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This content has been downloaded from IOPscience. Please scroll down to see the full text. Download details: IP Address: This content was downloaded on 08/01/2014 at 04:22 Please note that terms and conditions apply. Hair flow sensors: from bio-inspiration to bio-mimicking—a review View the table of contents for this issue, or go to the journal homepage for more 2012 Smart Mater. Struct. 21 113001 ( Home Search Collections Journals
  This content has been downloaded from IOPscience. Please scroll down to see the full text.Download details:IP Address: content was downloaded on 08/01/2014 at 04:22Please note that terms and conditions apply. Hair flow sensors: from bio-inspiration to bio-mimicking—a review View the table of contents for this issue, or go to the  journal homepage for more 2012 Smart Mater. Struct. 21 113001( usMy IOPscience  IOP P UBLISHING  S MART  M ATERIALS AND  S TRUCTURES Smart Mater. Struct.  21  (2012) 113001 (23pp) doi:10.1088/0964-1726/21/11/113001 TOPICAL REVIEW Hair flow sensors: from bio-inspiration tobio-mimicking—a review Junliang Tao 1 and Xiong (Bill) Yu 2,3 1 Department of Civil Engineering, Case Western Reserve University, Cleveland, OH 44106, USA 2 Department of Civil Engineering, Department of Electrical Engineering and Computer Science(courtesy appointment), Department of Mechanical and Aerospace Engineering (courtesy appointment),Case Western Reserve University, 2104 Adelbert Road, Bingham Building 206, Cleveland, OH 44106,USAE-mail: and Received 19 December 2011, in final form 13 June 2012Published 21 September 2012Online at Abstract A great many living beings, such as aquatics and arthropods, are equipped with highlysensitive flow sensors to help them survive in challenging environments. These sensors areexcellent sources of inspiration for developing application-driven artificial flow sensors withhigh sensitivity and performance. This paper reviews the bio-inspirations on flow sensing innature and the bio-mimicking efforts to emulate such sensing mechanisms in recent years. Thenatural flow sensing systems in aquatics and arthropods are reviewed to highlight inspirationsat multiple levels such as morphology, sensing mechanism and information processing.Biomimetic hair flow sensors based on different sensing mechanisms and fabrication tech-nologies are also reviewed to capture the recent accomplishments and to point out areas wherefurther progress is necessary. Biomimetic flow sensors are still in their early stages. Furtherefforts are required to unveil the sensing mechanisms in the natural biological systems and toachieve multi-level bio-mimicking of the natural system to develop their artificial counterparts.(Some figures may appear in colour only in the online journal) Contents 1. Introduction 12. Bio-inspiration: flow sensors in the nature 22.1. Lateral line system of aquatics 22.2. Hair flow sensors in arthropods (crickets andspiders) 83. Bio-mimicking: artificial hair flow sensors 103.1. Piezoresistive BHFS 113.2. Capacitive BHFS 123.3. BHFS based on other principles 154. Discussions and outlook  164.1. On the optimization of hair flow sensors 164.2. On the methods for information processing 16 3 Author to whom any correspondence should be addressed. 4.3. On the future trends 184.4. On the viscous coupling 18Acknowledgments 18References 18 1. Introduction Flow sensing is an essential technique involved in numerousapplications from traditional flow mapping to challengingturbulence characterization and liquid-dispensing, from ma-neuvering system of robots such as underwater autonomousvehicles(UAV)andself-stabilizingmicroairvehicles(MAVs)to biochemical and biomedical applications. Such applica-tions require that the flow sensors possess capabilities suchas multi-dimensional mapping, low-detection threshold, shortresponse time, least intrusion to the flow field of interests, and 10964-1726/12/113001 + 23$33.00  c  2012 IOP Publishing Ltd Printed in the UK & the USA  Smart Mater. Struct.  21  (2012) 113001 Topical Review Figure 1.  The lateral line system of fish. (a) Neuromasts distribution in Lake Michigan mottled sculpin, black dots in the red shaded canalarea represent the CNs and dots in other areas are SNs (Reprinted with permission and modified from Coombs (2001)). Copyright 2008Springer Science and Business Media). (b) The neuromasts canal diagram 3 , typically between an adjacent pore pair, there is one CN (CILIA2008). (c) A SN with Cupula is examined optically and the kinocilia are visible (modified from McHenry and van Netten (2007); the kinocilia are highlighted in yellow for better illustration). (d) A neuromast of zebra fish (modified and reprinted with permission fromRoberts  et al  2009). This neuromast consist of seven hair cells and the white arrows indicate that the staircase directions for the two groupsof hair bundles are nearly opposite. low costs and high durability (Chen  et al  2003). Traditionalflow sensing methods such as hot-wire anemometry (HWA),turbine flow meters, acoustic Doppler-shift velocimetryand particle image velocimetry (PIV) cannot fulfil theserequirements due to their large size, low sensitivity, complexsetup, etc (Fan  et al  2002).Nature has always been a source of inspiration and servesas a guide for technical developments (Fratzl and Barth 2009,Stroble  et al  2009). Creatures live in different media, e.g., fishin the water, flies in the air, and earthworms in sand or mud.Some of these media are changing rapidly and, therefore,creatures in them are equipped with flow sensitive sensorsin order to survive in these changing complex environments.It is inspiring to learn from nature and develop artificialcounterparts that emulate such high-performance biologicalflow sensors.Research on natural hair flow sensors has just begun andonly a few attempts have been made to create biomimeticsensors.Thispaperintendstoprovideacomprehensivereviewon bio-inspired hair flow sensors, which includes the basicsensing mechanisms in biological hair flow sensors andwhat has been achieved on biomimetic devices to date. Thebio-inspirations at different levels, namely hair morphology,sensing mechanism, information processing and so on, arehighlighted to emphasize that only through a multi-levelmimicking strategy can high performance be achieved inartificial hair flow sensors. Specifically, in the followingsections we will discuss two unique types of biologicalhair flow sensors and the different approaches that realizebio-mimicking of such sensors. 3 Fish: lateral line system of a fish. [Art]. Encyclopædia Britannica Online.Retrieved 16 December 2011, from 3409/Lateral-line-system-of-a-fish. 2. Bio-inspiration: flow sensors in the nature Flow sensors are ubiquitous in nature: Mexican blind fishrely on a lateral line system to navigate (von Campenhausen et al  1981); crickets escape from predators using the flowinformation provided by their hairy cerci (Tauber and Camhi1995); caterpillars detect flying wasps by hairs sensitiveto airborne disturbances(Tautz and Markl 1978); scorpions usually have patterned flow sensitive hair arrays on theirpedipalps (Hoffmann 1967); and bats monitor flow conditions by wing hairs to support flight control (Sterbing-D’Angelo et al  2011). Among the various flow sensors in nature, theinstinctive flow sensors of aquatics and arthropods are themost intensively studied. In this section, the morphology,function and biomechanics of the lateral line neuromasts of aquatics and the filiform hairs of arthropods are examined toshed light on the development of their artificial counterparts. 2.1. Lateral line system of aquatics The lateral line (figure 1), which can be found in all cartilaginous and bony fish and aquatic amphibians, has beenstudied extensively in the past few decades as a spatiallydistributedsystemofdirectionalflowsensors(Dijkgraaf 1963,Coombs 2001). Relying on the lateral line system, at leastmainly relying on it, fish ‘feel’ low frequency ( < 200 Hz)water motions created either by steady flow or turbulent flow.This information is then processed by its central nervoussystem to guide a number of different behavioral abilities,such as orienting, schooling and preying (Coombs 2001).The lateral line system is constituted by a number of neuromasts, which are the key sensory units in the system.The number of neuromasts varies in different species, fromless than 100 to over 1000, and they are distributed spatially 2  Smart Mater. Struct.  21  (2012) 113001 Topical Review in the head, trunk and tail of the fish body (Schmitz  et al  2008,Bleckmann 2008 and figure 1(a)). Neuromasts consist of  receptor cells called hair cells, which are mechano-electricaltransducers with directional sensitivities (Coombs 2001). Anumber of hair cells are encapsulated in a gelatinous cupula,which interacts with the surrounding fluid directly. Two majortypes of neuromasts exist. Superficial neuromasts (SNs) arethose situated on the surface of the fish body and directlyexposed to the external water flow (figure 1(c)), whereas the canal neuromasts (CNs) are those embedded in a canalstructure, which is connected with the outside flow throughdistributed pores (CILIA 2008 and figure 1(b)). 2.1.1. Number and distribution of canal neuromasts (CNs)and superficial neuromasts (SNs).  Hair cell numbers inneuromasts vary greatly, but typically a SN contains tens of hair cells and a CN contains hundreds to thousands of haircells (van Netten 2006). The numbers and distributions of SNsand CNs are believed to represent an adaption to a particularhydrodynamic environment. Still water habitats are oftenrelated to the supernumerary SNs, with widened canals andreduced canal lengths, while turbulent aquatic environmentsare often associated with fish with less SNs and narrowercanals (Schellart 1992, Bleckmann 1994, Engelmann  et al 2002). It is thought that adult fish, which have completesystems of lateral line canals, are capable of localizingthe prey stimuli from spatial variations in the pressuregradients along the trunk (Coombs and Conley 1997a, 1997b, Curcic-Blake and van Netten 2006); on the other hand,larvae, which are much smaller than the scale of prey stimuli(Higham  et al  2006) and on which only SNs exist, can detectonly a wide range of flow velocities and the high variability inthe frequency responses of their SNs may hinder their abilityto sense spatial cues (van Trump and McHenry 2008). To engineers, these findings can, of course, shed somelight on the design of flow sensors for different applications.For instance, for steady flow applications we can learn morefrom the SNs, while for the turbulent sensing CNs-typedesign may be more suitable. However, recent detailed studies(Beckmann  et al  2010, Klein  et al  2011) found that thenumber of SNs did not clearly predict the hydrodynamicenvironment. Therefore, to further uncover the sensingmechanism of the lateral line system, more informationabout the detailed anatomy of the peripheral lateral line andthe physiology of SNs and CNs, their cooperation and theprocessing of stimuli in the central nerve system are required(CILIA report 2008). 2.1.2. Directional sensitivity.  The directional sensitivityof the lateral line enables fish to locate stimulus sources.The directional sensitivity of a single neuromast is due tothe staircase structure of the hair cell, in which a bundle of relatively short stereocilia with increasing heights is followedby a longer kinocilium (figure 1(d)). The direction of the bundle’s staircase is along the long axis of the canal forCNs and parallel to the long cross-sectional axis of cupula(van Netten 2006), and SNs are oriented in a line which is either parallel or perpendicular to the nearby canal axis(Kroese and Schellart 1992). Depolarizing of the cell occurs when the stereociliary bundle bends towards the kinocilium,simultaneously with an increasing in the firing rate of afferentfibers, and vice versa (Coombs 2001). Along the intermediatedirections, displacement of the stereociliary bundle results inresponses that are an approximate cosine function of the inputdirection (Flock  1965). Since hair cells on each neuromast are oriented in two opposing directions (Coombs 2001), the overall directional sensitivity of a neuromast also follows acosine function. This directional feature of the neuromasts,together with the distribution of neuromasts in different partsof the fish body, may form the mechanism of how fishdetermine the water-flow direction and even the regionalhydrodynamic variations from different stimuli (Montgomery et al  2000). Furthermore, a recent study reveals that theexistence of flush ridges around the stereotyped array of SNsin the cephalic lateral line of the surface-feeding killifishchanges the hydrodynamic environment of the SNs and as aresult modifies the directional receptive range of this species(Schwarz  et al  2011). All such information has implicationsfor the development of artificial flow sensors. 2.1.3. Flowsensingmechanism:experimentaldiscoveriesand biomechanical sensing model.  Due to differences in themorphology and hydrodynamic environment, CNs and SNsalso differ from each other in terms of sensing mechanismsand functions. Experimental findings.  The firing rates of theafferent fibers are controlled by the displacements of theneuromasts (Flock  1965). When the hair bundles were displaced towards the kinocilium, the afferent firing ratesincrease and vice versa. Afferents can increase their firingrate in response to hydrodynamic deflections of a singleneuromast (Catton  et al  2007). The existence of the cupulacouples the motion of the surrounding water to the underlyingcilia through viscous forces (Coombs 2001). Kroese and van Netten (1987) reported that theneural response isproportionalto the displacement of the cupula, and in the meantime,the displacement of the cupula is largely proportional to thevelocity of the flowing water. Montgomery  et al  (2000) also established a linear relationship between the responses of theSNs of the eel and the different stimuli with variable flowvelocities. These findings indicate that the neurophysiologicalresponse of a neuromast depends on the degree to whichcupula mechanics permit the deflection of the kinocilia inresponse to the water flow (van Trump and McHenry 2008). Due to the difference in the hydrodynamic environmentwhere CNs and SNs are exposed, CNs act as a detector of water acceleration while SNs function as a detector of watervelocity. This has been proved by the measurements of themotions of the cupula (van Netten and Kroese 1987), the canalfluid (Denton and Gray 1983) and the response properties of afferent fibers innervate CNs and SNs (Kroese and Schellart1992, Voigt  et al  2000). Biologists have developed theoreticalmodels to unveil the mechanisms of the sensing abilitiesof the neuromasts and to study their underlying interactionswith fluids (van Netten 2006 (CNs modeling); McHenry  et al 3
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