**2.2 Wearable capacitive sensor**

As **Figure 1b** shows wearable capacitive sensor is based on capacitance change of capacitor. Among different capacitors, the most popular architecture is the parallelplate configuration because it is easy to be fabricated and its model is simple. The capacitive change can be expressed by the classic equation:

$$\mathbf{C} = \mathbf{x} \frac{A}{d} \tag{3}$$

in which κ, A, and d represent the permittivity of the medium between two plates, the overlap area, and the distance between two plates, respectively. When any of them is changed by the mechanical stimulus, the capacitance would be changed.

For capacitive strain sensor, when the strain ε is applied, the length of capacitor along the strain direction would be increased, which is expressed as (1 + ε)l0, while the width and thickness of dielectric layer would be decreased, which is expressed as (1 − νelectrode)w0 and (1 − νdielectric)d0, respectively. The νelectrode and νdielectric are used to represent the Poisson's ratios of flexible electrodes and dielectric layer, respectively. If both flexible electrodes and dielectric layer have same Poisson's ratio, then the capacitance upon stretching could be calculated as:

$$\mathbf{C} = \begin{pmatrix} \mathbf{1} + \mathbf{e} \end{pmatrix} \mathbf{C}\_0 \tag{4}$$

The equation indicates that the capacitance of capacitive strain sensor is linear with the applied strain. However, the linear relationship is only suitable for limited strain range. When the applied strain is higher than certain value, the relationship between different axes cannot be obtained simply by the Poisson's ratio.

For capacitive pressure sensor, the sensitivity (S) of capacitance to pressure is given by:

$$\mathbf{S} = \mathbf{\hat{s}} \{ \Delta \mathbf{C} / \mathbf{C}\_0 \} / \delta P \tag{5}$$

**79**

mechanical stimuli [15].

*Wearable Electromechanical Sensors and Its Applications DOI: http://dx.doi.org/10.5772/intechopen.85098*

*3.1.1 Sensitivity and linearity*

stretchability.

problem.

*3.1.2 Hysteresis and response time*

**3. Performance of wearable electromechanical sensor**

**3.1 Basic parameters of wearable electromechanical sensor**

Sensitivity is the magnitude of electrical response to measured mechanical stimulus, which is an important parameter. For strain sensor, sensitivity is called gauge factor (GF), which is defined as GF = ΔR/R0 for resistive type and GF = ΔC/C0 for capacitive type. For pressure sensor, pressure sensitivity (PS) is defined as PS = (ΔR/R0)/P. Sensitivity can be affected by functional material, sensing mechanism, and structural configuration. The materials with large piezoresistive or piezoelectric coefficient are desired. Tunneling effect and crack/ gap structures in piezoresistive sensors have been proven to be effective in promoting sensitivity. However, most highly sensitive sensors always show limited

Linearity characterizes degree of deviation from linear relationship between electrical signals and mechanical stimulus. High linearity is convenient for the calibration and data processing process. However, there is always a contradiction between sensitivity and linearity because crack propagation and tunnelingeffect-induced resistance change are usually exponential. For instance, piezoresistive strain sensors often exhibit varied sensitivity in different strain ranges, which is induced by the nonlinear heterogeneous deformation. In addition, capacitive sensors with microstructured dielectric also suffer the similar

Hysteresis and response time are another two important parameters in evaluating dynamical performance of electromechanical sensor. Hysteresis means the dependence of the performance on its history, which should be reduced or avoided. In general, capacitive sensors show immediate responding to the variation of overlapped area, featuring a lower hysteresis. Meanwhile, piezoresistive sensors have slower response due to the interactive motion between sensing material and polymer substrate. The interfacial binding between sensing material and substrate greatly affects the optimization of hysteresis. The full recovery of sensing material position is hindered by the interfacial slide, leading to a high hysteresis behavior. Meanwhile, to avoid the friction-induced buckling and facture in sensing materials, a weak adhesion is needed. It is reported that using low viscoelastic polymer substrate and improved configuration can partially eliminate hysteresis. However, it is still a large challenge to optimize hysteresis by novel material and structural engineering. Response time illustrates the speed to achieve steady response to applied mechanical stimulus, and response delay exists in nearly all composite-based sensors because of the viscoelastic property of polymers. Relatively, piezoresistive device has a larger response time than others because it needs more time to reestablish percolation network in resistive composites. In addition, lower modulus materials are popular for wearable electromechanical sensor, which can further decrease the response speed of resistive sensors. Moreover, based on structural design, the newly developed crack-based piezoresistive sensors show an appealing response time (about 20 ms) because cracks can reversibly connect and disconnect with loading and unloading of

where ΔC is the variation of capacitance (C–C0) and P presents applied pressure. The most popular structure for the wearable pressure sensor is interlock structure, which is hard to make accurate analysis.

#### **2.3 Iontronic sensors**

As **Figure 1c** shows, iontronic sensor is based on the iontronic interface sensing mechanism. The iontronic interface usually exists at the nanoscale interface between the electrode and the electrolyte. The electrode forms ionic-electronic contact with ionic gel. The electrons on the electrode and the counter ions from the iontronic film accumulate and attract to each other at a nanoscopic distance, leading to an ultrahigh unit-area capacitance. Compared to traditional parallel plate capacitive sensors, iontronic sensor has a higher surface area and its electrical capacitance is at last 1000 times larger. This excellent property is suitable for wearable electromechanical sensors. In addition, this special mechanism enables iontronic sensor immunity to environmental or body capacitive noises. So far, ion gels and ionic liquids are the most popular materials for iontronic sensor.

#### **2.4 Piezoelectric sensors**

As **Figure 1d** shows, the sensing mechanism of piezoelectric sensor is piezoelectric effect. Piezoelectric means that electric change accumulates in piezoelectric materials when mechanical stress is applied. Many materials have piezoelectric property, such as crystals, certain ceramics, and even biological matter. When strain or pressure is applied, there is a change in electrical polarization inside the material, resulting in a change in surface charge (voltage) at the surface of the piezoelectric material. In general, the electrical signal of piezoelectric sensor is voltage, which can be collected by measuring two different surfaces.


**3. Recent laboratory tests**

**2.5. Performance Estimation**

6

µm and 1.25 µm wavelengths, respectively.

edge [21]. Therefore, the fitting error variance *σ fit*

and 0.13 radian2

variance can be calculated approximately as *σ* 2 ≈*σ fit*

and 0.38 radian2

0.65 µm and 1.25 µm wavelengths, respectively.

length as *r*0∝*λ*

32 Adaptive Optics Progress

given as

*σtem*

*σtem*

2 is 0.44 radian2

iance of 0.90 radian2

As an example, the AO performance estimation will only focus on the Kitt Peak 1.6-meter McMP that has a large aperture size. The estimated performance should be better for other telescope with a smaller aperture size or a site with a better seeing condition. The Fried pa‐ rameter *r*0 is equal to ~ 5 cm at 0.5 µm for the daytime median seeing conditions at Kitt Peak, which is available almost every week for a clear sky. The seeing *r*0 is scalable with wave‐

A DM surface with a finite actuator number cannot exactly match the wave-front patterns of the atmospheric turbulence. For an atmospheric wave-front with Kolmogorov spectrum, the fitting error variance of a DM with finite actuator number is derived by Hudgin [20] and is

> 2 5/3 0 (/) *fit s*

where *rs* and *r*0 are the spaces between two actuators and the Fried parameter, respectively. *κ* is the fitting parameter. Since there are 12 actuators across the DM aperture that is conju‐ gated onto the 1.0-meter effective telescope aperture (see Section 4) although McMP has a 1.6-meter aperture, *rs* is equal to 83 mm. An extensive analysis of the fitting error showed that *κ* =0.349 was applicable for many influence functions that are not constrained at the

The temporal error of the wave-front correction is determined by the Greenwood frequency and the bandwidth of the AO system. The Greenwood frequency can be calculated as 0.43*v* /*r*0 [22], where *v* is the average wind speed. At McMP, when wind speed reaches 20 m/s, the telescope will be closed and observations will not take place. We assume that the AO system will operate with an average wind speed of 8.0 m/s. This results in a Greenwood frequency of 49 Hz and 23 Hz at the 0.65 µm and 1.25 µm wavelengths, respectively. The wave-front variance due to the temporal error of the correction can be calculated as

2 =( *f G* / *f BW* )5/3 , where *f G* is the Greenwood frequency and *f BW* is the bandwidth of the AO system. Since the AO closed-loop bandwidth is 80 Hz, the temporal wave-front variance

Read out noise is not a problem for solar wave-front sensing since plenty of photons are available for the wave-front sensing [10]. The corrected wave-front variance is the sum of all the error contributors. If only the fitting and temporal errors are considered, the wave-front

The overall performance of an AO system can be evaluated in terms of the Strehl ratio,

2 + *σtem*

 k

s

5 for Kolmogorov turbulence, and it reaches 70 mm and 150 mm at the 0.65

= *r r* (11)

and 0.25 radians2

2 . This results in a wave-front var‐

at the

2 is 0.46 radians2

at the 0.65 µm and 1.25 µm wavelengths, respectively.

at the 0.65 µm and 1.25 µm wavelengths, respectively.

The PSAO was first built in CSUN laboratory in 2009, with an OKO 37-actuator DM for soft‐ ware development and test purpose. In 2010, the DM was upgraded with the current BMC 140-actuator model. A cross target illuminated by a white-light fiber bundle is used for the 2-dimensional object test. A 32-actuator DM from Edmund Optics is used to generate a realtime wave-front aberration at a desired frequency, which is subsequently fed into the AO system, so that the AO performance can be evaluated in real-time. The whole optics of PSAO is built on a 900mm x 600mm optical breadboard as shown in the Figure 5, and can be easily carried to any solar observatory.

**Figure 2.** Graphic WFS interfaces for AO calibration with a point source (left) and for real-time correction with a cross target (right).

The AO software consists of two parts. The first part is used for the AO calibration, while the second part is the code for AO real-time correction. In principle, the calibration only needs to be done once a time, provided that the hardware has not been realigned. In the cali‐ bration, a single-mode fiber is served as a point source. Any possible incoming wave-front error will be filtered out by the single-mode fiber, and only the fundamental mode can prop‐ agate through the fiber. The output wave-front can be viewed as a perfect wave for the cali‐ bration. In the calibration, the fiber is switched into the optical path on the telescope focal plane just before the lens L1 (see Figure 1). In AO real-time correction, the fiber is switched out the optical path, and an extended target (a cross herein), including the wave-front error generated by the 32-element Edmund Optics DM, is allowed to input into the AO for test‐ ing. A lenslet array with 8.7-mm focal length is used for the WFS. Figure 2 shows the WFS interfaces in the AO calibration with a point source (Figure 2 left) and in real-time correction with a cross target (Figure 2 right): there are 69 effective WFS sub-apertures arranged in the 9x9-lenslet grid configuration and wave-front correction up to 65 modes of Zernike polyno‐ mials can be chosen. Please note that in this test, there is no central obstruction on the pupil, although our software can be used with a telescope with central obstruction.

The AO system was also tested with a 2-dimensional extended target with the same proce‐ dure for the point-source target. In this test, a cross was printed on a transmission film and was used as a 2-dimensional target. A fiber bundle light source located immediately behind the cross target was used to illuminate it, so that the image of the fibers was almost overlap‐ ped on the cross target, except for a slight defocus between them. Figure 4 shows the origi‐ nal cross-target image (left) without aberration applied, as well as the AO-off (center) and AO-on (right) images with the aberration applied, respectively. Please note that the image of the cross target and background small fibers is seriously blurred by the wave-front error ap‐ plied (center image). Compared the left and right images, however, it is clear that the AO recovers the original image perfectly, indicating that the AO's performance is excellent. In fact, our AO system can recover the original wave-front that is associated with the original

A Solar Adaptive Optics System http://dx.doi.org/10.5772/ 52834 35

The PSAO's small size makes it can be easily brought to any observatory for science obser‐ vations. Since 2010, we have carried out observations at two different sites. To demonstrate the AO's feasibility, an initial on-site observation was conducted by using the 0.61-m solar telescope at the San Fernando Observatory, California State University Northridge. This so‐ lar telescope is a three-aspheric mirror system with a central obstruction area of 14%, and a focal ratio of f/20. Because of the poor seeing conditions at the San Fernando Observatory, WFS with 9x9 sub-apertures (exception those in regions of the four-corners and the central obstruction) was used. The best observational results were acquired in October 2011. Using a sunspot as a target, the AO system was able to lock on the sunspot for wave-front sensing and provides high-resolution images at the wavelength of 0.75 µm [24], which indicates that our PSAO is able to provide wave-front correction at a site with a poor seeing condition.

After the successful observations on the San Fernando Observatory, we continued to test this system with the 1.6-m McMP. The McMP is located at the Kitt Peak, and is operated by the National Solar Observatory. It is one of the largest solar telescopes and is accessible to the solar community around the world. The medium seeing at the Kitt Peak is better than that at the SFO, but is still poor with a Fried parameter of ~ 5 cm at median seeing condition. The poor seeing condition at the Kitt Peak is a great challenge for an adaptive optics system. There are no AO available for routine observations with the McMP, although a prototype with 36 actuator DM was developed many years ago [25]. McMP is an off-axis telescope without central obstruction. The solar image is formed on a rotational station at f/54, where our PSAO can be conveniently placed for observations. Figure 5 shows the PSAO loaded on the McMP rota‐ tion station for an observation, in which the small size of the AO system is clearly referred. The non-common optical path error between the WFS and the science camera, which the WFS

During the latest observations in May 2012, the PSAO delivered excellent performance in the visible with the McMP. Solar images were captured at 0.6-µm visible wavelength. Figure 6

cannot measure, was calibrated by an approach we proposed recently [23].

image with accuracy up to 1/1000 wave-length in the visible [23].

**4. Recent on-site observations**

**Figure 3.** Point-source images: original image without aberration applied (left), with aberration applied and AO off (center), with aberration applied and AO on (right). 9x9 lenslets (without those on the four corners) are used for wavefront sensing, and 25 Zernike modes are corrected.

The PSAO had achieved excellent corrections in the test with a point source target. Figure 3 shows the original point-source image (left) without aberration applied, as well as the AOoff (center) and AO-on (right) images with the aberration applied, respectively. In this test, the wave-front error was introduced by the 32-actuator Edmund Optics DM in real-time, in which the wave-front is variable as a sinusoidal function with a frequency up to 80Hz. The central image of the distorted point-spread function shows that the wave-front error ap‐ plied is significantly large, while the right image clearly demonstrates that the AO system can recover the diffraction-limited image. The AO system demonstrated good results in differ‐ ent situations:

**Figure 4.** Cross-target images: original image without aberration applied (left), with aberration applied and AO off (center), with aberration applied and AO on (right). 9x9 lenslets (without those on the four corners) are used for wavefront sensing, and 25 Zernike modes are corrected.

The AO system was also tested with a 2-dimensional extended target with the same proce‐ dure for the point-source target. In this test, a cross was printed on a transmission film and was used as a 2-dimensional target. A fiber bundle light source located immediately behind the cross target was used to illuminate it, so that the image of the fibers was almost overlap‐ ped on the cross target, except for a slight defocus between them. Figure 4 shows the origi‐ nal cross-target image (left) without aberration applied, as well as the AO-off (center) and AO-on (right) images with the aberration applied, respectively. Please note that the image of the cross target and background small fibers is seriously blurred by the wave-front error ap‐ plied (center image). Compared the left and right images, however, it is clear that the AO recovers the original image perfectly, indicating that the AO's performance is excellent. In fact, our AO system can recover the original wave-front that is associated with the original image with accuracy up to 1/1000 wave-length in the visible [23].


**1.2. Arsenic: food and water security**

Arsenic is a metalloid that can be found naturally in the Earth, mainly in the Earth's crust, and can leak into the groundwater reserves. In many countries of the world, groundwater represents the main source of drinking water; therefore, exposure to arsenic from drinking water is considered a public health problem [8, 9]; not only the direct consumption of water contaminated with arsenic is a factor of exposure but also indirect consumption through food, when water is used in the preparation of food, in the irrigation of fields, and for consumption of animal, is an exposure factor [10].

The World Health Organization (WHO) and the guidelines of the Environmental Protection Agency (EPA) of the United States, established an allowed limit of 0.01 mg/L for arsenic in drinking water [11]. The Ministry of Health of Mexico establishes an allowable limit of 0.05 mg/L of arsenic in drinking water [11, 12], with current modifications at 0.025 mg/L, which are still above international standards.


**5. Control strategies for snails**

Snails are important both ecologically as well as economically due to a variety of factors. The prolific breeder *A. fulica*, soon after the introduction to a new habitat, reproduces at alarming rates making the control strategy very difficult. The control strategy of the pest is based on physical, chemical as well as biological methods. The physical control includes collection and destruction of snails and their eggs from the infested site or campaigns organized by local agencies voluntarily supported by health service officials, local people, students and teach‐ ers. After collection, snails are crushed and buried deep into the soil, covered with kaolin. Eradication of the species involves a huge amount of chemicals, hand collection and extensive public awareness programs like posters, documentaries, etc. Metaldehyde is the principal component of molluscicides and is indiscriminately used for the control of the snail *A. fulica,* consequently causing loss of productivity of local crops. For example, in Sao Paulo, farmers unknowingly used the molluscicide "metaldehyde" in banana fields to target snails, which killed many species including bats, skunks, lizards and small rodents which were beneficial as natural control agents of agricultural pests [12]. The physical methods are very time‐con‐ suming and tedious while the chemicals have resistance problems, killing the non‐target flora and fauna. Therefore, biological control is the option that seems very fruitful and ecofriendly. But predatory snails (e.g., rosy wolf snail: *Euglandina rosea*) and flatworms have also failed to control some species such as *A. fulica* [52, 53]. As snails are ecologically and economically important due to the pest nature, the bacterial flora present in the gastrointestinal (GI) tract of snails may have an important role in digestion. These functionally specialized GI tract regions may be unique microenvironments and could harbor unusual bacterial communities.