Performance summary of reported hydrovoltaic devices based on the carbon materials.
\r\n\tA quark exhibits confinement, which means that the quarks are not observed independently but always in combination with other quarks. This makes determining the properties (mass, spin, and parity) impossible to measure directly; these traits must be inferred from the particles composed of them. There are six flavors of quarks: up, down, strange, charm, bottom, and top. The flavor of the quark determines its properties.
\r\n\tThere are three generations of quarks, based on pairs of weak positive/negative, weak isospin. The first generation quarks are up and down quarks, the second-generation quarks are strange and charm quarks, the third generation quarks are top and bottom quarks. The up and down quarks make up protons and neutrons, seen in the nucleus of ordinary matter. They are the lightest and most stable. The heavier quarks are produced in high-energy collisions and rapidly decay into up and down quarks.
\r\n\tThe baryons and mesons known at the time fell into symmetric families of multiplets (octuplets, decuplets) sharing two identical quantum numbers (spin and parity), but differing in an ordered way in others (mass, charge, baryon number and strangeness). The mathematical group to fit this complex situation-SU3, the symmetric, unitary group of dimension 3-was proposed independently by Gell-Mann and Ne'eman. The validity of SU3 was demonstrated by the experiment. A major prediction was that a particle (the omega-minus), an isotopic singlet with spin = 3/2, positive parity, mass of roughly 1,680 MeV, negative charge, baryon number +1, strangeness = -3, and stable to strong decay, should exist to complete the 3/2+ baryon decuplet. It was therefore a major triumph for the scheme when the omega-minus, a baryon with the precise mass, charge, and strangeness predicted, was discovered in 1964. All these facts introduced a quark idea fully into modern physics.
\r\n\r\n\tThis book will be a self-contained collection of scholarly papers targeting an audience of practicing researchers, academics, PhD students and other scientists. The contents of the book will be written by multiple authors and edited by experts in the field.
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Water contains enormous energy (35% of the solar energy received by the Earth, 1015 W) in a variety of forms, such as chemical, thermal, and kinetic energy [1]. The chemical energy is harnessed through water splitting under the assistance of electricity or photocatalysts [2]. The thermal energy is exploited for salinity power generation [3]. Kinetic energy is widely utilized by hydroelectric station, which is a main form of electricity.
As the progress of nanomaterials and nanotechnology, a new strategy based on hydrovoltaic effect (HV) has been developed in recent years [1]. In solar cells, the electron-hole pairs are generated by the absorption of photons with higher energy than the bandgap of semiconductor [4]. With the help of the built-in field at the interface of p-n junction, the electron-hole pairs are separated and then accumulated at the terminal of solar cells, generating photovoltaic voltage (Figure 1a). HV effect is analogous to the photovoltaic effect described as above. For HV effect, the potential is generated through the interaction between nanostructured materials and water molecules [1]. The form of water can be liquid, droplet, moisture, and evaporation [5]. In brief, the basis of photovoltaic effect is the asymmetry of structural electronics (e.g., p-n junction). The nonuniformity of charge distribution at solid-liquid interface is the origin of HV effect. Figure 1b shows the electric double layer (EDL) at solid–liquid interface and the potential gradient as the distance increasing from solid surface into solution. Figure 1c illustrates the hydrovoltaic current is created in the graphene layer with the opposite orientation of the droplet flowing direction.
(a) Photovoltaic effect with p-n junction. The basis of photovoltaic effect is the asymmetry of structural electronics. (b) Schematic of EDL forms at the solid surface with negative charges (not shown). There are two charge layers near the surface of solid, which are stern and diffusion layers. The stern layer is formed due to the chemical interaction between solid and absorbed ions. The diffusion layer electrically screen the stern layer through coulomb interaction. The blue line is the electrical potential curve around the solid surface [1]. (c) Illustration of induced potential by drawing a droplet on graphene. An electric current is formed in graphene by two moving boundaries of the EDL at the front and rear of the running droplet, respectively [1].
Since the HV phenomenon was discovered with carbon nanotubes (CNTs) in 2003, carbon nanomaterials were extensively investigated and considered as the most promising candidates for HV generators [6]. So far plenty of carbon nanomaterials exhibit HV effect with no need of a pressure gradient, including 0D graphene quantum dots (GQDs), 1D CNTs, 2D graphene or graphene oxide (GO), 3D graphene foam, and so on [7, 8]. Yet, unlike photovoltaic effect, research on the HV effect is in its infancy and calls for continued efforts to materialize its great potential. In this chapter, starting by describing fundamental principle of hydrovoltaic effect, including water-carbon interactions and basic mechanisms of harvesting water energy with nanostructured materials, experimental advances in generating electricity from water flows, waves, natural evaporation, and moisture are then reviewed. We further discuss potential device applications of hydrovoltaic technologies, analyze main challenges in improving the energy conversion efficiency and scaling up the output power, and suggest prospects for developments of the emerging technology.
As in Figure 2a, in a nanochannel, the EDL layers form on the interface of solid–liquid and overlap each other due to the small size of nanochannel. Under pressure gradient, a steady current will be generated along with the ion transport from high to low pressure side. The voltage will prohibit the transport of more ions. Therefore the steady current named the streaming current is generated [9]. There is positive correlation between the flow rate, pressure gradient, channel height, and the streaming current.
(a) Schematic of electrokinetic effect in the nanochannel. Blue line illustrates the profile of flow velocity through the nanochannel [1]. (b) Illustration of waving potential induced in graphene by one moving boundary of EDL across a graphene sheet on a dielectric substrate [1]. (c) Schematic illustration of harvesting energy from raindrops [1].
When a nanomaterial (e.g., graphene) is inserted into the liquid level, the EDL layer will be created and changed as the immersed area of nanomaterials changes. This means, if the immersed area increases, the EDL layer will be charged and a voltage will be generated in the nanomaterials (Figure 2b). On the contrary, an inverse voltage can be observed due to the discharging of EDL layer. This wave-induced voltage was called waving potential [10]. The voltage and current are proportional to the velocity of graphene and can be scaled up by series and parallel connections of multiple graphene devices.
When the water contained ions is a droplet on graphene, the EDL is emerged only in the region of droplet followed the EDL theory. When the droplet is moving under the external force such as gravity, the EDL region will move accordingly. During this moving process, there is a charging state at the front of the droplet, while a discharging process at the rear of the droplet (Figure 1c). Therefore an electrical voltage called as drawing potential can be generated [11]. The voltage and current will increase as the velocity and number of droplets increase. The drawing potential can be developed to harvest raindrop energy (Figure 2c).
When moisture were adsorbed by nanomaterials with oxygen-containing functional groups, a gradient of H+ can form because of the local salvation effects, which can lead to the breakage of Oδ−-Hδ+ bonds. Due to the H+ concentration difference, the H+ will migrate along the reverse gradient direction. Then a voltage would increase continuously until the gradient of H+ vanishes. When the ingress of moisture stopped, the number of migratory ions decreased as free H+ and oxygen-containing functional groups recombined, resulting in an reverse voltage [12].
Based on the above discussion of mechanisms, an excellent hydrovoltaic material should possess the following characteristics: strong water-carbon interaction, rational pore and microstructure for the transport of water molecules, large interaction area for water adsorption and charge storage, sufficient electric conductivity for charge transportation, and abundant/gradient surface functional groups (oxygen functional groups in particular). Nowadays, the most reported hydrovoltaic materials are carbon nanomaterials [7, 8]. However, the hydrovoltaic effect is not limited to carbon nanomaterials but can be generic to other materials as long as they meet the above characteristics (Figure 3).
Characteristics of a good hydrovoltaic material.
Moisture is one important form of water in the nature. GQDs have unique properties due to quantum confinements and edge effects [13]. GQDs as the chemically active material have fabricated a moisture-triggered generator [14]. The size of GQDs is 2–5 nm. The GQDs contain an amount of oxygen-containing functional groups. To create a gradient of functional groups, GQDs are treated via electrochemical polarization. The GQD generator achieves a high voltage of 0.27 V, when the variation of relative humidity (RH) is 70%. After the optimization of the load resistor, a power density obtained is 1.86 mW/cm2. The gradient of oxygen-containing functional groups is the reason of electricity generation with moisture. Similarly, the porous carbon black, and GO framework with the functional group gradient, can also exhibit excellent HV performance under moisture [15, 16]. For example, a superhydrophilic 3D assembly of graphene oxide (g-3D-GO) with open framework exhibits a high power density of ca. 1 mW/cm2 and an energy conversion efficiency of ca. 52% [16]. With an RH variation of 75%, the g-3D-GO-based HV device could provide a voltage and current output of ca. 0.26 V and ca. 3.2 mA/cm2 within 2 s (Figure 4a and b). As in Figure 4c, a power source system consists of four HV cells in series which was fabricated to demonstrate the practical application. This system was attached onto the pendulum bob. The pendulum bob can swing between moist region (RH = 80%) and dry region (RH = 5%). When the system moves to the moist region, the moisture-induced positive voltage is applied on the light emitting diode (LED), and it lights up. Upon traveling to the dry region, the LED will switch off. As a consequence, this power source system could provide a steady power output.
(a) Voltage and (b) current output cycle of HV device based on g-3D-GO that is sandwiched by aluminum electrodes in response to the RH variation (ΔRH = 75%) [16]. (c) Schematic illustration of a HV device-based power source system [16]. (d) Schematic illustration of the mechanism of humidity-driven electricity generation [17]. (e) Condensation and evaporation of ionic liquids under different humidity [17]. (f and g) Voltage generated with wrinkle graphene/salt crystal nanogenerator under a sudden change in humidity [17].
Recently, Zhen et al. prepared a nanogenerator using the wrinkled graphene, which followed an unusual mechanism of HV effect [17]. In this work, a new cation-π interaction utilization strategy was developed. In other words, electricity is generated through water adsorption and desorption of salt crystals along with the humidity variation. The key of this nanogenerator is to deposit salt crystals onto the wrinkled graphene by manipulating the formation of ionic liquid microdroplets. The wrinkled graphene has many defects and uniform wrinkles, facilitating the ultrafast water evaporation, preventing excessive water accumulation and deposition of well-distributed salt crystals. Figure 4d and e schematically illustrates the mechanism of electricity generation. As the sudden change of humidity (25–75–25%), two inversed voltage peaks were observed sequentially (Figure 4f). This is attributed to the water vapor adsorption and desorption on the salt crystals. The sharper negative peak is due to the strong water adsorption ability of the salt crystals, while the broad positive peak is from the slow desorption process (Figure 4g). The voltage of 18 mV with the current of 37 nA was achieved with a 1 × 6 cm2 generator. Among various salts, NaCl exhibits the best performance due to its complete crystallization after each cycle.
As far as we know, the nanomaterials for moisture-induced electricity generation include carbon/graphene quantum dots, carbon black, GO film, and 3D GO frameworks. The main origin of electricity generation is similar to each other. The potential generation is dependent on the water adsorption difference due to the gradient of oxygen-containing functional groups and induced the concentration difference of charge carriers. Interestingly, the porous carbon black film treated partially by plasma could generate continuous electricity, which is totally different from other carbon nanomaterials. This discrepancy may be from the difference of the structure and/or the introducing manner of functional groups.
In 2014, Yin et al. firstly reported the electricity generation induced by droplet movement on monolayer graphene [11]. When a droplet of 0.6 M NaCl is drawn on graphene at a constant velocity of 2.25 cm/s, a voltage of 0.15 mV is generated. When the direction of droplet movement is opposite, the direction of potential is also reversed. When the movement is stopped, no potential is produced. Rain is one of important existent forms of water in nature. However, the energy in the rain is not yet utilized efficiently in the long term. There are amount of cations (such as Na+, NH4+, Ca2+, Mg2+) and anions (such as Cl−, NO3−, SO42−). Therefore harvesting energy from rain using HV effects is a promising approach [18, 19].
As previously reported, the low generated voltage of around 0.15 mV, and the external pressure needed, would limit the application of HV effect. Recently, Li et al. reported the electricity generation from water droplets on porous carbon film through capillary infiltrating [20]. Figure 5a illustrates the structure of the porous carbon film (PCF) device. When a droplet of 1 μL was dropped onto PCF, a sustainable voltage of 0.3 V was generated (Figure 5b). However, electricity generation by droplet movement on graphene or aligned single-walled nanotubes is pulse-like. The retention of voltage depends on the volume of water droplets as shown in Figure 5c, but the generated voltage value is nearly identical. More interesting, the dropping position of water droplet would influence the induced voltage (Figure 5d). Experimental results reveal the following key characteristics: (i) the merely directional water infiltration can induce the voltage, (ii) no direct correlation between the HV voltage and the position of droplets, and (iii) the direction of water infiltration influences the voltage sign. At last, the authors demonstrated a scale-up application with three devices in series (Figure 5e). Twelve 5 μL water droplets can generate a voltage up to 5.2 V and illuminate a liquid crystal display. This work powerfully demonstrate that a hydrophilic porous carbon film with water droplets could realize the energy harvested from rain and a practical application. However, there is no experimental results in real raining environment. More information on the droplet-induced electricity generation can refer one recent review paper [21].
(a) Schematic of the porous carbon film device with two ends modified with 1H,1H,2H,2H-perfluorodecyltriethoxysilane (PFDTS). The right inset shows a photograph of a typical device with dimensions of 50 × 7 mm2 [20]. (b) Open-circuit voltage obtained by repeatedly dropping 1 μL water droplets at the PFDTS@PCF/PCF interface under ambient conditions (~ 23.5°C and RH ~ 71.7%) [20]. (c) Measured Voc vs. time of the device when water droplets with various volumes were dropped onto the PFDTS@PCF/PCF interface [20]. (d) Wetting dependence of the induced voltage. Inset is schematic of the Voc measurement and the water-droplet position [20]. (e) Application demonstration of the water-droplet-induced voltage [20].
Ocean wave energy is a main form of ocean energy, which is considered as inexhaustible energy. In 2007, Liu and Dai reported that the flow-induced voltage can be greatly improved by aligning the nanotubes along the flow direction [23]. In 2017, Xu et al. fabricated a fluidic nanogenerator fiber with the aligned multi-walled carbon nanotube sheet (inset of Figure 6b) [22]. The device shows a power conversion efficiency of 23.3% and an excellent stability over 1,000,000 cycles. The flow direction, the flow distance, the flow velocity, and the NaCl concentration are positive correlation with the induced voltage (Figure 6a–6c). The authors also discovered that the ordered mesoporous carbon (OMC) can significantly enhance the flow-induced voltage (Figure 6d). After OMC introduction, the sustained voltage for over 1 h can be achieved. The maximum voltage output can reach up to 341 mV when the content of OMC is 5.1 μg/cm. Impressively, the stable performance of this device can be maintained even after over 1,000,000 bending cycles (Figure 6e). Moreover, the fiber nanogenerator is flexible and stretchable, indicating it can be woven into fabrics for large-scale applications.
(a) Voltage curve induced by a saturated NaCl flow at the velocity of 1.2 cm/s [22]. (b) Relationship between the voltage and the flow velocity. Solution is 0.6 M NaCl [22]. (c) The voltage vs. current relationship as the concentration variation of NaCl solution (flow velocity: 12.9 cm/s) [22]. (d) Dependence of the voltage on the OMC content in a saturated NaCl solution (flowing velocity: 20 cm/s) [22]. (e) Voltage generated by repeatedly dipping an OMC-incorporated device into a NaCl solution with an increasing number of bending cycles. The inserted graphs show the voltages after 200,000; 600,000; and 1,000,000 bending cycles in the NaCl solution [22].
Two-dimensional materials or devices have more advantages for ocean wave energy harvesting, which can well float on the surface of the ocean [1, 25]. Recently, Fei et al. achieved volt leveled waving potential using a pair of graphene sheets [24]. Figure 7a illustrates the device setup, where a pair of graphene-PET sheets with size of 2.5 × 1.5 cm2 is immersed in NaCl solution. As one of the graphene sheets moves through the liquid surface, electricity is generated (Figure 7b). The peak voltage can be around 60–120 mV. However, no voltage is observed either moving graphene underneath or parallel to the liquid level, indicating the waving potential is due to the dynamic EDL boundary. In this setup, the moving graphene is served as driving force for ion movement, while another graphene is as a reference electrode. Figure 7c shows the relationship between the peak voltage and the moving velocity of graphene. The peak voltage exhibits linear relations with velocity at low moving speeds and saturates to certain values at high speeds. This saturation may be due to the limit from the speed of ion adsorption/desorption. During the pulling process, when the load resistance is 0.6 MΩ, the largest power density of 1.6 mW/m2 can be obtained (Figure 7d). Moreover, the ion species can also influence the voltage value. The peak voltage values follow an order of LiCl > NaCl > KCl > BaCl2, indicating the smaller ions are better for higher voltage generation.
(a) Schematic of experimental setup with two graphene-PET sheets immersed vertically in an electrolyte container. GrL and GrR represent graphene samples on the left and right side, respectively [24]. (b) Generated voltage signals at resistor of 0.9 MΩ when separately moving GrL or GrR across NaCl solution [24]. (c) Peak voltage values collected as a function of velocity. The perpendicular moving distance of graphene is kept as 2 cm [24]. (d) Calculated output power per unit area of graphene [24].
Apart from the dynamic EDL boundary mechanism, the water-carbon interaction can also generate electricity [26]. In this case, the water does not need the cations and anions for the formation of EDL capacitance. More importantly, the electric signals are continuous, but the inherent mechanism is not very clear yet. More information on the liquid flow-induced electricity in carbon nanomaterials can refer the recent review papers [27, 28].
Water evaporation is a crucial step in the natural water circulation, releasing a huge amount of water energy. In 2017, Xue et al. reported that the water evaporation from centimeter-sized carbon black sheets can reliably generate sustained voltages of up to 1 V for 8 days under ambient conditions [29]. The annealing and plasma treatment introduced functional groups are essential for the electricity generation. Recently, Li et al. fabricated an evaporation-driven nanogenerator (1 cm × 5 cm) with a high open-circuit voltage of 3 V [30]. The film device is fabricated using carbon black and glass fiber. As in Figure 8a, the surface of the hybrid film was modified with several polymer molecules, such as polyethylene imine (PEI); 1,2,3,4-butane tetracarboxylic acid (BTCA); polydimethyl diallyl ammonium chloride (PDADMAC); and poly sodium-p-styrenesulfonate (PSS). The voltage of the glass-fiber-carbon-nanoparticle film (GCF) can vary from −3 V to 3 V, which rely on the difference of the surface functional groups (surface charges) as in Figure 8b. PEI- and PDADMAC-modified GCF have positive surface charges and thus positive zeta potentials. The ion-selective transport mechanism is schematically shown in Figure 8c. The concentration of polymer solution is one of critically experimental conditions, which can significantly affect the voltage from +1 V to −3.2 V by using 0–0.5 wt% PEI solution (Figure 8d). There is an optimal concentration of PEI solution (0.05 wt%), which should be attributed to the low conductivity of polymer and/or the partial channel blocking. A hybrid film device using two GCFs with opposite surface charges was also prepared. Therefore the generated voltage was enhanced to around 5 V (5 × 5 cm). They also connected the hybrid film device with supercapacitor. The supercapacitor can store the electric energy from GCFs and provide a high current output. The supercapacitor can be charged up to 2.8 V by the output voltage of GCFs, and then a blue LED can be lighted up for about 10 s without any auxiliary. This work shows the great potential of evaporation-induced electricity generation in the field of portable electronics.
(a) Schematic of the chemical modification with different molecules on carbon nanoparticles [30]. (b) Voc and zeta potentials of the pristine and modified GCFs [30]. (c) Schematic of the ion-selective transport mechanism in the nanochannels with (i) positively and (ii) negatively charged surface [30]. (d) Dependence of Voc of the device on the PEI concentration. The inset shows the evolution of Voc when a GCF was repeatedly modified by DADMAC(A) and PSS(B) [30]. (e) Voltage-time curve of three supercapacitors (SCs) connected in series in charging by the GCFs at ambient condition. Insets show the circuit diagram (top) and photos of the blue LED [30].
Because of the low power density (10−3–10 W/m2) of HV device, [1] it is a good choice to develop HV effect-based self-powered devices, such as self-powered liquid sensors. The factors which can affect the HV signal could be used for sensing applications, including flow rate, fluid volume, solution component, fluid movement, and so on. Yin et al. developed a monolayer graphene-based HV device in 2013 and demonstrated the self-powered liquid sensor application [11]. The configuration of HV devices is shown in Figure 9a. A droplet of 0.6 M NaCl was moved with a SiO2/Si wafer on the graphene film. During the moving of the droplet, the advancing and receding contact angles are ~91.9° and ~60.2°, respectively. As in Figure 9b, the voltage of device is linearly proportional to the velocity of droplet. The larger the size of the droplet would induce the larger voltage. The concentration of NaCl solution is also a critical factor, but the trend is not monotonic. The best concentration of NaCl solution is around 0.01 M. The voltage output is less than 0.5 mV. However, in carbon black, the potential is maximum ~1.0 V when deionized (DI) water is used for evaporation HV generator [29]. It is interesting that the voltage can be multiplied by drawing multiple droplets simultaneously as shown in Figure 9c. Experimental results indicate the voltage is near zero when two droplets are moving in the opposite directions. The voltage of device is closely related to the ion species, such as MgCl2, HCl, and NH3·H2O. However, there is no response for DI water. This indicates the drawing potential comes from the ion-induced EDL capacitance. The voltage induced with HCl solution is negative. The authors think that there is a H3O+ layer on the surface of graphene. Therefore the positively charged graphene would attract the negative Cl− anions, which dominate the electric double layer. At last the authors show a handwriting sensor with a Chinese brush and 0.01 M NaCl. Two pairs of electrodes, E1+–E1− (right–left) and E2+–E2− (bottom-top), were patterned perpendicularly on the four sides of the graphene to distinguish the handwriting direction (inset of Figure 9c). The drawing direction related to voltage signal can be well detected as in Figure 9f. Moreover, the force and speed of the handwriting can also be monitored.
(a) A liquid droplet is sandwiched between graphene and a SiO2/Si wafer and drawn by the wafer at specific velocities. Inset: A droplet of 0.6 M NaCl on a graphene surface [11]. (b) Dependence of the output voltage on the volume of a droplet of 0.6 M NaCl [11]. (c) Dependence of the output voltage on the concentration of the solution (three droplets of NaCl solution). Inset: Photograph of handwriting with a Chinese brush on graphene [11]. (d) Voltage induced by moving one, two, and three droplets of 0.6 M NaCl. Dashed lines are curves linearly fitted to the measured data [11]. (e) Voltage for various ionic solutions (three droplets) on monolayer graphene [11]. (f) Sensing the stroke directions (arrows) by the drawing potentials between electrodes E1+–E1− and E2+–E2− as shown in the inset of c [11].
Graphene oxide framework with lots of pores can facilitate the diffusion of water molecules. Along with the asymmetrical oxygen-containing groups, Zhao et al. observed that the transport of the ionic charge carriers is accelerated due to the ionic gradient [16]. When the RH variation is 75%, the potential can increase up to 260 mV in 2 s. The concentration gradient of Li ions in 3D PPy framework can also show good sensitivity for moisture [31]. The potential is 60 mV under the RH variation of 85%. More importantly, the stability of this material is stable during the several hundred cycles.
Besides the HV potential, other signals can also be utilized for sensing, such as the resistance of materials, the length of fibers, and the volume of materials [32, 33, 34, 35, 36]. Of course, the change of other physical fields, such as temperature and wind speed, can be detected by HV devices directly or indirectly [29, 37].
As we all know, the common solar cells only work on sunny days, but do not work in the night and on rainy days. Combining PV and HV effects, a hybrid cell for all-weather power generation was developed by integrating a solar cell with a HV device. Tang et al. fabricated a flexible solar cell made of a transparent graphene electrode and a dye-sensitized solar cell [38]. The hybrid cell can be excited by solar light on sunny days and raindrops on rainy days, yielding a solar-to-electric conversion efficiency of 6.53% under AM 1.5 irradiation and power in the range of 5.12–54.19 pW by simulated raindrops (Figure 10b). However, the output of this hybrid cell is still far lower than the actual requirement. Then Tang et al. changed the graphene to graphene/carbon black/polytetrafluorethylene (PTFE) for the hybrid cell fabrication [18]. But the voltage and current did not show significant improvement under simulated raindrops. Zhong et al. developed a 2D hybrid nanogenerator based on graphene and silicon for all-weather electricity generation [39]. In this hybrid cell, the graphene and silicon form the van der Waals Schottky diode (Figure 10c). This hybrid device delivers a maximum output power of 49.3 μW under light illumination. When the DI water flows on the graphene under light from Au electrode to Ag electrode, an additional potential of 2.54 mV can be generated (Figure 10d). However, there is no response in the dark, indicating no interaction exists between water and hybrid cell during the water flowing process. The authors think that the potential response should arise from the interaction between water and graphene/Si Schottky diode (the doping and dedoping at the front and rear side of water droplet, respectively) instead of the water-graphene interaction or the water-electrode interaction. Moreover, the negatively additional potential can be observed when the water flows toward Au electrode. This phenomenon should be attributed to the asymmetric potential profile of the graphene channel.
(a) The quasi-all-weather solar cell that can produce electricity from rain and sun [38]. (b) Power signals produced by dropping 0.6 M NaCl droplets on rGO film [38]. (c) Schematic structure of the 2D hybrid nanogenerator of graphene/SiO2/Si [θ = 30°, PDMS: Poly(dimethysiloxane)] [18]. (d) Voltage responses to the flow of DI water over graphene/Si Schottky diode in the dark and under room light illumination [18]. (e) Voltage responses to the flow of DI water toward different directions [18].
Due to the intermittency and randomness of raining, it is wise to harvest energy from the ocean wave energy. The ocean wave energy is renewable and inexhaustible because 70% of the Earth’s surface is covered by the ocean. The ocean wave energy is also called as blue energy [40]. Tan et al. fabricated a film type wave energy generator with graphene, carbon black, and polyurethane [25]. A voltage of > 20 mV and a current of > 10 μA are produced in a 15 cm2-sized generator. Moreover, the devices are sustainably stable upon persistent attack by waving ocean. The floating devices on the sea can be packaged into the wave energy stations by series and parallel connections. Tan et al. further proposed a photo-induced charge boosting liquid-solid electrokinetic generator with a structure of polyurethane/graphene oxide-carbon black-multi-walled carbon nanotube/carbon quantum dots/copper (PU/GO-CB-MWCNT/CQDs/Cu) [41]. Under AM1.5 illumination, the voltage, current, and power density achieved by this device are 0.1 V, 0.39 mA, and 26.6 mW/m2, respectively. The working mechanism is described in Figure 11a-c. Due to the difference of Na+ and Cl− ions on adsorption energy, the EDL can be formed. Same as the principle of waving potential as above, the potential signal would change along with the seawater moving and the capacity change. Figure 11d presents an enlarged one-cycle electrical signal generation. When the seawater moves to the highest position, the maximum peak electricity signal is obtained. In this work, CQDs are used for visible light absorption in the range of 330–490 nm. Then more electrons can be excited, the surface electron density increased as in Figure 11e. The authors lastly proposed a circuit design for scaling up the power output in large-scale networks (Figure 11f). However, a cost-effective, stable, and promising scalable approach for efficiently harvesting ocean wave energy is an open question.
(a) When the ocean wave meets with the PU/GO-CB-MWCNT/CQDs film, an EDL is generated due to the formation of Na+ cation layer and electron layer [25]. (b) When the ocean wave reaches the top of the film, a highest voltage can be observed due to the charge of EDL [25]. (c) When the ocean wave is falling downward, a decreasing voltage is obtained because of the discharging of EDL [25]. (d) the voltage and current change of HV cell in one cycle of ocean wave change [25]. (e) under illumination, the electron density is enhanced with CQDs through light excitation [25]. (f) Illustration of the HV networks assembled with HV cells in series and/or parallel. This network can float on the ocean and harvest wave energy and solar energy [25].
As shown in Table 1, the performance of reported HV devices based on the carbon materials is summarized. Even though the great progress has been achieved in recent years, there are several challenges to overcome in the future. The challenges include the following: (i) the power is far from the need of practical applications. (ii) The stability and durability in real environment is not clear. (iii) It is difficult to achieve large-scale integrated applications. (iv) More advanced experimental technologies should be developed to reveal the unclear mechanism. (v) Non-carbon hydrovoltaic materials should be synthesized or constructed.
Materials | Substrate | Solution | Flow type | Potential (mV) | Current (μA) | Refs. |
---|---|---|---|---|---|---|
GO film | / | DI water | Moisture | 700 | 25 | [42] |
GO framework | / | DI water | Moisture | 260 | / | [16] |
GO film | / | DI water | Moisture | 1500 | 136 | [43] |
GO film | / | DI water | Moisture | 700 | 0.2 | [44] |
GO | / | DI water | Moisture | 340 | ~1 | [45] |
GO nanoribbon | / | DI water | Moisture | 40 | 300 | [46] |
GQDs | PET | DI water | Moisture | 270 | / | [14] |
Wrinkled graphene | SiO2/Si | NaCl | Moisture | 20 | 0.045 | [17] |
Monolayer reduced GO | ITO/PET | Simulated raindrops | Droplet | 0.1 | 0.49 | [38] |
Graphene/carbon black/PTFE | ITO/FTO | Simulated raindrops | Droplet | 0.228 | 5.97 | [18] |
Monolayer graphene | SiO2/Si | DI water | Droplet | 28.14 | 1800 | [39] |
Monolayer graphene | PVDF | DI water | Droplet | 100 | / | [47] |
Monolayer graphene | SiO2/Si | DI water | Droplet | 10 | 0.5 | [48] |
Nitrogen-doped graphene | SiO2/Si | DI water | Droplet | 380 | / | [49] |
Graphene grid | PDMS | NaCl | Droplet | 0.1 | / | [50] |
Monolayer graphene | SiO2/Si | NaCl | Droplet | 0.15 | / | [11] |
Monolayer graphene | PET etc. | NaCl | Droplet | 500 | / | [51] |
Graphene foam | / | DI water | Flow | 0.001 | 100 | [52] |
Reduced GO | Paper-pencil | DI water/MgCl2 | Flow | 280 | 812.5 | [53] |
Graphene/carbon black | Glass, etc. | Simulated waving | Flow | 11.14 | 3 | [25] |
Monolayer graphene | PET | NaCl | Flow | 100 | 11 | [10] |
Carbon black | Quartz | DI water | Flow | 1000 | 0.15 | [29] |
Graphene hydrogel membrane | / | NaCl | Flow | / | 0.002 | [54] |
Few-layer graphene | SiO2/Si | HCl | Flow | 25 | 340 | [55] |
Few-layer graphene | SiO2/Si | HCl | Flow | 120 | / | [56] |
Pair of graphene sheets | / | NaCl | Flow | 1000 | 2 | [24] |
Aligned CNT fiber | / | NaCl | Flow | 341 | / | [22] |
Carbon black film | DI water | Evaporation | 1000 | / | [29] | |
Carbon Film | Al2O3 | DI water | Evaporation | 1000 | 0.6 | [57] |
Graphene/carbon cloth | / | NaCl | Evaporation | 370 | / | [58] |
Carbon black-glass fiber hybrid film | / | DI water | Evaporation | 3000 | / | [30] |
Partially reduced GO sponge | / | DI water | Evaporation | 630 | ~100 | [59] |
Performance summary of reported hydrovoltaic devices based on the carbon materials.
PMMA, polymethyl methacrylate; PVDF, polyvinylidene fluoride; ITO, indium tin oxide; FTO, fluorine-doped tin oxide; PDMS, polydimethylsiloxane.
Nowadays, the research of hydrovoltaic materials and technologies is still in its infancy. To solve the above issues, we may try to follow the following approaches. (i) Understand the interaction mechanism between water and carbon for the further improvement of hydrovoltaic device performance. This requires to controllably modify the electronic structure of carbon and manipulate the molecules/ions in flows. For example, heteroatom doping can be adopted to tune the electric properties. (ii) The composition and nanostructure of carbon materials can significantly affect the capability of electricity generation. Therefore the nanostructure should be optimized to enhance the effective surface area. Moreover, the composition tailoring can enhance the conductivity, reducing the loss during charge transport. (iii) To improve the output of hydrovoltaic devices, the carbon materials can be combined with other functional materials, such as photovoltaic materials, ferroelectric material, and piezoelectric materials. This route can additionally convert the solar energy and mechanical energy for higher voltage and current outputs. (iv) Develop new nanomaterials with hydrovoltaic properties. For example, graphdiyne as a new 2D carbon material has unique sp. and sp2 hybridized electronic structure, high theoretical conductivity, good chemical activity, good physical stability, and the intrinsic bandgap of ~0.5 eV. Moreover, the synthesis temperature is usually below 100°C. We believe this new carbon material will exhibit unique hydrovoltaic properties. (v) Some in situ and in operando technologies should be used to characterize the interface between water and solid at atom/nano level, such as atomic force microscopy, infrared/Raman spectroscopy, AC impedance spectroscopy, and so on. Nevertheless, hydrovoltaic materials and technologies are very promising for harvesting energy in water. More research efforts should be devoted to realize the practical applications in the near future. In hydrovoltaic field, China stands in the forefront of the world. To realize the practical applications, the multidisciplinary collaboration in research, the government support, and the market promotion are urgent needed in the following decade years.
In summary, we introduced the water-carbon interactions and the popular mechanisms of hydrovoltaic effects and reviewed the recent progress of hydrovoltaic devices. This field is in its infancy but is a promising direction in the future. Great achievements in moisture, droplet, flow, and evaporation-induced electricity generation have been gained. Since water evaporation is uninterrupted and available under any conditions, the hydrovoltaic devices have great advantages over other energy conversion devices if the power could meet the daily usage. As in Table 1, it is exciting that the optimized hydrovoltaic devices now can generate voltage of 3 V. We believe that the rapid growth will bring this emerging hydrovoltaic device into a viable and broad industry technology.
In China, the water resource is around 6% of the Earth’s water resource. However, 80% of the water resource is distributed in the South of China. Therefore the energy harvested from water through hydrovoltaic effect, not the conventional hydropower station, is an attracting and alternative approach in China because the hydrovoltaic effect can generate electricity from not only the water flow, but also the water moisture, droplet, and evaporation. This future technology is a very promising solution in the North of China. More importantly, this future technology can harvest ocean wave energy for island power supply in Chinese waters.
The authors are grateful for the financial support from the National Natural Science Foundation of China (Grant 21703150), the China Postdoctoral Science Foundation (Grant 2015 M582495), and the Sichuan Science and Technology Program (Grant 2018JY0015).
The authors declare no conflict of interest.
Three-dimensional (3D) shape measurement techniques are widely used in many different fields such as mechanical engineering, industry monitoring, robotics, biomedicine, dressmaking, among others [1]. These techniques can be classified as passive, like in stereo vision in which two or more cameras are used to obtain the 3D reconstruction of a scene, or as active, like in fringe projection profilometry (FPP) in which a projection device is used to project a pattern onto the object to be reconstructed. When compared with other 3D measurement techniques, FPP has the advantages of high measurement accuracy and high density. There are two types of FPP methods: phase shifting and Fourier-transform profilometry (FTP). Phase-shifting methods offer high-resolution measurement at the expense of projecting several patterns onto the object [2, 3, 4], whereas FTP is popular because only one deformed fringe pattern image is needed [5]. For this reason, FTP has been used in many dynamic applications [6] such as vibration measurement of micromechanical devices [7] and measurement of real-time deformation fields [8].
FTP was proposed by Takeda et al. [5, 9] in 1982 and has since become one of the most used methods [3, 10]. Its main advantages are full-field analysis, high precision, noise-robustness [11], among others. In FTP, a Ronchi grating, or a sinusoidal grating, or a fringe pattern from a digital projector is projected onto an object, and the depth information of the object is encoded into the deformed fringe pattern recorded by an image acquisition device as shown in Figure 1. The surface shape can be decoded by calculating the Fourier transform, filtering in the spatial frequency domain, and calculating the inverse Fourier transform. Compared with other fringe analysis methods, FTP can accomplish a fully automatic distinction between a depression and an elevation of the object shape. It requires no fringe order assignments or fringe center determination, and it needs no interpolation between fringes because it gives height distribution at each pixel over the entire field. Since FTP requires only one or two images of the deformed fringe pattern, it has become one of the most popular methods for real-time 3D reconstruction of dynamic scenes.
Fringe projection system.
Although FTP has been extensively studied and used in many applications, to the best of our knowledge a complete reference in which the implementation details are fully described is nonexistent. In this chapter, we describe the FTP fundamentals and the implementation of an FTP system in LabVIEW one of the most used engineering development platforms for data acquisition and laboratory automation. The chapter is organized as follows. In Section 2 we describe the FTP fundamentals and a general calibration method, in Section 3 we describe how FTP is implemented in LabVIEW, and finally in Section 4 we show three applications of FTP for 3D reconstruction.
There are many implementations of FPP. However, all share the same underlying principle. A typical FPP setup consists of a projection device and a camera as shown in Figure 1. A fringe pattern is projected onto a test object, and the resulting image is acquired by the camera from a different direction. The acquired fringe pattern image is distorted according to the object shape. In terms of information theory, it is said that the object shape is encoded into a deformed fringe pattern acquired by the camera. The object shape is recovered/decoded by comparison to the original (undeformed) fringe pattern image. Therefore, the phase shift between the reference and the deformed image contains the information of the object shape.
By projecting a fringe pattern onto the reference plane, the fringe pattern (with period
Likewise, when the object is placed on the reference plane, the deformed fringe pattern observed through the camera is given by
where
with
where
Principle of the filtering via Fourier transform (FT) method. IFT, inverse FT.
Next, the phase of the fringe patterns is recovered using the Fourier Transform method. Using one-dimensional notation for simplicity, when we compute the Fourier transform of Eqs. (1) and (2) the Fourier spectrum of the fringe signals splits intro three spectrum components separated from each other, which gives
as shown in two dimensions in Figure 2. With an appropriate filter function, for instance, a Hanning filter, the spectra are filtered to let only the fundamental component
where
The variable related to height distribution is the phase change
with
where
The calibration of FPP systems plays an essential role in the accuracy of the 3D reconstructions. Here we describe a simple yet extensively used calibration called the reference-plane-based technique, i.e., to convert the unwrapped phase map
The optical axis geometry of the FTP measurement system is depicted in Figure 3. The optical axis
Fringe projection system.
The triangles
Combining Eqs. (12) and (13) a proportional relation between the phase map and the surface height can be obtained for any point
where
where
We have shown how the object surface height is related to the recovered phase through FTP. The model described by Eq. (15) has many underlying assumptions and is often extended to cover more degrees of freedom. Moreover, a general calibration process in FPP can be carried out employing the methodology shown in Figure 4. First, we propose a model that best describes the system, while also considering metrological requirements such as speed, robustness, accuracy, flexibility and reconstruction scale. Some authors have proposed to use several calibration models based on polynomial or fractional fitting functions [13, 14], bilinear interpolation by look-up table (LUT) [15] and stereo triangulation [16, 17, 18]. These calibration models require different strategies or techniques that allow relating metric coordinates with phase values. In step II, we select or design a strategy that fits the proposed calibration model and characteristics of the elements to a given experimental setup, such as the type of projector (i.e., analog or digital projection) and camera (i.e., monochrome or color). These strategies consist in projecting and capturing fringe patterns onto 3D-objects [19] or 2D-targets [16, 20] with highly accurate known measurements. In some cases, the calibration consists in displacing the targets along the
General calibration methodology.
In this section, we explain the details of the FTP software implementation in LabVIEW. LabVIEW stands for Laboratory Virtual Instrument Engineering Workbench and is a system-design platform and development environment for a visual programming language from National Instruments [21]. It allows integrating hardware, acquiring and analyzing data, and sharing results. Because it is a visual programming language based on function blocks, it is a highly intuitive integrated development environment (IDE) for engineers and scientists familiar with block diagrams and flowcharts. Every LabVIEW block diagram also has an associated front panel, which is the user interface of the application.
The acquisition and processing strategies described in this section require the installation of the following software components:
NI vision acquisition software, which installs NI-IMAQdx. This software driver allows the integration of cameras with different control protocols such as USB3 Vision, GigE Vision devices, IEEE 1394 cameras compatible with IIDC, IP (Ethernet) and DirectShow compatible USB devices (e.g., cameras, webcams, microscopes, scanners). NI vision acquisition software also includes the driver NI-IMAQ for acquiring from analog cameras, digital parallel and Camera Link, as well as NI Smart Cameras. This hardware compatibility is the main advantage of using LabVIEW for vision systems. This compatibility greatly facilitates the development of applications for different types of cameras and busses.
NI vision development module (VDM). This package provides machine vision and image processing functions. It includes IMAQ Vision, a library of powerful functions for vision processing. In this library, there is a group of VIs that analyze and process images in the frequency domain. We will make use of these functions throughout the entire chapter.
NI VDM and Vision Acquisition Software are supported on the following operating systems:
• Windows 10; Windows 8.1; Windows 7 (SP1) 32-bit; Windows 7 (SP1) 64-bit; Windows Embedded Standard 7 (SP1); Windows Server 2012 R2 64-bit; Windows Server 2008 R2 (SP1) 64-bit.
There are two primary ways to obtain images in LabVIEW: loading an image file or acquiring directly from a camera. The wiring diagram in Figure 5(a) illustrates how to perform a continuous (grab) acquisition in LabVIEW using Vision Acquisition Software. A Grab acquisition begins by initializing the camera specified by the Camera Name Control and configuring the driver for acquiring images continuously. Using IMAQ Create, we create a temporary memory location for the acquired image. This function returns an IMAQ image reference to the buffer in memory where the image is stored. The reference is the input to the IMAQ Grab VI for starting the acquisition. The grabbed image is displayed on the LabVIEW front panel using an Image Indicator (see Figure 5(b)), which points to the location in memory referenced by the IMAQ image reference. A while loop statement allows adding each grabbed image to the image indicator as a single frame. Finally, the image acquisition is finished by calling the IMAQ close VI that releases resources associated with the camera and the interface.
Grab acquisition in LabVIEW. (a) Block diagram. (b) Image indicator in front panel.
The acquired image is written to a file in a specified format by using the IMAQ Write File 2 VI. The graphics file formats supported by this function are BMP (windows bitmap), JPEG, PNG (portable network graphics), and TIFF (tagged image file format). However, note that lossy compression formats, such as JPEG, introduce image artifacts and should be avoided to ensure accurate image-based measurements. The saved image can be displayed in a secondary image indicator by enabling the Snapshot option. When enabling the Snapshot Mode, the Image Display control continues to display the image as it was when the image was saved during the Case Structure execution, even when the inspection image has changed. To configure the Image Display control for working in Snapshot Mode, right-click on the control on the front panel and enable the Snapshot option.
Another way to acquire an image using a camera is presented in the Figure 6. This example uses the NI Vision Acquisition Express to perform the acquisition stage. The Vision Acquisition Express VI is located in the Vision Express palette in LabVIEW, and it is commonly used to quickly develop image acquisition applications due to its versatility and intuitive development environment. Double-clicking on the Vision Acquisition Express VI makes a configuration window appear which allows choosing a device from the list of available acquisition sources, selecting an acquisition type, and configuring the acquisition settings. Concerning the acquisition types, there are four main modes: single acquisition with processing, continuous acquisition with inline processing, finite acquisition with inline processing and finite acquisition with post-processing. The last two acquisition types are similar, except that for a finite acquisition with post-processing the images are only available after they are all acquired. The configuration of the acquisition settings is one of the most relevant processes during configuration and allows the simultaneous manipulation of camera attributes like Exposure Time, Trigger Mode, Gain, Gamma Factor, among others. For this example, we configured the acquisition for working in a continuous acquisition with inline processing mode, which continuously acquires images until an event stops the acquisition. Additionally, the Exposure Time attribute can be modified during the acquisition process by using a Numeric Control. As with the example in Figure 5, the captured image is displayed in a secondary image indicator during the Case Structure execution.
Continuous acquisition using IMAQ vision acquisition express. (a) Block diagram. (b) Image indicator in front panel.
In Fringe Projection systems, the manipulation of certain camera attributes (e.g., the Exposure Time attribute) is required to capture high-quality images and to enable to work under different lighting environments with different constraints. In the example above, we introduced the possibility of manipulating camera attributes during acquisition using the Vision Acquisition Express. This manipulation of attributes is also possible by programming a simple snap, grab, or sequence operation based on low-level VIs (as in the example in Figure 5) using IMAQdx property nodes. The attribute manipulation requires providing the property node with the name of the attribute we want to modify and identifying the attribute representation, which can be an integer, float, Boolean, enumeration, string or command. In general, cameras share many attributes; however, they often have specific attributes depending on the manufacturer. These attributes should be known beforehand to ensure good acquisition control. At the development stage, LabVIEW does not know or display the name of the attributes or representations. Furthermore, if the documentation is not available, we suggest using the Measurement and Automation Explorer (MAX). MAX is a tool that allows the configuration of different acquisition parameters and is useful when it is required to manipulate attributes of a device with a specific interface within the LabVIEW programming environment. For example, suppose we want to modify the exposure time of our camera (Basler Aca 1600-60gm), but we do not have information about supported attributes. Here is where MAX becomes a powerful tool for vision system developers. This attribute verification is done by selecting the desired attribute from the Camera Attributes tab in the Measurement and Automation Explorer and identifying its name (i.e., ExposureTimeAbs) and representation (i.e., floating-point format). Therefore, the section of the block diagram inside a red box in Figure 5 can be modified in order to allow setting the ExposureTimeAbs attribute value using a Property Node as shown in Figure 7.
Setting the ExposureTimeAbs attribute value using a property node.
Both acquisition methods have their advantages and disadvantages concerning their implementation in vision systems. On the one hand, the use of the NI Vision Acquisition Express allows to quickly and easily develop acquisition applications, even without having a high knowledge of the tools for image acquisition offered by LabVIEW. However, this could be a disadvantage if our purpose is to have complete control over the acquisition. On the other hand, the low-level VIs provide greater control and versatility over the application development, but the implementation of vision systems based on low-level VIs can be a complicated task for novice users of NI Vision Acquisition Software and LabVIEW.
Once the acquired fringe image file has been written to disk, it is loaded for processing. The block diagram in Figure 8 illustrates how to perform this procedure in LabVIEW. The IMAQ ReadFile VI opens and reads an image from a file stored on the computer into an image reference. The loaded pixels are converted automatically into the image type supplied by IMAQ Create VI. From now on we refer to the Fringe Image to the loaded fringe image.
Reading an image file in LabVIEW.
In the previous section, we described several acquisition methods for capturing images from a camera in LabVIEW. However, in fringe projection systems there are many different fringe pattern projection technologies and choosing the correct one becomes extremely important for an accurate three-dimensional reconstruction. A fringe pattern projector can be considered as an analog device (e.g., LED pattern projector) or as a digital device (e.g., DLP, LCoS, and LCD digital display technologies). LED pattern projectors are ideal for high-resolution three-dimensional reconstruction applications. If equipped with an objective lens and a stripe pattern reticle, these projectors offer great versatility for manipulating the optics of the system and obtaining results according to the metrological requirements. The main disadvantage of this type of projection system is the impossibility of manipulating the projected fringe pattern. Therefore, its use is often restricted to techniques in which only a single fringe image is necessary to obtain the 3D information, such as in the case of FTP.
Fringe Projection systems can also take advantage of a computer to generate sinusoidal fringe patterns that are projected using a digital projector. The key to a successful 3D reconstruction system based on digital fringe projection focuses on generating high-quality fringes to meet the metrological requirements. Ideally, assuming the projector is linear in that it projects grayscale values ranging from 0 to 255 (0 black, and 255 white), the computer-generated fringe patterns can be described as follows,
where
Block diagram for fringe pattern generation.
An alternative to a block diagram implementation of Eq. (16) LabVIEW provides a MathScript RT Module as a scripting language. The module allows the combination of textual and graphical approaches for algorithm development. In Figure 10 we provide an example on how to use the MathScript RT Module for fringe generation in LabVIEW.
Fringe pattern generation example using the LabVIEW MathScript RT module.
Once the fringe images have been generated, they are sent to a digital video projector for projection. A video projector is essentially a second monitor. Therefore the fringe image is displayed by using the External Display VIs provided by the NI Vision Development Module. Here, we use IMAQ WindDraw VI to display the image in an external image window. The image window appears automatically when the VI is executed. Having beforehand the information from all the available displays on the computer, including their resolution and bounding rectangles, we set the position of the image window to be displayed on the desired monitor. This setting is done with IMAQ WindMove VI. Additionally, using IMAQ WindSetup VI the appearance and attributes of the window can be modified to hide the title bar. Note that the default value for this attribute is TRUE which shows the title bar. The block diagram in Figure 11 illustrates a projection stage in LabVIEW. Here, we use a Property Node for obtaining the information about all the monitors on the computer. The Disp.AllMonitors property Returns information about their bounding rectangles and bit depths.
Second monitor configuration in LabVIEW.
Phase retrieval is carried out by Fourier transform profilometry. In LabVIEW, the IMAQ FFT VI computes the discrete Fourier transform of the fringe image. This function creates a complex image in which low frequencies are located at the edges, and high frequencies are grouped at the center of the image. Note that for the IMAQ FFT VI a reference to the destination image must be specified and configured as a Complex(CSG) image. Once the deformed fringe pattern is 2-D Fourier transformed, the resulting spectra are converted into a complex 2D array to perform the filtering procedure, thus obtaining the fundamental frequency spectrum in the frequency domain. The following step is to compute the inverse Fourier transform of the fundamental component. The Inverse FFT VI is for computing the inverse discrete Fourier transform (IDFT) of a complex 2D array. By using this function, we calculate the inverse FFT of the fundamental component which contains the 3D information. Finally, we obtain the phase by applying Eq. (11). Here, we use Complex To Re/Im Function to break the complex 2D array into its rectangular components and Inverse Tangent(2 Input) Function for performing the arctangent operation. With the example in Figure 12(a) we illustrate the phase retrieval process in LabVIEW. In this figure, the Fringe Image and Hanning W refer to the fringe pattern image shown in Figure 12(b) and the Hanning window filter array, respectively. The resultant wrapped phase map is shown in Figure 12(c).
Phase retrieval process in LabVIEW. (a) Block diagram. (b) Fringe pattern image. (c) Wrapped phase map.
In Section 2 we showed that in FTP a filtering procedure is performed to obtain the fundamental frequency spectrum in the frequency domain. Once the Fourier transform is computed, the resultant spectrum is filtered by a 2-D Hanning window defined by Eq. (6). In LabVIEW, the IMAQ Select Rectangle VI is commonly used to specify a rectangular region of interest (ROI) in an image. We use the IMAQ Select Rectangle VI for manually selecting the region in the Fourier spectrum corresponding to the fundamental frequency component. Here, the image is displayed in an external display window and through the use of the rectangle tools, provided by the IMAQ Select Rectangle VI, we estimate the optimal size and location of the filtering window that guarantees the separation between the fundamental frequency component and other unwanted contributions. The block diagram shown in Figure 13(a) indicates the IMAQ Select Rectangle VI to manually select the region corresponding to the first order spectrum. The Fringe Image is the fringe pattern image in Figure 12(b). The IMAQ FFT VI computes the discrete Fourier transform of the Fringe Image. The resultant complex spectrum is displayed using an external display window as shown in Figure 13(b). By using the selection tools located on the right side of the window, we can manually select the rectangular area of interest.
Manual selection of the filtering window. (a) Block diagram. (b) External display window and rectangle tools.
The IMAQ Select Rectangle VI returns the coordinates (i.e., left, top, right and button) of the chosen rectangle as a cluster. Therefore, it is necessary to access each element from the cluster to extract the window information. For this reason, we add the Unbundle By Name function to the block diagram which unbundles a cluster element by name. Based on this information, we calculate the size and location of the Hanning window filter. Finally, using the Hanning Window VI two 1-D Hanning windows are created whose lengths correspond to the size of
Hanning filter design in LabVIEW. (a) Continuation of the block diagram in Figure 13(a). (b) Fourier transform magnitude spectra displayed by the external window in Figure 13(b). dx and dy relate to the size in x and y of the filtering window, respectively. (c) 2D-hanning window.
The phase unwrapping process is carried out comparing the wrapped phase at neighborhoods and adding, or subtracting, an integer number of
Bidimensional phase unwrapping in LabVIEW. (a) Wrapped phase map. (b) Unwrapped phase map.
FPP is often used as a non-contact surface analysis technique in industry inspection. In this section, we show the 3D surface reconstruction of a dented steel pipe. A dent is a permanent plastic deformation of the cross-section of the pipe. In the example shown in Figure 16 the dent was produced penetrating the pipe with a diamond cone indenter. In Figure 16(a) and (b) we show the tested object, and the deformed fringe pattern image, respectively. The goal is to measure the depth of the dent with high accuracy and to obtain the surface shape of the pipe for subsequent deformation analysis. In Figure 16(c) and (d), we show the wrapped, and unwrapped phases obtained by FTP, respectively. The unwrapped phase map is converted to metric coordinates using a calibration model. In Figure 17(a), we show the reconstructed pipe shape with the texture map. A profile across the reconstructed pipe, thought the dent, is shown in Figure 17(b). Analyzing this profile, we can measure the depth of the dent to approximately 4 mm.
FTP analysis of a indented pipe. (a) Texture image. (b) Deformed fringe pattern. (c) Wrapped phase. (d) Unwrapped phase.
(a) 3D reconstructed shape. (b) Cross section of the 3D reconstruction.
Another application of FPP is in facial metrology, where several patterns are projected onto the face to obtain a 3D digital model. 3D shape measurement of faces plays an important role in several fields like in the biomedical sciences, biometrics, security, and entertainment. Human face models are widely used in medical applications for 3D facial expression recognition [24] and measurement of stretch marks [25]. Usually, the main challenge is the movement of the patient. The movement can produce errors or noise in the 3D reconstruction affecting its accuracy. Hence, 3D scanning techniques that require few images in the reconstruction process, like FTP, are commonly used. In Figure 18 we show an experimental result of reconstructing a live human face. The captured image with the deformed fringe pattern is shown in Figure 18(a). In Figure 18(b) and (c) we show the 3D geometry acquired rendered in shaded mode and with texture mapping, respectively. Note that several facial regions with hairs, like the eyebrows, are reconstructed with high detail. While other areas, under shadows, like the right side of the nose, are not correctly reconstructed.
(a) Fringe pattern onto face. (b) 3D rendered model in shaded mode. (c) 3D rendered model with color texture mapping.
Finally, another area where FPP has frequently been used is in cultural heritage preservation. The preservation of cultural heritage works requires accurately scanning sculptures, archeological remains, paintings, etc. In Figure 19 we show the 3D reconstruction of a sculpture replica.
FTP 3D reconstruction of a sculpture replica of “Figura reclinada 92 - Gertrudis” by Fernando Botero [23]. (a) Texture image. (b) 3D reconstruction.
This work has been partly funded by Colciencias (Fondo Nacional de Financiamiento para la Ciencia, la Tecnología y la Innovación Francisco José de Caldas) project (538871552485) and by the Universidad Tecnológica de Bolívar (Dirección de Investigación, Emprendimiento e Innovación). J. Pineda and R. Vargas thank Universidad Tecnológica de Bolívar for a Master’s degree scholarship
Authors are listed below with their open access chapters linked via author name:
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\n\nJunhong Chen 2017, 2018
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\n\nMark Connors 2015-18
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USA, CRC Press Taylor & Francis, Asia Pacific, Trans Tech Publications Ltd., Switzerland, and Materials Science Forum, USA. 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Omar obtained\nhis Bachelor degree in electrical and\nelectronics engineering from Universiti\nSains Malaysia in 2002, Master of Science in electronics\nengineering from Open University\nMalaysia in 2008 and PhD in optical physics from Universiti\nSains Malaysia in 2012. His research mainly\nfocuses on the development of optical\nand electronics systems for spectroscopy\napplication in environmental monitoring,\nagriculture and dermatology. He has\nmore than 10 years of teaching\nexperience in subjects related to\nelectronics, mathematics and applied optics for\nuniversity students and industrial engineers.",institutionString:null,institution:{name:"Universiti Sains Malaysia",country:{name:"Malaysia"}}},{id:"191072",title:"Prof.",name:"A. K. M. Aminul",middleName:null,surname:"Islam",slug:"a.-k.-m.-aminul-islam",fullName:"A. K. M. Aminul Islam",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/191072/images/system/191072.jpg",biography:"Prof. Dr. A. K. M. 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Conducting research on the development of hybrid vegetables, hybrid Brassica napus using CMS system, renewable energy research with Jatropha curcas.",institutionString:"Bangabandhu Sheikh Mujibur Rahman Agricultural University",institution:{name:"Bangabandhu Sheikh Mujibur Rahman Agricultural University",country:{name:"Bangladesh"}}},{id:"91977",title:"Dr.",name:"A.B.M. Sharif",middleName:null,surname:"Hossain",slug:"a.b.m.-sharif-hossain",fullName:"A.B.M. Sharif Hossain",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Malaya",country:{name:"Malaysia"}}},{id:"97123",title:"Prof.",name:"A.M.M.",middleName:null,surname:"Sharif Ullah",slug:"a.m.m.-sharif-ullah",fullName:"A.M.M. Sharif Ullah",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/97123/images/4209_n.jpg",biography:"AMM Sharif Ullah is currently an Associate Professor of Design and Manufacturing in Department of Mechanical Engineering at Kitami Institute of Technology, Japan. He received the Bachelor of Science Degree in Mechanical Engineering in 1992 from the Bangladesh University of Engineering and Technology, Dhaka, Bangladesh. In 1993, he moved to Japan for graduate studies. He received the Master of Engineering degree in 1996 from the Kansai University Graduate School of Engineering in Mechanical Engineering (Major: Manufacturing Engineering). He also received the Doctor of Engineering degree from the same institute in the same field in 1999. He began his academic career in 2000 as an Assistant Professor in the Industrial Systems Engineering Program at the Asian Institute of Technology, Thailand, as an Assistant Professor in the Industrial Systems Engineering Program. 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