*2.4. Training and Procedures for Data Collection*

Training was provided by the local team members/co-authors (Brazil C.R.L.A., Malawi L.M.S., Pakistan M.A.R.), with a consistency check visit by one of the other co-authors (Brazil P.K., Malawi M.G., Pakistan V.C.). Training took place over a period of 2–3 days at each site and was conducted in person at each site. The cost of the training depended on the cost of the trainer visit, hiring a room and transport costs to the training location for children and families attending. The training procedures were monitored so a formal "training the trainer" package could be developed. Training materials are available through the IYCD website (https://ezcollab.who.int/iycd, accessed on 2 June 2021).

Once trained, the country teams conducted assessments with caregivers. Generally, the IYCD assessment was conducted first, followed by the physical measurement of the child. In seven cases in Brazil, three in Malawi and none in Pakistan, the IYCD assessment had to be ceased and reprised on another occasion. The majority of the interviews were recorded using tablets run on Open Data Kit software [20].

#### 2.4.1. Reliability Testing

At the initial assessment, a random subsample (*n =* 96; 32 from each of the three sites) was invited to come back for a second assessment to test consistency of measurement. Inter-rater reliability (two different assessors within the same day at a different time and test-retest reliability (same assessor 3–7 days apart) were conducted in the same setting where the child was seen originally (in Brazil and Malawi, this was done in the health centers, and in Pakistan this was done in homes).


Interfacial Behavior of Saponin Based Surfactant for Potential Application in Cleaning

*Gajendra Rajput, Niki Pandya, Darshan Soni, Harshal Vala and Jainik Modi*

## **Abstract**

Amphiphilic molecules having a tendency to decrease the surface tension of the aqueous medium and those are widely used in the industrial and domestic sector. Nowadays they are in high demand to replace synthetic surfactants by naturally based molecules to reduce the environmental problem. Approx. more than 60% materials which are of surfactants-based enter into the marine water which dangerous for aquatic lives. We propose novel material which is a natural based surfactant which is biodegradable and eco-friendly alternatives. Here we are focused on tea saponin and investigated properties like surface tension, foaming, skin mildness, cleaning ability. This is maybe the first reporting a surfactant activity of tea based surfactant. Natural originated surfactants display well emulsion making capacity at the large amount as compared to synthetic. Tea is acidic in nature and it reduces surface tension to 31.4 mN/m, and greater foam, ultra-mildness, with excellent cleaning ability. The consequences suggest that tea have outstanding surface-activity which can be used as a green replacement for synthetic surfactants.

**Keywords:** green surfactant, saponin, foaming, emulsification, mildness

### **1. Introduction**

Amphiphilic molecules contain hydrophilic and hydrophobic units that improve interfacial properties. This characteristic of surfactant makes it suitable for the fields of detergent, wetting, emulsification, oil recovery, froth flotation and other fields [1–3]. A large number of non-natural surfactants are used in industrial and domestic work which are spread over underwater, soil, sediment, etc. Studies have shown that more than 60% of surfactant derivatives are released into the aquatic environment. Global surfactant production was approximately 12.5 million tons in 2006 [4], while production in Western Europe that is more than 3 million tons in 2007 [5]. After that year of 2010, the use of non-ionic surfactants (polyethoxylated nonylphenol) in the United States was approximately 172,000 tons [6]. These synthetic surfactants can disturb the environment and cause health risks such as respiratory tract, dermatitis, eye irritation, etc. [7, 8].

Ostroumov expressed how non-biodegradable cleaning product can reduce the cleaning capacity of oysters and mussels. Reduce the Water purifying capacity of bacteria like *Crassostrea gigas* and *Mytilus galloprovincialis* which cause an important impact on the ecosystem. Besides, aquatic plants, microorganisms, humans, also affected by the non-biodegradable surfactants [9].

Due to technological advancement methods for the mass production of surfactants are emerging, which have led to serious environmental problems [10]. Today's demand, surfactants should be biodegradable and have minimal toxicity to be surface active. Therefore, researchers are looking for environment-friendly surfactants, it may be a natural surfactant. Natural materials are gained from natural sources, such as plant, bacterial or fungal, animal fats. Various methods are needed, for extract and using them. The fatty acid esters or amides of these sugars can be used as substitutes for synthetic surfactants, as described by Salati et al. Humic acid extraction using biomass as a surfactant has been reported [11].

Saponins are well known plant-based surfactants. Natural surfactants have biodegradability, biocompatibility, and low toxicity, so they are less harmful to the environment [12]. These products can be manufactured with high production with cost effect which could be used in environmental control actions such as processing industrial emulsions, controlling oil pollution, detoxifying industrial wastewater, and bioremediation of contaminated soil [13].

Here we report that tea saponin is considered as a natural surfactant. A recent movement in the industry to rejecting used of synthetic products which inspiration for people to look for natural-based new materials. By these inspirations, we examined tea to estimate its physiochemical property such as critical micelle concentration (CMC), Foaming, viscosity behavior, emulsification, mildness pH, and conductivity. For comparison, we also examined the marketed product (ionic surfactants) just because they are commercially availability. We can reduce environmental issues just by replacing them with natural surfactant.

### **2. Experimental procedure**

#### **2.1 Materials**

Tea saponin powder was purchased from King diamond (65% approx.). Surf excel (Hindustan Unilever Limited), as a marketed surfactant, Soya bean oil (refined, batch number (AF) SB06C04), Coconut oil (Lot No. KB003), Paraffin wax (candles from the market) were used. Hexane was obtained from Loba Chemie. Milli-Q water of surface tension 71.6 ± 0.1 mN/m was used. Bleached cotton cloth Sort No. 22425003 from Akash Textiles, Ahmedabad.

#### **2.2 Surface tension measurements**

Surface tension of the individual surfactant was determined by a force tensiometer (*Type: K20, KRÜSS, Germany*) using a platinum plate method. The experiment was directed by using 25 mL of surfactant solution in a sample container. In this method, the liquid to be inspected is placed in a container, and its position can be changed by using a screw to move up and down. There is a platinum plate on the top of the sample. During this process, to ensure the plate is accurately immersed in the liquid alignment of platinum plate adjusted at 3 mm distance from the liquid. In the tensiometer, a pre-programmed software archives the surface tension of liquid under examination. For accuracy before apiece measurement instrument was calibrated using Milli-Q water (71.5 ± 0.1 mN/m) at 27 ± 0.5°C. After the completion of each reading, the platinum plate was carefully washed using deionized distilled water, followed by heating to ensure that the water evaporated from the surface of the plate. All measurements were recurrent three times to accord repeatability of the data.

*Interfacial Behavior of Saponin Based Surfactant for Potential Application in Cleaning DOI: http://dx.doi.org/10.5772/intechopen.102560*

### **2.3 Foam ability and stability**

The Ross Miles method was utilized to study the foaming behavior of surfactant. To study the foaming behavior 250 mL surfactant solution was prepared. Primary step to perform foam analysis was to wash the column with deionized water followed by rinsing of the column using surfactant solutions under examination. Afterwards, from 250 mL prepared surfactant solution gradually 50 mL of surfactant solution poured into the column and was stored at the bottom part of column. The remaining 200 mL surfactant solution was taken into the pipette and pipette containing surfactant solution positioned at top of the column. Then, faucet of pipette opened and at fixed flowrate surfactant solution run off into the column. As soon as the surfactant solution from pipette completely transfer to column, start the stop watch and note the initial foam height (termed as foamability) and after 5 min note foam height again (termed as foam stability).

### **2.4 pH and conductivity**

pH and conductivity were measured by Laqua pH 1100 (Horiba) at room temperature. The pH and conductivity electrode was calibrated with the help of standard solutions previously measurements.

#### **2.5 Viscosity**

Vibration viscometer (SV-10 series, A & D Company, Limited) was employed for viscosity measurement of samples. The working principle of instrument to determine viscosity followed constant parameters like frequency 30 Hz and amplitude <1 mm, at these fixed parameters the required current to resonate sensor plates used to detect viscosity.

#### **2.6 Emulsification**

This measurement carried out by the Kothekar method [14]. Initially 10 mL of sample solution and 10 mL of refined oil was taken into a graduated cylinder followed by shaking of this mixed solution. Now for this emulsion time required to separate 5 mL of sample solution was determined and denoted as emulsion persistence.

#### **2.7 Cleaning**

To determine the cleaning efficiency the Sharma method [15] was considered, which involved four steps. Firstly 5 × 5 cm sized cotton cloth was soaked in water for 12 h, dried and weighed. In second step simulate dirt was prepared by maxing 0.5 g coconut oil and 0.5 g paraffin in 50 mL of hexane. Now, in third step the dry cotton cloth was two times dipped into the simulated oil, dried and weighed. Lastly the cloth was treated with surfactant by soaking it for 10 min followed by washing, drying and weighing. To determine cleaning efficiency different surfactant concentrations ranging from 0.01% to 0.1% were utilized at room temperature.

#### **2.8 Protein solubilization**

The protein (zein) solubilization potential of surfactant was quantified by the gravimetric analysis. The 1.0% by weight surfactant solutions were prepared in vials and approximately 2.0% by weight zein powder added to the solution. Now

these mixtures of surfactant-zein protein were continuously stirred for 24 h. After 24 h continuous stirring the solutions were filtered using Whatmann filter paper to collect insoluble zein from solution. Lastly the collected insoluble solid zein was desiccated at 80°C for 24 h and weighed. Using the insoluble zein powder weight the solubilized zein% has been calculated.

### **2.9 Lipid solubilization**

The lipid (steric acid) solubilization potential of surfactant was quantified by the gravimetric analysis. The 1.0% by weight surfactant solutions were prepared in vials and approximately 2.0% by weight stearic acid powder added to the solution. Now these mixtures of surfactant-stearic acid were continuously stirred for 24 h. The surfactant-stearic acid mixture was then filtered using Whatman filter paper. Finally, the collected solid was dried at 60° C for 24 h. The weight percentage of the solubility of the lipid in the surfactant was calculated from the weight of the insoluble stearic acid after drying for 24 h.

## **3. Microstructure**

Microstructure of gas bubbles was measured through an Olympus STM7CB Digital microscope. The foam was generated by using the handshaking method.

### **4. Results and discussion**

#### **4.1 Surface tension**

The surface tension creates an imbalance in the intermolecular force in an interface between liquid, vapor or liquid and solid. **Figure 1** shows that the reduction in surface tension due to surfactant concentration increment and after certain concentration surface tension value was constant as this concentration is called the critical concentration of micelles (CMC). The reduction in surface tension is due to the break of hydrogen bonds aqueous, the reason was increased adsorption of monomer

**Figure 1.**

*Plot of equilibrium surface tension versus total surfactant concentration at a temperature of 27 ± 0.5°C.*

*Interfacial Behavior of Saponin Based Surfactant for Potential Application in Cleaning DOI: http://dx.doi.org/10.5772/intechopen.102560*

**Table 1.**

*Surface properties of surfactants.*

at the air-water interface [1]. The surface tension reduction value between 32 and 37 mN/m consider as the material has good detergency and surface activity [16]. Therefore, tea has a decent washing ability.

The properties of surfactants, such as detergents, solubilizers, etc., depending on their structure. The reduction in surface tension caused by natural surfactants is almost close [17] to the surface tension obtained by measuring surface tension (**Table 1**). The ionic surfactants tend to have relatively greater CMC value as of nonionic surfactant (as observed for surf), such behavior of ionic surfactants may be attribute to the repulsion between neighboring analogous head group charge. The tea saponin reveal greater capability to reduce surface tension as compared to surf may be due to absence of elementary electrostatic interaction in tea saponin and this also resembles faster micelle formation in water.

The effectiveness of surfactant (*π*CMC) is given by *π*CMC where γ0 is the surface tension of pure water and *γ*CMC is the surface tension of solution at CMC. Natural surfactant shows maximum effectiveness followed by synthetic.

$$
\pi\_{\rm CMC} = \mathcal{Y}\_0 - \mathcal{Y}\_{\rm CMC} \tag{1}
$$

The dynamic surface tension of surfactants solutions was studied by the pendant drop method. **Figure 2** displays the dependencies of dynamic surface tension for micellar solutions of natural and synthetic as functions of the effective lifetime [18]. The concentrations of surfactant are 0.005 wt% (below CMC). From the result shows that the natural surfactant leads to faster the surface tension changes because the Natural surfactant having no kind of change repulsive force at a head group like surf.

**Figure 2.** *Plot of surface tension as a function of time at a temperature of 27 ± 0.5°C.*

### **4.2 Foam ability and stability**

Foaming behavior and cleaning action is not much interrelated, but foam behavior is a significant condition in cleansing agent assessment by the consumers. Foam creation and durability are imperative in numerous applications [19–21]. Foam ability is the amount of foam creation due to the constant formation of new interfaces. Higher power of foaming requires faster adsorption, high surface elasticity. The foam results obtain by Ross Miles test are shown in **Figure 3** at a concentration range from 0.02 to 0.10 wt% for both surfactant sample. Foam generation by pouring method and the amount of foam formation was considered as Foam ability and after 5 min foam height measures that are termed as foam stability. The tea solution produces dense, high-quality foam, which may be due to the presence of high amounts of saponin. The existence of saponin group contributes to significantly reduce the dynamic surface tension as well as supports to produce the large surface for foam formation [13]. Foam formation upsurges with increased surfactant concentration as greater number of monomers could custom in film to enhanced foam stability.

We have also observed under the microscope to get an insight into the bubble size and foam structures as depicted in **Figure 4**. The foam engendered by surf solution speckled to contain more liquid portion with larger bubble size, while the foam generated using tea solution contained less liquid portion with smaller bubble size. These experimental results lead to better understanding of foam formed using natural surfactant and it indicates that such natural surfactants could deliberately consider as foaming agent for various applications. The foaming behavior studied by utilizing the Ross Miles method [22] and similar results were observed for foam generated by hand shake method as indicated in a picture (**Figure 4**) at 0.1 wt% concentration for both tea saponin and surf.

#### **4.3 Viscosity**

Micellization affects the viscosity of a solution, depending on the size and number of particles in the solution. **Figure 5a** shows that the viscosity gradually increases with increasing concentration. The absence of charge repulsion in between the head groups of surfactant monomers may lead to induce viscosity with

**Figure 3.** *Plot of foam height as a function of surfactant concentration in Milli-Q water at a temperature of 27 ± 0.5°C.*

*Interfacial Behavior of Saponin Based Surfactant for Potential Application in Cleaning DOI: http://dx.doi.org/10.5772/intechopen.102560*

**Figure 4.**

*Optical microscopy images of the foam generated from (a) surf (b) tea respectively at 27°C. Inset shows the photo of the foam for the corresponding surfactants.*

increased concentration of tea. The hydrophilic part of surfactant monomers surrounded by water assists to a rise in viscous resistance. The viscosity progress may escalate rapidly above CMC due to micelle shape transition at higher concentration. The increase in viscosity far exceeds CMC, which is due to more interactions among micelles, the interactions among micelles are starting to get closer and reduce the critical packing parameters [23].

#### **4.4 Conductivity**

Detergent mostly made by ionic surfactants which are ionized in an aqueous condition so it will show conductance from low to high depends on concentration. The change in conductivity at lower concentrations almost constant further increases at higher concentrations for natural surfactant **Figure 5b**. At lower surfactant concentration, the headgroups of surfactant were encircled by water consequently results to lower conductivity. The conductivity upsurges with the increasing concentration of surfactant due to ionization of surfactant molecules [24]. The conductivity also associates with mildness, which demonstrates inversely

#### **Figure 5.**

*Plot of (a) viscosity and (b) conductivity as a function of surfactant concentration at a temperature of 27 ± 0.5°C.*

proportional correlation [25]. The experimental outcome indicates that the conductivity increase at higher concentration for tea ensues to be more as compared to surf.

#### **4.5 pH measurements**

In surfactant science research investigation of pH is an indispensable study. The pH of a surfactant solution relies on the overall charge of the headgroup, which consequently changes the repulsion between headgroups [7]. The pH values for both tea and surf solutions for range of concentration 0.02–0.10 wt% in Milli Q water were observed to be 6.7 ± 0.2. The pH values of the surfactant solution at various concentrations are shown in **Table 2**. Tea with an acidic pH, possibly due to the hydrolysis of nonionic glucuronic groups. This pH of the solution near the skin (~5.5) causes less damage to the hair and skin. The surf shows alkaline in nature. The pH of the tea solution decreases with concentration, while that of surf increases.

#### **4.6 Emulsification**

The emulsion is a fine dispersion of one liquid into another stabilized by emulsifier such as protein, surfactant, polymers etc. The surfactants can be solubilized non-polar substances into polar due to its amphiphilic nature, e.g. Surfactant

*Interfacial Behavior of Saponin Based Surfactant for Potential Application in Cleaning DOI: http://dx.doi.org/10.5772/intechopen.102560*

**Table 2.**

*The values of pH at different concentrations of surfactants solutions.*

monomers adsorb between the water-oil interface and reduce its interfacial tension. Due to this reduction in interfacial tension less energy is required to form new interfaces that are essential to the preparation of a stable emulsion. In these studies, we make simple oil-in-water (o/w) emulsion, which showed the emulsifying power of the surfactant's solution **Figure 6**. Emulsion stability increases with concentration increases, natural surfactant formed more stable (almost 30 min) emulsions at high concentration. The results of surface tension show that the stability of the emulsion decreases in the area of micelle formation, possibly because less surfactant is adsorbed at the oil-water interface. Tea has the best emulsion stability, followed by the surf. Stable emulsions occur when the adsorbed surfactant makes repulsive interactions among the drops and generates an energy barrier against breakage.

#### **4.7 Cleaning**

Cleansing activity means, the removal of unwanted substances such as soil, grease and dirt, is the main target of any detergent. The cleaning activity was calculated by the below equation

$$\mathbf{C} = \left[ \left( \mathbf{W}\_2 - \mathbf{W}\_3 \right) / \left( \mathbf{W}\_2 - \mathbf{W}\_1 \right) \right] \times \mathbf{100}\mathbf{9} \tag{2}$$

where W1 = initial weight of cloth, W2 = weight of the cloth with simulated dirt and W3 = weight after cleaning with surfactant solution and water. As shown

**Figure 6.** *Plot of emulsification as a function of surfactant concentration at a temperature of 27 ± 0.5°C.*

in **Figure 7** The cleaning ability of the tea showed better at lower concentrations. The cleaning ability increases with concentration increases for natural surfactant. Although all surfactants have similar trends in cleaning performance, there is a significant difference in the amount of soil removed. The tea demonstrations decent cleaning efficacy at high concentration as compared to the surf, probably due to tea revealed greater efficiency to reduce surface tension.

#### **4.8 Skin mildness of surfactants**

In the skin structure first part is known as epidermis and the outer most layer of epidermis is stratum corneum (SC), this layer provides an important barrier function for skin. Surfactant can amend the function of SC by interacting with proteins and lipids of SC. These interactions lead to swelling and denaturation, however the comprehensive mechanism involved for such interaction has not been reported yet. But, predisposition of surfactant to interact with proteins could relate with its impact on human skin, generically termed as mildness to human skin [26].

**Figure 7.** *Plot of cleaning ability as a function of surfactant concentration at a temperature of 27 ± 0.5°C.*

**Figure 8.** *Plot of protein and lipid dissolution by surfactants (1.0 wt%) at a temperature of 27 ± 0.5°C.*

*Interfacial Behavior of Saponin Based Surfactant for Potential Application in Cleaning DOI: http://dx.doi.org/10.5772/intechopen.102560*

To comprehend impact of tea saponin and surf on skin, the solubilization potential of protein and lipid were determined using 1.0 wt% surfactant solution by dissolving model protein zein and model lipid stearic acid represented in **Figure 8**. The dissolution tendency of natural surfactants, zein and stearic acid is small compared to surf. This indicates that natural surfactants are milder than synthetic.


Income

Income calculation begins with *Indigofera* productivity, with total production calculated based on *Indigofera* leaf productivity per unit area. In 1 ha with a spacing of 100 × 100 cm, there will be 10,000 plants. Each individual plant produces an average of 35 g of *Indigofera* leaves per harvest. Harvesting on Timor Island can be done twice a year. Therefore, in

1 year, the production of *Indigofera* leaves is 700 kg ha−1. Based on the productive age of the plant, the Indigofera cultivation cycle is 4 years. Thus, the productivity and total production of *Indigofera* leaves in one business cycle for 4 years is 2800 kg ha−1.

The next step in the income calculation process is to calculate the yield of paste. Based on the results of the study, it was found that when 300 g of *Indigofera* leaves were added to 6 g of lime (2%) and 3 l of water, this yielded 177 g of paste (Agustarini et al. 2021). The calculation of paste yields based on the research activities is presented in Table 6.

**Table 6.** Indigo paste production from 1 hectare of *Indigofera* plant.

The productivity of paste yields per hectare per year is 273 kg (Table 6). The price of indigo paste is relatively inelastic due to both the supply and demand for indigo paste being relatively stable. Thus far, there has been no substitution of natural blue dye apart from *Indigofera*, and production competition is still limited. There is no large industry that produces an indigo paste that will potentially monopolize the market price. The price used for income analysis was the price of indigo paste in 2021, based on the selling price of indigo paste in the market place (IDR 110,000 kg−1, or USD 7.75). Therefore, the income derived from producing indigo paste is IDR 30,030,000 ha−1year−1 (or USD 2117) and income during the cultivation cycle is IDR 120,120,000 ha−1 (or USD 8469).

#### Results of the Financial Analysis

In conducting a financial feasibility analysis, it is necessary to consider changes in the value of currency over time. The cash flow on which the calculation of the investment eligibility criteria is based runs over a fairly long period of time (4 years). A calculation of the changes in the value of money over this period is calculated by including discounts in the analysis. Discounts are needed to convert the expected future flow of resources into an estimated present value (Riyanto 2018). The results of the financial analysis for the production of indigo paste are presented in Table 7, based on an assumption of a discount factor of 8%, based on the Bank Rakyat Indonesia credit interest rate in 2021. This follows (Nurmalina et al. 2018), who state that one of the factors for determining the discount rate is loan interest rates.

**Table 7.** The results of the financial analysis into the production of indigo paste with a factor discount of 8% over a 4-year business cycle.

The results of the analysis demonstrate the feasibility of indigo paste production. This is shown by the values of the investment parameters, with the NPV showing a positive value, with the BCR above 1, with the IRR above the discount rate, and the BEP within the period of the business cycle. The analysis shows that the production of indigo paste has the potential to yield profits of IDR 4631,501.50 ha−1 year−1, or USD 326,536 (exchange rate: USD 1 = IDR 14,183.73).

El Nino events impacted Timor Island in 2006–2007 (Kieft and Soekarjo 2007), with further impacts from La Nina in 2009 (Kota and Adiningtyas 2010), with these climatic anomalies having a severe impact on Timor's agricultural sector and on community welfare. The results of research related to the cultivation of *Indigofera* spp. show that climatic conditions are one of the most important factors for the cultivation of this plant, with significant implications for productivity. Thus, it can be concluded that the climatic anomalies associated with El Nino and La Nina would impact the productivity of agricultural crops.

With El Nino conditions resulting in extreme dry seasons, when these conditions occur, it is essential to water plants so that they do not die. To determine the impact of this on the cultivation of *Indigofera* spp., a sensitivity analysis was conducted, taking into account factors including decreased productivity and the increased production costs resulting from the need for watering.

The analysis involved two scenarios for decreased productivity, with the first assuming a 10% decrease (a reduction in income of IDR 3,003,000 year−1 or USD 211.72 year−1) and with the second assuming a 20% decrease (a reduction in income of IDR 6,006,000 year−1 or USD 423.44 year−1). The two scenarios involving increased production costs are respectively based on the need for watering activities with an intensity of 1 month in 1 year (resulting in an additional cost of IDR 3,741,497 year−1 or USD 263.79 year−1) and on an intensity of 2 months in 1 year (resulting in an additional cost of IDR 7,482,993 year−1 or USD 527.58 year−1), as shown in Table 8.

**Table 8.** Results of sensitivity analysis for the decreased leaf production of *Indigofera* spp.

The results of the sensitivity analysis for the decreased leaf production of *Indigofera* show that the production of *Indigofera* paste would remain feasible if the production of *Indigofera* leaves decreased by a figure not exceeding 10%. However, if the decrease is greater than 10%, then the production of indigo paste is not feasible. Sensitivity analysis is conducted if there is an increase in costs due to watering activities. It is carried out in 2 scenarios, namely 1 and 2 months of watering in a 1 year planting period (Table 9).

 1.07

 38%

 Third year −441.1122

−19%

 0.94

 Not achieved

**Table 9.** Results of sensitivity analysis to the increase in costs due to watering activities.

> IDR 6,135,695 or USD 432.5868

The results of this sensitivity analysis indicate that indigo paste production activities remain feasible if watering is required for a maximum of 1 month in 1 year. However, when watering is required for longer periods than this, indigo production becomes unfeasible.

3.5.2. Potential Economic and Social Value to Be Derived from the Development of *Indigofera* spp.

The potential economic value of *Indigofera* development is from the indigo paste selling activities in which its raw material is harvested from *Indigofera* potential cultivation areas. Based on the results of the various analyses, a number of findings related to the

NPV

BCR

IRR

BEP

development of *Indigofera* on Timor Island were obtained, including the following: the potential area to be prioritized for development covers ± 163 thousand ha; *I. suffruticosa* Mill. can generate production yields of 700 kg ha−1; resulting in yields of indigo paste of 273 kg ha−1, with the potential to generate profits of IDR 4,631,501.50 ha−1 year−1 or USD 326.54 (exchange rate: 14,183.73). The potential economic value to be derived from the development of *Indigofera* on Timor Island is distinguished based on three plantings scenarios: 100% targeted area development with intercropping (scenario 1); 50% targeted area development with intercropping (scenario 2); and 25% targeted area development with intercropping (scenario 3). A comprehensive outline of the potential economic and social value to be derived from the development of *Indigofera* can be seen in Table 10.

**Table 10.** Potential economic and social value to be derived from the development of *Indigofera*.

The potential social impact to be derived from the development of *Indigofera* takes the form of employment, given that the processes involved are labor-intensive. This potential is demonstrated in Figure 7, which shows that wages account for the largest proportion of the operational costs associated with the development of *Indigofera* (70%). The wage component for 4 years of *Indigofera* production cycle absorbs 1023 labor day (years 1: 348 labor day, years 2–4: 225 labor day) consists of the cultivation of *Indigofera* and the producing of indigo paste. In the first year, we need more labor for the cultivation activities. In the second to fourth year we only have maintenance and indigo paste production. In the cultivation of *Indigofera*, the wage component reaches 84% of the total cost of wages, while the proportion of the manufacture of indigo paste reaches 16%. Thus, this sector is suitable to be developed as it is able to absorb many workers in the rural sector.

**Figure 7.** Composition of costs related to the development of *Indigofera*.

*Ikat* weaving also has economic potential as a tourist attraction. Weaving from this region has significant aesthetic value and plays a major role in establishing cultural identity (Susanti 2021). Thus, it has the potential to act as a form of cultural tourist attraction that could create value for NTT as a market for tourists.

As shown by a study conducted by (Muda and Suwito 2019), *ikat* weaving could drive increased tourist visits to the region, with significant potential economic benefits for the community. The development of weaving crafts has the potential to generate significant employment and to reduce the unemployment rate. Thus, this innovation has the potential to generate strong positive social value (Salma et al. 2018). There is good potential to develop the *ikat* weaving business, given that most craft people work individually, with very few working together in groups (Muda and Suwito 2019). Weaving has strong market potential due to its recognition as an environmentally friendly art product by upper-middle consumers. According to (Samadara 2018), both national and international tourists prefer to buy *ikat* from Sumba with natural dye compared to synthetic dyes.

Together with increasing public awareness of the negative effects of synthetic colors on health and the environment, the market for natural dyes has good prospects (Salma et al. 2018). Therefore, *ikat* weaving can be regarded as a form of cultural heritage that could be developed through a community empowerment approach to generate significant positive economic and social benefits for members of communities in rural areas.

#### *3.6. Development Strategy the Sustainable Use of Indigofera Leaves to Produce Natural Dyes*

A development strategy for the sustainable use of *Indigofera* leaves to produce natural dye was formulated through a quantitative SWOT analysis. A quantitative SWOT analysis may be used to conduct an evaluation according to specific desired parameters and to develop management strategies (White et al. 2015). The main advantage of the SWOT approach is to show current and possible future constraints related to implementing a proposed initiative (Johnson et al. 1989). The SWOT analysis approach is an effective technique to formulate strategies (Hill and Westbrook 1997) because it categorizes factors related to the proposed initiative as either *internal* (strengths, weaknesses) or *external* (opportunities, threats) (Shrestha et al. 2004). Therefore, the approach can provide insights into how to transform threats into opportunities and offset weaknesses into strengths (Wang 2010; Liu 2013).

The SWOT diagram for the development of the use of *Indigofera* leaves as a natural dye in a sustainable manner on Timor Island, Indonesia, was constructed according to interviews with experts. Strengths and weaknesses are categorized as internal factors, and opportunities and threats are categorized as external factors (Table 11).

The determination of weights in the SWOT-AHP model is based on the results of comparisons between SWOT components using pairwise comparisons. The results of the analysis show that the value of the consistency ratio (Cr) is 0.03 for external factors and 0.04 for internal factors. This value is still below the maximum allowable Cr of 0.1, following the recommendations of (Saaty 2008). The results of the SWOT-AHP analysis show that the coordinate value for the internal factors stands at 2.10, while the external factor stands at 2.32 (Tables S5 and S6). Based on the position and action evaluation matrix strategy, there are four quadrant strategies: aggressive, competitive, defensive, and conservative (David et al. 2019).

These results indicate that the strategy for the production of *Indigofera* spp. on Timor Island lies in quadrant I, indicating that the strategy should be implemented aggressively. An aggressive strategy's leverage is the existing strengths and opportunities, rather than using defensive options to minimize weaknesses and threats (Rauch 2007).

Local wisdom relates not only to providing livelihoods and sustaining the lives of local communities but as also as a means to strengthen ecological sustainability and to leverage the community's unique resources to stimulate the sustainability of local wisdom itself (Lake et al. 2018). The strategy for the development of *Indigofera* spp. on Timor Island involves leveraging the community's culture and wisdom related to the use of this plant

acquired over generations, as a result of which it has developed the appropriate cultivation techniques, which consist of knowledge of the right time for planting, following good cultivation practices, and using appropriate planting materials. This provides a strong basis for leveraging the development of indigo paste in suitable areas to capture significant economic and social opportunities.

**Table 11.** Diagram of the SWOT analysis for the sustainable development of *Indigofera* to produce natural dyes.

Based on the SWOT-AHP analysis, it would be possible for the regional government of NTT to formulate a number of strategies for the development of *Indigofera* spp. on Timor Island, including the introduction of *Indigofera* spp. as an agro-industry commodity and its use for the rehabilitation of critical lands. These strategies could be implemented by developing partnerships with the private sector to increase the potential sale of indigo paste,

to expand markets, and to increase community capacity is to cultivate and process the plant. A number of stakeholders and actors could be involved in this initiative, including women's farmer groups, *Dewan Kerajinan Nasional Daerah/Dekranasda* (the Regional National Crafts Council), the Industry and Trade Office, the Women's Empowerment Service, the Agriculture and Plantation Service extension agencies, and private companies. Support for women's capacity building in terms of education is needed for the regeneration or further development needs. This is reinforced by (Chisamya et al. 2012), which states that by being educated, girls experienced a transformation of the inequitable gender relations they faced in society. Related to agriculture, although in Indonesia it is dominated by men, women appear to have equal access to productive resources, such as land and inputs, and greater control over household income than men (Akter et al. 2017). Funding and financing for these strategies could be sourced from related agencies, grant funds, business service agency (*Badan Layanan Umum*, BLU), and mutual cooperation funds managed by farmer groups.

Thus far, neither the NTT provincial government nor district governments have promulgated regulations related to the preservation of *ikat* or the use of natural dyes. Regional regulations could play a significant role in supporting strategies to develop the commodity, to create predictability and legal certainty, and to demonstrate a clear commitment to the development of cultural industries in NTT (Setiawan and Suwarningdyah 2014). In addition, local regulations related to weaving and tourism could play a strong role in facilitating the achievement of SDG 8 targets.

## **4. Conclusions**

The people of Timor Island are familiar with a range of plants that can be used to produce natural dyes, one of which is *Indigofera* spp. (*I. tinctoria* L. and *I. suffruticosa* Mill.), which is generally used to produce a black dye for *ikat* fabrics made by the community.

The cultivation of *Indigofera* is conducted in three stages: germination, seeding, and planting. Sowing media plays an important role in increasing the germination rate, with the best sowing media for the germination of *I. tinctoria* L. being sand media. At the seedling stage of *I. tinctoria* L, a mixed media consisting of soil, cocopeat, and rice husk charcoal (1:2:2) and placed in an unshaded area (0%) and mixed media consisting of soil and rice husk charcoal (1:2) and placed in 50% shade resulted in the highest biomass and the best quality index. At the planting stage of *I. suffruticosa* Mill., a spacing of 100 × 100 cm resulted in the best increases in height, diameter, and number of branches, while the use of fertilizer (100, 150, and 200 g plantings) resulted in better growth than without the use of fertilizer. Providing 100 g of fertilizer per plant results in the best results in terms of height, diameter, and number of branches.

Indigo paste is produced by adding quicklime CaO equivalent to 2% of the weight of the leaves and water equivalent to 10 times the weight of the leaves. This technique is more time-efficient and results in the production of a higher quality paste with a longer shelf life than through the techniques currently applied by the community.

The potential area suitable for the development of *Indigofera* spp. on Timor Island covers ±163,000 ha. The results of the financial feasibility analysis showed that the processing of *Indigofera* spp. to produce indigo paste is feasible. The results of the sensitivity analysis show that the tolerable decrease in production levels is only 10%, with a maximum of one month of watering in a year for the production of indigo paste to remain feasible. The total potential supply of raw materials to produce indigo paste from Timor Island stands at 11–44 tons/year, with the economic potential standing at USD 13–53 million/year. The cultivation and processing activities are labor-intensive, requiring a workforce of 1032 person/ha during the *Indigofera* spp. production cycle (4 years).

Strategies to develop the cultivation and processing of *Indigofera* spp. on Timor Island could be conducted aggressively by leveraging the identified strengths related to the cultivation of *Indigofera* spp. In particular, this would involve deleveraging local wisdom and cultural traditions related to the use of *Indigofera* spp. over generations by applying the appropriate cultivation techniques, together with innovations in the indigo

paste production process and the development of suitable areas to capture economic opportunities.

**Supplementary Materials:** The following are available online at https://www.mdpi.com/article/10 .3390/economies10020049/s1, Figure S1: Steps for Indigofera Land Suitability—Land Availability Analysis; Table S1. Effect of scarification and media on the percentage of germination of *Indigofera* spp. seeds; Table S2. The effect of media on germination percentage; Table S3. Effect of shade and media on growth parameters of height, diameter, number of branches, leaf production, and percent survival of *Indigofera* spp. seedlings; Table S4. The effect of spacing and manure application on increasing growth in height, diameter, and number of branches of *Indigofera* spp. four (4) months after planting; Table S5. Internal strategic factors based on weights, ratings, and scores; Table S6. Internal strategic factors based on weights, ratings, and scores.

**Author Contributions:** Each author (R.A., Y.H., Y.A., W.C.A., D.Y., R.A.F., G.E.S., A.H., H.L.T., W.I. and A.P.) has equal work as the main contributor who equally conceived and designed the outline of the manuscript, collected and contributed data, performed the analysis, provided constructive feedback for each section, and wrote and edited the manuscript. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by ACIAR, Kanoppi Project, FST/2016/141.

**Institutional Review Board Statement:** As the study does not involve any personal data and the respondent was well aware that they can opt-out anytime during the data collection phase, any written institutional review board statement is not required.

**Data Availability Statement:** The associated dataset of the study is available upon request to the corresponding author.

**Acknowledgments:** We are grateful to the anonymous reviewers and academic editors for helpful comments on the manuscript. We also thank project collaboration between The Standardization of Sustainable Forest Management (Indonesia Ministry of Environment and Forestry) and World Agroforestry (ICRAF). We thank Dani Pamungkas, Marthen Tualaka, and Melky for their assistance in collecting data in the field.

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


*3.2.1. Local relocation algorithm*

The proposed relocation algorithm consists of two stages:

At first stage, given the model placement and an additional module with its coordinates, the small area around the additional module is scanned to find proper range that has enough free space as the *local relocation range,* then necessary information that will be used at the second stage are extracted. At second stage, MFA algorithm starts to move or rotate some modules (movable modules) considering critical distances criteria using information of first stage. All of the seconcepts like movable modules, permissible distances and critical distances are defined at the following sections.


From Motor Neuron Specification to Function: Filling in the Gaps

*Mudassar Nazar Khan and Till Marquardt*

### **Abstract**

Motor neurons operate at the interface between nervous system and movement apparatus and play several roles in movement generation. During development, motor neurons emerge from progenitor cells in the ventral neural tube and eventually settle into stereotypic position that predict the identity of their target muscles. The specification of these 'positional' identities has been studied in detail and involves a coordinate grid of intersecting extrinsic signals that result in the activation of unique combinations of transcription factors acting as cell-autonomous determinants. Eventually, motor neurons diversify into 'functional' (e.g., fast/intermediate/ slow alpha, beta, and gamma) subtypes essential for proper movement execution, a process linked to the acquisition of unique sets of functional properties. Recent progress has provided insights into the molecular composition and specification of motor neuron functional identities, but little is known about their relationship to the mechanisms underlying the specification of positional identities. In this chapter, we attempt to provide a framework for consolidating both aspects of motor neuron diversification, in addition to outlining the gaps in our knowledge to guide future research directions aiming at understanding the events on a motor neuron's journey from specification to function.

**Keywords:** spinal motor neurons, motor neuron functional specification, motor neuron positional identities, motor neuron development, neurogenesis, movement control, neuronal development, gamma, beta, alpha motor neurons, fast, slow motor neurons

## **1. Introduction**

To paraphrase Sherrington, motor neurons represent the final common pathway in the generation of behaviors by linking the nervous system with the movement apparatus [1]. This role is reflected by two levels of organization, one spatial and one functional, that together allow motor neurons to serve as the interface through which the brain can engage with and act upon the external world. On the one hand, motor neurons are somatotopically organized, with the position of motor neuron somas in the spinal cord predicting the specific muscle it controls (**Figure 1**) [2]. These 'positional' identities of motor neurons are specified early in development, prior to the establishment of neuromuscular connections [2]. On the other hand, motor neuron diversity is defined by different roles in movement generation and by the different

#### **Figure 1.**

*Schematic summarizing the different levels of motor neuron specification. All spinal motor neurons originate from neuron progenitor cells in the ventral neural tube. Motor neuron positional identities (columns, divisions innervating certain muscle groups and motor pools innervating individual muscles) are acquired after cell-cycle exit. A typical motor pool of tetrapod vertebrates contains a mixture of different 'functional' motor neuron types: Alpha motor neurons (fast/f-MNs, intermediate/i-MNs and slow/s-MNs) as well as beta and gamma motor neurons, which all possess different biophysical properties such as firing rates (such as high firing rates for f-MNs and gamma motor neurons) and roles in movement execution and control. It remains unclear, when exactly motor neuron functional diversity is generated and to what extent, if at all, this is coordinated with the specification of motor pool identities.*

muscle fiber types that are innervated (**Figure 1**) [3]. In tetrapod vertebrates, a skeletal muscle typically contains a mixture of different muscle fiber types and is supplied by a range of motor neuron types involved in different aspects of movement generation or control [2, 4–6]. To avoid confusion, we will refer to the first instance of motor neuron diversity as 'positional' diversity and the second as 'functional' diversity. While the mechanisms promoting positional or functional diversity may operate independently form each other, an individual motor neuron can eventually be assigned both by a positional and a functional identity (**Figure 1**). Several excellent reviews have appeared in recent years covering either positional or functional motor neuron diversification [2, 3, 7]. To avoid redundancy, we will in this chapter just briefly touch upon the fundamentals of both spatial and functional motor neuron diversity and mostly focus on what is known—or rather what is not—about the link between the mechanisms that underlie the development of both types of motor neuron diversity necessary for proper movement execution and control.

### **2. Motor neuron positional identities**

#### **2.1 Initial specification of motor neuron identity**

Positionally distinct classes of motor neurons emerge in the developing spinal cord whose fates are specified under the influence of intersecting gradients of signaling molecules called morphogens along the dorsoventral (d-v) and rostrocaudal (r-c) axes of the neural tube [8, 9]. Morphogens are secreted from several embryonic structures to the neural tube: Sonic hedgehog (Shh) from the notochord and floor plate and bone morphogenetic protein (BMP) and wingless (Wnt) from the dorsal roof plate and surface ectoderm, retinoic acid (RA) from the paraxial mesoderm

#### *From Motor Neuron Specification to Function: Filling in the Gaps DOI: http://dx.doi.org/10.5772/intechopen.114298*

and fibroblast growth factors (FGFs) from the caudal mesoderm and tail bud, that together contribute to the specification of molecular identities of neural progenitor cells or domains along the d-v and r-c axes of the neural tube [8, 9]. Shh drives the specification of motor neuron progenitor (pMN) and interneuron populations (V0, V1, V2, V3) in the ventral spinal cord, while RA and FGFs also contribute to motor neuron progenitor identities [10–12]. The graded expression of Shh in the d-v axis regulates Gli (Glioma-associated oncogene) family transcription factors activity, which establishes the neural progenitor domains via the expression of Class I and II homeodomain transcription factors (TFs) [9].

Progenitor domain boundaries develop and are sustained through the crossrepressive actions of class I (Pax7, Pax6, Irx3, Dbx1, Dbx2) and Class II TFs (Nkx6.1 and Nkx2.2) [13]. The co-expression of Pax6 and Nkx6.1 activates the expression of basic helix-basic-helix (bHLH) protein Olig2, which together mark the motor neuron progenitor domain [14–16]. The expression of Olig2 and its repressive activities (specifically, the repression of a repressor that normally represses Ngn2 expression) leads to the indirect upregulation of Ngn2. This results in the expression of post-mitotic motor neuron genes of Lhx3/Isl1 and motor-neuron specific gene Mnx1 (Hb9) [13, 17, 18]. Moreover, the expression levels of Olig2/Ngn2 must be balanced to ensure timely motor neuron specific gene expression, since high expression of Olig2 sustains the pMN state, while high levels of Ngn2 activates the conversion of pMN to post-mitotic motor neurons [19]. Post-mitotic neuron classes, like the progenitor classes, are specified by the combinatorial expression of transcription factors, and in addition, develop specific patterns of connectivity, a neurotransmitter system and electrophysiological properties [8, 9]. Newly born post-mitotic motor neurons express a set of TFs like Lhx3, Isl1 and Mnx1 and send axons peripherally to muscles, use acetylcholine and glutamate as neurotransmitters [20, 21]. This set of early post-mitotic TFs bind to specific enhancers to specify and maintain motor neuron identity [22, 23].

### **2.2 Specification of motor neuron positional identities: columns, divisions and pools**

A second level of motor neuron organization within the spinal cord is the clustering of somas into columns within the r-c axis (**Figure 1**). These positional identities are patterned by reciprocally graded concentrations of fibroblast growth factors (FGFs) and retinoic acid (RA) that regulate the temporal and spatial expression of homeobox (*Hox*) family transcription factors [24–26]. *Hox* family transcription factors are sequentially arranged on chromosomes in four clusters (*HoxA, HoxB, HoxC, HoxD*) that encode 39 genes that are expressed in the spinal cord and hindbrain during both progenitor and post-mitotic phases of motor neuron differentiation [27, 28]. RA regulates the expression of *Hox4-Hox6* genes at the cervical/brachial levels of the spinal cord [7, 26]. While FGF induces *Hox4-Hox10* in cervical/brachial, thoracic and lumbar levels of the spinal cord and Gdf11/FGF8 induce *Hox10* in the lumbar spinal cord [7, 26].

*Hox* gene expression pattern in post-mitotic motor neurons determines their columnar subtype identity. For example, in the phrenic motor column (PMC), motor neurons at the cervical level of the spinal cord express *Hox5* [29]. Loss of *Hox5* in motor neurons results in failure of dendritic arborization in the diaphragm muscle and neuronal death leading to respiratory failure and perinatal death in mice [29]. The lumbar motor column (LMC) motor neurons of the brachial and lumbar levels express *Hox6* and *Hox10*, respectively [25, 30–34]. The specification of LMC depends

on the expression of *Hox* genes and their regulation of Foxp1 expression pattern [30, 32]. The preganglionic motor column (PGC) motor neuron identities depend on *Hoxc9* expression [35]. While, motor neurons that innervate axial muscles do not depend on *Hox* gene programs. For example, the medial motor column (MMC) columnar subtype motor neurons identity depends on the *Wnt* genes and are marked by the expression of Prdm family transcription factor Mecom [7, 36, 37]. Still, the programs involved in the specification of other motor columns, like hypaxial motor column (HMC) which innervates axial muscles are unknown.

A third level of motor neuron organization is known as divisional identity which is defined by the motor axons and the muscles they target. The genetic programs that are involved in the specification of muscle targets have been studied extensively in the lateral motor column (LMC) motor neurons. While the generation of LMC motor neurons depends on *Hox* genes and the expression pattern of transcription factors they regulate, the maturation of LMC neurons is dependent on both the limb-derived cues and molecular programs intrinsic to motor neurons. *Hox* genes promote high *Foxp1* expression levels, which is required for the expression of Raldh2, an enzyme that catalyzes retinoic acid (RA) synthesis. Localized synthesis of RA in motor neurons at the brachial and lumbar spinal cord levels establish LMC divisional identities [38–40]. The LMC motor neurons express specific Lim homeodomain (HD) proteins and their axons target specific muscles groups of the limbs, which confers their divisional identity: the lateral division (LMCl) motor neurons express Lhx1 and send axons to the muscles within the dorsal compartment, while the medial division (LMCm) motor neurons express Isl1 and send axons to the muscles within the dorsal compartment [41]. To ensure LMCl identity, Lhx1 expression is maintained by the repressive interaction between Lhx1 and Isl1, the protein signaling controlled by the sources of RA from Raldh2+ motor neuron and the paraxial mesoderm [7, 38–40]. Moreover, the routes that LMCl motor neuron axons select within the limbs is dependent upon Lhx1 expression. Lhx1 expression within LMCl regulates the expression of axonal guidance receptor Eph4 (which repels ventral axons that express ephrin), leading to the dorsal projection of axons [7, 42, 43]. Moreover, ventrally projecting LMCm axons use similar signaling strategies: Lim HD proteins regulate the expression of axonal guidance molecules like ephrin and Eph receptors that ultimately determine axonal trajectories [7, 44].

A fourth level of motor neuron organization observed in the spinal cord are motor pools (**Figure 1**). Motor neurons within a specific motor pool cluster in a specific position in the spinal cord, innervate a single muscle, have specific type (alpha/gamma) ratios and show motor pool-specific molecular marker expression, morphology, central and peripheral connectivity, and electrophysiological patterns. These features are determined by *Hox* genes, in part, and by molecular cues from peripheral targets. Early on in development, motor pool diversity is regulated by *Hox* genes and their regulation of transcription factor expression levels. Combinatorial expression of *Hoxc8* and *Hoxc6* determines the expression of several proteins, including ETS domain transcription factor Pea3, the Pou domain protein Scip (also known as Pou3f1), and Nkx6.1, which ultimately characterize LMC motor pool identities [7]. Thus, the loss of *Hoxc8* and *Hoxc6* in mice leads to the decrease in Pea3-expressing motor neurons and decrease in axonal arborization of targeted muscles [31, 45, 46]. Moreover, specific muscle innervation in mice is disrupted in motor pools that no longer express Nkx6.1, a downstream target of *Hox* signaling [47], while *Foxp1* mutant mice show reduced expression of motor pool markers *Pea3*, *Scip* and *Nkx6.1* [32]. Motor pool identities are also regulated by the molecular

cues from peripheral muscles. For example, neurotrophic factors like glial-derived neurotrophic factor (GDNF), regulate the expression of Pea3 and thus, determine motor pool soma position and motor pool targeted muscle innervation [48].


Preface

This book provides readers with a comprehensive overview of recent trends in innate immunity as well as general concepts of immuno-biology of infections, immune-pathology, immuno-microbiology, immuno-genomics, and immunopharmacology. It also examines current clinical recommendations in the management of various diseases, highlighting ongoing issues, recent advances, and future directions of diagnostic approaches and therapeutic strategies.

The book focuses on various aspects and properties of innate immunity, whose deep understanding is very important for safeguarding the human race from further loss of resources and economies due to innate immune response-mediated diseases. Throughout this book we will examine the individual mechanisms by which the innate immune response acts to protect the host from pathogenic infectious agents and other non-communicable diseases.

Macrophages are ubiquitous and integrated parts of both innate and adaptive immunity. These cells have been researched extensively in different contexts. Macrophages display a range of plasticity in their phenotype in different pathological conditions. Together, peripheral and tissue macrophages constitute the reticuloendothelial system where they play a major role in sensing pathogens and tumor antigens for their effective eradication. Macrophages display a range of plasticity, which qualifies them as potential target cells for managing various human diseases clinically. Due to their plastic nature, these cells are potentially involved in most immunological and physiological responses.

Several groups including ours have demonstrated that several multidrug resistant (MDR)/extensively drug resistant (XTR) bacteria polarize M1 effector alveolar macrophages towards their M2 phenotype during persistent infection. This seems to be a potential link to the sensitization for infection and possibly the development of cancer.

Current research in the field focuses on managing the M1/M2 imbalance to minimize the risk of cancer arising from chronic and persistent lung infection with intracellular pathogens like *Chlamydia* or *Mycobacteria*. This may be achieved by targeting major signaling pathways that drive the M2 phenotype and are involved in cancer development (e.g., sphingolipids, Th2/Th17 responses).

In view of the preceding, this book discusses research methodologies, resources, and technologies for identifying the molecular signature involved in the polarization of M1 effector macrophages to M2 macrophages during disease. It also explores how selective phenotypes of macrophages can improve existing therapies, with special emphasis on infection and cancer, particularly lung cancers, and various gastric inflammatory diseases like inflammatory bowel disease.

Written by experts in the field, this volume is a self-contained collection of scholarly papers targeting an audience of practicing researchers, academics, Ph.D. students, and other scientists.

## **Dr. Shailendra K. Saxena**

Professor, Center for Advanced Research (CFAR), King George's Medical University (KGMU), Lucknow, India

#### **Dr. Hridayesh Prakash**

**1**

Section 1

Innate Immunity in

Infection Management

Amity Institute of Virology and Immunology, Amity University, Noida, India Section 1


**2. The Mechanism of Microparticle Formation**

The primary settings for MPs release are cellular activation and death. An increase in the intracellular concentration of calcium ions secreted by cytoplasmic reticulum is a response to multiple factors, including: An increase in the number of free radicals, increased shear force, adenosine diphosphate (ADP) secreted by activated platelets, expression of CD40 ligand (CD40L) on T-cells [26]. In these conditions, activation of calcium concentration-dependent enzymes takes place; these include gelsolin, which facilitates the separation of actin fibers from platelet cytoskeleton [27]; aminophospholipid translocase, which transports aminophospholipids from the outer membrane into the cell interior [28]; floppase, which transports phospholipids from the inner lipid bilayer outwards [29]; calpain, which destroys cytoskeleton actin fibers [30]; and scramblase, which affects transmembrane phospholipid transport [31] (Figure 1). The activated enzymes contribute to the loss of asymmetric distribution of phospholipids in the cell membrane in which phosphatidylserine (PS) and phosphatidylethanolamine (PE) are present mainly in the inner cytoplasmic layer, and phosphatidylcholine (PC) and sphingomyelin (SM) are present in the outer layer of the lipid bilayer. The asymmetry loss process resulting from transferring phosphatidylserine and phosphatidylethanolamine to the outer layer of the cell membrane and simultaneous cytoskeleton destabilization allow the formation and secretion of microparticles [26,32].

**Figure 1.** Microparicle formation following cellular activation and cytoskeletal.

An increase in the intracellular concentration of calcium, as a response to cell activation or apoptotic stimuli, results in the activation of calcium concentration-dependent enzymes: Gelsolin, aminophospholipid translocase, floppase, calpain, and scramblase. During this process, membrane asymmetry is lost, leading to the exposure of phosphatidylserine and phosphatidylethanolamine normally present in the inner leaflet of the membrane bilayer. Destabilization of the cytoskeleton results in cellular contraction and membrane blebbing.

#### **3. Methods of Microparticle Detection**

There are no standardized microparticle testing techniques and each laboratory conducting such testing develops its own MPs detection methods and standards. However, common protocol among researchers is to start collecting MPs from blood with a centrifugation to collect platelet-free plasma to avoid the activation and subsequent release of microparticles from platelets. Microparticle identification is a technical challenge because they are much smaller than cells of origin (diameters of 10–100× less). Microparticles can be isolated from blood, other biological fluids, or from cell cultures. Since MPs are cell-derived structures, a cytometric test is the "gold standard" and is the most widely

used method of microparticle detection on the basis of light scattering as well as binding of marker antibodies to identify the cell of origin [33]. The MPs population to the flow cytometry settings is defined by using size calibration beads. However, MPs smaller than approximately 0.5 μm in diameter are not efficiently resolved by conventional flow cytometers. Available digital flow cytometers do not count all MPs because of their limited forward scatter (FS) sensitivity [34]. Recently, high-sensitivity flow cytometers with significantly improved light scatter detection became available and provide sufficient size resolution for the identification of MPs subtypes [35]. Microparticles generally expose phosphatidylserine and membrane antigens of their parent cells. MPs as cell-derived membrane structures in the cytometry test are commonly determined with annexin V, usually stained with phycoerythrin and antibodies against specific cell antigens, stained with fluorescein isothiocyanate. Labeled annexin V binds mainly to phosphatidylserine, which is typical of all types of microparticles and which is present on the MPs membranous surface and less frequently in membrane permeability disorders; it can also connect to phosphatidylserine located inside microparticles [36]. Annexin V is often used to identify microparticles, but some MPs may not show a tendency to bind to this protein [37]. The use of antibodies against different membranous antigens enables identification of the cellular origin of microparticles.

Another method of microparticle determination is based on ELISA (enzyme-linked immunosorbent assay), which makes use of test plates coated with annexin V or antibodies specific to cell membrane antigens [38]. When total phosphorus or phospholipid activity is determined, ELISA enables quantitative determination of microparticles [39].


**2. Theory**

## *2.1. Transport Regions*

In addition to electrical conduction, ionic conductivity in the electrodes and electrolyte is necessary for the completion of electrochemical reactions. Charged species, including Li-ions can pass through a media under two driving forces: An externally applied electric field and/or a concentration gradient which is described in the Nernst–Planck relation:

$$j\_{\rm ion} = -c\_{\rm ion}v\_{\rm ion} = \frac{u\_{\rm ion}}{z\_{\rm ion}q}kT(\nabla c\_{\rm ion} + \frac{F}{kT}c\_{\rm ion}\nabla \phi) = D\_{\rm ion}(\nabla c\_{\rm ion} + \frac{F}{kT}c\_{\rm ion}\nabla \phi) \tag{1}$$

where *jion* is the ionic current density, *vion* is the drift velocity, *uion* is the electrical mobility of ions, *zion* is the valence number, and *q* is the charge. *K* is the Boltzamn constant, *Dion* is the diffusion coefficient, and ∇*φ* is the gradient of potential. Ohm's law is the relation between current density (*i*), conductivity (*σ*), and the electric field (*ζ*). By substituting the ionic current density (*j*) for *i* into Ohm's relation and including chemical potential in driving forces, as it is needed for the ions, then rewriting the derived formula for *σion* and finally comparing it with Nernst–Plank relation we come to an equation called the Nernst–Einstein:

$$
\sigma\_{ion} = \frac{c\_{ion} D\_{ion} z\_{ion}^2 F^2}{RT} \,\text{.}\tag{2}
$$

During charge and discharge, Li+ transfers from one side to the other. To study the transport mechanisms in the cell we consider five regions (see Figure 1), which can be defined as follows:

**Figure 1.** Schematic view of the five transport regions considered in a full lithium ion cell.

During the charge period, intercalated Li diffuses from the cathode particle bulk to the interface (E). Particles of the solid active materials at the cathode and anode are assumed to be spherical. At the surface, it donates one electron and crosses the formed SEI layer on particles at the interface (D) to enter the electrolyte. This electrochemical reaction can be explained by the Butler–Volmer equation. Li+ diffuses further in the electrolyte towards the anode because of the concentration gradient and electric field (C). By reaching the anode particles, the Li+ ion transfers through the surface layer formed on the particles and receives one electron (B) and enters into the particle. Intercalated lithium diffuses away from the surface due to the concentration gradient (A). Once the cell is fully charged and the discharge starts, the whole transport process takes place in a reverse direction from the anode side to the cathode.

Calculating the ionic conductivity for each of the five regions can give us a simple way to compare the transport performances at different conditions. In this work we calculate ionic conductivity of transport for (1) solid active materials A and E, (2) electrolyte C, and (3) interfaces B and D based on the Nernst–Einstein relation.

### *2.2. Model Description*

The required data for calculation comes from an implemented model which is based on the pseudo two-dimensional (P2D) approach [28]. We used COMSOL Multiphysics® 5.4 software for simulation. A detailed explanation of the governing partial differential equations can be found in the literature [28–31]. The model considers charge and species transport along the electrodes thicknesses direction (*x*) and in the solid particles (*r*) of active materials. Equations governing the *x* and *r* directions are coupled via the electrochemical reactions on the surface of active material particles described by the Butler–Volmer relation. Lithium plating and SEI formation are considered aging mechanisms, so that they are the anode side reactions competing the intercalation reaction during the charge process. This means:

$$j\_{tot} = j\_{int} + j\_{SEI} + j\_{Lip} \tag{3}$$

where *jint* is the intercalation current density, *jSEI* is the current density of the SEI formation, and *jLiP* is the current density of lithium plating. From the Butler–Volmer relation for the intercalation current density we have the definition of:

$$j\_{int} = j\_{0, int} (\exp(\frac{\alpha\_a F \eta\_{int}}{RT}) - \exp(\frac{-\alpha\_c F \eta\_{int}}{RT})) \tag{4}$$

where *j*0,*int* is the intercalation exchange current density, *αa* and *αc* are anodic and cathodic transfer coefficients respectively. Exchange current density for intercalation can be calculated as follows:

$$j\_{0,int} = Fk\_c^{a\_d}k\_a^{a\_c}(c\_{s,max-c\_s})^{a\_d}(c\_s)^{a\_c}(\\\frac{c\_l}{c\_{l,ref}})^{a\_d} \tag{5}$$

*ka* and *kc* are the rate constants of the anodic and cathodic reactions respectively. The maximum possible concentration is *cs*,*max*, and *cs* is the local concentration of solid particles. *cl* represents the electrolyte concentration. SEI formation, which we assumed in this model is the reaction of ethylene carbonate (EC) from electrolyte with lithium ions and electrons. A detailed description about the simulation of the SEI layer can be found in previous works [32,33]. The surface overpotential for each of the reactions (intercalation, SEI, Li-plating) is:

$$\eta\_{(int,SEL,Lip)} = \phi\_{\mathfrak{k}} - \phi\_{l} - E\_{\text{eq}(int,SEL,Lip)} - \Delta\phi\_{fillm} \tag{6}$$

where *φs* and *φl* are the potential of solid and electrolyte phase respectively. Δ*φfilm* is the potential drop over the film which is forming because of the SEI and Li-plating side reactions and *Eeq* is the equilibrium potential of the corresponding reaction.

Both side reactions are assumed to be irreversible. The additional oxidation of plated lithium and consequently the formation of the secondary SEI layer on the plated lithium is neglected. Additionally, it is assumed that no partial dissolution of deposited Li during the discharge occurs. The current density of each of the side reactions is calculated, considering only the cathodic part of the Butler–Volmer relation [34]:

$$j\_{(SEI,Lip)} = -j\_{0(SEI,Lip)} \exp(\frac{-\mathfrak{a}^c(\_{SEI,Lip})F\eta\_{(SEI,Lip)}}{RT}) \tag{7}$$

Cell parameters originate from an experimental cell. Key cell parameters and simulation conditions are listed in Table 1. Diffusion coefficient of electrodes are adjusted based on Cabanero's

work [35] to include a degree of lithiation dependency. The cycling is simulated using a constant current/constant voltage (CC/CV) charging strategy. Discharge is simulated using CC only. Since no thermal modeling is included, a 0.5 C charge/discharge rate is applied for the whole simulation so that the temperature variation over charge and discharge can be neglected [36].

**Table 1.** Cell parameters and simulation conditions used in the model.

**Figure 2.** Diffusion coefficient (**A**) of the anode and (**B**) of the cathode as a function of the lithiation degree.

Based on the Nernst–Einstein relation, we defined ionic conductivity for each of the regions as follows:

$$
\sigma\_{l(a,c,s)} = \frac{c\_l D\_{l(a,c,s)}^{eff} F^2}{RT} \tag{8}
$$

*σl* is the ionic conductivity of electrolyte (region C). *Deff l* is the effective diffusion coefficient of electrolyte which is defined depending on its medium i.e., anode, cathode, or separator. It is calculated with the Bruggeman correlation. So that:

$$D\_{l(a,c,s)}^{eff} = D\_l \mathfrak{e}\_{l(a,c,s)}^{\gamma} \tag{9}$$

consisting of the electrolyte diffusion coefficient *Dl*, the electrolyte volume fraction *l*(*a*,*c*,*s*), and the Bruggeman exponent *γ*.

For the particles of solid active material (region A and E) we have:

$$
\sigma\_{s(a,c)} = \frac{(\mathfrak{c}\_{s,\text{max}(a,c)} - \mathfrak{c}\_{s,\text{ave}(a,c)})D\_{s(a,c)}F^2}{RT} \tag{10}
$$

in which *Ds* is the diffusion coefficient of the solid particles.

To calculate the ionic conductivity for the last two regions (B and D) we assume the interface reactions to consume Li ion from electrolyte at electrode/electrolyte interface. Therefore ionic conductivity is:

$$
\sigma\_{i(a,c)} = \frac{c\_{s(a,c)} \left(\frac{c\_l}{c\_{l-ref}}\right)^{\alpha\_d} F^2}{RT} \tag{11}
$$

including surface and electrolyte concentrations (*cs* and *cl*) coming from the Butler–Volmer relation definitions and effective electrolyte diffusion coefficients.