Coupling Distances
\r\n\t[2] J. V. Moloney, A. C. Newell. Nonlinear Optics. Westview Press, Oxford, 2004.
\r\n\t[3] M. Kauranen, A. V. Zayats. Nonlinear Plasmonics. Nature Photonics, vol. 6, 2012, pp. 737-748.
\r\n\t[4] P. Dombi, Z. Pápa, J. Vogelsang et al. Strong-field nano-optics. Reviews of Modern Physics, vol. 92, 2020, pp. 025003-1 – 025003-66.
\r\n\t[5] N. C. Panoiu, W. E. I. Sha, D.Y. Lei, G.-C. Li. Nonlinear optics in plasmonic nanostructures. Journal of Optics, 20, 2018, pp. 1-36.
\r\n\t[6] A. Krasnok, A. Alu. Active nanophotonics. Proceedings of IEEE, vol. 108, 2020, pp. 628-654.
\r\n\t[7] M. Lapine, I.V. Shadrivov, Yu. S. Kivshar. Colloquium: Nonlinear metamaterials. Reviews of Modern Physics, vol. 86, 2014, pp. 1093-1123.
\r\n\t[8] Iam Choon Khoo. Nonlinear optics, active plasmonics and metamaterials with liquid crystals. Progress in Quantum Electronics, vol. 38, 2014, pp. 77- 117.
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The rapid development of radar, communication and weapons guidance systems generates an urgent need for microwave receivers to detect possible threats at the earliest stage of a military mission. The microwave receivers used to intercept the RF signals must be able to meet these challenges. Thus, microwave receivers have become an important research area because of their applications to electronic warfare (EW) [1].
The instantaneous frequency measurement (IFM) receiver has been mostly incorporated in advanced EW systems. As to perform the fundamental function, which is to detect threat signals and provide information to the aircrafts, ships, missiles or ground forces, the IFM receiver offers high probability of intercept over wide instantaneous RF bandwidths, high dynamic ranges, moderately good sensitivity, high frequency measurement accuracy, real time frequency measurement and relatively low cost.
IFM started out as a simple technique to extract digital RF carrier frequency over a wide instantaneous bandwidth mainly for pulsed RF inputs. It is been gradually developed to a resourceful system for real time encoding of the RF input frequency, amplitude, pulse width, angle of arrival (AOA) and time of arrival (TOA) for both pulsed and continuous wave (CW) RF inputs. For many electronic support measures (ESM) applications, the carrier frequency is considered to be one of the most important radar parameters, since it is employed in many tasks: sorting, even in dense signal environments; emitter identification and classification; and correlation of similar emitter reports from different stations or over long time intervals, to allow emitter location [2,3].
An IFM receiver is an important component in many signal detection systems. Though numerous improvements have been made to the design of these systems over the years, the basic principle of operation remains relatively unchanged, in that the frequency of an incoming signal is converted into a voltage proportional to the frequency. Microwave interferometers are usually base circuits of the IFM systems. These interferometers most often consist of directional couplers, power combiners/dividers and delay lines [4-8]. As a good example, a coplanar interferometer based on interdigital delay line with different finger lengths, will be presented. Another example of interferometers, but now, implemented with micro strip multi-band-stop filters to obtain signals similar to those supplied by the interferometers was published recently and will be presented here as well [9,10].
The system is based on frequency mapping, going from analogical signal into digital words. Any frequency value in the operating band of the system corresponds to a unique digital word. In the process, there is no need to adjust or tune any device. The signal is identified instantaneously. The frequency resolution depends on the longest delay and the number of discriminators.
Let us see how the IFMS maps the incoming signal x(t) into digital words. First of all, consider a sinusoidal signal x(t) = sin(ωt) split into two parts, as shown in Fig. 1.
Interferometer used in instantaneous frequency measurement subsystem.
The signals x1(t) and x2(t) are then described as
Because of different delays τ1 and τ2, one has
and
S1(t) and S2(t) are the signals after passing the delay τ1 and τ2, respectively. Then the output s(t) is given by the addition of (2) and (3), and after some trigonometric manipulations that sum can be written as
From (4), one can see that the frequency interval between two consecutive maxima or minima of s(t) are given by
where
As in [1], the frequency resolution is given by
A binary code can be generated if
And this way, the resolution fr of an n-bits subsystem can be rewritten as
Fig. 2 shows the architecture of a traditional instantaneous frequency measurement subsystem (IFMS), where delay lines are used to implement five interferometers as discrimination channels.
Architecture of a traditional IFM subsystem.
Each discriminator provides one bit of the output binary word that is assigned to a certain sub-band of frequency [1]. Wilkinson power dividers are used at the input and output of each interferometer [3]. The output of each discriminator is connected to a detector. The 1 bit A/D converter receives the signal from the amplifier, and attributes “0” or “1” to the output to form the digital word for each frequency sub-band. These values depend on the power level of the received signal. A limiting amplifier is used in IFM input to control the signal gain, to increase sensitivity, and clean up the signal within the band of interest [1], [7].
The schematic drawing of the interdigital delay is shown in figure 3. The particular line consists of 164 interdigital fingers of equal length
Coplanar interdigital delay line under test.
For the structure shown in figure 3 the phase velocity and the characteristic impedance Z0, become: [(C + 2C0/d) LS ]-1/2 and [LS /( C + 2 C0/d )]1/2, respectively. Here, LS is the series inductance [11]. Due to the fringing electric fields about the fingers, the amount by which the capacitance per unit length increases is greater than the corresponding amount by which the inductance per unit length decreases. In order to exploit the fringing electric fields produced by the fingers, one needs to increase the finger length and keep the finger width fixed.
The ABCD matrix of a lossless transmission line section of length L, line impedance Z0 and phase constant
From the above equation one can relate Z0 to only B and C elements. If we use the conversion from ABCD matrix to S-parameters and assume the source and load reference impedance as Z, we then have [12]
Note that the ABCD matrix is not for a unit cell of the line, it represents the entire transmission line.
Group delay is the measurement of signal transmission time through a test device. It is defined as the derivative of the phase characteristic with respect to frequency. Assuming linear phase change ϕ21(2)ϕ21(1) over a specified frequency aperture f(2)f(1), the group delay can, in practice, be obtained approximately by
The structure shown in figure 3 was etched on only one side of an RT/duroid 6010 with relative permittivity εr = 10.8, dielectric thickness h = 0.64 mm, conductor thickness t =35
The simulation used sonnet software in order to find the magnitude and phase of the S-parameters, assuming a lossless conductor. Afterwards, equations 10 and 11 were used to find Z0 and τg, respectively. In the experimental procedure each device was connected with coaxial connectors to a HP8720A network analyzer. After carrying out a proper calibration, the devices were then measured. This way, the group delay measurement was implemented, and figure 4 summarizes the group delay results from both measurement and simulation for a frequency range of 0.5-3 GHz. As the finger length increases the lumped capacitance per unit length increases. It slows down the group velocity leading to an increase in the group delay. The longer the finger length, compared to the finger width, the closer it is to a purely capacitive element.
The experimental data of Z0 were obtained using a reflection measurement in time domain low pass function of the HP8720A. The same devices were all measured again and the results are summarized in figure 5. Looking at the beginning of the curve on the left hand side, the figure 5 seems to agree with the classical coplanar strips formulation, as we found Z0= 99
Group delay as a function of finger length at a Frequency range of 0.5-3 GHz.
Characteristic Impedance as a function of finger length at a frequency range of 0.5-3 GHz.
These results look promising as far as an IFM application is concerned. Referring to a single stage of a typical IFM, a coplanar unequal output impedance power splitter can be designed to feed two delays with different characteristic impedances. The length of the second delay of each discriminator may be increased to achieve better resolution. The results from figures 4 and 5 may be used together to redesign the coplanar unequal output impedance power splitter to achieve the exact impedance matching. Figure 6 shows a prototype system fabricated based on results of figures 4 and 5. Coplanar wave guide, coplanar strips, coplanar unequal output impedance power splitter and coplanar interdigital delay line are integrated without bends or air bridges. The chip resistors used to increase the isolations between the outputs of the power splitter (and the input of the combiner) are not shown below.
Uniplanar single stage of the IFM under test, scale 1/1
The design has a delay difference of 1.6ns. Two output traces versus frequency from 1.5GHz to 3GHz are presented in figure 7. The theoretical one was obtained using the design equations for a single stage of a typical IFM subsystem [14]. The oscillations in the experimental trace originated from the coaxial connections and the chip resistors bonds.
Theoretical interferometer output and measured scattering parameter in dB versus frequency.
The IFMS presented now is based on band-stop filter and is shown in Fig. 8. The advantage of using the new architecture is that one has in each channel only multi band-stop filters instead of delay lines and power splitter, as one finds in classical IFMS.
Architecture of an instantaneous frequency measurement subsystem (IFMS) using band-stop filters.
Each word is assigned to only one frequency sub-band to generate a one-step binary code. The response of each multi band-stop filter should be like the one shown in Fig. 9 (a) with discriminators 0, 1, 2, 3 and 4. The discriminator 0 provides the least-significant bit (LSB) and the discriminator 4 provides the most-significant bit (MSB). The form of these responses is suitable to implement the 1 bit A/D converters. Here, let us attribute value 1 if the insertion loss response for the multi band-stop filter is greater than 5 dB, and value 0 for the opposite case. Fig. 9(b) shows the wave form of each 1 bit A/D converter output. According to this example the waveforms at the 1 bit A/D converter outputs are shown in Fig. 9(c). As seen in Fig. 4, this subsystem has its operating band from 2 to 4 GHz, which was divided into 32 sub-bands. Therefore, the resolution obtained was fR = 62.5 MHz.
Responses for the IFMS from Fig. 8: (a) desired |S21|, (b) A/D converters output, and (c) generated code.
Rectangular microstrip open loop resonators were chosen to design every discriminator of a five bit IFMS. Frequency response of those resonators presents a narrow rejection band and wide pass band [5] with first spurious out of the working band. Fig. 10(a) shows the top view of a resonator with resonance frequency at 1.9375 GHz. One can see in Fig. 10(b) that the first spurious occurs at 6.140 GHz. Still in this section, it will be shown how this response makes possible the fabrication of a wideband discriminator.
That resonator is placed near to a 50 Ω microstrip transmission line, which was designed with aid of quasi-static analysis and quasi-TEM approximation [8]-[9]. Fig. 11 shows the resonance frequency adjusted by the length l1 + l2 + l3+ l4 of the resonator, which must be approximately half wavelength long [8]. Additionally, there is a coupling gap g given by l2 - l3 - l4. Moreover, the coupling distance between the resonator and the main transmission line affects this resonance frequency. This distance also affects the bandwidth of the resonator [8].
Despite the narrow band of the isolated resonators, wide rejection bands are created from coupled arrays. Fig. 12(a) presents 3 sketches of one, two and three resonators, whose resonant frequencies are 2.02, 2.07 and 2.12 GHz, respectively. The line width for the resonators is fixed to be 0.5 mm along this chapter. The ideal coupling distance between resonators is obtained varying di,j using EM full wave software.
Fig. 12(b) shows the frequency response obtained at ideal coupling distance between them. These distances are chosen to obtain the insertion loss greater than 10 dB over rejection band and also to get this band as large as required. One notices that the coupling between non-adjacent resonators is almost zero. This happens because their resonance frequencies are not very close and the distance between them is large enough. Therefore, the insertion of a new resonator does not change the position of the others already inserted.
A model of two coupled resonators has been developed by the authors and will be presented in the full chapter.
(a) Physical structure of a resonator with resonance frequency at 1.9375 GHz, and (b) frequency response of the resonator over a wideband.
Open loop resonator.
As the desired insertion loss of the discriminator 1 is shown in Fig. 9(a), there must be four rejection bands, where the first one is from 2.125 GHz to 2.375 GHz, regarding the chosen operating band. The resonators are arranged one by one. Fig.13 (a) shows this discriminator with its numbered resonators. The device is designed on a RT6010.2 substrate of relative dielectric constant εr = 10.2 and thickness h = 1.27 mm. The 50 Ω transmission line width is 1.2 mm. The gap of every resonator and the distance between the main transmission line and the resonators are kept 0.1 mm for whole structure. Table I shows the coupling distances between the resonators for this device.
(a) The open loop resonator arrays. The scale has been enhanced for a better comprehension of the devices, and (b) frequency response of 1, 2, and 3 resonators.
Still in Fig. 13(a) one sees four groups of resonators, whose frequency responses and A/D converter outputs are shown in Fig. 15(b). Looking carefully their correlation, Group 1 gives the rejection band over 2 GHz; Group 2 gives the rejection band over 2.5 GHz, and so on. Fig. 13(b) presents the simulated results of the discriminator 1, which agree with the results shown in Fig. 9. One can see the insertion loss level is greater than 10 dB over all rejection bands, and is less than 5 dB over the pass bands. The output A/D converter should generate level zero for |S21| < - 5 dB and level 1 for |S21| > - 5 dB. Concerning all the involved di,j, the dimensions of this discriminator are 3 cm wide and 15 cm long. Following the same procedure, the others discriminators are projected, where new resonators configurations will give new desired rejection bands.
(a) Layout of the discriminator 1, and (b) frequency response of the discriminator 1, and the output of the 1-bit A/D converter; 250 MHz for each rejected band.
\n\t\t\t\tCoupling distance between “i” and “j” resonators (mm)\n\t\t\t | \n\t\t|
\n\t\t\t\td1,2\n\t\t\t\t= 0.6 | \n\t\t\t\n\t\t\t\td13,14\n\t\t\t\t = 1.4 | \n\t\t
\n\t\t\t\td2,3\n\t\t\t\t = 0.8 | \n\t\t\t\n\t\t\t\td14,15\n\t\t\t\t = 1.6 | \n\t\t
\n\t\t\t\td3,4\n\t\t\t\t = 0.5 | \n\t\t\t\n\t\t\t\td15,16\n\t\t\t\t = 1.3 | \n\t\t
\n\t\t\t\td4,5\n\t\t\t\t = 0.3 | \n\t\t\t\n\t\t\t\td16,17\n\t\t\t\t = 0.7 | \n\t\t
\n\t\t\t\td5,6\n\t\t\t\t = 0.2 | \n\t\t\t\n\t\t\t\td17,18\n\t\t\t\t = 0.4 | \n\t\t
\n\t\t\t\td7,8\n\t\t\t\t = 0.6 | \n\t\t\t\n\t\t\t\td19,20\n\t\t\t\t = 1.3 | \n\t\t
\n\t\t\t\td8,9\n\t\t\t\t = 1.2 | \n\t\t\t\n\t\t\t\td20,21\n\t\t\t\t = 1.4 | \n\t\t
\n\t\t\t\td9,10\n\t\t\t\t = 0.4 | \n\t\t\t\n\t\t\t\td21,22\n\t\t\t\t = 1.6 | \n\t\t
\n\t\t\t\td10,11\n\t\t\t\t = 1.1 | \n\t\t\t\n\t\t\t\td22,23\n\t\t\t\t = 1.2 | \n\t\t
\n\t\t\t\td11,12\n\t\t\t\t = 1.1 | \n\t\t\t\n\t\t\t\td23,24\n\t\t\t\t = 1.1 | \n\t\t
Coupling Distances
The Fig. 14(a)-(e) presents all the projected IFMS discriminators from Fig. 8, having between 23 and 25 resonators. The number of resonators depends on the desired rejection bands. Following the same principle, each group gives only one rejection band, so that discriminators with eight groups have eight rejection bands, as shown in Fig. 14(e). The others, without any specified group, have only one as shown in Fig. 14 (a) and (b). Fig. 15 shows that the simulated and measured results of the five discriminators are in reasonable agreement with each other.
Bandstop filters for implementation of the: (a) discriminator 4 – MSB, (b) discriminator 3, (c) discriminator 2, (d) discriminator 1, and (e) discriminator 0 – LSB.
Frequency response of the: (A) Discriminator 4 – MSB, (B) Discriminator 3, (C) Discriminator 2, (D) Discriminator 1, and (E) Discriminator 0 – LSB.
Fixed IFM designs like the ones discussed in section IV have the advantage of providing instantaneous frequency identification while reconfigurable designs should do a sweep but are very compact in size, making them suitable for portable and handheld systems. RFMs include tuning elements [15] embedded in the designs to produce multibit frequency identification using reconfigurable measurement branches.
An example of RFM architecture is shown in Fig. 16, this design includes a reconfigurable phase shifter used to produce more than one bit. The number of bits will depend on the amount of phase shifts produced by the reconfigurable design; each phase shift will correspond to a specific control voltage in the case of varactors, otherwise switches will be in “on” or “off” state to produce the different phase shifts. The other components shown in Fig. 16 operate in a similar way to the ones exposed in section IV. The RFM can also include reconfigurable bandstop filters [16] instead of the phase shifter to produce a branch that can produce more than one bit as an alternative design.
The switching speed of the tuning elements used in the reconfigurable phase shifter design will mainly determine the detection speed of the subsystem. Solid state components like PIN, varactor diodes, transistors and the use of ferroelectric materials will provide high tuning speeds, (10-6 seconds for the PIN and varactor diodes, 10-9 seconds for transistors and 10-10 seconds for the ferroelectric varactors) while the Micro Electromechanical Systems (MEMS) counterpart will provide slower tuning speeds (10-5 seconds) but with the advantage of low power consumption compared with the solid state components. The use of ferroelectric materials results in high tuning speeds with the drawback of having generally high dielectric losses. When designing an RFM it is important to decide which type of technology is adequate for a given application in terms of detection speed, power consumption and device size.
Architecture of a reconfigurable frequency measurement subsystem (RFM) based on phase shifters.
Device size will be mainly determined by the type of technology used to implement the subsystem; the most compact designs can be achieved monolithically, by having the components integrated into a single chip. A monolithic design can include all solid state, MEMS and ferroelectric implementations. Hybrid integrations use microwave laminates or substrates and tuning elements, these include solid state, MEMS and ferroelectric surface mountable components that can be embedded into the design. Hybrid integrations normally involve much larger circuit size compared to the monolithic counterpart, however these components normally involve low cost and simple manufacturing and prototyping techniques.
The most reliable technology is the solid state transistor and the ferroelectric films, followed by the PIN and varactor diode ending with the MEMS components. MEMS packaging can improve device reliability by avoiding contamination or humidity of the movable parts of a switch or varactor. The objective of an RFM is to reduce the size of fixed IFMs by designing branches that can produce more than one bit in the identification subsystem. Size reduction is the main advantage of an RFM over a fixed IFM. A disadvantage over fixed IFMs is that there will be a switching time for the device, so the frequency measurement is not instantaneous.
This chapter presented two kinds of interferometers for IFM applications, the first type was a Coplanar Intedigital Interferometer and the second one was based on Multi band-stop filters, which can substitute the interferometers in the IFM Architecture. For the first case, coplanar strips interdigital delay lines were fabricated, simulated and measured at a frequency range of 0.5-3 GHz. As the finger length varied from 0.6 mm to 4.2 mm, keeping all the other parameters fixed, the group delay increased by about 150% and the characteristic impedance decreased about 45%. A prototype of uniplanar IFM with a delay difference of 1.6ns was fabricated and measured based on the results of the characteristic impedance and the group delay.
For the second case, Multi band-stop filters were designed, simulated and measured over a frequency range of 2 GHz. The results show that the use of loop resonators to design the discriminators, instead of delay lines and power splitters, make the simulation and the fabrication easier, as there are no more bends or sloping strips. In addition, one has more control over the resolution, as one can couple the resonators one by one and create the rejection bands. In this process, the association of loop resonators was used to design multi band-stop filters. In light of the above, the use of multi band-stop looks promising as far as planar interferometer identifier is concerned.
The use of loop resonators instead of delay lines and power dividers/combiners, to design IFM systems, decreases the simulating time of the whole structure, as there are no more bends or sloping strips. In addition, one has more control over the resolution, as one can couple the resonators one by one and create the rejection bands. The multi-band-stop filters can substitute interferometers in the IFM system architecture, in a very efficient way. Reconfigurable frequency measurement circuits can considerably reduce the size of the IFMs by using tuning elements embedded into the topologies, resulting in multiple bit circuits by means of reconfigurable frequency measurement branches. RFMs switch between states, thus tuning speed determines the sweep time required for signal detection.
There is no doubt that streams and rivers are important freshwater sources for man due to their influence on social and economic development of human societies. However, the quality of water in most streams and rivers is being threatened worldwide due to pollution connected with human activities [1]. The situation is worsened with increasing industrial pollution and use of fertilizers and other agro-chemicals in agriculture, rapid urbanization, and continuing use of improper sanitation systems especially in developing countries [2]. Consequently, aquatic ecosystems that depend on water flows and seasonal changes within these water bodies are often threatened by poor water quality [3]. Water quality problems represent a major global challenge. For example, pollution of water bodies, especially nutrient loading, has worsened water quality in almost all rivers in Africa, Asia, and Latin America. Therefore, future global water demands cannot be met unless concerted efforts are made to address water quality and wastewater management challenges.
\nTherefore, sustainable management of freshwater resources needs to aim at protecting or reducing pollution load of freshwater sources especially streams and rivers to avoid negative impacts on water quality and ecosystems. In this regard, constructed wetlands are recognized as potential technology for meeting water quality and other requirements of these important freshwater sources. The use of constructed wetlands for water quality improvement is increasing with new applications and technological possibilities [4, 5]. In recent times, the use of river diversion wetlands is gaining more relevance for improving quality of water in riverine systems [6, 7, 8]. The incorporation of constructed wetlands into management strategies for rivers and streams may help to reduce pollution load and enhance their absorbing capacity against impacts [9].
\nDespite the recognition of constructed wetlands as an effective and economical way of improving water quality, many of those in operation are underperforming. The shortcomings are partly attributed to limitation and inconsistencies of equations used in designing them [10, 11, 12]. Besides, most of the available design methods are either related to municipal wastewater treatment or stormwater quality improvement with the primary aim of peak flow retention to attenuate flood water which may lead to overestimation. For river diversion wetlands, specific design criteria have not been fully established, and further research is needed to optimize existing approach in order to enhance performance capabilities of these types of wetlands [7]. However, the design of constructed wetlands is generally based on empirical equations using zero- or first-order plug flow kinetics as basis for predicting pollutants’ removal and improving water quality [13].
\nThis chapter aimed to provide guidance on the design of a typical river diversion constructed wetland intended to improve quality of river water. The chapter provides an overview of factors to be considered for the wetland design, water quality characterization, wetland inflow estimation, computation of the wetland hydrodynamic parameters, wetland sizing, and configuration and guide on designing of conveying and inlet and outlet structures. The approach presented may be useful to wetland experts as some of the procedures adopted are not popular in wetland studies.
\nBasically, two main types of constructed wetlands exist. These are free water surface (FWS) flow and subsurface flow (SSF) systems. FWS flow wetlands operate with water surface open to the atmosphere, while for SSF, water flow is below the ground through a sand or gravel bed without direct contact with the atmosphere [14, 15]. Both are characterized by shallow basins usually less than 1 m deep. FWS wetlands require more land than SSF wetlands for the same pollution reduction but are easier and cheaper to design and build [16].
\nFWS flow wetlands are further sub-classified based on the dominant type of vegetation planted in them such as emergent, submerged, or floating aquatic plants. SSF wetlands which are often planted with emergent aquatic plants are best sub-classified according to their flow direction as horizontal subsurface flow (HSSF), vertical subsurface flow (VSSF), and hybrid system [17]. Another sub-division of constructed wetland types which have emerged recently is river diversion wetlands. These are mostly FWS wetlands located near or within a stream or river system. They are distinguished according to their location as off-stream and in-stream wetlands. Off-stream wetlands are constructed nearby a river or stream where only a portion of the river flow enters the wetland. On the other hand, in-stream wetlands are constructed within the river bed, and all flows of the river enter into the wetland [18]. Figure 1 shows a typical arrangement of both types.
\nArrangement of off-stream and in-stream river diversion wetlands. (a) Off-stream river diversion wetland and (b) in-stream river diversion wetland.
Potential benefits of river diversion wetlands include merits relating to river water quality improvement, flood attenuation, increasing connectivity between rivers and floodplains, and creation of mixed habitat of flora and fauna communities [8, 19]. The systems are also cost-effective due to their simple designs and construction when compared to conventional treatment systems. Major drawbacks of these types of wetland systems relate to emissions of greenhouse gases and losses of biodiversity which may result from continued pollution loading [20]. Unlike the in-stream wetlands, a major advantage of the off-stream river diversion wetlands is that they can be used to mitigate non-point source pollution from agricultural lands before reaching the river channel. However, off-stream wetlands may require storage and flow control structures to regulate flow and a large space for layout of the wetlands which may result in high initial costs for land easements. Additionally, only part of the river flow volume can be treated at a time. On the other hand, space availability may not be a big issue for in-stream wetlands as they are constructed within the river bed, and as such the whole river flow volume can be subjected to treatment. However, it may be difficult to regulate flow especially during river peak flows and consequently retention time which is an important aspect of wetland for effective pollutant removal.
\nThe design of a constructed river diversion wetland is an iterative process involving site-specific data. Prior to design and construction, site conditions must be evaluated to assess the appropriateness of the site for the proposed constructed wetland system [4]. Thus, the following are recommended as part of the design process:
Investigation of site characteristics
Water quality characterization
Wetland design inflow estimation
Site condition is a very important factor in the design of a constructed river diversion wetland. This is particularly necessary when a suitable site or land is not readily available as the situation often limits possible options the designer may utilize. Thus, site investigation enables the designer to have an idea of the site characteristics including size of area or land available for the design. However, where there is sufficient suitable site or land, it gives the designer the latitude and flexibility of several design options. Therefore, identifying the required area available for optimal layout of the wetland is vital for effective reduction of pollutants.
\nSite characteristics to be evaluated when designing and possibly constructing a river diversion wetland include:
Proximity of the site to the river system (the site should be situated close to the source of water to be treated for easy diversion or within the river channel depending on the type (in-stream or off-stream))
Climate (climate can affect type and size of the space required for the wetland; climatic factors that are important include rainfall, evaporation, evapotranspiration, insolation, and wind velocity)
Topography of the land (topographic conditions such as natural depressions and slopes are important consideration; the gradient of the land should preferably have a gentle slope so that water can easily flow by gravity)
Groundwater condition (assess groundwater levels within the site in different seasons to guide against possible contamination)
Soil and environmental condition of the site (the site should contain soils that can be sufficiently compacted to minimize seepage to groundwater, or necessary measures should be put in place to minimize groundwater contamination)
Distance of the site from residential buildings to avoid creating an environment that is not conducive for inhabitants
After due consideration of the above conditions, a suitable location can be selected for siting the wetland system, and the designer can then take cognizance of the space available for the system design.
\nCharacterization of pollutant concentration of the river water to be treated is essential for sizing of a constructed river diversion wetland and in creating a clear understanding of whether the wetland can effectively treat the water or not. Thus, the constituents of the river water and their respective concentrations need to be known before beginning the design process of the constructed river diversion wetland. However, water quality is highly variable especially in rivers due to fluctuations and variability of discharge and contaminant concentration from pollution sources [21]. Thus, a clear definition of water quality is essential, and it may be necessary to take into account previous distribution of the contaminants’ concentrations in the water over time [4]. According to [22], characterization of the river water quality can be done based on available data which provides information on temporal and spatial distribution of parameters of interest and their level of concentrations in the water to be treated. Water quality parameters that are characterized in most situations include biochemical oxygen demand (BOD), nitrogen, phosphorus, suspended solids, and coliform bacteria [23]. These are pollutants that originate mostly from organic sources and are considered of most interest in treatment wetland design [24]. Others include metals, phenols, pesticides, and surfactants which may also be treated. However, these parameters require specific applications as opposed to organic pollutants [18].
\nBOD reflects the degree of organic matter pollution, and it is a measure of the amount oxygen removed by aerobic microorganisms for their metabolic requirement during decomposition of organic materials. Nitrogen and phosphorus are considered as primary drivers of nutrient pollution, and they occur in organic and inorganic forms. Nitrogen in water is usually measured as total nitrogen, ammonium ion, nitrate, nitrite, and total Kjeldahl nitrogen (sum of organic nitrogen and ammonium ion) or as a combination of these parameters to estimate organic or inorganic nitrogen concentrations [25]. Phosphorus in water is usually measured as total phosphorus which is the sum of organic and inorganic forms of phosphorus and includes orthophosphate (PO4\n3−), polyphosphates, and organic phosphates [1]. For microbial contamination, indicator organisms are used to detect the presence of pathogens (disease causing organisms). Microorganisms mostly considered are those of fecal origin, and coliform bacteria are most often used to indicate the presence of fecal pollution [26]. Suspended solids are constituents that remain in solid state in water and often occur as part of sediments carried in the water. Measurement of suspended solids is essential as sediments are responsible for contaminant transport in water. Metals can exist as dissolved, colloidal, or suspended forms in water, and their toxicity depends on the degree of oxidation of the metal ion together with the forms in which it occurs [1]. Metals mostly considered with high priority in water pollution are arsenic (As), cadmium (Cd), copper (Cu), chromium (Cr), lead (Pb), mercury (Hg), nickel (Ni), and zinc (Zn) [23]. Nevertheless, selection of any pollutant or combination of pollutants for water quality improvement will depend on the objectives for which the wetland is designed. Based on the river water quality characterization, appropriate equations can be used to determine the required area and organic loading rates of the wetland system.
\nThe amount of water flow per unit time that passes through a wetland system is one of the important parameters required in the design of a constructed river diversion wetland. Flow rate of water is an important hydrological parameter required to facilitate sizing of a constructed wetland [4]. Even though flow into a wetland can be continuous or intermittent, it however passes through the system at low velocities. There are different approaches employed to determine the quantity of inflow (volumetric inflow rate) into a wetland, depending on the wetland type, treatment objectives, and incoming water to be treated.
\nFor wastewater treatment wetlands, inflow is mostly based on wastewater concentration and generation rates [27]. Mass loading charts with reference to the required level of pollutant removal are mostly used in the United States, while in Europe estimation is based on wastewater generation volume and pollutant concentration [27, 28]. For stormwater constructed wetlands, a range of hydrologic methods are applied to estimate design flows. Typical approaches include the use of routing in response to a storm event like the average recurrence interval (ARI) flow criterion, level-pool routing, and estimation of peak runoff flow rate using curve number (CN) model and rational method [29, 30, 31]. The ARI is applied in Australia and level-pool in Malaysia, and the CN is mostly used in the United States. While all these methods are mainly applied to stormwater treatment wetlands, they are however used with reference to specific available data and scenarios in these countries [29]. Moreover, not all wetlands are designed for treatment of maximum expected peak flows; otherwise the vegetation are likely to be damaged due to high flows, and the wetland system would need to be extremely large or the outflow water quality requirement considerably relaxed. Furthermore, the CN model has been examined to be inaccurate due to inherent limitation associated with inconsistency of the fixed ratio (λ) between initial abstraction (\n
For river diversion wetlands, a specific method for estimating design inflow has not been fully established [7]. However, more recently, [8] evaluated the performance of a river diversion wetland for improving quality of river water using relations that can be used to estimate design inflow for a similar wetland system. These relations are presented below.
\nwhere α = wetland/river catchment area ratio; \n
Application of the above equations requires estimation of average flow volume of a river. However, flow rates vary over time because of normal variability in precipitation patterns, and a key factor governing hydrological regime of rivers is their discharge variability [35]. Therefore, to determine river flow or discharge regimes, historical flow data are required, including possible seasonality trend of the flows, pattern of past flows (low, moderate, and high flows), and stream gauge information close to the wetland site location [4, 36]. Flow data are important to facilitate understanding of fluctuations in the amount of flowing water in the river and to support development of a rating curve for the river where it is not available. The rating curve has been an important tool widely used for routing purposes in hydrology to estimate discharge in natural rivers [37]. It is a graphical representation that gives relationship between flow regimes and stage heights or water levels of a river at a given site and over a period of time [35, 38]. However, very few rivers have absolutely stable flow characteristics, and thus the rating curve may require revision over time and under unsteady conditions. A comprehensive review of the various equations developed by several authors for correcting unsteady to steady flow condition was presented by [38].
\nAnother crucial aspect of wetland design is the estimation of average river flow regimes. The river flow regimes are required to:
Guide in determining the amount of water per unit time that can be diverted into the wetland system without compromising the flow needed for survival of the river ecosystem.
Aid the design and estimation of inflow regime(s) for which the wetland system will be operated since the goal of the wetland is to improve quality of river water.
Therefore, obtaining or developing appropriate rating curve may be necessary to facilitate characterization of flow regimes of the river. Based on the rating curve, the river flows can be classified into low, moderate, and high flows. Figure 2 shows a typical river rating curve with flows classified into three regimes as indicated. For example, based on the rating curve (Figure 2), three flow regimes (0.29 m3/s, 1.97 m3/s, and 3.96 m3/s) (marked with dotted red lines) were selected corresponding to low, moderate, and high flows of the river, respectively. The classification of the flow regimes into low, moderate, and high flows was based on their computed flow velocities as presented in Table 1.
\nTypical river rating curve with flows classified into three regimes.
Flow regimes (m3/s) | \nMean cross-sectional area of river gauging section (m2) | \nVelocity (m/s) | \nVelocity\n*\n groups (m/s) | \nClassification | \n
---|---|---|---|---|
0.29 | \n3.34 | \n0.09 | \n<0.10 | \nLow flow | \n
1.97 | \n3.34 | \n0.59 | \n0.10–0.60 | \nModerate flow | \n
3.96 | \n3.34 | \n1.19 | \n≥0.70 | \nHigh flow | \n
For flood or peak flow control wetlands, high flows are often considered for the design, while for water quality improvement, moderate to low flows are mostly the target. Where high flow is to be used for design of river diversion wetland intended for water quality improvement, it may be necessary to include a retention basin in the design to slow down flow energy and allow for gradual release into the system.
\nSince the river diversion wetland under discussion is intended to be designed for water quality improvement, only a portion of the river flow regimes is required to be diverted into the wetland system per unit time. Thus, to determine the quantity of the design inflow rate of the wetland, Eq. (3) derived from Eqs. (1) and (2) can be used together with the average river flow regime(s).
\nwhere all parameters remain the same as previously defined in Eqs. (1) and (2).
\nThe wetland can be designed to operate with the three river flow regimes (low, moderate, and high) to take into account seasonal flow variability or a single flow regime depending on the objective and availability of space within the site.
\nThe design of constructed wetlands is generally based on empirical equations using zero- or first-order plug flow kinetics as basis for predicting pollutant removal and improving water quality [13]. With zero-order kinetics, the reaction rate does not change with concentration but varies with temperature [4], while first-order kinetics simply implies that the rate of removal of a particular pollutant is directly proportional to the remaining concentration of the pollutant at any point within the wetland [40]. Plug flow means that every portion of flow entering into the wetland takes almost the same amount of time to pass through it which is rarely the case [41]. The kinetic equations also considered FWS wetlands as attached growth biological reactors similar to those found in conventional wastewater treatment systems [23]. Generally, two types of equations are popular that use two different approaches in the design of FWS wetlands based on “rule-of-thumb” (no account for the many complex reactions that occur in a constructed wetland). There is the volume-based or zero-order kinetic equation which uses hydraulic retention time to optimize pollutant removal [42, 43]. The second is the area-based or first-order kinetic equation where the entire wetland area is used to provide the desired pollutant treatment [44]. The key difference between the two equations is in the use of kinetic rate constants. Volume-based equation assumes horizontal or linear kinetics and uses volumetric and temperature-dependent rate constant, with calculations being based on available volume of the wetland and average water temperature. The area-based equation assumes vertical or areal kinetics and uses rate constants which are independent of temperature but related to the wetland surface area. The volume-based equation was developed by [43], and the equations are presented below:
\nwhere \n
The area-based model equation was developed by [44], and the equations are presented below:
\nwhere \n
The volume-based model was developed based on those parameters that are removed primarily by biological processes such as biochemical oxygen demand (BOD), ammonia (NH4), and nitrate (NO3). The areal equation considered more parameters and in addition includes total suspended solids (TSS), total phosphorus (TP), total nitrogen (TN), and fecal coliform (FC). According to [45], while the [43] method provides a relatively conservative area estimate, [44] approach may require considerable land space, depending on the pollutant concentration limit. Furthermore, the [45] model appears to be less sensitive to different climatic conditions as temperature changes are only considered significant for nitrogen removal [46]. However, temperature plays an important role in constructed wetland systems as it enhances higher biological activity and productivity which may lead to better performance of the systems [47, 48]. For this reason, the use of these models may lead to wide variations in performance due to effect of changes in climatic conditions. Additionally, many authors have developed more complex models like the Monod-type and mechanistic compartmental models [49, 50]. However, the [43, 44] models appear to be more straightforward and can be applied with ease by wetland designers [13]. Data limitation on operational performance of constructed wetlands prevented the development of equations which can clearly describe the kinetics of known wetland processes [23]. Thus, optimal design of constructed wetland systems has not yet been determined. However, in order to take advantage of [43, 44] models and ease complexity of computation, [24] presented a simplified approach for the design and sizing of FWS constructed wetlands using the two equations. The approach was based on performance criteria for the removal of four water quality parameters that included BOD, nitrogen, phosphorus, and coliform bacteria. According to [24], rates of BOD and nitrogen removal are principally temperature dependent and therefore utilized equations proposed by [43] model for removal of these parameters. On the other hand, the reduction of phosphorus and coliform bacteria was assumed to be governed by physical processes which are less temperature-dependent, and thus [44] equations were used. In addition, [24, 51] proposed the following relationships for nominal hydraulic retention time and removal of total nitrogen (TN), respectively.
\nwhere \n
The authors recommended that the above equations can be used together with those presented by [43, 44] to determine the hydrodynamic and size parameters of a new FWS flow constructed wetland, depending on the target pollutant or combination of pollutants (BOD, nitrogen, phosphorus, and coliform bacteria) required to be removed from the wastewater. As indicated by [52], the approach presented by [24] is useful in the design of a new FWS constructed wetland and for performance evaluation of existing ones.
\nThe use of a combination of wetland design equations proposed by [24, 51] was found to be useful for determination of river diversion wetlands’ hydrodynamic parameters. These parameters include nominal hydraulic retention time and hydraulic loading rate.
\nDetermination of nominal hydraulic retention time is important for design guide and estimating possible pollutant removal ability of the wetland system. Thus, the nominal HRT for a river diversion wetland can be estimated based on the kinetic equations governing the removal of basic water quality parameters (BOD, nitrogen, phosphorus, and coliform bacteria), often used for sizing of constructed wetlands. Table 2 shows the parameters and kinetic equation used for determining the nominal HRT.
\nParameter | \nEmpirical equations | \nEquation no. | \nSource | \n
---|---|---|---|
BOD (mg/l) | \n\n\n | \n(1) | \n[41] | \n
\n | \n\n | \n(11) | \n[12] | \n
TN (mg/l) | \n\n\n | \n(9) | \n[12] | \n
\n | \n\n | \n(12) | \n\n |
\n | \n\n | \n(13) | \n\n |
TP (mg/l) | \n\n\n | \n(10) | \n[22] | \n
\n | \n\n | \n(14) | \n[13] | \n
FC (CFU/100 ml) | \n\n\n | \n(10) | \n[22] | \n
\n | \n\n | \n(14) | \n[13] | \n
Parameters and equations for computing design HRT.
Note: \n
The HLR of the wetlands system can be computed using Eq. (7) by [44]. The determination of the HLR is essential to guide in the design and can assist to avoid overloading the system. Thus, the design may confirm the organic loading rate is within the wetland limit; an equation developed by [51] can be used to compare the \n
where all parameters remain the same as defined in Eqs. (1), (2), and (4).
\nSizing is an important component of wetland design and vital for pollutant removal processes to take place. Most of the design recommendations provided certain approaches to wetland sizing to maximize removal of pollutants. For wastewater treatment wetlands, population equivalent (PE) is mostly employed for the determination of design wetland area. The required surface area is usually expressed as unit area per population equivalent (m2/PE). For example, 5–10 m2/PE was recommended for FWS, while for SSF it ranges between 2 and 5 m2/PE depending on the type (HSSF, VSF, and hybrids) [27]. For stormwater wetlands, the typical approach is to consider relative percentage of the contributing catchment area or connected impervious area, and 1–5% of the contributing watershed was recommended as actual sizing criterion [4]. For full-scale river diversion wetlands, a minimum of 2–7% of the total catchment area was recommended as wetland area [20]. However, such sizing criteria pose challenges of overestimation and do not account for any performance consideration [53]. Therefore, such prescribed wetland sizing criteria may be unrealistic due to space limitation and cost. Nevertheless, an approach derived based on empirical determination of actual area required for pollutant removal with reference to hydraulic loading rate as presented by [24] appears to be more realistic for estimating actual area of river diversion wetlands intended for water quality improvement. Thus, the actual area required for such a wetland system can be determined using Eq. (16) which was derived from Eq. (8) by [24].
\nwhere \n
For ease of operational control (flow control and water level adjustment) and increased removal efficiency, multiple wetland units often referred to as cells may be used where possible than a single unit wetland. This is particularly more applicable to design of off-stream river diversion wetland. Multiple cells have the advantages of providing greater flexibility in design and operation and enhancing the performance of the system by decreasing the potential for short-circuiting. Wetland cell size depends primarily on water quality treatment needs and cost considerations.
\nThe actual area of the wetland is then computed using Eq. (16). Based on the computed values, the actual area of the wetland is thus selected as the maximum of areas obtained for each of the target pollutants (BOD, nitrogen, phosphorus, and coliform bacteria).
\nWetland system configuration is an important element in the design of river diversion constructed wetland technology. After determining an appropriate wetland size, it is necessary to define the system configuration or layout by choosing an appropriate aspect ratio. Aspect ratio represents length (L) to width (W) ratio (L/W) of the wetland. It was suggested that choosing a good aspect ratio can assist to minimize short-circuiting and maximize flow distribution within the wetland system for biological activities [54]. Aspect ratio of as low as 1:1 was recommended for SSF [55], while length to width ratio of between 3:1 and 5:1 was recommended for FWS from an optimal point of view by [23]. However, based on findings by [56], 10:1 was recommended for FWS for good hydraulic efficiency. For water quality improvement, a river diversion wetland should be designed to operate with the most efficient aspect ratio.
\nWetland bed slopes are also critical to maintain a uniform water depth throughout the wetland system and facilitate drainage. In order to minimize short-circuiting, a uniform bed slope from inlet to outlet is recommended. Thus, the bed slope for SSF should be 2% or less, while that for FWS should be 0.5% or less [14]. A river diversion wetland can also be designed to operate with similar bed slope as recommended for FWS since they are related in mode of operation.
\nThis aspect of the wetland design focused on selecting or designing a water conveying system, inlet and outlet control structures that can facilitate flow and distribute inflow and drain outflow water from the wetland effectively. Depending on the type of river diversion wetland, flow diversion structure may be designed to consist of either a pipe or channel system and should function to provide a controlled flow of water to the wetland. However, it is necessary to be explicit about flow capacity at the time of design so that appropriate sizing of flow diversion structure can be made. Generally, the design flow conveyance structure is based on hydraulic; therefore the reader is referred to hydraulic books for detailed information.
\nIn order to ensure that the inflow water is uniformly distributed across the entire wetland area, multiple entry openings or gates should be considered rather than single to deliver the range of design flow regimes required. Flow control structures should be used to control inflow rate and maintain water levels. Control valves or weirs or a combination can be used depending on the type of inlet structured selected. Since the wetland system is for water quality improvement, high incoming water velocities should be discouraged. Therefore, energy dissipation system may be required for the incoming water to provide protection for the wetland inlet. The inlet openings should be designed large enough to avoid obstruction. Inlet zones should provide access for sampling and flow monitoring.
\nWetland outlet design is essential in avoiding possible dead zones and controlling water level and for monitoring flow and water quality. Depending on the size of the wetland, a combination of outlets (primary and secondary) or multiple outlets consisting of hydraulic control structures can be considered to collect and discharge treated water for the range of design flow regimes and maintain required water storage level. The purpose of the primary outlets is for water quality control, while the secondary is to act as a spillway and control flows in excess of the maximum design flow regime. Different types of control structures are available that can be used to control water level within the wetland. These may include number of individual pipes that fit together in a combination to obtain the desired water level, drop structures, or weirs. The design requirements of drop control structures and weirs can be found in hydraulic books. The outlet or water level control structure should be able to completely dewater the wetland when needed and allow for changes to be made easily.
\nThe management and restoration of water bodies like rivers should go beyond protection through the use of regulations. It should also make the most of opportunities that arise from using ecosystem properties to enhance self-purification capacity of rivers for water quality improvement. A key consideration is the use of constructed river diversion wetlands.
\nThis chapter provided guidance on the design of a river diversion constructed wetland aimed at improving quality of river water. The use of a combination of empirical equations was presented to guide in the estimation of the actual wetland area rather than relying on an assumed rate. The design approach using these equations may present a promising method for the design of river diversion wetlands. Furthermore, this novel approach may be useful to wetland experts as some of the procedures adopted are not popular in wetland studies. This may provide opportunity for wetland designers to document approaches that have been found promising and come up with suitable design criteria for constructed river diversion wetlands.
\nThe authors wish to express appreciation for the funding support provided by the Regional Water and Environmental Sanitation Centre, Kumasi (RWESCK), at the Kwame Nkrumah University of Science and Technology (KNUST), Kumasi, which is funded by the Government of Ghana and the World Bank under the Africa Centres of Excellence Project. Also, the authors wish to thank the National Water Resources Institute (NWRI), Nigeria, for providing opportunity and support for the study that led to the generation of knowledge and information presented in this chapter. We declare that the views expressed in this chapter are those of the authors and do not necessarily reflect those of the World Bank, Ghana Government, KNUST, and NWRI.
\nNone declared.
Edited by Jan Oxholm Gordeladze, ISBN 978-953-51-3020-8, Print ISBN 978-953-51-3019-2, 336 pages,
\nPublisher: IntechOpen
\nChapters published March 22, 2017 under CC BY 3.0 license
\nDOI: 10.5772/61430
\nEdited Volume
This book serves as a comprehensive survey of the impact of vitamin K2 on cellular functions and organ systems, indicating that vitamin K2 plays an important role in the differentiation/preservation of various cell phenotypes and as a stimulator and/or mediator of interorgan cross talk. Vitamin K2 binds to the transcription factor SXR/PXR, thus acting like a hormone (very much in the same manner as vitamin A and vitamin D). Therefore, vitamin K2 affects a multitude of organ systems, and it is reckoned to be one positive factor in bringing about "longevity" to the human body, e.g., supporting the functions/health of different organ systems, as well as correcting the functioning or even "curing" ailments striking several organs in our body.
\\n\\nChapter 1 Introductory Chapter: Vitamin K2 by Jan Oxholm Gordeladze
\\n\\nChapter 2 Vitamin K, SXR, and GGCX by Kotaro Azuma and Satoshi Inoue
\\n\\nChapter 3 Vitamin K2 Rich Food Products by Muhammad Yasin, Masood Sadiq Butt and Aurang Zeb
\\n\\nChapter 4 Menaquinones, Bacteria, and Foods: Vitamin K2 in the Diet by Barbara Walther and Magali Chollet
\\n\\nChapter 5 The Impact of Vitamin K2 on Energy Metabolism by Mona Møller, Serena Tonstad, Tone Bathen and Jan Oxholm Gordeladze
\\n\\nChapter 6 Vitamin K2 and Bone Health by Niels Erik Frandsen and Jan Oxholm Gordeladze
\\n\\nChapter 7 Vitamin K2 and its Impact on Tooth Epigenetics by Jan Oxholm Gordeladze, Maria A. Landin, Gaute Floer Johnsen, Håvard Jostein Haugen and Harald Osmundsen
\\n\\nChapter 8 Anti-Inflammatory Actions of Vitamin K by Stephen J. Hodges, Andrew A. Pitsillides, Lars M. Ytrebø and Robin Soper
\\n\\nChapter 9 Vitamin K2: Implications for Cardiovascular Health in the Context of Plant-Based Diets, with Applications for Prostate Health by Michael S. Donaldson
\\n\\nChapter 11 Vitamin K2 Facilitating Inter-Organ Cross-Talk by Jan O. Gordeladze, Håvard J. Haugen, Gaute Floer Johnsen and Mona Møller
\\n\\nChapter 13 Medicinal Chemistry of Vitamin K Derivatives and Metabolites by Shinya Fujii and Hiroyuki Kagechika
\\n"}]'},components:[{type:"htmlEditorComponent",content:'This book serves as a comprehensive survey of the impact of vitamin K2 on cellular functions and organ systems, indicating that vitamin K2 plays an important role in the differentiation/preservation of various cell phenotypes and as a stimulator and/or mediator of interorgan cross talk. Vitamin K2 binds to the transcription factor SXR/PXR, thus acting like a hormone (very much in the same manner as vitamin A and vitamin D). Therefore, vitamin K2 affects a multitude of organ systems, and it is reckoned to be one positive factor in bringing about "longevity" to the human body, e.g., supporting the functions/health of different organ systems, as well as correcting the functioning or even "curing" ailments striking several organs in our body.
\n\nChapter 1 Introductory Chapter: Vitamin K2 by Jan Oxholm Gordeladze
\n\nChapter 2 Vitamin K, SXR, and GGCX by Kotaro Azuma and Satoshi Inoue
\n\nChapter 3 Vitamin K2 Rich Food Products by Muhammad Yasin, Masood Sadiq Butt and Aurang Zeb
\n\nChapter 4 Menaquinones, Bacteria, and Foods: Vitamin K2 in the Diet by Barbara Walther and Magali Chollet
\n\nChapter 5 The Impact of Vitamin K2 on Energy Metabolism by Mona Møller, Serena Tonstad, Tone Bathen and Jan Oxholm Gordeladze
\n\nChapter 6 Vitamin K2 and Bone Health by Niels Erik Frandsen and Jan Oxholm Gordeladze
\n\nChapter 7 Vitamin K2 and its Impact on Tooth Epigenetics by Jan Oxholm Gordeladze, Maria A. Landin, Gaute Floer Johnsen, Håvard Jostein Haugen and Harald Osmundsen
\n\nChapter 8 Anti-Inflammatory Actions of Vitamin K by Stephen J. Hodges, Andrew A. Pitsillides, Lars M. Ytrebø and Robin Soper
\n\nChapter 9 Vitamin K2: Implications for Cardiovascular Health in the Context of Plant-Based Diets, with Applications for Prostate Health by Michael S. Donaldson
\n\nChapter 11 Vitamin K2 Facilitating Inter-Organ Cross-Talk by Jan O. Gordeladze, Håvard J. Haugen, Gaute Floer Johnsen and Mona Møller
\n\nChapter 13 Medicinal Chemistry of Vitamin K Derivatives and Metabolites by Shinya Fujii and Hiroyuki Kagechika
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I am also a member of the team in charge for the supervision of Ph.D. students in the fields of development of silicon based planar waveguide sensor devices, study of inelastic electron tunnelling in planar tunnelling nanostructures for sensing applications and development of organotellurium(IV) compounds for semiconductor applications. I am a specialist in data analysis techniques and nanosurface structure. 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After obtaining a Master's degree in Mechanical Engineering, he continued his PhD studies in Robotics at the Vienna University of Technology. Here he worked as a robotic researcher with the university's Intelligent Manufacturing Systems Group as well as a guest researcher at various European universities, including the Swiss Federal Institute of Technology Lausanne (EPFL). During this time he published more than 20 scientific papers, gave presentations, served as a reviewer for major robotic journals and conferences and most importantly he co-founded and built the International Journal of Advanced Robotic Systems- world's first Open Access journal in the field of robotics. Starting this journal was a pivotal point in his career, since it was a pathway to founding IntechOpen - Open Access publisher focused on addressing academic researchers needs. Alex is a personification of IntechOpen key values being trusted, open and entrepreneurial. Today his focus is on defining the growth and development strategy for the company.",institutionString:null,institution:{name:"TU Wien",country:{name:"Austria"}}},{id:"19816",title:"Prof.",name:"Alexander",middleName:null,surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/19816/images/1607_n.jpg",biography:"Alexander I. Kokorin: born: 1947, Moscow; DSc., PhD; Principal Research Fellow (Research Professor) of Department of Kinetics and Catalysis, N. Semenov Institute of Chemical Physics, Russian Academy of Sciences, Moscow.\r\nArea of research interests: physical chemistry of complex-organized molecular and nanosized systems, including polymer-metal complexes; the surface of doped oxide semiconductors. 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