**1. Introduction**

Deep Level Transient Spectroscopy (DLTS) is an efficient and powerful method used for observing and characterizing deep level impurities in semiconductors. The method was initially introduced by D. V. Lang [1] in 1974. DLTS is a capacitance transient thermal scanning technique, operating in the high frequency (Megahertz) range. It uses the capacitance of a pn junction or Schottky barrier as a probe to monitor the changes in charge state of a deep centre. The capacitance techniques [2-10] used before DLTS lacked either in sensitivity, speed, range of observable trap depths, or the spectroscopic nature, thus rendering the techniques inade‐ quate for a complete characterization of a deep level. DLTS has the advantage over all the techniques used to-date in that it fulfils almost all the requirements for a quick and complete characterization of a deep centre. DLTS is a technique, which is sensitive enough, rapid and easy to analyze. It is able to distinguish between majority- and minority-carrier traps [2]. DLTS can also give the concentrations, energy and capture rates of both kinds of traps. It is spectro‐ scopic in the sense that it can also resolve signals due to different traps. In the many variants of the basic DLTS technique the deep levels are filled with free carriers by electrical or optical methods. Subsequent thermal emission processes give rise to a capacitance transient. The transient is analyzed by signal processing while the temperature is varied at a constant rate. This results in a full spectroscopic analysis of the semiconductor band-gap.

For a complete understanding of DLTS we must have some basic knowledge of capacitance transients arising from the depletion region of a p-n junction. The use of capacitance transients for studying the properties of defect centers is well known [4-6]. These transients provide

© 2015 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

information about an impurity level in the depletion region by observation of the capacitance transient originating from the return to thermal equilibrium after a perturbation is applied to the system. A brief description of the capacitance change due to the change in occupancy of the deep levels in the depletion region is given below.


*2.1. Animal Models and Induction of Liver Fibrosis*

C57Bl6/J mice were housed under specific pathogen free conditions in the animal facility of the University Hospital, Aachen. All experiments that are described were approved by the corresponding legal authorities (Landesamt für Natur, Umwelt und Verbraucherschutz NRW, LANUV NRW). To induce hepatic fibrosis, mice received intraperitoneal injections of 0.5 mL per kg bodyweight carbon tetrachloride (CCl4) three times per week for a total of three weeks. Controls received an equivalent amount of oil. After three weeks, mice were euthanized and analyzed as detailed below.


**8. Whey protein role in metabolic syndrome related to amelioration in pro-inflammatory cytokines**

Hormones, cytokines and various other compounds involved to prohibit insulin sensitivity are produced by adipose tissue, an endocrine organ [66]. It is established that in the obese people, there is an increased discharge of adipocytokines through adipose tissue and macrophages [92]. Pal & Ellis [93] found that supplementing whey protein (54 g) for 12 weeks had little effect on pro-inflammatory markers (IL-6, C-reactive protein-CRP, and TNF-α) in overweight and obese participants. In rats with D-galactosamine-induced hepatitis and liver fibrosis, however, whey protein intake significantly decreased plasma amounts of pro-inflammatory cytokines

*Bio-Molecular Characteristics of Whey Proteins with Relation to Inflammation DOI: http://dx.doi.org/10.5772/intechopen.99220*

**Figure 3.**

*Cytokine profile related to whey proteins; by inhibiting IL-1*α*, IL-1*β*, IL-6, TNF-* α*, CRP and IL-10 whey protein show anti-inflammatory effect while release of IL-2, IL-4, IL-7, IL-8 and TNF-* α *showed its immunity related function [60, 96, 97].*

(IL-1β: 59 percent and IL-6: 29 percent) as paralleled to casein consumption [37]. Since consuming whey protein and its amino acids, a decrease in pro-inflammatory cytokines can be linked to a reduction in body weight gain [94]. In streptozotocininduced diabetic rats, whey protein fed in 100 mg amount related to body weight in kg and results showed the decrease in number of oxidative stress markers, like; MDA, nitric oxide, and ROS concentrations, as well as pro-inflammatory cytokines (IL-1, TNF-α, IL-6, and IL-4) and raised glutathione amount [81].  Hence it can be concluded that whey proteins plays a vital role to enhance endogenous antioxidant enzymes (glutathione peroxidase, catalase, and superoxide dismutase) and limits oxidative stress markers in obese, diabetic, or stroke patients, along with low expression of pro-inflammatory cytokines (IL-1, IL-6, and TNF-α) [94]. TBARs are commonly used to measure lipid peroxidation products in cell and MDA is used as an oxidative stress marker. Summary of all cytokines related to whey proteins is discussed in **Figure 3**.


4.1.3. Variations between Models

Keeping a common design, we introduced different geological histories for the models in the set. Our objective is to appraise the geological control on failure preconditioning in passive margin sediments, and to assess the development of weakness areas in terms of timing and expansion. Among the numerous geological factors varying through a margin history, we specifically focused on the sedimentation rate and on the angle of the basal slope, which usually undergoes a progressive tilt during the subsidence of the sedimentary stack.

High sedimentation rates favor fluid overpressure build-up, and in the literature previous physical simulations illustrate how rapid deposition can create weakness zones in deeply buried parts of the sedimentary wedges [35,46]. However, weakness and rock failure are also observed in margins with slow sedimentation, and other published simulation studies showed that additional preconditioning factors than overpressure should be considered [33,49]. In this regard, the models presented in this work are a way to specifically evaluate the impact of the basal angle tilt during sedimentation.

Table 1 presents the three sedimentation rate scenarios simulated in this study. In each case, a specific duration is assigned to all sedimentation events regardless of their thickness, which makes the sedimentation rate vary with time. However, the resulting values are consistent with the ones estimated on actual passive margins around the world [26,81,82]. Table 2 presents the three tilting scenarios simulated. In every model, the bottom horizon starts horizontal and then tilts at an increment angle at each transgressive event, until reaching the target value in the present day.

**Table 1.** Values of sedimentation rate in the models set.

**Table 2.** Values of subsidence tilt angle in the models set.


**5. Applications of normative connectome (including DBS)**

As a powerful tool to study the intriguing network in the brain, normative connectome can be used in different areas.

First, normative connectome can unveil the underlying complicated pathways of various neurological and psychiatric diseases and so may bring insights to the identification of new treatment targets. Let us take **Figure 1** as an example. By mapping the connectomes of a group of patients with visual hallucination, lesions that cause the symptom were found to be connected to the occipital cortex. This aid in the discovery of novel treatment targets, as in this example, transcranial magnetic stimulation at the occipital cortex can suppress visual hallucination [50].

Second, normative connectome can enhance surgical precision and hence improve treatment outcome of different neurosurgical procedures. For instance, for glioma patients who plan for resection surgery, the application of normative connectome can help in the identification of eloquent areas and motor tracts before operation, hence reducing the number of intra-operative stimulations required to safely confirm a tract, decreasing the likelihood of disruptive seizures, lowering the risks of postoperative neurological deficits, facilitating the resection, and making patients more comfortable during the operation [51, 52]. Epilepsy surgery is another example. It has been reported that with the use of connectome, the surgical outcome of epilepsy surgery can be improved and the risks of post-operative neurocognitive sequelae, including memory and language impairment, can be reduced [53–56].

Last but not the least, normative connectome has been used widely in the field of neuromodulation, especially DBS. Theoretically, DBS works by depolarization blockade, synaptic inhibition and depression, as well as, stimulation-induced modulation of pathological network activity, of which is regarded the most important mechanism of action [57, 58]. Thus, when normative connectome allows us to map out the pathological pathways in the brain, stimulation at certain points along the circuits may restore the disrupted information flow and so alleviate patients' symptoms. Besides, DBS electrodes, which function as probes, can become *seeds* or regions-of-interest

*Perspective Chapter: Functional Human Brain Connectome in Deep Brain Stimulation (DBS)… DOI: http://dx.doi.org/10.5772/intechopen.109855*

(ROIs) when they are used to compute their connectivity profiles with normative connectome to perform network-based analyses. In this way, we can identify the optimal site for stimulation and avoid undesirable side effects, thereby facilitating DBS programming and improving surgical outcome [59].


> − *dBD* − *dBC λB*

where Δ*ϕi* is, again, the relative phase offset of the envelope frequencies at the two receivers, *dXY* is the distance of the corresponding nodes *X* and *Y*, and *λi* is defined as *λi* = 2*c*/(*fA* +

Again, Equation 48 provides the q-range value with a 2*π* ambiguity, which is removed in the method in Section 2 by repeating the same measurement over a set of different carrier frequencies to obtain the q-range. The stochastic method takes a different approach here, as shown in Figure 13. Rather than calculating the q-range error distribution first, then estimating the q-range values and then the node positions, it calculates only the q-range error

> *M* ∑ *j*=1

> *M* ∑ *j*=1

> *M* ∑ *j*=1

That is, the stochastic method minimizes Equation 51, where Δ*ϕ*ˆ*i*,*j* is the estimated phase offset calculated by the location estimate, *di*,*j* is defined by Equation 17, *q*ˆ*j* is the q-range estimate and error(*q*ˆ*j*) is the q-range error distribution. As the latter one oscillates rapidly, it is not

*N* ∑ *i*=1

*N* ∑ *i*=1 *di*,*j* − qˆ*j*

error(qˆ*j*

Δ*ϕi*,*j* − Δ*ϕ*ˆ*i*,*j*

2

2

) (51)

mod (2*π*) (47)

(49)

(50)

mod (2*π*), (48)

**6.1 Stochastic interferometric localization**

*fB* of transmitters *A* and *B*, where *fA* > *fB* is assumed:

≈ 2*π*

distributions and uses them to estimate the positions directly:

(*x*ˆ, *y*ˆ) = arg min

*x*ˆ,*y*ˆ

= arg min *x*ˆ,*y*ˆ

= arg min *x*ˆ,*y*ˆ

 *dAD* − *dAC λA*

 *dABCD λi*

Δ*ϕi* = 2*π*

*fB*).

Fig. 13. Comparison of the original (RIPS) and the stochastic interferometric localization algorithms

minimized directly. Rather, it is smoothed first by taking its envelope and then, the minimum of the envelope is searched.

## **6.2 Platform description and results**

The key motivation behind the Stochastic Radio Interferometric Positioning System (SRIPS), detailed in Dil & Havinga (2011), was to make inteferometric localization available on WSN platforms using radio chips operating in the 2.4 GHz band. Popular 2.4 GHz radio chips, such as the CC2430 Texas Instruments (2007b), offer no mechanism to fine tune the transmit frequency. However, they support much higher sampling rates than the CC1000 radio. As a result, the synthesized interference signal envelope frequency has a larger variance, with high confidence in the 200 Hz to 14 kHz range, but it is sampled at a rate of 62.5 kHz.

The SRIPS phase measurement starts with two nodes transmitting unmodulated carriers at nominally same frequencies – without making attempts to fine tuning. The receiver nodes measure the envelope phase and transmit it to a PC. The same phase measurement is carried out over a set of frequencies and is repeated for different transmitter pair combinations. Due to the inaccurately set interference signals, several of the phase measurements are invalid. To cope with the large number of inaccurate measurements, SRIPS skips the q-range calculation step and optimizes Equation 51 on the PC using the phase offsets directly.

SRIPS experiments on a 20×20 m field resulted in an average error of 50 cm.

## **7. Summary**

The algorithms presented in this chapter all rely on radio interferometry in order to localize or track nodes in WSNs. In each case, the interference signal is generated the same way,

RIPS bearing, Doppler tracking and Doppler bearing leverage the prior knowledge of the anchor node locations and allow for distributed calculation of the locations, e.g. the nodes can

Interferometry in Wireless Sensor Networks 461

The operating range of the presented radio interferometric platforms is generally in the order of tens of meters up to a few hundred meters. In multipath free environments, the original RIPS proves to be the most accurate, which is, again, mainly due to the favorable properties of the CC1000. This is followed by the Doppler bearing, SRIPS and RIPS bearing, each of which achieves sub-meter accuracy. The average error of Doppler tracking is difficult to compare as it assumes the initial positions to be known and because the error is calculated over several

**RIPS** Radio Interferometric Positioning System

Amundson, I., Sallai, J., Koutsoukos, X. & Lédeczi, A. (2010). Radio Interferometric Angle

Chang, H.-l., Tian, J.-b., Lai, T.-T., Chu, H.-H. & Huang, P. (2008). *Spinning beacons for precise*

Dil, B. J. & Havinga, P. J. M. (2011). Stochastic Radio Interferometric Positioning in the 2.4

Fontana, R. J., Richley, E. & Barney, J. (2003). Commercialization of an ultra wideband

URL: *http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=1267866* Kusý, B., Balogh, G., Völgyesi, P., Sallai, J., Nádas, A., Maróti, M., Meertens, L. & Lédeczi,

Kusý, B., Lédeczi, A. & Koutsoukos, X. (2007). *Tracking mobile nodes using RF Doppler shifts*,

of Arrival Estimation, *7th European Conference on Wireless Sensor Networks*, Springer,

GHz Range, *Proceedings of the 9th ACM conference on Embedded network sensor systems*

precision asset location system, *IEEE Conference on Ultra Wideband Systems and*

A. (2006). Node-density independent localization, *Information Processing in Sensor*

**SRIPS** Stochastic Radio Interferometric Positioning System

**Anchor node** Node with a priori known location **COTS** Commercial Off-The Shelf **IF** Intermediate Frequency

**RSS** Received Signal Strength

**WSN** Wireless Sensor Network

*indoor localization*, ACM Press, New York, New York, USA. URL: *http://portal.acm.org/citation.cfm?id=1460412.1460426*

*Technologies 2003*, number November, IEEE, IEEE, pp. 369–373.

URL: *http://portal.acm.org/citation.cfm?id=1322263.1322267*

**LO** Local Oscillator **PC** Personal Computer

URL: *http://www.isis.vanderbilt.edu/node/4139*

*- SenSys'11*, ACM Press, Seattle, WA, USA.

*Networks (IPSN 06) SPOTS Track*.

ACM Press, New York, New York, USA.

estimate their own positions.

iterations.

**8. Nomenclature**

Table 3. List of acronyms

Coimbra, Portugal.

**9. References**

by two independent transmitters, but the different algorithms exploit features of this signal in different ways. Table 2 compares the algorithms with an emphasis on the experimental implementation and key results. All the prototype systems expect for SRIPS rely on the CC1000 radio chipset, which is designed operate in the 433 MHz and 900 MHz bands. They are characterized by a precisely tuned interference frequency which allows for processing at the limited 9 kHz sampling rate. Clearly, it is the unique fine-tuning capability of the CC1000 LO that made this radio chip - along with the MICA2 platform - the most favorable choice. SRIPS, on the other hand, aimed to make interferometric localization available on platforms that lack a fine-tunable LO. Driven by the reduced control over the interference frequency in the CC2430, SRIPS decided to drop tuning altogether. The available higher 62.5 kHz sampling rate made the processing of the highly varying frequency interference signal possible. This came at the cost of significantly increased number of inaccurate phase measurements, which SRIPS deals with in later processing stages.

Table 2. Comparison of RI localization systems

An important aspect in several applications is whether the localization or tracking service requires additional hardware. RIPS, SRIPS, RIPS bearing and Doppler tracking are especially favorable as they rely on no extra hardware. RIPS bearing, however, uses a special arrangement of certain nodes, which might be unfavorable in certain scenarios. Doppler bearing approaches utilize either a physically rotating antenna, or a relatively complex antenna array—both of which are impractical in low-cost WSNs.

RIPS and SRIPS are unique in the sense that they are able to calculate the relative node locations without anchor nodes, that is, without prior knowledge of locations of a subset of nodes. In this mode, they generally require orders of magnitude more time to do the self-localization. The presence of anchor nodes may potentially speed up the localization process both for RIPS and SRIPS, while it is essential for the other systems. In general, RIPS bearing, Doppler tracking and Doppler bearing leverage the prior knowledge of the anchor node locations and allow for distributed calculation of the locations, e.g. the nodes can estimate their own positions.

The operating range of the presented radio interferometric platforms is generally in the order of tens of meters up to a few hundred meters. In multipath free environments, the original RIPS proves to be the most accurate, which is, again, mainly due to the favorable properties of the CC1000. This is followed by the Doppler bearing, SRIPS and RIPS bearing, each of which achieves sub-meter accuracy. The average error of Doppler tracking is difficult to compare as it assumes the initial positions to be known and because the error is calculated over several iterations.

## **8. Nomenclature**

24 Will-be-set-by-IN-TECH

by two independent transmitters, but the different algorithms exploit features of this signal in different ways. Table 2 compares the algorithms with an emphasis on the experimental implementation and key results. All the prototype systems expect for SRIPS rely on the CC1000 radio chipset, which is designed operate in the 433 MHz and 900 MHz bands. They are characterized by a precisely tuned interference frequency which allows for processing at the limited 9 kHz sampling rate. Clearly, it is the unique fine-tuning capability of the CC1000 LO that made this radio chip - along with the MICA2 platform - the most favorable choice. SRIPS, on the other hand, aimed to make interferometric localization available on platforms that lack a fine-tunable LO. Driven by the reduced control over the interference frequency in the CC2430, SRIPS decided to drop tuning altogether. The available higher 62.5 kHz sampling rate made the processing of the highly varying frequency interference signal possible. This came at the cost of significantly increased number of inaccurate phase measurements, which

bearing

433 MHz 2400 MHz 433 MHz 433 MHz 900 MHz

300-700 Hz 200-12000 Hz 300-700 Hz 300-700 Hz 300-700 Hz

9 kHz 62.5 kHz 9 kHz 9 kHz 9 kHz

No No No No Yes

No No Yes Yes Yes

Few seconds

< 5 cm 50 cm 80 cm 1.5 m 40 cm

An important aspect in several applications is whether the localization or tracking service requires additional hardware. RIPS, SRIPS, RIPS bearing and Doppler tracking are especially favorable as they rely on no extra hardware. RIPS bearing, however, uses a special arrangement of certain nodes, which might be unfavorable in certain scenarios. Doppler bearing approaches utilize either a physically rotating antenna, or a relatively complex

RIPS and SRIPS are unique in the sense that they are able to calculate the relative node locations without anchor nodes, that is, without prior knowledge of locations of a subset of nodes. In this mode, they generally require orders of magnitude more time to do the self-localization. The presence of anchor nodes may potentially speed up the localization process both for RIPS and SRIPS, while it is essential for the other systems. In general,

Radio chip CC1000 CC2430 CC1000 CC1000 CC1000

Doppler tracking

Few seconds Doppler bearing

Few seconds

SRIPS deals with in later processing stages.

Operating band

Sampling frequency

Special hardware required

Anchor nodes required

time

Average error

Localization

Several minutes

Table 2. Comparison of RI localization systems

Interference frequency

RIPS SRIPS RIPS

Several minutes

antenna array—both of which are impractical in low-cost WSNs.

**Anchor node** Node with a priori known location

	- **IF** Intermediate Frequency
	- **LO** Local Oscillator
	- **PC** Personal Computer

Table 3. List of acronyms

## **9. References**

Amundson, I., Sallai, J., Koutsoukos, X. & Lédeczi, A. (2010). Radio Interferometric Angle of Arrival Estimation, *7th European Conference on Wireless Sensor Networks*, Springer, Coimbra, Portugal.

URL: *http://www.isis.vanderbilt.edu/node/4139*

URL: *http://portal.acm.org/citation.cfm?id=1602165.1602195*

URL: *http://www.ti.com/lit/ds/symlink/cc1000.pdf*

URL: *http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=1570023*

26 Will-be-set-by-IN-TECH

462 Interferometry – Research and Applications in Science and Technology

Lédeczi, A., Völgyesi, P., Sallai, J. & Thibodeaux, R. (2008). *A novel RF ranging method*, IEEE.

Maróti, M., Völgyesi, P., Dóra, S., Kusý, B., Nádas, A., Lédeczi, A., Balogh, G. & Molnár, K. (2005). *Radio interferometric geolocation*, ACM Press, New York, New York, USA.

Sallai, J., Völgyesi, P. & Lédeczi, A. (2009). Radio interferometric Quasi Doppler bearing

Taubenheim, D., Kyperountas, S. & Correal, N. (2005). Distributed radiolocation hardware core for ieee 802.15.4, *Technical report*, Motorola Labs, Plantation, Florida. Texas Instruments (2007a). Single-chip very low power RF transceiver, *Technical report*, Texas

Texas Instruments (2007b). A true system-on-chip solution for 2.4 GHz IEEE 802.15.4 /

Tuchler, M., Schwarz, V. & Huber, A. (2005). Location accuracy of an UWB localization system

URL: *http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=1570023*

in a multi-path environment, *2005 IEEE International Conference on UltraWideband*

estimation, *IPSN '09 Proceedings of the 2009 International Conference on Information Processing in Sensor Networks*, IEEE Computer Society, Washington, DC, USA,

URL: *http://ieeexplore.ieee.org/xpl/freeabs\_all.jsp?arnumber=4623303*

URL: *http://portal.acm.org/citation.cfm?id=1098918.1098920*

URL: *http://portal.acm.org/citation.cfm?id=1602165.1602195*

URL: *http://www.ti.com/lit/ds/symlink/cc1000.pdf*

zigbee(tm), *Technical report*, Texas Instruments. URL: *http://www.ti.com/lit/ds/symlink/cc2430.pdf*

pp. 325–336.

Instruments.

(September): 414–419.

## *Edited by Ivan Padron*

This book provides the most recent studies on interferometry and its applications in science and technology. It is an outline of theoretical and experimental aspects of interferometry and their applications. The book is divided in two sections. The first one is an overview of different interferometry techniques and their general applications, while the second section is devoted to more specific interferometry applications comprising from interferometry for magnetic fusion plasmas to interferometry in wireless networks. The book is an excellent reference of current interferometry applications in science and technology. It offers the opportunity to increase our knowledge about interferometry and encourage researchers in development of new applications.

Photo by andsem / iStock

Interferometry - Research and Applications in Science and Technology

Interferometry

Research and Applications in

Science and Technology


Meet the editor

Dr. Mahmood S. Choudhery is Associate Professor of Biomedical Sciences, working at the Tissue Engineering and Regenerative Medicine Laboratory of the Department of Biomedical Sciences at King Edward Medical University. He received his BSc (Hons) from Punjab University and worked for his PhD at the National Center of Excellence in Molecular Biology (CEMB), Punjab University, and the University of Arizona, USA. He continued his

research on stem cells at the University of Arizona in the Dr. David T. Harris Laboratory. At King Edward Medical University, along with teaching, Dr. Choudhery researches stem cell-based regenerative therapies. Dr. Choudhery is an editorial board member of several international journals of high repute and has published his research work in many leading journals.

## *Dedication*

To my Daughter, Hafsa, and my sons, Kashan and Affan. You are my love and the center of my world. To my mother and father who are the "wind beneath my wings."

Contents

*and Kenneth Mautner*

*and Ick Soo Kim*

**Preface III**

**Chapter 1 1**

**Chapter 2 41**

**Chapter 3 67**

**Chapter 4 93**

**Chapter 5 119**

**Chapter 6 137**

Hypoxic Preconditioning as a Strategy to Maintain the Regenerative

*by Bushra Bashir, Mahmood S. Choudhery and Ijaz Hussain*

The Rationale of Autologously Prepared Bone Marrow Aspirate Concentrate for use in Regenerative Medicine Applications *by Peter A. Everts, Glenn Flanagan II, Joshua Rothenberg*

Adipose-Derived Stem Cells (ADSCs) and Growth Differentiation Factor 11 (GDF11): Regenerative and Antiaging Capacity for the Skin

Isolation, Activation, and Mechanism of Action of Platelet-Rich Plasma

*by Mikel Sánchez, Maider Beitia, Orlando Pompei, Cristina Jorquera,*

*Pello Sánchez, Jorge Knörr, Francisco Soldado, Leonor López, Jaime Oraa, Ane Miren Bilbao, Nicolás Fiz, Jorge Guadilla,*

*by Enzo Maria Vingolo, Laura Contento, Antonio Florido,*

Application of Nanowires for Retinal Regeneration *by Davood Kharaghani, Zahra Tajbakhsh, Phan Duy Nam*

*by Loubna Mazini, Luc Rochette and Gabriel Malka*

*Beatriz Aizpurua, Juan Azofra and Diego Delgado*

Regenerative Medicine and Eye Diseases

*Filippo Avogaro and Paolo Giuseppe Limoli*

Potential of Mesenchymal Stem Cells

and Its Applications for Joint Repair


**1. Introduction**

One of the most significant bottlenecks to increased agricultural productivity in developing economies is continuous soil degradation due to land use change and erosion [1, 2]. Human activities have primarily destabilized the distribution of carbon in the universe that has released too much carbon to the atmosphere than what plants can utilize via the photosynthesis process (**Figure 1**). As a consequence, climate-change-related risks such as erratic rainfalls, floods, and fluctuating temperatures have ensued, causing soils to lose their nutrients through erosion and leaching. This has further led to the depletion of soil productivity, increased soil acidity, and a heightened need for mineral fertilizer application [4]. Acidity in soils is caused by factors ranging from nature of the soil, agroecological condition, and fertilization systems. For instance, the non-calcareous parent materials that are intrinsically acidic naturally undergo bleaching, especially in humid climates like East Africa and in high rainfall conditions. Further, reclaimed swampy soils (peats) and soils that have been highly treated with nitrogenous fertilizers tend to be acidic over time [5, 6].

### **Figure 1.**

*Carbon cycle representing the global natural and anthropogenic contributions. Source: Brewer [3].*

As a consequence, the production of biochar from various biomass sources has attracted immense attention among scientists and agricultural practitioners. This is because of its potential in revitalizing the fertility of degraded soils by sequestering carbon and reducing greenhouse gas emissions, thereby mitigating climate change [7].

Biochar is a black carbonaceous solid product that results from thermochemical decompositions of various biomass feedstocks at elevated temperatures under oxygen-deficient conditions [3, 8, 9]. Pyrolysis is the most common thermochemical process that yields biochar depending on the type of feedstock used and the variations in temperature regimes and the rate of heat application. Biochar from various types of biomass has been used predominantly as a soil amendment, carbon sequestrating tool, an agent for nutrient recycling, waste management tool [10–12], and as a solid fuel source [13]. Charcoal, one of the ancient products used for cooking, has also been investigated on its influence on agricultural productivity. In general, unlike biochar, charcoal has not been effective in fixing carbon into the soil except when it is mixed with mineral fertilizers or other organic manures. This is because mixing charcoal with organic fertilizers has the potential of enhancing nutrient accumulations at the crops root zone. Further, this mixture can minimize nutrient leaching in the vastly weathered tropical soils besides boosting crop productivity in acidic soils [14, 15].

According to Obi [16], more than 998 million tons of agricultural wastes result from crops, livestock, and aquaculture productions annually across entire Africa. Most of these wastes are reused as fuel sources, and others are left to decompose on the farms as organic manures or as feedstock for anaerobic digestions like biogas generation. From crop production alone, enormous amounts of plant-based biomasses are generated annually across East African countries. However, a clear focus toward their use as precursors of value-added products as biochar is mostly missing. The primary reason is the lack of appropriate technology to employ and limited informed strategies to spur biochar production in the region. Prudent implementation of sustainable biochar production is a potent stimulant to spanning agricultural productivity, better economic growth while minimizing negative environmental impacts in the area. Notably, the steady population increase in the region is exerting

**125**

**2.2 Slow pyrolysis**

*Biochar Potential in Improving Agricultural Production in East Africa*

much pressure on the exponentially shrinking arable land besides other implications as climate change and land use change. Thus, ardent efforts to restore the degraded soils through the use of biochar are a potential remedy. This must be done while ensuring that the present and future regional agricultural production standards, food security, and renewable energy sources are uncompromised. This is a fundamental element in the biochar bio-economy discourse, which is aimed at revolutionizing agronomic operations in East Africa while underscoring the spectrum of

In this chapter, we highlight the overview of the biochar production process, its usefulness, and potentials in improving agricultural productivity in East Africa.

In principle, biochar can be produced from a range of carbonaceous feedstocks

The principle behind pyrolysis and volatilization is combustion reaction of biomass in an inert atmosphere. The inert atmosphere is ensured by flushing through the reactors with argon or nitrogen gases [9]. The application of heat to biomass feedstock causes disintegration of chemical bonds leading to smaller molecules vaporizing into gas oxidation state [18]. Due to the oxygen-deficient condition, the products formed are water, methane, carbon monoxide, and carbon dioxide, otherwise, in the presence of excess oxygen, heat and light results. Thus, the lack of oxygen causes the volatiles to form into dense gases or liquid tar and soot. Consequently, once all the volatile components are eliminated or oxidized, the remaining slow-burning residue undergoes the final stage of combustion called solid-phase oxidation to yield radiant coal. Therefore, each thermochemical decomposition process is dependent on the heat energy applied, pressure, the quantity of

Slow pyrolysis takes place at low heating temperatures of 400°C and a long solids residence time, causing secondary cracking of the primary products. In a slow pyrolysis process, biochar yields are higher (up to 45%) compared to bio-oil (30%).

oxygen supplied, type of precursor, and the residence time [9].

subjected to various thermochemical processes. The feedstocks can include agricultural wastes, municipal solid wastes, residues from forests, used building materials, and hydrocarbon substances like used tires, among others. Important to note is that the suitability of various feedstocks principally depends on their availability, biosafety regulations, and the targeted market conditions. Depending on the desired end use, biochar production for agricultural production should take into considerations the environmental aspects and an understanding of soil condition as well as its properties. Discrete processes employed in biochar production are outlined in **Table 1**. The methods span from slow pyrolysis, fast pyrolysis, flash pyrolysis, intermediate pyrolysis, vacuum pyrolysis, hydropyrolysis, torrefaction, and gasification, receiving varied treatments according to the quality and quantity of the desired final product [17]. Generally, temperature, pressure, heating rate, residence time, reactive or inactive environment, type of the purifying gas, and its flow rate are engineered to yield the targeted products. In all the pyrolysis processes, three main products are generated: solid biochar or ash, bio-oil or tar liquid,

*DOI: http://dx.doi.org/10.5772/intechopen.92195*

its usefulness and viability [11].

**2. Biochar production process**

and non-condensable gases or syngas [1, 3].

**2.1 Biochar sources**

*Biochar Potential in Improving Agricultural Production in East Africa DOI: http://dx.doi.org/10.5772/intechopen.92195*

much pressure on the exponentially shrinking arable land besides other implications as climate change and land use change. Thus, ardent efforts to restore the degraded soils through the use of biochar are a potential remedy. This must be done while ensuring that the present and future regional agricultural production standards, food security, and renewable energy sources are uncompromised. This is a fundamental element in the biochar bio-economy discourse, which is aimed at revolutionizing agronomic operations in East Africa while underscoring the spectrum of its usefulness and viability [11].

### **2. Biochar production process**

In this chapter, we highlight the overview of the biochar production process, its usefulness, and potentials in improving agricultural productivity in East Africa.

### **2.1 Biochar sources**

*Applications of Biochar for Environmental Safety*

As a consequence, the production of biochar from various biomass sources has attracted immense attention among scientists and agricultural practitioners. This is because of its potential in revitalizing the fertility of degraded soils by sequestering carbon and reducing greenhouse gas emissions, thereby mitigating climate

*Carbon cycle representing the global natural and anthropogenic contributions. Source: Brewer [3].*

Biochar is a black carbonaceous solid product that results from thermochemical decompositions of various biomass feedstocks at elevated temperatures under oxygen-deficient conditions [3, 8, 9]. Pyrolysis is the most common thermochemical process that yields biochar depending on the type of feedstock used and the variations in temperature regimes and the rate of heat application. Biochar from various types of biomass has been used predominantly as a soil amendment, carbon sequestrating tool, an agent for nutrient recycling, waste management tool [10–12], and as a solid fuel source [13]. Charcoal, one of the ancient products used for cooking, has also been investigated on its influence on agricultural productivity. In general, unlike biochar, charcoal has not been effective in fixing carbon into the soil except when it is mixed with mineral fertilizers or other organic manures. This is because mixing charcoal with organic fertilizers has the potential of enhancing nutrient accumulations at the crops root zone. Further, this mixture can minimize nutrient leaching in the vastly weathered tropical soils besides boosting crop

According to Obi [16], more than 998 million tons of agricultural wastes result from crops, livestock, and aquaculture productions annually across entire Africa. Most of these wastes are reused as fuel sources, and others are left to decompose on the farms as organic manures or as feedstock for anaerobic digestions like biogas generation. From crop production alone, enormous amounts of plant-based biomasses are generated annually across East African countries. However, a clear focus toward their use as precursors of value-added products as biochar is mostly missing. The primary reason is the lack of appropriate technology to employ and limited informed strategies to spur biochar production in the region. Prudent implementation of sustainable biochar production is a potent stimulant to spanning agricultural productivity, better economic growth while minimizing negative environmental impacts in the area. Notably, the steady population increase in the region is exerting

**124**

change [7].

**Figure 1.**

productivity in acidic soils [14, 15].

In principle, biochar can be produced from a range of carbonaceous feedstocks subjected to various thermochemical processes. The feedstocks can include agricultural wastes, municipal solid wastes, residues from forests, used building materials, and hydrocarbon substances like used tires, among others. Important to note is that the suitability of various feedstocks principally depends on their availability, biosafety regulations, and the targeted market conditions. Depending on the desired end use, biochar production for agricultural production should take into considerations the environmental aspects and an understanding of soil condition as well as its properties. Discrete processes employed in biochar production are outlined in **Table 1**. The methods span from slow pyrolysis, fast pyrolysis, flash pyrolysis, intermediate pyrolysis, vacuum pyrolysis, hydropyrolysis, torrefaction, and gasification, receiving varied treatments according to the quality and quantity of the desired final product [17]. Generally, temperature, pressure, heating rate, residence time, reactive or inactive environment, type of the purifying gas, and its flow rate are engineered to yield the targeted products. In all the pyrolysis processes, three main products are generated: solid biochar or ash, bio-oil or tar liquid, and non-condensable gases or syngas [1, 3].

The principle behind pyrolysis and volatilization is combustion reaction of biomass in an inert atmosphere. The inert atmosphere is ensured by flushing through the reactors with argon or nitrogen gases [9]. The application of heat to biomass feedstock causes disintegration of chemical bonds leading to smaller molecules vaporizing into gas oxidation state [18]. Due to the oxygen-deficient condition, the products formed are water, methane, carbon monoxide, and carbon dioxide, otherwise, in the presence of excess oxygen, heat and light results. Thus, the lack of oxygen causes the volatiles to form into dense gases or liquid tar and soot. Consequently, once all the volatile components are eliminated or oxidized, the remaining slow-burning residue undergoes the final stage of combustion called solid-phase oxidation to yield radiant coal. Therefore, each thermochemical decomposition process is dependent on the heat energy applied, pressure, the quantity of oxygen supplied, type of precursor, and the residence time [9].

### **2.2 Slow pyrolysis**

Slow pyrolysis takes place at low heating temperatures of 400°C and a long solids residence time, causing secondary cracking of the primary products. In a slow pyrolysis process, biochar yields are higher (up to 45%) compared to bio-oil (30%).

### **Table 1.**

*Thermochemical processes, their representative reaction conditions, particle residence times, and primary products.*

The lower heating rates and longer retention time enable vapor formed from complete secondary reactions to be eliminated, thus forming the solid carbonaceous biochar [10, 11, 19–21].

### **2.3 Slow pyrolysis versus traditional charcoal making**

Charcoal has been used as a perennial fuel for domestic heating. In practice, charcoal is made by slow burning of wood in the absence of oxygen at mild to high temperatures [22]. Even though the charcoal making process can be referred as slow pyrolysis, the initial heat required to ignite the reaction is generated by burning part of the wood or the feedstock making it hard to achieve inert environment. For a typical slow pyrolysis process, the heat needed to decompose the feedstock thermally is supplied externally via an indirect heating medium. In contrast to the charcoal making process, the feedstock remains in airtight vessels or reactors [8]. Thus, the goal of slow pyrolysis is to yield a biochar product with high energy and carbon content. This is besides other by-products like pyroligneous acid or wood tar and non-combustible or syngas.


**4. Conclusions**

The proposed method for assessing the specific grinding energy of torrefied biomass pellets is a promising tool that could allow further characterization of torrefied pellets beyond the existing physical-mechanical and chemical parameters already being measured. The method itself is simple enough to be conducted by specialized laboratories and pellet producers alike without great investment cost or training. Even though the impact mill seems to have the most promising features and results, further investigations could be necessary to decide whether the results obtained by this mill type represent the characteristics most relevant to plant operators. To increase the impact of the newly introduced parameter (SGE), it would also be necessary for plant operators to acquire first-hand experience on how the specific grinding energy values translate into adjustment of their mills on-site. Therefore, if the specific grinding energy is to be used as a quality parameter, it might also be useful to complement the single value information with either a particle size distribution curve or the distribution width as part of the characterization.

**Supplementary Materials:** The following are available online at www.mdpi.com/1996-1073/9/10/794/s1. Figure S1: Picture of the anvil and plunger used during the hardness test, Figure S2: View of the grinding chamber of each of the three mills used, Figure S3: Typical power curve when recoding the power consumption during grinding, Figure S4: Cumulative particle size distribution obtained when using a ball mill on three selected torrefied biomass pellets and four standardized HGI coals, Table S1: Characteristic values of the particle size distributions obtained by the different mills for the different biomass types.

**Acknowledgments:** The authors would like to thank the managers of the SECTOR-Project for their organizational skills and the European Union for their funding (The SECTOR-Project has received funding from the European Union's Seventh Programme for research, technological development and demonstration under grant agreement n◦ 282826GA). Furthermore, the authors would like to thank the laboratory staff at the Deutsches Biomasseforschungszentrum who did a great load of the practical work for this study.

**Author Contributions:** Diana Leistner (D.L.), Nadja Weller (N.W.) and Leilani Darvell (L.I.D.) conceived and designed the experiments while Jan Hari Arti Khalsa (J.H.A.K.) and D.L. did most of the data analyses. J.H.A.K. did most of the writing with input from D.L., N.W. and L.I.D. Ben Dooley (B.D.) and L.I.D. were responsible for the HGI measurements while all other parameters were measured under supervision of D.L. The torrefied biomass pellets were a courtesy of the SECTOR-Project with numerous researchers being involved in the production. These researchers are not included in the list of authors yet their contribution is greatly appreciated.

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