Transducer Technologies for Biosensors and Their Wearable Applications: Comparison
Please note this is a comparison between Version 2 by Catherine Yang and Version 1 by EMRE OZAN POLAT.

Biosensors refer to the collaboration of receptors that recognize target analytes and transducers that translate this recognition into a detectable signal. Biological molecules such as enzymes, nucleic acids, antibodies, or their synthetic analogues can serve as bio-receptors to bind the analyte of interest. To form a biosensor device that detects or measures the biological events or changes, the targeted matching of the bio-receptor and the analyte should be evaluated quantitatively, making the transducers indispensable components of a biosensor [5,6]. Availability of various bio-receptors, transducers, and possible combinations of both components constitute various ways to classify biosensors. Compared to conventional sensors based on rigid semiconductors, metals, and ceramics, elastomers are advantageous since they exhibit the highest level of strain behavior for wearable applications. 

  • transducers
  • biosensors
  • wearables
  • analytes
  • bioreceptors

1. Construction and Classification of Transducers for Biosensors and Wearables

Biosensors have been evolved as the combination of bio-receptors and transducers based on electrochemical, optical, thermal, and gravimetric methods converting the signals from analytes containing antibodies, nucleic acids, and immunological agents, microorganisms, hormones, enzymes, cells, tissues, chemical receptors, and other detectable biological inputs. For most biosensors, device construction entails three steps; (i) implementation of a bio-receptor that reacts with a specific analyte, (ii) integration of a transducer, and (iii) the fixation/immobilization of a biological component to the transducer. Therefore, creating a biosensing device strongly depends on these construction steps together with device design strategies and integration of readout electronics for wearable devices that will provide continuous use on the human body. It is also important to acknowledge that the isolation from external factors such as chemical/physical conditions (temperature, contaminants, and pH), should be further taken into consideration while constructing wearable biosensors for specific applications [31,32][1][2].
The variety of available transducing mechanisms provides wearable biosensors worn on the head, neck, torso, legs, feet, arms, hands, and fingers. Figure 21A shows the body locations for which the multiple wearable form factors have been reported [33,34,35][3][4][5]. The wearable market and reported research results include a broad spectrum of device designs from smart helmets to skin patches (Figure 21A), for which user adoption and accuracy are the critical factors to present a sustainable and user-friendly technology. To that end, the construction of transducing mechanism is a key factor that can convert many biosensing technologies to be used as wearable devices.
Figure 21. Wearable biosensors and their transducer specifications. (A) Body locations for which a variety of wearable form factors are reported [33,34,35][3][4][5]. The variety of the transducing mechanisms and the miniaturization of electronics provide wearables that can be worn on the head, neck, torso, legs, feet, arms, and hands. (B) Transducer specifications for construction of biosensors. Transducers are the main components in the biosensors that register the biological signal and convert it to a detectable output that can be in various formats. Active sensing material can be inorganic, organic, conductor, insulator, semiconductor, or in the form of a biological substance. While the transducer specifications are defined by the active material, the design of the transducer device can affect the resulting wearable device specifications such as sensitivity, stability, cost, and variety of the specifications that are given under the transducer specifications (middle panel of (B). Signal conversion mechanisms are conventionally classified as the means of electrochemical, optical, thermal, and gravimetric transducing. A more specific classification of the transducers with the signal conversion mechanisms is given in the panel on the right-hand side of (B). (A) Created with BioRender.com accessed on 27 April 2022, (B) Adapted from the data shown in ref. [36][6].
Transducers for biosensors depend on the type of material used, specifications of the sensor device, and the actual signal conversion mechanism (Figure 21B). The transducer materials are generally classified as inorganic, organic, conductor, insulator, and semiconductor, and can also be found in the form of a biological substance. While the specifications of the transducer are mostly defined by the capabilities of the active sensing material, the design of the device also plays an important role in the definition of the final specifications (Figure 21B). To that end, transducer mechanism simply defines the class of the biosensors, for instance, a biosensor is classified as an “electrochemical biosensor” if it uses an electrochemical transducer.

2. Supplementary Technologies for Wearable Biosensing

2.1. Microfluidics and Biomedical Microelectromechanical Systems (Bio-MEMS)

Microfluidics provides an efficient platform for transducers by forming the core of the sensing elements and providing advanced liquid holding and storage capabilities [190][7]. Microfluidics-based point of care medical sensors are of ever-growing interest due to their versatility as wearable lab-on-the-body systems [191,192,193][8][9][10]. This section is dedicated to a brief outlook on the importance and varieties of the conformal biomedical microelectromechanical (bio-MEMS) sensors as a supplementary technology to the wearable transducer-based sweat analysis. Sweat is a compatible substance with wearable medical sensor systems owing to its transparent and hypotonic properties [191,194][8][11]. Furthermore, natural sweating induces high enough pressure (~70 kPa) to sustain pressure different for the microfluidic channels [191,194,195][8][11][12]. However, sweat-based sensing is prone to false-concentration-based analyte readings due to vaporization [191,194,196,197][8][11][13][14] and inhomogeneous local sweat gland density [195][12]. It has been demonstrated that the measurement error could be as high as 114% [192][9]. Furthermore, the sensory system should include microfluidics, electronics, and a power supply to collate and relay data on the collected and analyzed sweat in real-time, which results in a rather bulky system. Moreover, the said system should be conformal if desired to be employed in direct contact with the skin on different parts of the body. Despite these shortcomings and constraints, analysis of the constituents of sweat is a crucial part of medical diagnosis with an ever-growing focus owing to the variety of critical biomarkers that can be detected in tiny volumes. Biomarkers present in the sweat that can be detected by wearable microfluidics-based medical sensors are (i) glucose, (ii) lactate, (iii) pH, (iv) chloride, (v) creatinine, (vi) tyrosine, (vii) uric acid, (viii) potassium, (ix) sodium, (x) ascorbic acid, (xi) cortisol, (xii) dopamine, and (xiii) adrenaline. The concentration of glucose in sweat is 1% of that of the plasma and its accurate measurement is very important for continuous monitoring of diabetes [193,198][10][15]. On the other hand, lactate is an indicator of physical effort signaling the transition from aerobic to anaerobic metabolism [194,199][11][16]. Determining the level of pH helps in ascertaining the neuromuscular condition of the subject [194][11], while chloride levels are used to help with the diagnosis of cystic fibrosis [194,195][11][12]. The level of creatinine is an important indicator in confirming the hydration status of the metabolism and renal health [195][12] and determining tyrosine level from sweat is generally used for the diagnosis of liver diseases and various psychiatric disorders [197][14]. Thus, the analytes found in sweat serve as crucial markers for the performance of vital organs and critical systems in the human body; therefore, using sweat as a working fluid in a wearable Bio-MEMS sensor is crucial. The reported wearable microfluidic devices that use sweat to analyze the abovementioned analytes are summarized in Table 21.
Table 21.
 Biomarkers and driving mechanisms in wearable microfluidic Bio-MEMS *.
[195][12]. In addition to colorimetric sensors, there are systems with hybrid sensing compartments or completely different transducing approaches, e.g., galvanic, strain, piezoresistive effect, and electrochemical. The reported devices with various transducing mechanisms are summarized in Table 32. Electrochemical biosensors are exclusively microfluidic and rely on custom electrodes coated with specific mediator molecules, such as carbon nanomaterials, to choose and react to a targeted analyte [196,197,199][13][14][16]. The induced current by the transducer is directly related to the flux of the molecules of interest in the vicinity of the electrode [192][9]. Thus, the transportation and diffusion of analytes in the microfluidic network and reservoirs are of utmost importance [197][14]. Also, strain sensors could exploit swelling of bulk material, e.g., hydrogels, as sweat gets absorbed while stretching a conductive fabric changing its resistance [202][19]. Similarly, the sweat rate can be detected based on the change in galvanic properties of specifically designed microchannel geometries.

2.1.3. Biocompatibility

In microfluidic wearables, the sensors and antennas, along with a printed electrical interface to the necessary PCB, are expected to be implemented on biocompatible conformal structures to seamlessly fit on any surface. Furthermore, the sensor is supposed to be attached to the skin via medical-grade adhesives. Sweat itself is supposed to be collected and stored by the carefully tailored network of microfluidic channels and chambers embedded in this elastic structure. Such a network can be implemented in PDMS [196,198,199[13][15][16][18],201], silicone rubber [194][11], and medical adhesives [197][14]. Polyimide (PI) [197,198][14][15], PDMS [193][10], and polyethylene terephthalate (PET) [197][14] are demonstrated as a cover to the microchannels, as well as for the printing of the sensory electrodes. Moreover, different porous materials could be used to collect sweat, such as hydrogel [202][19] and Ecoflex [198][15], in certain volumes. Likewise, paper and cotton can be used for sweat collection, fluid flow compartmentalization, and sensor placement [191,193][8][10]. In addition, the thermo-responsive valves can be manufactured out of poly(N-isopropylacrylamide) [192][9]. To reach the desired wearable microfluidic device components, microchannels and reservoirs can be implemented via different fabrication techniques such as photolithography, screen printing, laser engraving, laying cotton fibers, and embossment. Table 43 presents the list of the reported wearable fabrication techniques and biocompatible microfluidic device materials. The said techniques are utilized to manufacture tailored microchannels and electrodes meeting different production constraints. For instance, laser engraving is utilized as a faster and cheaper solution as it reduces the need for infrastructure and expertise required for photolithography [195,196,197,199,201,203][12][13][14][16][18][20]. Also, making use of easy-to-obtain materials such as cotton or paper when feasible greatly simplifies the manufacturing procedure and reduces the overall cost [191,193][8][10]. Likewise, the techniques of screen-printing and embossment are employed as a cost-effective solution to microchannel manufacturing while preserving the dexterity to obtain a channel network with bifurcations [194,195,196,197][11][12][13][14]. Nevertheless, manufacturing electrodes for electrochemical or galvanic sensing or building the associated interfacial electronics entails the use of photolithography [195,196,199,203][12][13][16][20].
Table 43.
 Biocompatible materials and fabrication techniques for microfluidic-based wearable biosensors.

References

  1. McNaught, A.D.; Wilkinson, A. IUPAC. Compendium of Chemical Terminology, 2nd ed.; (the “Gold Book”); Blackwell Science: Oxford, UK, 1997.
  2. Kissinger, P.T. Biosensors—A Perspective. Biosens. Bioelectron. 2005, 20, 2512–2516.
  3. Polat, E.O. Seamlessly Integrable Optoelectronics for Clinical Grade Wearables. Adv. Mater. Technol. 2021, 6, 2000853.
  4. Saleem, K.; Shahzad, B.; Orgun, M.A.; Al-Muhtadi, J.; Rodrigues, J.J.P.C.; Zakariah, M. Design and Deployment Challenges in Immersive and Wearable Technologies. Behav. Inf. Technol. 2017, 36, 687–698.
  5. Rodrigues, J.J.P.C.; de Rezende Segundo, D.B.; Junqueira, H.A.; Sabino, M.H.; Prince, R.M.; Al-Muhtadi, J.; de Albuquerque, V.H.C. Enabling Technologies for the Internet of Health Things. IEEE Access 2018, 6, 13129–13141.
  6. White, R.M. A Sensor Classification Scheme. IEEE Trans. Ultrason. Ferroelectr. Freq. Control 1987, 34, 124–126.
  7. Yeo, J.C.; Kenry, K.; Lim, C.T. Emergence of Microfluidic Wearable Technologies. Lab Chip 2016, 16, 4082–4090.
  8. Ardalan, S.; Hosseinifard, M.; Vosough, M.; Golmohammadi, H. Towards Smart Personalized Perspiration Analysis: An IoT-Integrated Cellulose-Based Microfluidic Wearable Patch for Smartphone Fluorimetric Multi-Sensing of Sweat Biomarkers. Biosens. Bioelectron. 2020, 168, 112450.
  9. Lin, H.; Tan, J.; Zhu, J.; Lin, S.; Zhao, Y.; Yu, W.; Hojaiji, H.; Wang, B.; Yang, S.; Cheng, X.; et al. A Programmable Epidermal Microfluidic Valving System for Wearable Biofluid Management and Contextual Biomarker Analysis. Nat. Commun. 2020, 11, 4405.
  10. Gualandi, I.; Marzocchi, M.; Achilli, A.; Cavedale, D.; Bonfiglio, A.; Fraboni, B. Textile Organic Electrochemical Transistors as a Platform for Wearable Biosensors. Sci. Rep. 2016, 6, 33637.
  11. Vinoth, R.; Nakagawa, T.; Mathiyarasu, J.; Mohan, A.M.V. Fully Printed Wearable Microfluidic Devices for High-Throughput Sweat Sampling and Multiplexed Electrochemical Analysis. ACS Sens. 2021, 6, 1174–1186.
  12. Koh, A.; Kang, D.; Xue, Y.; Lee, S.; Pielak, R.M.; Kim, J.; Hwang, T.; Min, S.; Banks, A.; Bastien, P.; et al. A Soft, Wearable Microfluidic Device for the Capture, Storage, and Colorimetric Sensing of Sweat. Sci. Transl. Med. 2016, 8, 366ra165.
  13. Nyein, H.Y.Y.; Tai, L.-C.; Ngo, Q.P.; Chao, M.; Zhang, G.B.; Gao, W.; Bariya, M.; Bullock, J.; Kim, H.; Fahad, H.M.; et al. A Wearable Microfluidic Sensing Patch for Dynamic Sweat Secretion Analysis. ACS Sens. 2018, 3, 944–952.
  14. Yang, Y.; Song, Y.; Bo, X.; Min, J.; Pak, O.S.; Zhu, L.; Wang, M.; Tu, J.; Kogan, A.; Zhang, H.; et al. A Laser-Engraved Wearable Sensor for Sensitive Detection of Uric Acid and Tyrosine in Sweat. Nat. Biotechnol. 2020, 38, 217–224.
  15. Zhu, J.; Liu, S.; Hu, Z.; Zhang, X.; Yi, N.; Tang, K.; Dexheimer, M.G.; Lian, X.; Wang, Q.; Yang, J.; et al. Laser-Induced Graphene Non-Enzymatic Glucose Sensors for on-Body Measurements. Biosens. Bioelectron. 2021, 193, 113606.
  16. Shitanda, I.; Mitsumoto, M.; Loew, N.; Yoshihara, Y.; Watanabe, H.; Mikawa, T.; Tsujimura, S.; Itagaki, M.; Motosuke, M. Continuous Sweat Lactate Monitoring System with Integrated Screen-Printed MgO-Templated Carbon-Lactate Oxidase Biosensor and Microfluidic Sweat Collector. Electrochim. Acta 2021, 368, 137620.
  17. Kim, S.; Lee, B.; Reeder, J.T.; Seo, S.H.; Lee, S.-U.; Hourlier-Fargette, A.; Shin, J.; Sekine, Y.; Jeong, H.; Oh, Y.S.; et al. Soft, Skin-Interfaced Microfluidic Systems with Integrated Immunoassays, Fluorometric Sensors, and Impedance Measurement Capabilities. Proc. Natl. Acad. Sci. USA 2020, 117, 27906–27915.
  18. Song, Y.; Min, J.; Yu, Y.; Wang, H.; Yang, Y.; Zhang, H.; Gao, W. Wireless Battery-Free Wearable Sweat Sensor Powered by Human Motion. Sci. Adv. 2020, 6, eaay9842.
  19. Wang, L.; Xu, T.; Fan, C.; Zhang, X. Wearable Strain Sensor for Real-Time Sweat Volume Monitoring. iScience 2021, 24, 102028.
  20. Ziaie, B. Hard and Soft Micromachining for BioMEMS: Review of Techniques and Examples of Applications in Microfluidics and Drug Delivery. Adv. Drug Deliv. Rev. 2004, 56, 145–172.
  21. Hofmann-Wellenhof, B.; Lichtenegger, H.; Collins, J. Global Positioning System: Theory and Practice; Hofmann-Wellenhof, B., Lichtenegger, H., Collins, J., Eds.; Springer Science & Business Media: Berlin/Heidelberg, Germany, 2001; ISBN 9783211835340.
  22. Djuknic, G.M.; Richton, R.E. Geolocation and Assisted GPS. Computer 2001, 34, 123–125.
  23. Huang, H.; Gartner, G. A Survey of Mobile Indoor Navigation Systems. In Cartography in Central and Eastern Europe; Springer: Berlin/Heidelberg, Germany, 2009; pp. 305–319.
  24. Liu, H.; Darabi, H.; Banerjee, P.; Liu, J. Survey of Wireless Indoor Positioning Techniques and Systems. IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.) 2007, 37, 1067–1080.
  25. Ram, S.; Sharf, J. The People Sensor: A Mobility Aid for the Visually Impaired. In Proceedings of the Digest of Papers. Second International Symposium on Wearable Computers (Cat. No.98EX215), Pittsburgh, PA, USA, 19–20 October 1998; IEEE Computer Society: Washington, DC, USA, 1998; pp. 166–167.
  26. Gu, Y.; Lo, A.; Niemegeers, I. A Survey of Indoor Positioning Systems for Wireless Personal Networks. IEEE Commun. Surv. Tutor. 2009, 11, 13–32.
  27. Komine, T.; Nakagawa, M. Fundamental Analysis for Visible-Light Communication System Using LED Lights. IEEE Trans. Consum. Electron. 2004, 50, 100–107.
  28. Kumar, N.; Lourenco, N.; Spiez, M.; Aguiar, R. Visible Light Communication Systems Conception and VIDAS. IETE Tech. Rev. (Inst. Electron. Telecommun. Eng. India) 2008, 25, 359–367.
  29. Galván-Tejada, C.; García-Vázquez, J.; Brena, R. Magnetic Field Feature Extraction and Selection for Indoor Location Estimation. Sensors 2014, 14, 11001–11015.
  30. Shao, W.; Zhao, F.; Wang, C.; Luo, H.; Muhammad Zahid, T.; Wang, Q.; Li, D. Location Fingerprint Extraction for Magnetic Field Magnitude Based Indoor Positioning. J. Sens. 2016, 2016, 1945695.
  31. Alsindi, N.A.; Alavi, B.; Pahlavan, K. Measurement and Modeling of Ultrawideband TOA-Based Ranging in Indoor Multipath Environments. IEEE Trans. Veh. Technol. 2009, 58, 1046–1058.
  32. Breed, G. A Summary of FCC Rules for Ultra Wideband Communications. High Freq. Electron. 2005, 4, 42–44.
  33. Kopta, V.; Farserotu, J.; Enz, C. FM-UWB: Towards a Robust, Low-Power Radio for Body Area Networks. Sensors 2017, 17, 1043.
  34. Nasr, K.M. Hybrid Channel Modelling for Ultra-Wideband Portable Multimedia Applications. IET Microw. Antennas Propag. 2008, 2, 229.
  35. De Santis, V.; Feliziani, M.; Maradei, F. Safety Assessment of UWB Radio Systems for Body Area Network by the FD2TD Method. IEEE Trans. Magn. 2010, 46, 3245–3248.
  36. Fort, A.; Ryckaert, J.; Desset, C.; de Doncker, P.; Wambacq, P.; van Biesen, L. Ultra-Wideband Channel Model for Communication around the Human Body. IEEE J. Sel. Areas Commun. 2006, 24, 927–933.
  37. Rowe, N.C.; Fathy, A.E.; Kuhn, M.J.; Mahfouz, M.R. A UWB Transmit-Only Based Scheme for Multi-Tag Support in a Millimeter Accuracy Localization System. In Proceedings of the 2013 IEEE Topical Conference on Wireless Sensors and Sensor Networks (WiSNet), Austin, TX, USA, 20–23 January 2013; IEEE: Piscataway, NJ, USA, 2013; pp. 7–9.
  38. Kuhn, M.J.; Mahfouz, M.R.; Turnmire, J.; Wang, Y.; Fathy, A.E. A Multi-Tag Access Scheme for Indoor UWB Localization Systems Used in Medical Environments. In Proceedings of the 2011 IEEE Topical Conference on Biomedical Wireless Technologies, Networks, and Sensing Systems, Phoenix, AZ, USA, 16–19 January 2011; IEEE: Piscataway, NJ, USA, 2011; pp. 75–78.
  39. Mahfouz, M.R.; Kuhn, M.J.; Wang, Y.; Turnmire, J.; Fathy, A.E. Towards Sub-Millimeter Accuracy in UWB Positioning for Indoor Medical Environments. In Proceedings of the 2011 IEEE Topical Conference on Biomedical Wireless Technologies, Networks, and Sensing Systems, Phoenix, AZ, USA, 16–19 January 2011; IEEE: Piscataway, NJ, USA, 2011; pp. 83–86.
  40. Arsan, T.; Kepez, O. Early Steps in Automated Behavior Mapping via Indoor Sensors. Sensors 2017, 17, 2925.
  41. Moghtadaiee, V.; Dempster, A.G. Determining the Best Vector Distance Measure for Use in Location Fingerprinting. Pervasive Mob. Comput. 2015, 23, 59–79.
  42. Alarifi, A.; Al-Salman, A.; Alsaleh, M.; Alnafessah, A.; Al-Hadhrami, S.; Al-Ammar, M.; Al-Khalifa, H. Ultra Wideband Indoor Positioning Technologies: Analysis and Recent Advances. Sensors 2016, 16, 707.
  43. Jiang, S.; Skibniewski, M.J.; Yuan, Y.; Sun, C.; Lu, Y. Ultra-Wide Band Applications in Industry: A Critical Review/Ultraplačios Juostos Bangų Taikymas Pramonėje: Kritinė Apžvalga. J. Civ. Eng. Manag. 2011, 17, 437–444.
More
ScholarVision Creations