations, i.e. opposing contributions of work hardening and the dynamic recovery due to the change of dislocation density with deformation.

**7. SPD impacts on the structure and mechanical properties of steels**

indirectly associated with material stability and durability.

SFE between 15 to 50 mJ/m2 [31, 85, 87].

steelmakers Kobo steel, Nippon, and Sumitomo steel organizations.

Severe plastic deformation where metal grains are heavily deformed is realized by using several setups of plastic deformations like high-pressure torsion, equal channel angle pressing, multi-axial forging, twist extrusion, accumulated roll bending, and constrained groove pressing [22, 34, 80]. Severe deformation produces not only a strong direct impact on the mechanical properties i.e. high strength, lowtemperature toughness, superior plasticity, good ductility, and good wear resistance of high manganese grades steel but also on other important properties such as thermal stability, diffusion, radiation tolerance, and corrosion properties, which are

The high manganese steels (Mn) are advanced high strength austenitic steels that contain Mn between 3 to 31% wt. These steels are known as Hadfield steel, damping steel, complex steel, transformation induced plasticity steel (TRIP), and twinning induced plasticity steel (TWIP) [81, 82]. In all of these, Hadfield steel was firstly discovered in 1882 by Sir Robert Hadfield [83] while TWIP steel is one of the latest fully austenitic steel which is developed in the early 1990s by Japanese

It is well known that ultrafine and nanocrystalline structure depends on three mechanisms; martensitic transformation, dislocation motion, and twinning and twin evolution where stacking fault energy (SFE) of material plays an important role. **Figure 10** reflects the relation between strain-induced mechanism vs. temperature and SFE for Fe-20Mn-4Cr-0.5C steel. It shows that retained austenite can be converted into ε-martensite and strain-induced by a twinning mechanism at a lower temperature. Thus, the calculation of martensitic start temperature *and* SFE value is necessary to achieve the right combination of mechanical and other properties in low SFE high Mn steels. It is well known that the SFE of materials depends on chemical composition and on temperature [84–86]. The high Mn steels have a low

Allain et al. [86] reported results for Fe-22Mn-0.06C steel where the temperature influences the SFE values and strain-induced mechanism, which can be seen in **Table 3**. The strain hardening and mechanical behavior of steels strongly depend

Souza et al. [45] have documented results of hot deformation testing at elevated

*DOI: http://dx.doi.org/10.5772/intechopen.97607*

restoration mechanism [79].

softening stage.

**Figure 9.** *The true stress–strain curves at different temperatures and strain rates for 42CrMo steel [44].* *Plastic Deformation Behavior in Steels during Metal Forming Processes: A Review DOI: http://dx.doi.org/10.5772/intechopen.97607*

a steady-state, is generally associated with dynamic recovery being the dominant restoration mechanism [79].

Zhang et al. [31] have reported that several original grains were broken and recrystallized new grains showed up along the grain boundaries at the deformed condition at 900°C with a high strain rate 10 s−1, which indicates that the deformed morphology is inhomogeneous. In contrast, at the same temperature but with a lower strain rate (1 s−1), DRX was observed along the grain boundaries. This is due to local temperature rise within the samples during deformation. It is interesting to note that at the initial stage of strain, the flow stress increases steeply due to work hardening phenomena in materials having higher carbon content and less austenite stabilizing alloying elements. It reaches a peak value before going into the softening stage.

Souza et al. [45] have documented results of hot deformation testing at elevated temperatures with different strain rates in austenitic stainless steel. Also, some differences could be seen in the work hardening phenomena; the slopes of the flow stress curves changed. In the initial work hardening region, the increase of dislocation density during deformation is controlled by the competition between storage and annihilation of dislocations, i.e. opposing contributions of work hardening and the dynamic recovery due to the change of dislocation density with deformation.

### **7. SPD impacts on the structure and mechanical properties of steels**

Severe plastic deformation where metal grains are heavily deformed is realized by using several setups of plastic deformations like high-pressure torsion, equal channel angle pressing, multi-axial forging, twist extrusion, accumulated roll bending, and constrained groove pressing [22, 34, 80]. Severe deformation produces not only a strong direct impact on the mechanical properties i.e. high strength, lowtemperature toughness, superior plasticity, good ductility, and good wear resistance of high manganese grades steel but also on other important properties such as thermal stability, diffusion, radiation tolerance, and corrosion properties, which are indirectly associated with material stability and durability.

The high manganese steels (Mn) are advanced high strength austenitic steels that contain Mn between 3 to 31% wt. These steels are known as Hadfield steel, damping steel, complex steel, transformation induced plasticity steel (TRIP), and twinning induced plasticity steel (TWIP) [81, 82]. In all of these, Hadfield steel was firstly discovered in 1882 by Sir Robert Hadfield [83] while TWIP steel is one of the latest fully austenitic steel which is developed in the early 1990s by Japanese steelmakers Kobo steel, Nippon, and Sumitomo steel organizations.

It is well known that ultrafine and nanocrystalline structure depends on three mechanisms; martensitic transformation, dislocation motion, and twinning and twin evolution where stacking fault energy (SFE) of material plays an important role. **Figure 10** reflects the relation between strain-induced mechanism vs. temperature and SFE for Fe-20Mn-4Cr-0.5C steel. It shows that retained austenite can be converted into ε-martensite and strain-induced by a twinning mechanism at a lower temperature. Thus, the calculation of martensitic start temperature *and* SFE value is necessary to achieve the right combination of mechanical and other properties in low SFE high Mn steels. It is well known that the SFE of materials depends on chemical composition and on temperature [84–86]. The high Mn steels have a low SFE between 15 to 50 mJ/m2 [31, 85, 87].

Allain et al. [86] reported results for Fe-22Mn-0.06C steel where the temperature influences the SFE values and strain-induced mechanism, which can be seen in **Table 3**. The strain hardening and mechanical behavior of steels strongly depend

*Material Flow Analysis*

and SRX [76, 77]. I.

phenomena are discussed.

In addition, processing temperatures and strain rates are equally important for the plastic deformation behavior. Therefore, the dynamics of TMP can be understood through the investigation of microstructural changes combined with interpretations of trends of flow stress–strain curves which depend on DRV and DRX,

It is noticed in most cases that flow stress decreases with increase in temperature and depends upon the applied strain rate [27, 32, 36, 78]. In terms of temperature,

> ( ) ( ) 2 , exp

εε

Where m stands for strain rate sensitivity, n represents the strain hardening

In the following, some flow curve trends of different steels and underlying

Researchers reported that the series of flow curves are subjected to different temperatures and strain rates for different grades of steels [26, 27, 44, 45, 64]. Lin et al. [44] have reported interesting results for hot deformation of 42CrMo grade high strength steel in which they found that flow stress increased with decreasing temperature (**Figure 9a**) while it increased with strain rates (**Figure 9b**). At the slower strain rate in different ranges of temperatures, the flow stress will decrease with increase in temperature due to increase in the amount of cross slip screw dislocations and climb of edge dislocations, as well as vacancy diffusion. This results in an increase of grain boundary mobility and energy accumulation at boundaries for the nucleation and growth in DRX grains and dislocation annihilation which is

Kumar et al. [27] have found for hot deformed condition in high strength steel that flow stress increases continuously at lower deformation temperature (750–850°C) due to continuation of work hardening phenomena High temperature showed the higher steady-state condition where DRX was dominant. While both DRV and DRX were dominant at all strain rates with decreasing temperature, the dDRX phenomena was more prominent at slow strain rate (0.001 s-1) at 900°C due to nucleation of unstrained grains that occurs normally in low SFE high strength steels. A flow curve without pronounced peak stress, but which exhibits

*The true stress–strain curves at different temperatures and strain rates for 42CrMo steel [44].*

− −

*n m K T*

 β

(5)

strain, and strain rates, the flow curves can be expressed by Eq. (5) [75].

*m*

3 1

σ=

exponent, and K, β represents material constant.

responsible for the decrease in flow stress [27, 64].

**12**

**Figure 9.**

#### **Figure 10.**

*Effect of SFE and temperature on deformation mechanism in Fe-20Mn-4Cr-0.5C steel [84].*

#### **Table 3.**

*The deformation mechanism at different temperatures for Fe-22Mn-0.6C steel [86].*

on the SFE, which is responsible for the activation energy of a deformation mechanism [87].

The mechanical properties of Hadfield Mn austenitic steels can be improved through high rate strain hardening where two phenomena (i.e. dislocation accumulation and twinning) act preferentially during plastic deformation [82]. This is attributed to the strain hardening transformation where austenite phase transforms into ε or α-martersite, and twinning, dynamic strain aging, dispute between dislocations with stacking faults occurs. In this connection, Yan et al. [88] have tried to improve the hardness values by shot pinning method, whereby hardness values could be increased with increasing in operation time. This was attributed to the increment in density of dislocations, dislocation accumulation, and formation of twinning. The influence of higher strain rate (between 103 to 105 /s) attains great impact on mechanical behavior and wear resistance properties of high austenitic Mn steel which may be linked to dynamic strain aging and may delay fracture [81, 89–93].

Over the past few years, many researchers have reported work on TRIP and TWIP steels and achieved better mechanical properties by plastic deformation at high strains (more than 1) [22, 73, 74, 81, 89, 91, 94–96]. Both TRIP and TWIP steels are fully austenitic steels with less carbon content than hadfield steel. The initial microstructure of TRIP steel is consisted of martensite, bainite and ferrite with retained austenite. The fraction of carbon enriched retained austenite in TRIP steels is between 5 to 30% which transforms into martensite by displacive mechanism during SPD process. This behavior has attained great improvement in strength and toughness properties [96, 97].

A critical issue remains hydrogen embrittlement in TRIP steels, promoted by a displacive mechanism where the relevance of different solubility and diffusivity in the parent austenite has been discussed [96].

Sevsek et al. [90] reported the effect of strain rate on medium Mn X6MnAl12–3 steel. The softer austenite region was strained locally and transformed into

**15**

was higher.

**Figure 11.**

*(b) 18%, (c) 26%, and (d) 34% [81].*

**8. Concluding remarks**

*Plastic Deformation Behavior in Steels during Metal Forming Processes: A Review*

martensite which depended on the strain rate sensibility. The deformation induced phase transformation of austenite to martensite is partially suppressed at lower and higher strain rates. The impact of high strains in fully austenitic Fe-22Mn-0.6C steel is predominantly controlled by twinning plasticity mechanism (see **Figure 11**) as suggested by Jacob et al. [81]. The initial microstructure of Fe-22Mn-0.6C steel is a single-phase austenitic steel with few twinned grains (**Figure 11a**). They found that the fraction of twinning is increased with increasing strains (**Figure 11b–d**), where most of the internal energy was used for recrystallization and rest for grain growth [87]. They concluded that twin boundaries act as a hindrance to the dislocation

*Optical morphologies of Fe-22Mn-0.6C steel subjected to high strain deformation: (a) unstained; strained with*

In the same way, Kang et al. [98] have reported HPT tests for TWIP steel, in which they found that both stress and hardness values increased with an increase in the number of turns. This was related to grain refinement. It was also noticed that the inhomogeneity in morphology and volume of low and high angle grain boundaries increased with the increase in the number of turns, associated with higher stress and lower elongation. The hardness at tip location was found to be lower in all strain ranges due to the lower extent of plastic deformation while at the edge it

This review chapter focuses on plastic deformation behavior which can be controlled via processing parameters. Their optimisation is responsible for a refined microstructure, typically associated with beneficial mechanical properties of metals and steel due to metal forming. In other words, an appropriate combination of processing parameters enables one to fabricate products that will be defect-free on

*DOI: http://dx.doi.org/10.5772/intechopen.97607*

glide providing work hardening effect.

*Plastic Deformation Behavior in Steels during Metal Forming Processes: A Review DOI: http://dx.doi.org/10.5772/intechopen.97607*

**Figure 11.**

*Material Flow Analysis*

mechanism [87].

**Table 3.**

**Figure 10.**

influence of higher strain rate (between 103

and toughness properties [96, 97].

the parent austenite has been discussed [96].

on the SFE, which is responsible for the activation energy of a deformation

**Temperature, K SFE value, mJ/m2 Plasticity mechanism** 77 10 Dislocation gliding

293 19 Dislocation gliding and twinning

*Effect of SFE and temperature on deformation mechanism in Fe-20Mn-4Cr-0.5C steel [84].*

*The deformation mechanism at different temperatures for Fe-22Mn-0.6C steel [86].*

The mechanical properties of Hadfield Mn austenitic steels can be improved through high rate strain hardening where two phenomena (i.e. dislocation accumulation and twinning) act preferentially during plastic deformation [82]. This is attributed to the strain hardening transformation where austenite phase transforms into ε or α-martersite, and twinning, dynamic strain aging, dispute between dislocations with stacking faults occurs. In this connection, Yan et al. [88] have tried to improve the hardness values by shot pinning method, whereby hardness values could be increased with increasing in operation time. This was attributed to the increment in density of dislocations, dislocation accumulation, and formation of twinning. The

673 80 Dislocation gliding and ε-martensitic transformation.

to 105

ical behavior and wear resistance properties of high austenitic Mn steel which may

Over the past few years, many researchers have reported work on TRIP and TWIP steels and achieved better mechanical properties by plastic deformation at high strains (more than 1) [22, 73, 74, 81, 89, 91, 94–96]. Both TRIP and TWIP steels are fully austenitic steels with less carbon content than hadfield steel. The initial microstructure of TRIP steel is consisted of martensite, bainite and ferrite with retained austenite. The fraction of carbon enriched retained austenite in TRIP steels is between 5 to 30% which transforms into martensite by displacive mechanism during SPD process. This behavior has attained great improvement in strength

A critical issue remains hydrogen embrittlement in TRIP steels, promoted by a displacive mechanism where the relevance of different solubility and diffusivity in

Sevsek et al. [90] reported the effect of strain rate on medium Mn X6MnAl12–3

steel. The softer austenite region was strained locally and transformed into

be linked to dynamic strain aging and may delay fracture [81, 89–93].

/s) attains great impact on mechan-

**14**

*Optical morphologies of Fe-22Mn-0.6C steel subjected to high strain deformation: (a) unstained; strained with (b) 18%, (c) 26%, and (d) 34% [81].*

martensite which depended on the strain rate sensibility. The deformation induced phase transformation of austenite to martensite is partially suppressed at lower and higher strain rates. The impact of high strains in fully austenitic Fe-22Mn-0.6C steel is predominantly controlled by twinning plasticity mechanism (see **Figure 11**) as suggested by Jacob et al. [81]. The initial microstructure of Fe-22Mn-0.6C steel is a single-phase austenitic steel with few twinned grains (**Figure 11a**). They found that the fraction of twinning is increased with increasing strains (**Figure 11b–d**), where most of the internal energy was used for recrystallization and rest for grain growth [87]. They concluded that twin boundaries act as a hindrance to the dislocation glide providing work hardening effect.

In the same way, Kang et al. [98] have reported HPT tests for TWIP steel, in which they found that both stress and hardness values increased with an increase in the number of turns. This was related to grain refinement. It was also noticed that the inhomogeneity in morphology and volume of low and high angle grain boundaries increased with the increase in the number of turns, associated with higher stress and lower elongation. The hardness at tip location was found to be lower in all strain ranges due to the lower extent of plastic deformation while at the edge it was higher.

#### **8. Concluding remarks**

This review chapter focuses on plastic deformation behavior which can be controlled via processing parameters. Their optimisation is responsible for a refined microstructure, typically associated with beneficial mechanical properties of metals and steel due to metal forming. In other words, an appropriate combination of processing parameters enables one to fabricate products that will be defect-free on

#### *Material Flow Analysis*

the microscale, which represents an important demand of customers. It is noticed that the flow stress increases with an increase in strain rate when the temperature is constant while it decreases with an increase in temperature when the strain rate is constant. The dDRX phenomena occur under axial stress hot deformation conditions while cDRX phenomena are linked to torsion deformation conditions during severe plastic deformation at relatively low temperatures. Plastic deformation acts differently in the case of high Mn austenitic TRIP steels where retained austenite is transformed into martensite by displacive mechanism and induced strain forms twinning which improves strength and toughness of steels. In contrast, the high-Mn fully austenitic steel such as TWIP steels generate huge amount of twinning structure by induced high strains and do not show phase transformation like TRIP steels.


**1. Introduction**

An important movement control strategy used by the central nervous system (CNS) is the activation of multiple muscles acting in concert with each other to achieve a specific movement [1-6]. The specific temporal pattern of motor unit firing, the number of recruited motor units, and the intensity of the firing units result in a state of Multiple Muscle Activation (MMA) during each movement. We hypothesized that quantifying the MMA would provide infor‐ mation about certain states of muscle activation used by the CNS at any moment of a given movement activity. As neuromuscular disorders disrupt the firing characteristics of the motor units by which the CNS controls human movement, quantifying the degree of MMA might be useful as an assessment tool for these disorders. Additionally, monitoring changes in MMA over time might provide valuable information about the progression of a disease state or conversely, a recovery profile. Currently, no easily implemented screening technique for use in clinical settings exists that allow the changes in the MMA to be measured and tracked over time. Therefore, developing a single value that quantifies the degree of activation among multiple muscles that accounts for temporal and magnitude changes in muscle activity in a combinatorial fashion may be of value to clinicians and researchers interested in evaluating alterations in muscle activation due to different physiological and environmental constraints. In this chapter, a new index, "SYNERGOS" (from the Greek word for "working together") is introduced to quantify the level of MMA. At its core, SYNERGOS systematically identifies the changes in muscle activity depicted by electromyography (EMG) signals obtained from multiple muscles and summarizes the coactivity among these muscles into a single scalar quantifying the MMA over a predefined period (i.e. in this report, the gait cycle) during a variety of movements.

© 2013 Pourmoghaddam et al.; licensee InTech. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2013 Pourmoghaddam et al.; licensee InTech. This is a paper distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Surface EMG is commonly used to investigate neuromuscular activation during human movements. EMG can provide insight into the relationship between the underlying activity of the CNS and the resulting muscle contractions that produce the observable movement. Such insight sheds light on the different neuromuscular activation patterns in both healthy move‐ ments and disease states [7, 8]. However, several studies identified the inherent stochastic characteristics of EMG signals and recommended applying nonlinear data analysis to inves‐ tigate the underlying nonlinear features (i.e. determinism) of the signal [9-16]. Recurrence Quantification Analysis (RQA) has shown promising results to detect subtle changes in EMG signals which can be missed by the application of linear data analysis. RQA quantifies several parameters such as percentage of recurrence (% REC) and percentage of determinism (% DET) to detect such subtle changes in dynamical state of EMG signal [13, 15-18]. % DET is an indication of the underlying deterministic patterns in a dynamical system that is associated with the degree that an EMG signal "recurs," or is predictable [15, 16, 18] as a result of increasing firing rate in recruited motor units [9, 10, 16, 18]. % DET has shown significant sensitivity to the change in the amount of loading and duration of a movement during both static and dynamic movements [13-18]. In this study, the % DET obtained from the RQA for each recorded muscle during predefined duration (i.e. gait cycles within each gait speed) is a single value used as an input for calculation of SYNERGOS index for each gait cycle.

coactivation associated with higher gait speed (modeled by faster knee extension/flexion) [23, 24]. In this study, the cyclic movement of walking and running was chosen, which by their nature, maintain their general kinematic form as speed increases but the underlying MMA is modulated in concordance with increasing speed. We believe that if SYNERGOS can detect minute changes in MMA associated with slight increases in locomotion speed it may have

SYNERGOS: A Multiple Muscle Activation Index

http://dx.doi.org/10.5772/56168

133

Ten, healthy University of Houston students participated in the study at the Laboratory of Integrated Physiology (5 male and 5 female; age range: 20-25 years, Mean 22, SD 2.3 years, weight: 55-80Kg Mean 67.45, SD 12.90 Kg.). The exercise readiness of each subject was monitored by using a physical activity questionnaire (modified International Physical Activity Questionnaire) in which several wellness aspects such as cardiovascular fitness, discomfort during exercise, history of dizziness, joint problems, pregnancy, diabetes, breathing problems, and history of major surgery were questioned [25]. In addition, the subjects were required to report any history of neuromuscular disorder and lower extremity injury. No subject reported any of the aforementioned exercise readiness risks. These selection criteria were chosen to minimize the known effects of neuromuscular disorders and aging on changes in neuromus‐ cular activation. [26, 27]. All procedures were reviewed and approved by the University of Houston Committee for the Protection of Human Subjects. The subjects were fully informed

Each experiment was conducted in a single laboratory session. To alter the gait pattern from walking to running, the participants were instructed to walk at a comfortable initial speed (different between subjects) on a treadmill whose speed increased by 0.045 m/s every five strides up to a maximum duration of 180 seconds. Subjects were encouraged to stop the trial if at any point they became uncomfortable. All subjects successfully completed this protocol by transitioning their gait from walking to running. The transition speed (speed in which gait pattern was changed from walk to run) was recorded for each subject by observation to be used during analyses (see Statistical Analysis below). In addition, to verify the transition between walk and run, the vertical hip velocity and the stride time variability were evaluated. Both of these measures are sensitive to the sudden changes in gait pattern (i.e. walk to run transition). This approach confirmed the observed transitional strides [28]. The mean initial speed across all participants was a slow walk at 1.12 ± 0.13 m/s with final speed at a run of 3.30

functional utility to evaluate MMA during a variety of movements.

about the test protocol and provided signed consent prior to participating.

**2. Method**

**2.1. Subjects**

**2.2. Experimental protocol**

± 0.11 m/s.

Previous authors have proposed techniques that have demonstrated that individual muscles are not independently controlled by the CNS during movement [2, 4, 6, 19]. These methods of quantifying multiple muscle activities provide several time-variant and time-invariant parameters [2, 4] or several modes [6, 20, 21] demonstrating the level of activation of each muscle during a movement. These techniques share some features in common with SYNER‐ GOS, namely the application of EMG to detect the multi-muscle actions during various activities and the use of sophisticated techniques to explore the underlying dynamics associ‐ ated with the muscle activation controlling limb motion. While having these features in common with SYNERGOS, these techniques are designed to investigate and parameterize the underlying synergies by quantifying different modes of multiple muscle actions during body movements so that relative time-invariant contributions of different muscles can be identified. Conversely, SYNERGOS is not designed to identify multiple modes of activation synergies but rather SYNERGOS is unique in that a single index value accounting for the simultaneous activity of all muscles during a given movement, or over time in the case of cyclical movements (e.g., one index for each gait cycle), can be conveniently assessed.

In this report, we assess the potential of the SYNERGOS technique to evaluate changes in MMA between walking and running conditions that use different muscle activation strategies due to differences in the complexity of these movements [22]. We hypothesized that the SYNER‐ GOS method can detect significant differences in quantified MMA during walking vs. running due to higher level of coactivities resulted from more frequent and intense simultaneous neuromuscular activities depicted by EMG signals. Additionally we hypothesized that the SYNERGOS method was significantly sensitive to identify the changes in MMA associated with small but incrementally increasing gait speed beginning with a slow walk and concluding with running. These hypotheses were based on previous studies indicating an elevated muscle coactivation associated with higher gait speed (modeled by faster knee extension/flexion) [23, 24]. In this study, the cyclic movement of walking and running was chosen, which by their nature, maintain their general kinematic form as speed increases but the underlying MMA is modulated in concordance with increasing speed. We believe that if SYNERGOS can detect minute changes in MMA associated with slight increases in locomotion speed it may have functional utility to evaluate MMA during a variety of movements.


*Article* **Determining the Effects of Compost Substitution on Carbon Sequestration, Greenhouse Gas Emission, Soil Microbial Community Changes, and Crop Yield in a Wheat Field**

**Hongzhi Min 1 , Xingchen Huang 1 , Daoqing Xu 2 , Qingqin Shao 1 , Qing Li 3 , Hong Wang 1,\* and Lantian Ren 1,\***

**\*** Correspondence<EMAIL_ADDRESS>(H.W<EMAIL_ADDRESS>(L.R.)

**Abstract:** Compost produced by straw and livestock and poultry manure under the action of microorganisms is one of the main forms of organic alternative fertilizers at present. The present study explored the effects of compost substitution on soil greenhouse gas emissions, soil microbial community changes, and wheat yield to determine the best substitution ratio for reducing greenhouse gas emissions and soil microbial community changes and increasing wheat yield. Using the single-factor randomized block trial design, four treatments were employed, the characteristics of greenhouse gas emission, yield and yield components, and the changes of soil microbial community under different compost substitution ratio in the whole wheat growing season were determined by static box-gas chromatography. During the wheat season, both CO2 and N2O emissions were reduced, whereas CH4 emission was increased. That all treatments reduced the Global Warming Potential (GWP) and Greenhouse gas emission intensity (GHGI) in wheat season compared with T0. Compost substitution can alleviate the global warming potential to some extent. Under the condition of compost substitution, the wheat yield under T2 and T3 increased significantly compared with that under the control; however, the spike number and 1000-grain weight did not differ significantly among the treatments. When compost replacement was 30%, the yield was the highest. Under different ratios of compost substitution, the microbial communities mainly comprised Proteobacteria, Actinobacteria, Firmicutes, Patescibacteria, Chloroflexi, Acidobacteria, Bacteroidetes, Gemmatimonadetes, and Verrucomicrobia. The soil microbial community structure differed mainly due to the difference in the compost substitution ratio and was clustered into different groups. In conclusion, to achieve high wheat yield and low greenhouse gas emissions, compost replacement of 30% is the most reasonable means for soil improvement and fertilization.

**Keywords:** composting; wheat; yield; greenhouse gas; soil micro-organism

## **1. Introduction**

In China, obtaining high crop yield requires the application of a large amount of chemical fertilizers, leading to an increase in the production cost. Moreover, the long-term application of chemical fertilizers leads to soil acidification and consolidation, and the utilization rate of nitrogen fertilizer is low. Increasing use of chemical fertilizers has deteriorated soil fertility and quality of agricultural products, which has not only affected the comprehensive production capacity of soil but also seriously affected the ecological environment [1]. High-temperature composting of crop straw is one of the effective methods to replace chemical fertilizers [2–4]. Straw composting can increase the content of soil organic matter, improve soil physiology and microbial characteristics, promote microbial fixation of nutrients, reduce nutrient loss [5,6], and reduce CH4 emissions [7]. However, at the same time, it can lead to an increase in N2O emissions [8,9], and promote the recycling of natural

**Citation:** Min, H.; Huang, X.; Xu, D.; Shao, Q.; Li, Q.; Wang, H.; Ren, L. Determining the Effects of Compost Substitution on Carbon Sequestration, Greenhouse Gas Emission, Soil Microbial Community Changes, and Crop Yield in a Wheat Field. *Life* **2022**, *12*, 1382. https:// doi.org/10.3390/life12091382

Academic Editor: Tanya Soule and Ling Zhang

Received: 1 August 2022 Accepted: 30 August 2022 Published: 5 September 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 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 (https:// creativecommons.org/licenses/by/ 4.0/).

resources. Therefore, transformation from inorganic agriculture to ecological and green agriculture is necessary [10]. Use of compost can promote resource utilization of agricultural waste, reduce the use of chemical fertilizers, and improve crop yield and quality, indicating that it has a high practical application value. The combined application of organic and chemical fertilizers is the most efficient approach for improving soil quality, increasing crop yield, and reducing greenhouse gas emissions. The use of chemical fertilizer alone in farmland can lead to an increase in greenhouse gas emissions [11]. Wang et al. [12] reported that the combined application of bio-organic and chemical fertilizers could increase the number of tillers of wheat by 12.4–18.9% and significantly increase the number of effective spikes, grains per spike, and 1000-grain weight of wheat. Other studies have reported that composting can promote plant growth and development and increase crop yield [13]. According to Chaoui et al. [14], composting can increase the number of micro-organisms in soil and improve the availability of soil nutrients. The effect of straw returning on N2O emission is uncertain in China and abroad; straw returning has been reported to not only promote N2O emission [15], but also inhibit N2O emission [16]. Additionally, a few studies reported that using straw compost instead of some part of chemical fertilizer can increase crop yield. The present study investigated the effect of replacing some part of chemical fertilizer with straw compost in terms of differences in growth indices and yield and quality of wheat with different straw compost substitution ratios. The findings may be useful in promoting resource utilization of agricultural waste and reducing the use of chemical fertilizers, thereby providing a theoretical basis for wheat high-yield cultivation techniques.

#### **2. Materials and Methods**

## *2.1. Experimental Field and Design*

This experiment was conducted in the Anhui University of Science and Technology (E117◦33039", W32◦52049") from December 2020 to June 2021, during the field experiment average temperature of 17–8 ◦C, during the field experiment rainfall of 145 mm. The former stubble of the experiment was rice; the soil was yellow cinnamon soil. The organic matter content in the 0–20-cm soil layer is 20.8 g/kg, with the alkali-hydrolysable nitrogen content of 110.9 mg/kg, available phosphorus content of 25.8 mg/kg, and available potassium content of 115.2 mg/kg.

#### *2.2. Test Materials*

#### 2.2.1. Production of Compost

The compost used in the experiment comprised cow manure and rice straw harvested after ripening according to the mass ratio of 2:1, and the raw materials were pre-treated before composting. Large chunks of cow dung were simply crushed, and the straw was crushed into small pieces (smaller than 1 cm), which were stacked and set aside. We mixed layers by layers and sprayed water while mixing; the pile was turned repeatedly with a lawn grabber until it was evenly mixed. The overall moisture content was adjusted to 55–65%. The C/N ratio was 25:30, and the oxygen content was 8–18%. The pH was maintained at 6.5–8.0. A small pile with a bottom width of 1.5 m, a height of 1–1.2 m, and a length of 2–3 m was fermented at a high temperature for 50–60 days, and a compost with weak ammonia and manure odor was obtained; the color of rice straw and cow manure changed to granular black–brown after maturity. The nitrogen, P2O5, K2O, and organic matter contents of the compost were 1.03%, 0.87%, 1.35%, and 47.8%, respectively, and the pH was 6.67.

#### 2.2.2. Experimental Wheat

The experimental wheat variety 'Huaimai 44' was selected. The seedling stage of this variety is semi-creeping; the leaves are short and green in color. After attaining maturity, the plants become compact, their sword leaf is erect, and the ripening phase is better. The plants exhibit a strong tillering ability, a large number of panicles, earlier yellowing, and strong ability of cold and lodging resistance.

#### *2.3. Single Factor Experimental Design*

The types of straw compost used as a substitute for chemical fertilizer treatment (T) were as follows: 1. conventional fertilizer control (T0 treatment; compound fertilizer (N18%-P2O5-18%-K2O18%): 600 kg/hm2 , urea: 300 kg/hm2 ); 2. composting to replace 10% chemical fertilizer (T1 treatment; compound fertilizer (N18%-P2O5-18%-K2O18%): 540 kg/hm2 , urea: 270 kg/hm2 , straw compost: 3 t/hm2 ); 3. composting to replace 20% chemical fertilizer (T2 treatment; compound fertilizer (N18%-P2O5-18%-K2O18%): 480 kg/hm2 , urea: 240 kg/hm2 , compost: 6 t/hm2 ); and 4. composting to replace 30% chemical fertilizer (T3 treatment; compound fertilizer (N18%-P2O5-18%-K2O18%): 420 kg/hm2 , urea: 210 kg/hm2 , compost: 9 t/hm2 ). Each treatment was performed in triplicate. On 5 December 2020, the seeds were sown with an equal row spacing of 15 cm. The sowing rate of 14 m2 was 52.50 g/row and that of 15 m2 was 56.25 g/row. Except for different fertilization treatments, the other field-management measures adopted the unified management mode of local high-yield wheat field in each district. Wheat was harvested on 9 June 2021.

#### *2.4. Observation Indicators and Methods*

#### 2.4.1. Collection and Determination of Greenhouse Gases

The closed static chamber method was used to measure the emission fluxes of CH4, CO2, and N2O in wheat field. The sampling device consists of three parts made of opaque organic plastic, namely top box, middle box, and base. In the inner surface of the box (50 cm × 50 cm × 50 cm), a small fan is placed that ensures the uniform distribution of gas. The outer surface consists of a sponge aluminum foil, which is used for reflection and heat insulation, and a thermometer, which is inserted in the upper part of the box and measures the temperature of the box. The upper surface of the base (50 cm × 50 cm × 25 cm) has grooves. During gas production, the grooves are sealed with water, and the box is covered to prevent air leakage between the box and base. The sample was collected through a syringe from the sampling port. The base was buried between the crop rows, exposing only grooves, and it did not move throughout the wheat growing season.

Gas samples were collected during the jointing stage (13 March 2021), booting stage (10 April 2021), flowering stage (24 April 2021), filling stage (9 May 2021), and mature stage (6 May 2021). The sampling time was fixed from 8:00 am to 11:00 am. Water injection in the base tank was closed during gas production; the small fan box at the top of the top box was opened and placed on the base, and the samples were collected every 5 min. Air was extracted from the gas inlet by using a 60 mL syringe at the time intervals of 5, 10, and 15 min, and 60 mL gas was injected into the vacuum air bag for preservation. A total of 3 gas samples were collected. Simultaneously, the temperatures of air and the box were observed and recorded.

The collected samples were analyzed using the Agilent7890B (Agilent, Waldbronn, Germany) gas chromatograph in the laboratory. The analysis column was Porpak.Q packed column. The temperature of the column box was 40 ◦C, and the carrier gas was high purity N2. N2O electron capture detector (ECD) was used. The working temperature was 300 ◦C, and the lowest detection limit was 32 µg·kg−1 . CO2 and CH4 were determined using hydrogen detector (FID). The working temperature was 300 ◦C. The lowest detection limit of CO2 and CH4 was 4 and 0.2 mg·kg−1 , respectively. The standard gas of the national standard metrology center was used to calibrate the gas chromatograph, and the external standard working curve was obtained for all 60 samples. The unobserved daily emission fluxes were calculated using the interpolation method; further, the measured values were summed up with the calculated values, and the respective emissions of CO2, CH4, and N2O were obtained. The greenhouse gas emission fluxes were calculated using the following formula:

$$\mathbf{F} = \frac{\mathbf{dc}}{\mathbf{dt}} \times \frac{\mathbf{M}}{\mathbf{V}\_0} \times \frac{\mathbf{P}}{\mathbf{P}\_0} \times \frac{\mathbf{T}\_0}{\mathbf{T}} \times \mathbf{H} \tag{1}$$

where F is the greenhouse gas emission flux (mg/(m2 ·h)); dc/dt is the slope of the regression curve of the gas volume fraction with time during sampling; M is the molar mass of the gas (g/mol); V0 is the molar volume of the gas under the standard gas (22.41 L/mol); P and P0 are the air pressure at the sampling point (Pa) and the air pressure at the standard state (101,325 Pa), respectively; T and T0 are the absolute temperatures at the time of sampling (K) and in the standard state (273.15 K), respectively; and H is the height of the sampling box (m).

The cumulative greenhouse gas emissions during the wheat growth period were obtained by multiplying the average greenhouse gas emission flux during the two adjacent sampling periods with the time interval between two sampling periods.

## 2.4.2. Calculation of Comprehensive Warming Potential and Greenhouse Gas Emission Intensity

Using comprehensive warming potential (*GWP*), other greenhouse gas emissions can be converted into equivalent CO2, and their climates can be compared [17]. In this study, the *GWP* was used to express the potential effects of different greenhouse gases on global warming. On the 100-year warming scale, the warming potential of CH4 and N2O is 28 and 265 times of that of CO2. The *GWP* can be calculated from the cumulative emission of each gas and its corresponding temperature increasing coefficient.

$$\text{GWP} = \text{CO}\_2 + 25 \times \text{CH}\_4 + 298 \times \text{N}\_2\text{O} \tag{2}$$

*GHGI* was used to evaluate the comprehensive greenhouse effect of each treatment. The algorithm of *GHGI* is:

$$\text{GHHI} = \text{GWP}/\text{Y} \tag{3}$$

where *GHGI* is the greenhouse gas emission intensity of the treatment, and *Y* is the crop yield of each treatment (kg·hm−2 ).

#### 2.4.3. Determination of Yield

Representative 1 m, 2-row samples were selected from each plot during the wheat maturity stage. The average spike number was investigated, and the spike number per unit area was calculated. Overall, 20 plants were selected from each plot for indoor seed testing, and the average grain number per spike and 1000-grain weight were investigated.

#### 2.4.4. Soil sample Collection and Treatment

Soil samples were collected in June 2021 (after wheat harvest). After removing the surface floating soil, according to the 'Z'-shaped sampling route, 0–20 cm soil cores were randomly selected using a sampler (diameter 2.5 cm). Then, the samples were mixed evenly to remove impurities such as gravel and plant residual roots. The fresh soil samples were divided into two parts: one part was air-dried for the determination of soil physical and chemical properties, and the other part was preserved at −80 ◦C for the extraction of soil genomic DNA.

## 2.4.5. Extraction and Sequencing of Soil DNA

In total, 0.5 g of soil stored in a refrigerator at −80 ◦C was taken, and total DNA of the soil was extracted using the PowerSoil® DNA extraction kit (MoBio Laboratories, Inc., Carlsbad, CA, USA), according to the manufacturer's instructions. DNA content was quantified using a NanoDrop spectrophotometer (ND-2000, Thermo Scientific, Waltham, MA, USA), diluted to 10 ng·µ L −1 , and stored in a refrigerator at −20 ◦C util use. Each DNA sample had 9 PCR repeats and included 2 negative controls without DNA template. The PCR reaction system and reaction procedure were based on the method described by Zhao et al. [18]. The PCR product was purified through gel cutting, and the concentration was determined using PicoGreen® (Promega, Madison, WI, USA). After equimolar mixing, the product was sent to Guangdong MAGIGENE Technology limited company for sequencing.

**Treatment**

**N2O Cumulative Emission (kg N∙hm−2)**

#### 2.4.6. Statistical Analysis 2.4.6. Statistical Analysis

*Life* **2022**, *12*, x FOR PEER REVIEW 5 of 12

The test data were processed and analyzed using MS Excel 2010 and DPS7.05 software, and the significance was tested using the LSD method. The relationship between soil physical and chemical properties and greenhouse gases and their functional bacteria was analyzed using the 'psych' package in R, and the principal coordinate analysis (pCoA) and redundancy analysis (RDA) of community structure differences and environmental factors on community structure were completed using 'vegan' in R. The 'pheatmap' and 'ggplot2' packages in Excel and R were used for drawing. The test data were processed and analyzed using MS Excel 2010 and DPS7.05 software, and the significance was tested using the LSD method. The relationship between soil physical and chemical properties and greenhouse gases and their functional bacteria was analyzed using the 'psych' package in R, and the principal coordinate analysis (pCoA) and redundancy analysis (RDA) of community structure differences and environmental factors on community structure were completed using 'vegan' in R. The 'pheatmap' and 'ggplot2' packages in Excel and R were used for drawing.

Carlsbad, CA, USA), according to the manufacturer's instructions. DNA content was quantified using a NanoDrop spectrophotometer (ND-2000, Thermo Scientific, Waltham, MA, USA), diluted to 10 ng·μ L−1, and stored in a refrigerator at −20 °C util use. Each DNA sample had 9 PCR repeats and included 2 negative controls without DNA template. The PCR reaction system and reaction procedure were based on the method described by Zhao et al. [18]. The PCR product was purified through gel cutting, and the concentration was determined using PicoGreen® (Promega, Madison, WI, USA). After equimolar mixing, the product was sent to Guangdong MAGIGENE Technology limited company for sequenc-

#### **3. Results 3. Results**

ing.

#### *3.1. Effects of Different Compost Substitution Ratios on Soil N2O, CH4, and CO2 Emissions 3.1. Effects of Different Compost Substitution Ratios on Soil N2O, CH4, and CO2 Emissions*

Figure 1 shows the dynamic changes in N2O, CH4, and CO2 emission fluxes during the wheat growth period under different compost substitution ratios. As shown in Figure 1, under different compost substitution ratios, the flux emission of each treatment displayed a unimodal pattern, and the emission flux demonstrated first an increasing trend and then a decreasing trend with the seasonal change; the maximum emission flux was obtained at the jointing stage after sowing. The emission of N2O after T1 treatment was higher than that after T0 treatment, and the amount of N2O uptake after T2 treatment was higher than that after T3 treatment. Figure 1 shows the dynamic changes in N2O, CH4, and CO2 emission fluxes during the wheat growth period under different compost substitution ratios. As shown in Figure 1, under different compost substitution ratios, the flux emission of each treatment displayed a unimodal pattern, and the emission flux demonstrated first an increasing trend and then a decreasing trend with the seasonal change; the maximum emission flux was obtained at the jointing stage after sowing. The emission of N2O after T1 treatment was higher than that after T0 treatment, and the amount of N2O uptake after T2 treatment was higher than that after T3 treatment.

**Figure 1.** Dynamics of (**A**) N2O flux, (**B**) CH4 flux, (**C**) CO2 flux under different treatments. **Figure 1.** Dynamics of (**A**) N2O flux, (**B**) CH4 flux, (**C**) CO2 flux under different treatments.

**Table 1.** Accumulation of greenhouse gas emissions, comprehensive greenhouse gas warming po-

**CO2 Cumulative Emission (kg C∙hm−2)**

Note: Values followed by different lowercase letters in a column indicate significant difference

The emission flux of CO2 from each treatment clearly changed during the growth period of wheat, showing a single peak. The emission flux of CO2 increased gradually after low performance at the booting stage, reached the peak at the filling stage, and then decreased. According to Table 1, the cumulative CO2 emission of each treatment was in the order T0 > T1 > T3 > T2 during the wheat growth period. The cumulative emission of 55,731.98 kgC·hm−2 under T1, T2, and T3 decreased significantly by 13.6%, 59.7%, and 30.5%, respectively, compared with that under T0 treatment (*p* < 0.05). Outcomes under

The estimated results of GWP and GHGI under different compost substitution ratios during the wheat growth period are presented in Table 1. T0 treatment made the greatest contribution to the comprehensive warming potential of farmland. The comprehensive warming potential of TI, T2, and T3 decreased by 13.8%, 149.8%, and 42.6%, respectively, compared with that of T0. With significant variations in the yield under each treatment, both GHGI and GWP exhibited a different trend. Under different compost substitution ratios, the GHGI under different treatments was in the order T1 > T3 > T2 > T0. The GHGI

The flux of CH4 was both positive and negative, and it fluctuated rapidly. As shown in Table 1, during the wheat growth period, the cumulative emission of CH4 in each treatment was negative, suggesting absorption. The amount of absorption under T0 treatment was higher than that under T2 treatment, and there was a positive value. The emission under T3 treatment was higher than that under T1 treatment. Outcomes under each treat-The flux of CH4 was both positive and negative, and it fluctuated rapidly. As shown in Table 1, during the wheat growth period, the cumulative emission of CH4 in each treatment was negative, suggesting absorption. The amount of absorption under T0 treatment was higher than that under T2 treatment, and there was a positive value. The emission under

> **GWP (kgCO2-eq∙hm−2)**

**GHGI (g CO2-eq∙kg−1)**

each treatment differed significantly from those under T0 (*p* < 0.05).

*3.2. GWP and GHGI under Different Compost Substitution Ratios*

T0 0.32 ± 0.01 b −28.21 ± 0.71 d 55,731.98 ± 1393.30 a 55,121.75 a 6.93 a T1 0.74 ± 0.02 a 3.74 ± 0.09 b 48,128.64 ± 1203.22 b 48,442.61 b 6.56 a T2 −1.26 ± 0.03 c −0.22 ± 0.01 c 22,451.49 ± 561.29 d 22,070.57 d 2.63 c T3 −1.07 ± 0.03 c 9.39 ± 0.23 a 38,729.71 ± 968.24 c 38,645.56 c 4.01 b

ment differed significantly from those under T0 (*p* < 0.05).

**CH4 Cumulative Emission (kgC∙hm−2)**

among treatments at the 0.05 level.

T3 treatment was higher than that under T1 treatment. Outcomes under each treatment differed significantly from those under T0 (*p* < 0.05).

**Table 1.** Accumulation of greenhouse gas emissions, comprehensive greenhouse gas warming potential, and greenhouse gas emission intensity under different compost substitution ratios.

Note: Values followed by different lowercase letters in a column indicate significant difference among treatments at the 0.05 level.

The emission flux of CO2 from each treatment clearly changed during the growth period of wheat, showing a single peak. The emission flux of CO2 increased gradually after low performance at the booting stage, reached the peak at the filling stage, and then decreased. According to Table 1, the cumulative CO2 emission of each treatment was in the order T0 > T1 > T3 > T2 during the wheat growth period. The cumulative emission of 55,731.98 kgC·hm−2 under T1, T2, and T3 decreased significantly by 13.6%, 59.7%, and 30.5%, respectively, compared with that under T0 treatment (*p* < 0.05). Outcomes under each treatment differed significantly from those under T0 (*p* < 0.05).

#### *3.2. GWP and GHGI under Different Compost Substitution Ratios*

The estimated results of GWP and GHGI under different compost substitution ratios during the wheat growth period are presented in Table 1. T0 treatment made the greatest contribution to the comprehensive warming potential of farmland. The comprehensive warming potential of TI, T2, and T3 decreased by 13.8%, 149.8%, and 42.6%, respectively, compared with that of T0. With significant variations in the yield under each treatment, both GHGI and GWP exhibited a different trend. Under different compost substitution ratios, the GHGI under different treatments was in the order T1 > T3 > T2 > T0. The GHGI under T1 was the highest (42.53 g·CO2-eq kg−1 ), although T1 exhibited the lowest yield among all treatments.

#### *3.3. Effects of Different Compost Substitution Ratios on Wheat Yield and Yield Components*

The effects of different compost substitution ratios on wheat yield, quality, and yield components are shown in Table 2. The number of grains per spike in T2 was significantly higher than those in T0, T1, and T3; however, the difference in the number of grains per spike between T0 and T3 was nonsignificant. The number of panicles in T1 was significantly lower than those in T0, T2, and T3. In terms of the 1000-grain weight, T0, T1, and T2 exhibited no significant difference; however, 1000-grain weight after T3 was significantly higher than that after other treatments. Overall, 30% substitution of compost significantly increased the wheat yield, and the yield after T3 (9647.01 kg·hm−2 ) increased by 21.3% compared with that under T0. The yield under T0 and T2 was significantly higher than that under T1; however, it did not differ significantly between T0 and T2.

**Table 2.** Effects of different compost substitution ratios on wheat yield and yield components.

Note: Values followed by different lowercase letters in a column indicate significant difference among treatments at the 0.05 level.

#### *3.4. Effect of Microbial Community Composition under Different Compost Substitution Ratios*

Figure 2A shows the relative abundance of microbial community structure under different compost substitution ratios. The microbial community under different treatments comprised mainly Proteobacteria, Actinobacteria, Firmicutes, Patescibacteria, Chloroflexi, Acidobacteria, Bacteroidetes, Gemmatimonadetes, and Verrucomicrobia. With an increase in the compost substitution ratio, the relative abundance of Actinomycetes under T0 increased significantly compared with those under T1, T2, and T3 (*p* < 0.05), but it decreased when the substitution ratio increased to 30%. The relative abundance of Proteus was significantly lower under T2 than those under T0, T1, and T3 (*p* < 0.01). The relative abundance of thick-walled bacteria was significantly higher under T2 than those under T0, T1, and T3 (*p* < 0.01). The relative abundance of Patescibacteria was significantly lower under T3 than those under T0, T1, and T2 (*p* < 0.01). The relative abundance of Campylobacter was significantly higher under T0 and T3 than that under T1 and T2 (*p* < 0.01). Likewise, the relative abundance of acid bacilli was significantly lower under T1 and T2 than that under T0 and T3 (*p* < 0.01). *Life* **2022**, *12*, x FOR PEER REVIEW 8 of 12 and T2 could be well separated on the first axis, and the amount of variance explained was 51.8%. Figure 2D shows the results of Shannon index diversity analysis of the microbial community structure. The higher the Shannon index, the richer is the microbial community diversity, and vice-a-versa. The Shannon index under T2 was significantly lower than that under other three treatments (*p* < 0.05); however, no significant difference was observed in terms of diversity among the other three treatments.

*3.5. Redundancy Analysis of the Microbial Community Structure, Environmental Factors, and*

**Figure 2.** The relative abundance (**A**), the relative abundance of dominant functional bacteria (>1%) (**B**), the PCoA (**C**), and fragrance index (**D**) of the differences in community structure under different compost substitution ratios. **Figure 2.** The relative abundance (**A**), the relative abundance of dominant functional bacteria (>1%) (**B**), the PCoA (**C**), and fragrance index (**D**) of the differences in community structure under different compost substitution ratios.

*Soil Nutrient Content*  The correlation between the microbial community structure, environmental factors, and soil nutrient content was further examined, and the results are shown in Figure 3. A total of 80% of the relationship between microbial community structure, environmental factors, and soil nutrient content could be explained by the two axes, reflecting the effects of environmental factors and soil nutrient content on the soil microbial community structure. Further analysis indicated that the amount of variance explained by RDA1 and RDA2 was 67.1% and 12.9%, respectively, and the responses of soil microbial communities to environmental factors and soil nutrients were different under different compost treat-Figure 2B shows the relative abundance of microbial communities under different compost substitution ratios at the genus level, including 13 dominant functional bacteria (relative abundance more than 1%) and various α-, β-, and γ-type amoeba (Proteobacteria). The relative abundance of *Bacillus* was significantly higher under T2 than those in T0, T1, and T3. For *Sphingomonas*, the relative abundance was significantly higher under T1 and T3 than that under T0 and T2. The relative abundance of *Gemmatimonas* was significantly lower under T0 and T2 than that under T1 and T3. The relative abundance of *Nocardioides* was significantly higher under T1 than that under T0, T2, and T3 (*p* < 0.01).

ments. Among them, T0, T1, and T3 positively correlated with AN, N2O, SOM, and CO2, whereas T2 positively correlated with TN and CH4. Figure 2C presents the OTU data based on 97% similarity, and the weighted (Weighted Unifrac) algorithm was used to stack the results of pCoA of microbial community structure under different compost substitution ratios. Among them, the variance contribution rates of the first and second principal components were 51.8% and 29.4%, respectively, and the cumulative variance contribution rate was 81.2%. On the first axis, T1 and T2 could be well separated on the first axis, and the amount of variance explained was 51.8%.

Figure 2D shows the results of Shannon index diversity analysis of the microbial community structure. The higher the Shannon index, the richer is the microbial community diversity, and vice-a-versa. The Shannon index under T2 was significantly lower than that under other three treatments (*p* < 0.05); however, no significant difference was observed in terms of diversity among the other three treatments.

### *3.5. Redundancy Analysis of the Microbial Community Structure, Environmental Factors, and Soil Nutrient Content*

The correlation between the microbial community structure, environmental factors, and soil nutrient content was further examined, and the results are shown in Figure 3. A total of 80% of the relationship between microbial community structure, environmental factors, and soil nutrient content could be explained by the two axes, reflecting the effects of environmental factors and soil nutrient content on the soil microbial community structure. Further analysis indicated that the amount of variance explained by RDA1 and RDA2 was 67.1% and 12.9%, respectively, and the responses of soil microbial communities to environmental factors and soil nutrients were different under different compost treatments. Among them, T0, T1, and T3 positively correlated with AN, N2O, SOM, and CO2, whereas T2 positively correlated with TN and CH4. *Life* **2022**, *12*, x FOR PEER REVIEW 9 of 12

**Figure 3.** Redundancy analysis of environmental factors, soil nutrient content, and soil microbial community structure. **Figure 3.** Redundancy analysis of environmental factors, soil nutrient content, and soil microbial community structure.

#### **4. Discussion 4. Discussion**

emissions.

Many studies have reported that the application of compost can improve the physical and chemical properties of soil [19–22], increase the content of soil nutrients [19,20,23,24], and increase the diversity and abundance of soil micro-organisms [14,25–27]. Moreover, compost application is beneficial to the transformation and release of soil nutrients [14]. Greenhouse gas emissions from farmland are affected by many factors such as climatic conditions, soil characteristics, fertilizer types, and agricultural management measures [27–30]. Many studies have reported that the application of compost can improve the physical and chemical properties of soil [19–22], increase the content of soil nutrients [19,20,23,24], and increase the diversity and abundance of soil micro-organisms [14,25–27]. Moreover, compost application is beneficial to the transformation and release of soil nutrients [14]. Greenhouse gas emissions from farmland are affected by many factors such as climatic conditions, soil characteristics, fertilizer types, and agricultural management measures [27–30].

N2O is emitted from soil by the production of ammonium salts during the process of oxidation involving nitrifying bacteria [31]. Some studies have suggested that compost substitution can change the soil characteristics [32], and stimulate the soil microbial activity to increase soil microbial biomass, thereby promoting denitrification to increase N2O emission [33–35]. A study by Guo et al. [36] indicated that compost substitution can sig-N2O is emitted from soil by the production of ammonium salts during the process of oxidation involving nitrifying bacteria [31]. Some studies have suggested that compost substitution can change the soil characteristics [32], and stimulate the soil microbial activity to increase soil microbial biomass, thereby promoting denitrification to increase N2O

The emission flux of N2O is relatively high at the jointing stage, which may be related

Previous studies have reported the involvement of micro-organisms in various carbon cycle metabolic processes such as the conversion of inorganic carbon to organic carbon, production of methane and methane oxidation, and decomposition of organic matter [42]. Some studies have reported that the amount of CH4 emitted from dryland soil is relatively low, which is mostly shown as absorption. This may be due to soil dryness,

nificantly reduce soil N2O emission, consistent with the findings of the present study.

promoting N2O emission [37]. Under the compost substitution ratio of 20%, the cumulative N2O emission was the lowest, which may be attributed to the low Shannon index and poor microbial community diversity. Studies have reported that micro-organisms, particularly Proteus, thick-walled bacteria, and Actinomycetes, are involved in straw decomposition [38]. Nitrogen-fixing micro-organisms mainly include various α-, β-, and γ-type Proteobacteria [39]. Among these groups, Firmicutes can participate in straw decomposition and are the most abundant bacteria that play a leading role in nitrogen utilization and compete with nitrifying bacteria for nitrogen sources; thus, they promote the fixation of plant available nitrogen, reduce the substrate for N2O production [40,41], and inhibit N2O emission [33–35]. A study by Guo et al. [36] indicated that compost substitution can significantly reduce soil N2O emission, consistent with the findings of the present study.

The emission flux of N2O is relatively high at the jointing stage, which may be related to fertilization. Fertilization can provide a large amount of available nitrogen to soil micro-organisms and accelerate the processes of microbial nitrification and denitrification, thus promoting N2O emission [37]. Under the compost substitution ratio of 20%, the cumulative N2O emission was the lowest, which may be attributed to the low Shannon index and poor microbial community diversity. Studies have reported that micro-organisms, particularly Proteus, thick-walled bacteria, and Actinomycetes, are involved in straw decomposition [38]. Nitrogen-fixing micro-organisms mainly include various α-, β-, and γ-type Proteobacteria [39]. Among these groups, Firmicutes can participate in straw decomposition and are the most abundant bacteria that play a leading role in nitrogen utilization and compete with nitrifying bacteria for nitrogen sources; thus, they promote the fixation of plant available nitrogen, reduce the substrate for N2O production [40,41], and inhibit N2O emissions.

Previous studies have reported the involvement of micro-organisms in various carbon cycle metabolic processes such as the conversion of inorganic carbon to organic carbon, production of methane and methane oxidation, and decomposition of organic matter [42]. Some studies have reported that the amount of CH4 emitted from dryland soil is relatively low, which is mostly shown as absorption. This may be due to soil dryness, good aeration conditions, and abundant oxygen in the soil, which make CH4 easy to be oxidized [43]. It may also be due to the rapid decomposition of organic matter and slow accumulation of organic carbon in dryland soil, thus affecting the production and emission of CH4 [44]. In the present study, the cumulative emission of CH4 increased under different compost substitution ratios, and the soil emission of CH4 was increased by the compost substitution treatment. This may be attributed to the fact that the relative abundance of *Bacillus*, Actinobacteria, and *Sphingomonas* increased after compost substitution, which reduced the decomposition of soil organic matter and promoted its rapid accumulation, thereby increasing the production and emission of CH4. The absorption under T0 was higher than that under T1, T2, and T3, which may be due to the presence of fewer *Proteus* in the soil without composting and increased fixation of organic matter in the soil [39], leading to a decrease in the methanogen matrix and CH4 emission.

In this study, CO2 emissions reduced under different compost substitution ratios, and the relative abundance of *Bacillus* increased after compost substitution. This resulted in soil carbon sequestration, leading to a decrease in the decomposition of organic matter by microorganisms and the transformation of mineral nutrients. Different compost substitution ratios lead to differences in the amount of soil organic carbon fixed. The stability of soil organic carbon increases gradually, and the deep organic carbon is not easy to be used by biology. Therefore, soil respiration is relatively weak, and CO2 emissions decrease.

In this study, all treatments reduced the comprehensive warming potential of greenhouse gases in the wheat season, and the comprehensive warming potential under T3 treatment was relatively low, which may be due to the lack of carbon-fixing micro-organisms in T3, rendering the soil to fix a limited amount of organic carbon. The comprehensive warming potential under T2 decreased by 60% compared with that under T0, which indicated that soil organic carbon was fixed due to compost substitution. The GHGI under T2 was the weakest among all compost substitution treatments, indicating that compost substitution could not only reduce the GHGI but also enhance the carbon sequestration of soil micro-organisms and improve soil productivity.

Additionally, compost substitution increased wheat yield, which is consistent with the findings of a study by Wang Jiabao [12]. The yield increased the most under compost replacement of 30%. The compost replacement of 30% increased the abundance of microorganisms causing soil carbon sequestration and improved soil productivity, in addition to significantly increasing the effective panicles and 1000-grain weight of wheat. Thus, compost substitution is an ideal cultivation measure for soil improvement and fertilization.

#### **5. Conclusions**

Throughout the wheat season, compost substitution significantly reduced CO2 and N2O emissions, and the accumulation of CO2 under all treatments was lower than that under T0. 20% of compost substitution was the smallest and the largest without composting. N2O accumulation was also the lowest under compost replacement of 20%; whereas for CH4, it was emission. In terms of GWP and GHGI, compost substitution of 30% could reduce GHGI and significantly increase wheat yield by 21.3% on a 100-year scale. Therefore, compost replacement of 30% could relatively reduce greenhouse gas emissions and significantly increase wheat production, indicating that compost replacement of 30% could be beneficial to both the economy and environment.

**Author Contributions:** Conceptualization, H.W. and L.R.; methodology, H.W.; software, H.M.; validation, H.M., X.H. and Q.S.; formal analysis, Q.L.; investigation, H.M.; resources, L.R.; data curation, H.M.; writing—original draft preparation, H.M.; writing—review and editing, H.W.; visualization, H.M.; supervision, L.R.; project administration, D.X.; funding acquisition, D.X. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the Natural Science Foundation of Anhui Province, China, grant number 2008085QD181; and the Major Provincial Science and Technology Projects, grant numbers 201903a06020001, 201903A06020023, 202003a06020003.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

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

#### **References**


Analysis of Wideband Second-Order Microwave Integrators

*Usha Gautam and Tarun Kumar Rawat*

## **Abstract**

This chapter presents the implementation of stable, accurate, and wideband second-order microwave integrators (SOMIs). These SOMI designs are obtained by the use of various cascading combinations of transmission line sections and shunt stubs. In order to obtain the optimal values of the characteristic impedances of these line elements, the particle swarm optimization (PSO), cuckoo search algorithm (CSA) and gravitational search algorithm (GSA) are used to approximate the magnitude response of the ideal second-order integrator (SOI). Based on magnitude response, absolute magnitude error, phase response, convergence rate, pole-zero plot, and improvement graph, the performance measure criteria for the proposed SOMIs are performed. The results of the simulation and statistical analysis reveal that GSA exceeds the PSO and CSA in order to approximate the ideal SOI in all state-of-the-art eligible for wide-band microwave integrator. The designed SOMI is compact and suitable for applications covering ultra-wideband (UWB). The designed SOMI structure is also simulated on Advanced Design Software (ADS) in the form of a microstrip line on a dielectric constant 2.2 RT/Duroid substrate with a height of 0.762 mm. In the 3–15 GHz frequency range, the simulated magnitude result agrees well with the ideal one.

**Keywords:** Cuckoo search algorithm, gravitational search algorithm, particle swarm optimization, line elements, microwave integrator


Down Syndrome and Other Chromosome Abnormalities

*Edited by Subrata Kumar Dey*

Published in London, United Kingdom

### *Supporting open minds since 2005*

Down Syndrome and Other Chromosome Abnormalities http://dx.doi.org/10.5772/intechopen.91519 Edited by Subrata Kumar Dey

#### Contributors

Emine Ikbal Atli, Zainab Al-Suhaymi, Fatma Söylemez, Madhavilatha Routhu, Shiva Surya Varalakshmi Koneru, Subhadra Poornima, Saranya Vadrevu, Imran Ali Khan, Hariharan Sreedharan, Van Hieu Van Pham, Subrata Kumar Dey, Poulami Majumder

#### © The Editor(s) and the Author(s) 2022

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**7. Conclusions**

By using the Laplace transformation, the analytical form of the solutions in the Laplace space and thus, by inversion, in the time domain are obtained. The approach provides an efficient and accurate way to calculate the rock and fluid temperatures. Through several preliminary case studies, some brief conclusions are obtained:


**Abstract**

The study was designed to enable researchers the opportunities to investigate the number sense performance and methods used by both the gifted and general students. A mixed-method design was used, and 48 gifted students and 95 general students in fourth grade from two elementary schools in Southern Taiwan were selected. The sample was chosen using a convenience sampling method. Nine students in each group were randomly selected and interviewed. The results showed that the gifted students performed significantly higher than the general students on the whole test and in each component of number sense. The contributions of this study based on the findings are discussed.

**Keywords:** fourth graders, general student, gifted student, number sense, elementary school


**Abstract**

Over the last century, there has been increasing debate concerning the use of biomass for different purposes such as foods, feeds, energy fuels, heating, cooling and most importantly biorefinery feedstock. The biorefinery products were aimed to replace fossil fuels and chemicals as they are renewable form of energy. Biomass is a biodegradable product from agricultural wastes and residues, forestry and aquaculture. Biomass could be sourced from a variety of raw materials such as wood and wood processing by-products, manure, fractions of organic waste products and agricultural crops. As a form of renewable energy, they have the advantages of easy storage, transportation, flexible load utilization and versatile applications. The aim of this study is to provide an overview for thermochemical and biochemical biomass conversion technologies that were employed currently. Attention was also paid to manufacture of biofuels because of their potentials as key market for largescale green sustainable biomass product.

**Keywords:** biomass, bioenergy, biochemicals, conversion routes, green chemistry


**Chapter 5**

*Geodetic Sciences - Theory, Applications and Recent Developments*

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org/10.1002/2014GL059323.

[73] Ramillien G, Seoane L,

doi.org/10.3390/rs12081299.

[74] GRACE Tellus [Internet] 2020. Available from http://grace.jpl.nasa.gov

(accessed December 18, 2020).

[75] GRACE FO [Internet] 2020. Available from https://gracefo.jpl.nasa. gov/ (accessed January 18, 2021).

GRACE-based water storage deficit approach for hydrological drought characterization. Geophysical Research Letters 2014;41:1537-1545. https://doi.

Schumacher M, Forootan E, Frappart F, Darrozes J. Recovery of Rapid Water Mass Changes (RWMC) by Kalman Filtering of GRACE Observations. Remote Sensing 2020;12:1299. https://

**76**


**3. Anthocyanin recovered from by-products**

#### **3.1 Extraction methods**

As anthocyanins are found as secondary plant metabolites in the case of purple corn cob, it is important to take into consideration their extraction and isolation from the tissue with adequate method, as their main issue is their high susceptibility to degradation, when isolated. Among the factors that affect anthocyanins' stability, the most referred are pH, when it has values above 7, explaining the requirement of acidified environment for most of the extraction protocols and temperature. The last one impacts the levels of anthocyanin extracted, a higher temperature being likely to induce degradation and impact in this way the extraction factor [39]. An ideal extraction procedure for food-grade anthocyanins would maximize pigment output while reducing degradation and changing the natural state of the target compounds [40]. When choosing the extraction method, it is important to take into consideration the application, as for food industry the solvents play a decisive role. In most of the times, anthocyanins' extraction is conducted by grinding, drying or lyophilizing the matrices, or soaking fresh materials into solvents such as water, ethanol, methanol, acetone, or others. As mentioned, the solvents are frequently acidified to make the extraction process easier and to keep the pigments stable during the process. Other possible influences on anthocyanin extraction that should be considered beside pH and temperature are time, solid:liquid ratio, assistance with microwave, ultrasound, and sonication [3]. In the case of purple corn cob, different optimized extraction techniques have been developed, both conventional and new methods (**Table 1**), taking into consideration the time, temperature, and solid: liquid ratio. In this way, Yang and Zhai [17] macerated the matrices and used methanol as solvent, the same one used by Li et al. in their study [41]. Another solvent used by Nisi et al. is acetone, but due to safety concerns, these two, methanol and acetone, are not desirable if the extract is used in food applications. The same author tested also the efficacy of water and ethanol solvent, by comparing with acetone, and the results were similar [42]. Also, Lao and Giusti optimized an extraction protocol using a combination of water and ethanol (EtOH), which is food-friendly. Besides analyzing the best extraction ratio, they focused also on its acidity in order to increase extraction efficiency [3]. Yang et al.'s work focused on choosing the optimum conditions for the extraction of anthocyanins from purple corn cob taking into account the main factors related to the process, including solvent, acid, solvent concentration, and acid concentration, using a mixture of solvent and water. The maximum yield for purple corn cob extraction was obtained with 80% methanol and 1% citric acid, and using ethanol, the best conditions were obtained with 80% solvent and 0.5% citric acid [43]. Based on safety concerns related to certain organic solvents, Rajha et al. managed to study the efficiency in terms of anthocyanins' extraction using β-cyclodextrin-water as solvent, β-cyclodextrin being generally recognized as safe (GRAS) solvent. They suggested a low-cost extraction protocol for the valorization of purple corn cobs, obtaining the best extraction rate using 39.8 mg/mL β-cyclodextrin at 68.8°C for 60.4 min [44].

Recently, there is a focus on reducing or eliminating the use of toxic solvents by taking advantage of the potential that emerging extraction strategies have, which are capable of reducing the processing time, maintaining also the bioactivity of the compounds [48]. Piyapanrungrueang et al. realized a comparison study between the conventional extraction methods and the emerging ones, having a purple corn cob hybrid as matrices. They characterized and optimized methods for conventional, ultrasound-assisted extraction, microwave-assisted one, and for ohmic heating. By taking into consideration the amount of anthocyanin extracted, energy efficiency, and color value, the best method for extracting anthocyanin from purple corn cob is the microwave-assisted one [45]. There are also other authors that optimized emerging methods for extracting anthocyanins as presented in **Table 1**. When choosing the extracting protocol, the future applications should be taken into consideration, as

*Purple Corn Cob: Rich Source of Anthocyanins with Potential Application in the Food Industry DOI: http://dx.doi.org/10.5772/intechopen.107258*

*Optimized extraction techniques: conventional and new methods.*

there are plenty possibilities for anthocyanin from purple corn cob. Related to this, the next chapter focuses its attention on studies that emphasize purple corn cob's applications in different industries.

#### **3.2 Recent studies and future perspective**

In terms of using the cobs in order to minimize the waste production, Nisi et al. proposed a quick and cheap extraction method to obtain anthocyanin with the aim of using them as dye for different natural fibers. Moreover, they managed to recover and use all the material to produce nutraceuticals and pigmented litter for pets [42]. In this framework of the biorefinery approach in valorizing purple corn cob, authors proposed a natural dye for fabrics, showing also ultraviolet protection of the dyed clothes. They present the final product as having good durability, acceptable stability, excellent aesthetic appearance and also sustainable, since are dyed with natural pigments. Also, they demonstrated that the extract possesses good anti-inflammatory properties, highlighting the possibility of incorporating in food products to take advantage of its full potential related to health benefits. The purple lignocellulosic solid residue is considered feasible for animal bedding, which can be compostable, nulling in this way the waste produced [42].

Within the same frame, Gullón et al. characterized purple corn cob's potential in order to comprehend whether it can be used as multifunctional ingredient for food and pharmaceutical industries and what properties does it possess [5]. After the extraction conditions of bioactive compounds from purple corn cob were successfully performed, oligosaccharides and phenolic compounds, including flavonoids and anthocyanins were characterized. The bioactive compounds showed complex structures as the extract was stable at high temperatures when subjected to thermogravimetric analysis. Overall, their research concluded that purple corn cob represents a sustainable source of bioactive compounds with economical value, therefore improving the food industry's competitiveness [5].

Regarding its culinary applications, purple corn cob is used as a base ingredient in the process of obtaining traditional drink *chicha morada* and dessert named *mazamorra morada.* For both of then, the whole corn including the corn cob is boiled in order to extract the color. To obtain the drink, some fruits, spices, and sugar are mixed together. Besides these, the dessert requires a binder followed by cooking. Regarding the heat treatments that can affect the product nutritional quality, some studies concluded that thermal processing such as boiling, roasting, or frying is associated with a decrease in bioactive compounds, as they are not stable in such conditions [19].

Also related to food industry, a recent study developed and optimized a low-calorie tea formulation containing purple corn cob, stevia as sweetener, cinnamon, and clove as flavoring and quince as a pH regulator that improves the color. The product exhibits enhanced antioxidant capacity, which, in future studies, will be evaluated on human volunteers in order to offer information on the true impact of the new antioxidant product [4]. In a similar area of products, Wattanathorn et al. [49] obtained a functional drink containing the extract of purple corn cob and pandan leaves, being the first study that showed that a polyphenol-rich supplement can improve cognitive function in a menopausal animal model. The functional drink has a cognitive boosting effect that is comparable to donepezil, a common medicine used to treat memory problems today. In this way, the product based on purple corn cob extract and pandan leaves can be used as a viable supplement to lower the risk of memory deterioration in menopausal women, which is simple to achieve, based on its advantages and low raw material prices. Clinical trial research and subchronic toxicity, one the other hand, are necessary [49].

As presented, purple corn cob is a valuable source of bioactive compounds, with different possible applications, but related to food industry, several studies need to be made in order to benefits from its full potential.