Piezoceramic and Nanotube Materials for Health Monitoring

Transcription

1 SPIE NDE Piezoceramic and Nanotube Materials for Health Monitoring Mark J. Schulz a, Goutham R. Kirikera a, Saurabh Datta a, Mannur J. Sundaresan b a Laboratory for Smart Structures and Active Materials Research Department of Mechanical Engineering, University of Cincinnati, Cincinnati, OH b Intelligent Structures and Mechanisms Laboratory, Department of Mechanical Engineering North Carolina A&T State University, Greensboro, NC ABSTRACT This paper discusses the potential for using Piezoceramic and Nanotube materials to develop an artificial neural system for structural health monitoring. An artificial neural system array was modeled using piezoceramic nerves and electronic components. The neural system was simulated using one hundred dual-output sensor nodes on a four-foot square composite panel. The nodal outputs were combined into twenty neuron firing signals, one row time signal, and one column time signal. This system was able to detect and locate acoustic waves and large strains in the panel. Also discussed, is the potential for using nanotubes for building the artificial neural system. In carbon nanotubes, an electrochemical process can be used to achieve low voltage actuation at high strain, but the process velocity is slow and a structural polymer electrolyte must be used for ion exchange. Carbon and boron nitride nanotubes can be piezoelectric, and piezonanotechnolgy may be useful for building high bandwidth neural systems. The operating temperature of boron nitride is high and the amount of material needed to build artificial nerves is small, but the piezoelectric coefficients appear to be small. Nanotube molecular electronics and the change in conductance of nanotubes might also be used to develop artificial nerves. Key words: Structural Health Monitoring, Artificial Neural System, Piezoceramic fibers, Nanotubes, Composites. 1. INTRODUCTION Advanced materials including fiber composite materials exhibit complex damage modes, some of which are difficult to foresee in the early part of their technology development cycle. There is a pressing need for continuous monitoring of these new classes of materials or combinations of materials to avert unforeseen catastrophic failures and provide confidence for the rapid introduction of these high performance and heterogeneous materials into service. Currently, structural health monitoring is not widely adopted because the sensors that are essential for the structural health monitoring are too large, too expensive, or are not rugged enough for use for periods of 20 years or more. This paper explores the possibility of developing small rugged sensors using piezoceramic (PZT) and nanotube fibers that can be integrated within fiber composite materials as well as other structural materials such as concrete. These sensors can also be bonded onto the surface of metals and other materials where it is not possible to embed them within the structure. It may be possible to build a network of such miniaturized sensor elements to form an Artificial Neural System (ANS) [1]. Since it is feasible to have a dense array of these sensor elements distributed over the critical regions of the structure, the ANS will potentially have increased sensitivity to damage. The ANS can be designed to be simultaneously sensitive to low frequency dynamic strains caused by structural vibrations as well as the high frequency Acoustic Emission (AE) signals that accompany damage growth. PZT materials are discussed first for use in sensors. Modeling of an ANS is then discussed and simulation results are given. Next, possible ways in which nanotubes can be used to make fiber sensors is discussed. Conclusions are then given and continuing work is outlined.

2 2. ACTIVE FIBER SENSORS Active Fiber Composite (AFC) Materials is a new class of material that combines several of the desirable properties of an active material. AFC s were developed in 1997 [2, 3] at MIT. In 2000, a variation of this material was developed by NASA, called the Macro Fiber Composite (MFC) [4]. The AFC/MFC material is built using parallel piezoceramic fibers or ribbons in an epoxy matrix with an interdigitated electrode pattern on the top and bottom faces of the composite layer, but not touching the fiber surface. The material is poled to yield an axial actuation capability that produces up to 0.2% strain with an applied voltage of 2500 volts. These AFC s have been integrated into carbon fiber composites to produce smart structures that can actuate. This smart material is being used successfully for actuation of helicopter blades and for vibration and noise reduction on torpedo s and rocket casings [3]. Overall, AFC s represent a large advance in the development of high-impedance actuators. However, the AFC s are not optimized for sensing. The capacitance of the AFC is too high and the AFC s that have been produced are too large for sensing high frequency waves. There is a need for AFC materials that can be easily reconfigured for different sensing purposes. Health monitoring of large structures such as aircraft, bridges, pipelines and composite wind turbine blades are some of the applications for sensing using AFC materials. Sensing has different requirements than actuation, including high efficiency transduction, low cost, and spatial filtering. This paper discusses the design of an Active Fiber Sensor (AFS) that has high transduction efficiency and low cost such that it can be used for measuring small strains in composite and metallic materials. Applications for this sensor include monitoring strain levels in structures for health monitoring, and also feedback of vibration signals for vibration control of wings, blades and other flexible structures. The modeling of the AFS is discussed next. Because of the piezoelectric properties of the AFC fibers, the sensor can be modeled as a capacitor in parallel with a current source [5]. The piezoelectric constitutive equations are listed in the IEEE standard ANSI/IEEE Std This standard is used to derive the basis of the AFS electrical modeling. If the electric field is only applied through one axis, and we neglect any actuation transverse to the AFS fiber direction, then the one dimensional equation can be used, assuming that the PZT ribbon fibers exhibit linear bulk piezoceramic properties. The output voltage equation for the sensor with n nodes connected in a series arrangement is: d n n i ea e ( i) + = S & (1) j dt RC RC j= 1 where C is the capacitance of the AFS with an effective capacitor area of A e, and effective plate separation distance h, i c represents the component of the current going through the capacitor of the model, and i g represents the component of the current generated by the piezoelectric fibers. The product of the current i(t), and the impedance R of the measuring device equals the voltage of the series connected sensors as a function of time. Thus we solve for the current to get the voltage V 0 = IR. This voltage is proportional to the dynamic strain in the structure at the sensor. A continuous sensor is made by connecting a plurality of sensor nodes together usually in a series configuration. The individual sensor nodes can have two channels of output as shown in Figure 1. The continuous sensor is analogous to a dendrite in the neural system. Many dendrites can be connected to form an artificial nerve cell. Figure 1. Concept for two-channel active fiber sensors; (a) a horizontal unidirectional sensor (channel 1) and a vertical unidirectional sensor (channel 2) give separate and generally different electrical outputs, and (b) two bi-directional sensors channel 1 and channel 2 give separate and nearly identical electrical outputs for a small sensor size.

3 It will be possible to substitute different materials [6, 7] in place of the PZT ribbons in the sensor shown in Fig. 1. For example, if we want to build a low impedance sensor, the Polyvinlyidene Fluouride (PVDF) material can be used. To build a miniature sensor, carbon nanotube fibers with a structural polymer electrolyte may be used. To build a high temperature sensor, boron nitride nanotubes can be used. The nanotube materials are currently extremely expensive, but the cost is expected come down, and the sensor only requires a small amount of material. Thus, sensors may be a feasible application of nanotechnology. Nanotubes may be useful for enhancement of the AFS design, and nanotubes may also be useful for other types of sensor designs. A description of how the ANS works is given in the next section and then different possible approaches for using Nanotube (NT) sensors are discussed based on results from a literature search. 3. SIMULATION OF AN ARTIFICIAL NEURAL SYSTEM This simulation assumes that an artificial neural system can be constructed using piezoceramic nodes connected in series to a resistor, an operational amplifier, and a full wave rectifier. The electrical modeling of the neurons is shown in Figure 2. Multiple nerves are then connected together to form an Artificial Neural System (ANS). The nerves can be arranged in horizontal rows and vertical columns with coincident nodes that are not interconnected. In the configuration examined here, multiple neurons are used as rows and columns of an array. The dual output sensor elements allow the strain at a point to be sensed by intersecting rows and columns of the array without having the rows and columns electrically connected. An array designed using 10 row neurons and 10 column neurons is shown in Figure 2. This array has 100 sensor nodes, 20 neurons, and 20 neuronal outputs that tell if each neuron is firing or off. The bidirectional sensor node shown in Figure 1(b) is modeled in the neural system. The 10 row neurons are connected into a single channel output and the 10 column neurons are connected into another channel output. Each nerve has a threshold voltage at which it fires or opens. The output is rectified and all column outputs are combined into a single output, and all row outputs are combined into a second output channel of data acquisition. This arrangement allows transient excitations to be identified by the outputs of the rows and columns. Each row and column nerve has a switch to tell if it is firing, but only the combined responses of the rows and columns are measured. The switches tell where the signals are coming from, and the output signals tell the level and characteristics of the excitation. A Programmable Signal Transducer (PST) is envisioned that can control at the user s option the connection of the neurons, in either series (voltage adding) or parallel (current adding) configurations, switch to allow all or only peak channel output, acquire the signal, filter at low or high frequency, and check the integrity of the sensor elements. The PST processes the neuronal outputs (Figure 2(a)) for input to the computer which contains the intelligence and diagnostic functions to interpret the health of the structure. This approach can locate and quantify damage events in the structure. In particular, acoustic emissions and low frequency dynamic strains can be measured using the ANS. A step input to simulate an AE is used at the center of the fiberglass panel. The panel is 4 feet square, 0.25 inches thick and is modeled as being simply supported. The arrangement of the nodes on the panel is exactly symmetric. The wave propagation response of the panel is shown in Figure 3. The step input produces low amplitude high frequency waves at the beginning of the response, and a large low frequency downward deflection of the panel later in the response. The time step used in computing the response is 1 microsecond and 100 vibration modes of the panel are used in the response solution. The 100 th mode natural frequency is 100 KHz. Modeling of the electronic components representing the processing functions of the biological nerve cell is discussed briefly. A 1 megaohm resistor was connected to the output of each neuron and each neuron signal is amplified by a factor of 10. The bridge rectifier model was generated by taking the absolute value of the neuron signal which produces only a positive rectified signal whenever the neuron voltage exceeds the 0.1 volt switching level Using the absolute value along with an on/off switch models the characteristic of the ridge rectifier. The neurons are assumed to be connected in series in the PST for this simulation. The code developed can be used for any number of sensor elements and neurons. Further work on the code is being done to develop the signal processing logic to identify acoustic signals from cracks and high dynamic strains. The initial response of the panel is shown in Figure 4 in which the higher frequency waves are contained. The firing of the row neurons, the output of the 10 row neurons, and the combined output of the row neurons are shown in Figure 4. The firing of the column neurons, the output of the 10 column neurons, and the combined output of the column neurons are shown in Figure 5. In the initial part of the response, neurons 5, 6, 15 and 16 are firing. The intersection of these row and column neurons is at the center of the panel. Later in the response the voltage output increases because the strain levels in the panel are increasing and because more nodes are being strained. The output of the neurons adds

4 and the row and column output voltages increase due to the addition of all the neurons that are firing. Note that in the beginning of the response only four neurons are firing and later they all are firing. The symmetry in the panel causes the signals to overlap. There are actually 10 curves in Fig. 4 and in Fig. 5, but, due to overlapping, only five are obvious. Also, the row and column responses are identical because of the symmetry of the panel and because the excitation is at the center of the panel. These figures show that the ANS can locate the source of an AE by noting the intersection of the row and columns that are firing. The row and column outputs give a composite response that characterizes the frequency and amplitude level of the excitation. The ANS presented is an efficient method for parallel processing of structural responses and determining the health of structures. Some techniques for SHM rely on sequential processing which may be difficult to apply for very large structures and structures with complex geometry. Programmable Signal Transducer (PST) a. Series/Parallel neurons, b. Channel switching, c. Signal acquisition, d. Low/high freq. filtering, e. Sensor self-check Computer Signal Processing Diagnostics Prognostics Signals from neurons (a) (b) Figure 2. Design of an ANS: (a) electrical schematic of the ANS with n nodes in each neuron, m neurons, and the PST/Computer system, (b) an ANS in a composite panel in which one-hundred bi-directional AFS nodes (N1-N100) form 20 neurons with outputs (V1-V20) where the final output of the neural system consists of 20 on/off neuron firing signals and two time history signals. Figure 3. Wave propagation due to a step input at the center of the panel for 0.01 seconds.

5 (a) (b) (c) Figure 4. Signal processing in the columns of the ANS; (a) the neuron switches close (0v=open, 1v=closed) when the voltage level of the neuron exceeds 0.1 volt, (b) the voltage output of the amplifier and diodes in each neuron (only 5 curves are shown due to symmetry of the plate), and (c) the final time signal output which is the sum of the voltages from all the column neurons.

6 (a) (b) (c) Figure 5. Signal processing in the rows of the ANS; (a) the neuron switches close (0v=open, 1v=closed) when the voltage level of the neuron exceeds 0.1 volt, (b) the voltage output of the amplifier and diodes in each neuron (only 5 curves are shown due to symmetry of the plate), and (c) the final time signal output which is the sum of the voltages from all the row neurons.

7 4. NANOTUBE MATERIALS If you were to envision the characteristics an ideal material should have for advanced active structures applications, these would include high stiffness, superelasticity, light weight, large strain actuation, thermal stability and low cost. 4 The properties of single wall Carbon Nanotubes (CNT) include a high length/diameter aspect ratio of up to 10, an 3 elastic modulus of 1TPa, a tensile strength of 50GPa, light weight with a density of 1.4g / cm, high temperature capability, and a predicted actuation energy density times greater than existing smart materials. When loaded in compression, the tubes will bend over to large angles then ripple and buckle. However, these deformations are elastic. Upon removal of the load, the nanotube will return to its original undeformed shape. With the property of superelasticity, if nanotubes could be put together to make macro-scale structures, these structures might become almost unbreakable. These amazing properties make CNT and Single Wall Boron Nitride Nanotubes (BNT) (which are structurally equivalent to carbon) potentially the materials of choice for reinforcing and actuating polymer composite materials. CNT/BNTs have a good potential to make sensors and actuators because of their nanoscale size and electromechanical transduction properties and high power density. A key advantage of using CNTs and BNTs for actuation is that they are also load-bearing. In this sense, the use of nanotubes provides a great potential for health monitoring of structures because the structure is also the sensor. In reality, salient problems including high cost must be solved before Smart Nanocomposites can become practical. The following sections discuss recent research [8-62] in the area of nanotube actuation and sensing and the possibilities for using nanotubes for sensing and actuation for health monitoring of structures. 4.1 Carbon Nanotube Electrochemical Sensors/Actuators Actuation of CNT was developed in 1999 by Ray Baughman et al [8-10]; in CNT ropes and aligned membranes were developed [11-13]; in 2001 composite materials strengthened with CNT were studied using Raman scattering [14]; in 2001 the piezoelectric effect in Single Wall Carbon Nanotubes (SWCNT) and Boron Nitride Nanotubes (BNT) was modeled [15], and experiments to study the actuation properties of a single CNT were begun [16]. When positive charges are injected into the SWCNT graphite crystal, the graphite tends to shrink, and conversely, negative charges cause the graphite to expand. This structural deformation and conversion of electrical energy to mechanical energy through radial and longitudinal expansion or contraction is mainly caused by the change in electronic structures rather than coulomb interactions, and large actuation capabilities are predicted for a SWCNT. The mechanisms for actuation can be electrochemical reduction and oxidation reactions, capacitive injection of charge, a change in the electric or magnetic fields, or by changing the localized PH or ion or solvent concentration. The input/output of ions and the resulting increase/decrease of the double layer charge around the CNT causes the actuation effect and the CNT to behave as a large capacitor filled with an electrolyte. Development of these CNT actuators is in an early stage [8-10] and is mostly focused on artificial muscles, structural actuation/sensing is a more difficult problem. As produced, SWNTs form ropes that are diameters in size and the tube-tube interactions within the ropes are weak, similar to the coupling between adjacent graphene planes in 3-D crystalline graphite. This weak intertube coupling is dominated by the van der Waals interaction, but contains a nonzero covalent component that has a significant effect on the vibration and electronic states of the CNT. As the rope diameter increases [14], shear deformation reduces the effective moduli of nanoropes by an order of magnitude with respect to that of a SWNT. Thus, transferring charge to the inner tubes in a rope, and transferring the shear load through the rope are current roadblocks to realizing the theoretically predicted performance of CNT actuators. Boron Nitride Nanotubes (BNT) in chiral or zigzag symmetric structures are intrinsically polar and therefore the piezoelectric effect can be used to develop structural actuators. Most nanotubes have the chiral or helical arrangement of hexagons around the tube axis. The armchair or zigzag structures have high symmetry and the BN tubes are insulating. No satisfactory methods exist for controlling the chirality and producing aligned BNTs for actuator applications. The research efforts discussed verify that CNT based materials have extraordinarily high strength, super-elasticity and actuation capability at the same time. Therefore, CNTs are potentially a game changing material [8] that can improve, by one or two orders of magnitude, the actuation properties of AFC materials. Practically, however, the actual performance of nanophase composites and CNT actuators has been far below the predicted performance, and significant advances must be made before useful implementation of CNT materials can be realized. This is shown in Table 1, where the theoretical Energy Density of CNTs is over two orders of magnitude greater than AFCs, but the achieved Energy Density of CNT actuators is comparable to AFCs. Carbon nanotubes have the potential to provide a unique combination of electrical properties for new generations of nanoelectronic devices and outstanding mechanical

8 properties for use in composites. Synthesis of carbon nanotubes has been achieved by different approaches such as laser ablation, arc discharge, pyrolysis, and plasma-enhanced chemical vapor deposition [17]. Although intensive research on carbon nanotubes has been performed, controlling length, diameter, and preferable alignment of CNTs is still a big challenge [18, 19]. Uniform synthesis of well-aligned carbon nanotubes with high quality is a prerequisite for their applications in sophisticated nanoscale devices. In particular, the electrochemical behavior of CNT strongly depends on the density, structure and purity of the CNT. Purification and functionalization appear to be essential in defining active surface area, charge transfer rate, and adsorption/desorption at the electrode surface. The synthesis of SWCNTs requires a catalyst and as of now there is no method known to control the chirality of the nanotubes produced. This is a problem for electrical/actuation applications, but the elastic behavior of CNTs is not strongly dependent on the chirality. Material Table 1. Preliminary Properties of Active Materials Elastic Bandwidth Range Voltage Density Peak Strain-Volt. Strain Energy Modulus 3 ( Kg / m ) Strain Coeff. (peak 2 3 Yε m / 2 ( J / m ) Y (GPa) (Hz) ε strain/volt) m Energy Density 2 Y ε / 2ρ ( J / Kg) Active Fiber Composite Kv x 10 56, Bundled SWCNT (test data / x 10 11, Q.M. Zhang/R. Baughman) (approx) (approx) (estimated) (estimated) Nonbundled SWCNT theory (est) +1/ x 10 29x 10 2, CNT Sensors using Raman Spectroscopy Raman spectroscopy can be used to characterize single-wall and multi-wall nanotubes. The intensities of the low frequency radial ( cm -1 ) and high frequency tangential ( cm -1 ) modes of single wall carbon nanotubes exhibit strong resonance effects related to the tube diameter, and the frequency of the radial mode is inversely proportional to the radius of the tube. Thus, measurements of the frequency of this mode can be used to determine the radius of SWNTs. Raman spectroscopy can also be used [46, 49-54] to map the stress distribution in the vicinity of discontinuities in a polymer using single wall nanotubes seeded in a composite material. Direction sensitive strain mapping [54] using 0.1 % weight of randomly dispersed single wall nanotubes is also possible because the strain transferred from the composite matrix to the nanotubes causes a large wavenumber shift in the Raman spectrum. The limitation of the spectroscopy technique is that is it a NDI technique and would be difficult to use for in-situ monitoring of structural health. 4.3 CNT Sensors using Conductance Measurements The conductance in carbon nanotubes can be significantly altered by functionalization. The conductance change can possibly be used for sensing by creating artificial nerves and monitoring their conductance. Phase-coherent transport in ropes of single wall nanotubes with damage to the ropes is shown to be strongly affected by temperature [58]. The nanotube ropes may be useful as sensors of temperature, strain and chemical species. The dynamic conductance of nanotubes differs significantly from the DC conductance, displaying both capacitive and inductive responses. It has been shown that the resistance of carbon nanotubes remains constant regardless of their length or width, for lengths up to 5 microns. This property might be used to design long neural systems with nanotubes or ropes that are sensitive to nanoscale damages. This could improve the sensitivity of damage detection while using a small number of monitoring signals. 4.4 BNT Sensors/Actuators Equally interesting are non-carbon nanotubes, which have received much less attention in recent years and remain relatively unexplored. Similar to carbon, III-V compounds found in the hexagonal graphite structure could also lead to a microscopic tubular structure. The III-V material most closely related to C is boron nitride, which like carbon, is found in both sp 2 - and sp 3 - bonded structures. Hexagonal BN nanotubes have been theoretically predicted [20, 21] and experimentally observed for the first time [22]. This was followed by findings of other groups [23]. Thus, BN nanotubes have exceptional thermal, electrical and mechanical properties and may serve as a perfect nano-insulating tubular shield for any conducting material encapsulated within, and, moreover, the B-N bonding also tends to be dipolar. These characteristics offer the possibility of piezoelectric actuation tailored over a wide bandwidth. The electronic structure of such tubes is controlled by their chemistry rather than their geometry (helicity and diameter) as for their carbon counterparts. We envision that future generations of actuators, sensors, devices and active structures will be based on m

9 carbon, boron nitride and other nanotubes that have extraordinary actuation and mechanical properties. The production of high quality and bulk amounts of the carbon and, especially of BN nanotubes, is an exceedingly challenging scientific and technological problem. Current processing techniques such as plasma arc discharge, substitution reaction, laser ablation, electron beam irradiation, reactive ball milling, and thermal CVD need improvements to make nanotubes of controlled quality. Up to now, there has been limited success on mass production of carbon and particularly of BN nanotubes [62]. Also, the product often contains metal or carbon impurities and structural defects like poorly crystalline or polycrystalline walls, which may affect the electrical and mechanical properties of the tubes. Thus, development of new approaches for synthesis of carbon and BN nanotubes is required. Although the synthesis of BN nanotubes by Laser Ablation is a challenge that offers better control over process variables, only a few reports have addressed this issue [23-25]. The target material is usually prepared by mixing h-bn with metal catalyst powder followed by hot pressing and sintering. In some cases, no catalysts are used. The target is ablated by an excimer laser at 1200 C in an inert atmosphere of Ar, or N 2 or He. Another approach utilizes single crystal c-bn specimens heated above 5000K by CO 2 laser under nitrogen pressure of 5-15 GPa [25]. 4.5 CNT Electronics CNT s are molecular quantum wires that can have conducting or semiconductor properties depending upon the exact geometry of the tube. Perfect tubes can have ballistic conductance in which the resistance is independent of the length and large currents can be carried. In a magnetic field, the resistance of the nanotube can be modulated. If a cobalt material that is magnetic is attached to the ends of the tube, the resistance of the tube depends on the relative orientation of the magnetization, and the direction of the electron spins must be maintained as they move along the tube. Spintronic sensors that exploit the spin rather than the charge of electrons may be possible. Intermolecular junctions of nanotubes can form nanoscale electronic devices. A metal-semiconductor kink junction behaves like a molecular diode [59]. Single-molecule transistors have been made that consist of a semiconducting nanotube on two metal nanoelectrodes with a substrate that is a gate electrode. A small quantum dot that is the active element of a transistor was formed by introducing sharp bends in a nanotube. Single molecule nanotube digital logic circuits have also been formed. Based on these recent advances, molecular electronics has the potential to miniaturize health monitoring systems such as the artificial neural system discussed. This could enable damage detection at the molecular level by replacing sensor elements, operational amplifiers, diodes, wires, switches and signal processing instrumentation with nanotube equivalents. Full coverage of structures with sensors might then be possible. A major challenge will be to communicate between the possible millions of nanolevel sensors/computers and humans. Biomimitecs and neuroscience may provide these solutions. 5. CONCLUSIONS While there are many uses for sensors in composites, there are not many composites used with sensors. This is because present sensor systems are complex and expensive. Simulations presented in this paper show that an artificial neural system may be able to replace tens or hundreds of conventional sensors and still obtain critical information for health monitoring of structures. This can be achieved because the neural system performs parallel processing and uses a hierarchy of processing steps to combine raw structural response measurements into signals that contain only the essential information for SHM. The neural system can also be used in an active interrogation method by applying an excitation to the system and monitoring the neural system response. Concepts for active fiber sensors and artificial nerves show the potential for building neural systems that can cover large structures. Nanotube materials have the potential to miniaturize the sensor elements and the electronics of the artificial neural system. Much of the study of nanotubes is in the area of condensed matter physics and chemistry. Putting nanotubes into applications such as bulk nanocomposites, sensors/actuators and incorporating biomimetics into structures requires interdisciplinary and multiuniversity efforts. Our current work in health monitoring is focusing on building large scale sensor systems with miniaturized components that have high sensitivity to damage. This includes developing the signal processing methods and artificial intelligence to interpret the output from highly distributed neural sensors, developing and building the sensor nodes, building the neural system using electronic components, developing nanotube sensors using the different properties discussed in the article, and testing the neural system on structures under cyclic loading to failure. Bio- Nanotechnology (combining biomimetics and nanotechnology) can have a profound effect on the health monitoring of structures, and humans. It may become feasible to perform health monitoring on a molecular scale.

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