1. Introduction
The advent of wearable sensors is the integration of technologies in multiple areas. This integration is facilitated by breakthroughs in material science, microfabrication, and the Internet of Things (IoT), which collectively enable the development of sophisticated, compact, and efficient wearable devices. Wearable sensors represent an advanced approach to health monitoring and lifestyle management. These sensors, designed to be seamlessly integrated with the human body, offer real-time data collection and analysis. As a result, they can provide insights into various physiological parameters that eventually are used to evaluate our health condition.
Wearable sensor is a wide concept. Some sensors are used to monitor biophysical signals such as heart rate, body temperature, and movement. Some are designed to monitor biochemical signals like glucose levels, lactate concentration, and pH balance. These sensors are typically embedded in accessories such as wristbands, patches, and clothing, ensuring that they are comfortable for the user. The advanced materials are the key components that are not only biocompatible but also able to stands certain stress from our daily life. Innovations in flexible and stretchable materials have made it possible to design devices that can be comfortably attached on our body, maintaining intimate contact with the skin for accurate detection.
Meanwhile microfabrication also plays an important role in it. These techniques allow for the miniaturization of electronic components which make the device lighter and more portable. For example, microfabricated chips have enabled the creation of highly sensitive and selective sensors capable of detecting minute changes in physiological conditions. Also, the incorporation of microfluidics into wearable sensors has enhanced their capability to analyze biological fluids efficiently, paving the way for non-invasive monitoring methods.
Recent advancements in wearable sensor technology include the development of graphene-based chemical sensors, biofuel-powered electronic skins, and devices that utilize near-field communication (NFC) for data transmission. These innovations have significantly enhanced the functionality, sensitivity, and user-friendliness of wearable sensors. For example, graphene-based sensors offer high sensitivity and rapid response times, making them ideal for detecting biochemical signals. Biofuel-powered sensors eliminate the need for conventional batteries, thereby reducing the device's environmental impact and extending its operational lifespan. NFC technology simplifies the design of wearable sensors, making them easier to use and more accessible to a broader audience.
Wearable sensors represent a cutting-edge convergence of technology and biology, with the potential to revolutionize personal health management and clinical diagnostics. By providing continuous, non-invasive monitoring of vital physiological parameters, these devices empower users with actionable health insights and enable healthcare providers to deliver more personalized and effective care. As research and development in this field continue to advance, the future of wearable sensors promises even greater integration into everyday life, heralding a new era of smart, connected health solutions. In this paper, we’ll introduce some widely spread sensors. Then we will discuss how microfabrication technique minimize the sensors. Finally, we’ll focus on some examples of microfluidic based wearable sensors.
2. Approaches for chemical sensing
2.1. Amperometric sensors
Amperometric sensors are highly favored because of their ability to continuously and instantaneously monitor conditions using straightforward current detection. These sensors function by applying a fixed potential, with the resulting current reflecting the varying concentrations of components over time. Factors such as electrode material are important to consider. Typically, wearable amperometric biosensors utilize carbon or platinum electrodes that are modified with redox enzymes.
Current biosensors, like enzymatic sensors, are highly selective when analyzing complex bio-fluid samples. Wearable platforms based on oxidase enzymes usually detect the oxidation of hydrogen peroxide, the product of the enzymatic reaction. Second-generation oxidase biosensors improve upon this by using mediators as electron acceptors instead of oxygen. These mediators transfer electrons from the enzyme to the electrode surface, which lowers the overpotential and reduces the likelihood of interference from other redox reactions.
2.2. Affinity sensors
The majority of wearable platforms focus on monitoring common metabolites and electrolytes using enzyme-based or ionophore-based recognition elements for continuous operation.
Antibody-based sensors, while highly specific, suffer from limitations such as slow recognition and difficulty in regeneration, hindering their continuous use. To overcome these limitations, interest has turned to synthetic receptors like molecular imprinted polymers (MIP) and aptamers. MIP offer higher stability and scalability compared to antibodies, while aptamers show promise for continuous, real-time monitoring due to their reversible binding nature and high sensitivity, although their limited commercial availability is still a problem [1].
2.3. Energy harvester sensors
The main challenge facing wearable electrochemical sensors is the requirement for a continuous power supply, as conventional batteries are rigid and bulky, limiting performance and comfort. To overcome this, flexible and stretchable wearable energy harvesters have been developed to extract energy from bio-fluids. These energy harvesters, similar to fuel cells that utilize enzymes to oxidize bio-fuels in the bio-fluid, generating electrical current.
Efficient bio-fuel cells (BFC) have been demonstrated using enzymes like lactate oxidase and bilirubin oxidase, capable of powering external devices like LED during exercise [2]. Integration of BFC with micro fluid devices enables energy harvesting from sweat. Hybrid devices combining energy harvesting and storage have also been explored, allowing simultaneous energy harvesting and sensing, with power output proportional to analyte concentration.
3. Approaches for physical sensing
3.1. Electrophysiological
For this type of measurement, the focus is primarily on changes in electrical impedance. The impedance measured can reveal the concentration and consistency of bio-fluids, which can provide insights into health status. Before taking measurements, certain factors need careful consideration. The magnitude and skin-electrode impedance significantly impact the quality of the recordings. High and unstable impedance can lead to poor signal quality. Additionally, optimizing the geometry and material composition of the electrodes is crucial to minimize skin irritation and maximize signal quality. Traditional methods for measuring potential signals involve the use of conductive gels, adhesive tapes, and hardware connections.
To enable wireless, gel-free measurements, a demonstrated approach utilizes a conductive nanomesh structure. The open structure and ultra-thin geometry allow for high permeability to gases and various bio-fluids without compromising measurement capabilities, even outperforming traditional gel-electrode methods [3]. Another similar technique uses micro-scale mesh constructs formed with photolithographically defined thin metal filaments arranged in serpentine or fractal patterns. This structure provides soft, bio-compatible contact with the body. More recent demonstrations have leveraged graphene in similar filamentary serpentine designs that are also optically transparent [4].
To address the need for direct contact between the sensor and the skin, insulating coatings and capacitive approaches have been introduced. The reason for avoiding direct contact between the sensor and skin is to prevent irritation and avoid direct current passing through the skin. Additionally, measurements taken using capacitive approaches can be influenced by the wearer's movement. These issues have been resolved in current designs. A notable example is the use of electrodes with thin silicone elastomer coatings as insulating layers [5]. These electrodes provide strong capacitive coupling to the skin and can capture electrophysiological data with a high signal-to-noise ratio.
3.2. Kinematic
The dynamic motion signals captured by soft wearable sensors can provide important insight across broad range of applications from clinical diagnostic to athletic performance monitoring. These kinds of sensors are usually consisted of soft materials and thin film design which allows the intimate and direct contact with human skin. Other contemporary reviews provide related, complementary perspectives on strain sensing technologies and on the deployment of discrete accelerometer and gyroscope-based multiaxial motion sensing systems.
The resistive constrain sensors commonly use thin metal film traces which serves as the functional materials react to the constrain. Then a Wheatstone bridge configuration with in the sensor turn the constrain into data we can actually detected. Beside thin metal film, we can also exploit liquid metals in micro-channels embedded in elastomeric PDMS sheets [6].
Another crack-based sensor which use another transduction method also perform good with sensibility and the stretchability. Remarkably, this kind of sensors can support good cyclic performance. The crack in the materials reopen in a repeatable manner. But due to the absence of FS structure, the constrain that a crack-based sensors can take is limited. Later, work refines the cracking process with the application of a controlled tensile force to the film during bending resulting in a dramatically increased sensitivity.
3.3. Thermal regulatory
Thermal regulation is crucial for the human body, as maintaining our core temperature within the narrow range of 36 to 37 degrees Celsius is essential for basic life-sustaining processes. Therefore, it's important to capture subtle and dynamic temperature data. The body regulates temperature through mechanisms like sweating, changes in blood flow, and adjusting the exposed surface area of the skin.
Temperature measurement typically depends on how temperature changes affect the resistance of semiconductors or optical materials. Many materials exhibit these characteristics, including metals, block copolymer composites, conducting polymers, liquid metals, carbon nanomaterials, 2D materials, crystalline semiconducting molecules, silk, and hydrogels.
Advanced materials have been developed that offer better temperature coefficients (TCR) than simple metals. The electrical resistance of conductive polymers, in particular, is highly sensitive to temperature. A recent study demonstrated that a semicrystalline acrylate copolymer with conductive graphite particles exhibits a phase change, depending on the concentration of octadecyl acrylate, from crystalline to amorphous within the physiological temperature range of 25 to 50 degrees Celsius [7].
4. Microfabrication and microfluidic devices
Microfabrication techniques are vital in the area of miniaturized wearable sensors. These methods enable the production of complex, small-scale structures that significantly enhance the performance and functionality without losing its stability of sensors while maintaining their compact size. The key microfabrication techniques include photolithography, micro-contact printing, thin film deposition, and etching. Each of these techniques contributes to the precision and miniaturization necessary for advanced wearable sensor technology.
Photolithography is used to transfer the pattern you designed on to the material. When you design a pattern in the computer, a mask is made by the mask vendor. A mask is usually a glass plate covered with opaque material in the pattern you designed. Then a substrate is prepared by being spin-coated with photoresist, a photosensitive organic polymer. Now the preparation has done. The mask is covered on the substrate. And they’ll be exposed under the UV light. Photoresist under the transparent portion of the mask will make direct contact with the UV light, causing it to become soluble in a developing solution.
Micro-contact printing (μCP) is a technique using elastomeric stamp to generate SAMs pattern on the surface of the substrate. The elastomeric stamp, made by casting a PDMS prepolymer can be reused over 50 times. The "ink" is a solution of hexadecanethiol (HDT) in ethanol which can be applied to the PDMS stamp. Then it contacts the substrate to form patterned SAMs. This method, followed by selective wet etching, allows for the creation of microstructures with controlled shapes and sizes, useful in fabricating sensors, microelectrodes, and diffraction gratings.
Thin film deposition techniques, including physical vapor deposition (PVD) and chemical vapor deposition (CVD), are essential for creating uniform layers of materials on substrates. PVD processes, such as sputtering and evaporation, and CVD processes, which involve chemical reactions on the substrate surface, are used to form conductive paths, protective coatings, and functional layers in sensors. The ability to deposit thin films with precise thicknesses and compositions is crucial for optimizing sensor performance and enabling further miniaturization.
Etching techniques, both wet and dry, are used to selectively remove material from substrates to create the desired microstructures. Wet etching employs liquid chemicals to dissolve material, while dry etching uses gases or plasmas to achieve more controlled and intricate features. These techniques are vital for defining sensor features such as cavities, channels, and vias. Precise etching allows for the integration of multiple functions within a compact sensor, contributing to their overall miniaturization and performance enhancement.
An important aspect of microfabrication is the concept of microfluidics, which involves the manipulation of fluids at the microscale. The basic requirement for wearable devices is that the materials have to be compatible with the skin. It’s a technology for precise manipulation of minute amount of fluids in a confined micro space. Microfluidics is chosen for some really good reasons. First microfluidics has good mechanical and adhesion properties. It utilizes elastomer materials like PDMS which minimize the slippage against the skin without detachment [8]. Second, the micro size channel and structure ensure the accuracy of the measurement. In the meantime, it allows the miniature of the device without losing the sensitivity [9]. Third it can be easily integrated with multiple functions [10]. Last, the cost of microfluidics fabrication is relatively low [11].
5. Examples of microfluidic devices
A common example is called flow cytometer. The basic definition of a flow cytometer is the one-by-one measurement of cells or particles as they flow through an analysis volume. The particle-by-particle analysis makes detection of discrete particle populations possible. Next, we’ll discuss how this works.
First, we’ll do sample preparation. Here we discuss a special preparation used widely in the analyze of flow cytometer called optimization of particle concentration. One example of this approach shows in white blood cell (WBC) population analysis in whole blood. Since the number of the red blood cell (RBC) is far greater than the number of WBC, the reduction of RBC is needed. We can solve this problem by dilution. But as the target cell become rarer, dilution approach will require greater diluted sample. A more practical approach called magnetic immunocapture techniques are able to enrich the target cell based on the specific markers on the cell surface [12]. However, it can’t be exploited on the cells that do not have markers. As such, label free microfluidic methods that specifically enrich cells of interest prior to flow cytometry analysis remains of interest. One examples of such approach is CTC-iChip [13]. In this device, RBC are removed through a micro-posts field. Larger cells move into a flow path for inertial focusing, and, finally, the magnetically labeled CTC.
Next, we’ll do sample delivery. For sample delivery, it is important to maintain the sample flow in a stable velocity. Stable velocity can contribute a lot to the accuracy of your measurement. Pressure is a typical way to deliver sample fluids in flow cytometer which includes syringe pumps, Peristaltic pumps and etc. Besides common solutions above, there are also devices using electrophoresis or electro-osmotic flow, on-chip peristaltic pumps, or pneumatic channels [14,15] to drive sample and sheath for analysis. The absence of moving part makes the fabrication and integration easy. Also, more complicated fabrication techniques are needed due to the need of controlling fluid properties.
Sample analysis is the next part. The first step is particle focusing. The most straightforward flow system to fabricate is clearly the non-tapered microfluidic channel. But the sample fluids under this situation will have parabolic flow profiles which causes the reduction of accuracy. There are devices could address concerns above and provide focusing in two dimension (top to bottom and side to side) using simple structure. A chevron structure in PDMS micro-channel could adjust the profiles of the fluids without using sheath fluids. Besides this, particle focusing approaches like bulk acoustic standing waves (BAW) [16,17,18] dielectrophoresis (DEP) [19,20,21] and Inertial particle focusing also seem promising.
Final step is the optical detection. In a optical detection process, the forward scatter (FSC) is roughly proportional to a particles relative size while the side scatter is roughly proportional to the internal granularity of the particle. In the process of detection, we also need to take fluorescent crosswalk into consideration. This problem could be solved by a math process called compensation. Also, a set of calibrated microsphere standards were made in order to standardize the result of evaluation.
6. Conclusion
The advancements in sensor technology and microfabrication techniques are transforming the landscape of wearable electronics. The wearable devices will play a bigger role in improving health care. These innovations are leading to the development of more efficient, reliable, and user-friendly health monitoring systems. By integrating advanced sensors with sustainable energy solutions, wearable electronic devices are becoming increasingly applicable and effective in various settings, significantly enhancing their potential impact on health and wellness.
References
[1]. Parlak, O., Keene, S.T., Marais, A., Curto, V.F., Salleo, A., 2018. Molecularly selective nanoporous membrane-based wearable organic electrochemical device for noninvasive cortisol sensing. Sci. Adv. 4, eaar2904.
[2]. Bandodkar, A.J., Gutruf, P., Choi, J., Lee, K.H., Sekine, Y., Reeder, J.T., Jeang, W.J., Aranyosi, A.J., Lee, S.P., Model, J.B., Ghaffari, R., Su, C.J., Leshock, J.P., Ray, T., Verrillo, A., Thomas, K., Krishnamurthi, V., Han, S., Kim, J., Krishnan, S., Hang, T., Rogers, J.A., 2019. Battery-free, skin-interfaced microfluidic/electronic systems for simultaneous electrochemical, colorimetric, and volumetric analysis of sweat. Sci. Adv. 5, eaav3294.
[3]. Miyamoto, A.; Lee, S.; Cooray, N. F.; Lee, S.; Mori, M.; Matsuhisa, N.; Jin, H.; Yoda, L.; Yokota, T.; Itoh, A.; etal. Inflammation-Free, Gas-Permeable, Lightweight, Stretchable on-Skin Electronics with Nanomeshes. Nat. Nanotechnol. 2017, 12, 907−913.
[4]. Kabiri Ameri, S.; Ho, R.; Jang, H.; Tao, L.; Wang, Y.; Wang, L.; Schnyer, D. M.; Akinwande, D.; Lu, N. Graphene Electronic Tattoo Sensors. ACS Nano 2017, 11, 7634−7641.
[5]. Jeong, J. W.; Kim, M. K.; Cheng, H.; Yeo, W. H.; Huang, X.; Liu, Y.; Zhang, Y.; Huang, Y.; Rogers, J. A. Capacitive Epidermal Electronics for Electrically Safe, Long-Term Electrophysiological Measurements. Adv. Healthcare Mater. 2014, 3, 642−648.
[6]. Kramer, R. K.; Majidi, C.; Wood, R. J. Wearable Tactile Keypad with Stretchable Artificial Skin. 2011 IEEE International Conference on Robotics and Automation, May 9−13, 2011; pp 1103−1107.
[7]. Yokota, T.; Inoue, Y.; Terakawa, Y.; Reeder, J.; Kaltenbrunner, M.; Ware, T.; Yang, K.; Mabuchi, K.; Murakawa, T.; Sekino, M.; et al. Ultraflexible, Large-Area, Physiological Temperature Sensors for Multipoint Measurements. Proc. Natl. Acad. Sci. U. S. A. 2015, 112,14533−14538.
[8]. B. K. Lee, J. H. Ryu, I. B. Baek, Y . Kim, W . I. Jang, S. H. Kim, Y . S. Yoon, S. H. Kim, S. G. Hong, S. Byun, H. Y . Yu, Adv. Healthcare Mater. 2017, 6, 1700621.
[9]. A. J. Bandodkar, P. Gutruf, J. Choi, K. Lee, Y . Sekine, J. T . Reeder, W . J. Jeang, A. J. Aranyosi, S. P. Lee, J. B. Model, R. Ghaffari, C.-J. Su, J. P. Leshock, T . Ray, A. Verrillo, K. Thomas, V . Krishnamurthi, S. Han, J. Kim, S. Krishnan, T . Hang, J. A. Rogers, Sci. Adv. 2019, 5, eaav3294.
[10]. J. Q. Hu, R. Li, Y . Liu, Y . W . Su, Sci. China: Phys., Mech. Astron. 2018, 61, 5.
[11]. A. K. Yetisen, J. L. Martinez-Hurtado, B. Unal, A. Khademhosseini, H. Butt, Adv. Mater. 2018, 30, 1706910.
[12]. V. M. Martin, C. Siewert, A. Scharl, T. Harms, R. Heinze, S. Ohl, A. Radbruch, S. Miltenyi and J. Schmitz, Experimental hematology, 1998, 26, 252-264.
[13]. E. Ozkumur, A. M. Shah, J. C. Ciciliano, B. L. Emmink, D. T. Miyamoto, E. Brachtel, M. Yu, P. I. Chen, B. Morgan, J. Trautwein, A. Kimura, S. Sengupta, S. L. Stott, N. M. Karabacak, T. A. Barber, J. R. Walsh, K. Smith, P. S. Spuhler, J. P. Sullivan, R. J. Lee, D. T. Ting, X. Luo, A. T. Shaw, A. Bardia, L. V. Sequist, D. N. Louis, S. Maheswaran, R. Kapur, D. A. Haber and M. Toner, Science translational medicine, 2013, 5, 179ra147.
[14]. P. Woias, Sensors and Actuators B: Chemical, 2005, 105, 28-38.
[15]. J. Atencia and D. J. Beebe, Lab on a chip, 2006, 6, 567-574.
[16]. P. P. Austin Suthanthiraraj, M. E. Piyasena, T. A. Woods, M. A. Naivar, G. P. Lopez and S. W. Graves, Methods, 2012, 57, 259-271.
[17]. M. E. Piyasena, P. P. Austin Suthanthiraraj, R. W. Applegate Jr, A. M. Goumas, T. A. Woods, G. P. López and S. W. Graves, Analytical chemistry, 2012, 84, 1831-1839.
[18]. A. Lenshof, C. Magnusson and T. Laurell, Lab on a chip, 2012, 12, 1210-1223.
[19]. M. Li, S. Li, W. Cao, W. Li, W. Wen and G. Alici, Journal of Micromechanics and Microengineering, 2012, 22, 095001.
[20]. D. Holmes, H. Morgan and N. G. Green, Biosensors and Bioelectronics, 2006, 21, 1621-1630.
[21]. C. Yu, J. Vykoukal, D. M. Vykoukal, J. A. Schwartz, L. Shi and P. R. C. Gascoyne, Microelectromechanical Systems, Journal of, 2005, 14, 480-487.
Cite this article
Xia,Y. (2025). Wearable Sensors for Smart Electronics. Applied and Computational Engineering,122,136-142.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
Disclaimer/Publisher's Note
The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of EWA Publishing and/or the editor(s). EWA Publishing and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
About volume
Volume title: Proceedings of the 5th International Conference on Materials Chemistry and Environmental Engineering
© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and
conditions of the Creative Commons Attribution (CC BY) license. Authors who
publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons
Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this
series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published
version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial
publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and
during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See
Open access policy for details).
References
[1]. Parlak, O., Keene, S.T., Marais, A., Curto, V.F., Salleo, A., 2018. Molecularly selective nanoporous membrane-based wearable organic electrochemical device for noninvasive cortisol sensing. Sci. Adv. 4, eaar2904.
[2]. Bandodkar, A.J., Gutruf, P., Choi, J., Lee, K.H., Sekine, Y., Reeder, J.T., Jeang, W.J., Aranyosi, A.J., Lee, S.P., Model, J.B., Ghaffari, R., Su, C.J., Leshock, J.P., Ray, T., Verrillo, A., Thomas, K., Krishnamurthi, V., Han, S., Kim, J., Krishnan, S., Hang, T., Rogers, J.A., 2019. Battery-free, skin-interfaced microfluidic/electronic systems for simultaneous electrochemical, colorimetric, and volumetric analysis of sweat. Sci. Adv. 5, eaav3294.
[3]. Miyamoto, A.; Lee, S.; Cooray, N. F.; Lee, S.; Mori, M.; Matsuhisa, N.; Jin, H.; Yoda, L.; Yokota, T.; Itoh, A.; etal. Inflammation-Free, Gas-Permeable, Lightweight, Stretchable on-Skin Electronics with Nanomeshes. Nat. Nanotechnol. 2017, 12, 907−913.
[4]. Kabiri Ameri, S.; Ho, R.; Jang, H.; Tao, L.; Wang, Y.; Wang, L.; Schnyer, D. M.; Akinwande, D.; Lu, N. Graphene Electronic Tattoo Sensors. ACS Nano 2017, 11, 7634−7641.
[5]. Jeong, J. W.; Kim, M. K.; Cheng, H.; Yeo, W. H.; Huang, X.; Liu, Y.; Zhang, Y.; Huang, Y.; Rogers, J. A. Capacitive Epidermal Electronics for Electrically Safe, Long-Term Electrophysiological Measurements. Adv. Healthcare Mater. 2014, 3, 642−648.
[6]. Kramer, R. K.; Majidi, C.; Wood, R. J. Wearable Tactile Keypad with Stretchable Artificial Skin. 2011 IEEE International Conference on Robotics and Automation, May 9−13, 2011; pp 1103−1107.
[7]. Yokota, T.; Inoue, Y.; Terakawa, Y.; Reeder, J.; Kaltenbrunner, M.; Ware, T.; Yang, K.; Mabuchi, K.; Murakawa, T.; Sekino, M.; et al. Ultraflexible, Large-Area, Physiological Temperature Sensors for Multipoint Measurements. Proc. Natl. Acad. Sci. U. S. A. 2015, 112,14533−14538.
[8]. B. K. Lee, J. H. Ryu, I. B. Baek, Y . Kim, W . I. Jang, S. H. Kim, Y . S. Yoon, S. H. Kim, S. G. Hong, S. Byun, H. Y . Yu, Adv. Healthcare Mater. 2017, 6, 1700621.
[9]. A. J. Bandodkar, P. Gutruf, J. Choi, K. Lee, Y . Sekine, J. T . Reeder, W . J. Jeang, A. J. Aranyosi, S. P. Lee, J. B. Model, R. Ghaffari, C.-J. Su, J. P. Leshock, T . Ray, A. Verrillo, K. Thomas, V . Krishnamurthi, S. Han, J. Kim, S. Krishnan, T . Hang, J. A. Rogers, Sci. Adv. 2019, 5, eaav3294.
[10]. J. Q. Hu, R. Li, Y . Liu, Y . W . Su, Sci. China: Phys., Mech. Astron. 2018, 61, 5.
[11]. A. K. Yetisen, J. L. Martinez-Hurtado, B. Unal, A. Khademhosseini, H. Butt, Adv. Mater. 2018, 30, 1706910.
[12]. V. M. Martin, C. Siewert, A. Scharl, T. Harms, R. Heinze, S. Ohl, A. Radbruch, S. Miltenyi and J. Schmitz, Experimental hematology, 1998, 26, 252-264.
[13]. E. Ozkumur, A. M. Shah, J. C. Ciciliano, B. L. Emmink, D. T. Miyamoto, E. Brachtel, M. Yu, P. I. Chen, B. Morgan, J. Trautwein, A. Kimura, S. Sengupta, S. L. Stott, N. M. Karabacak, T. A. Barber, J. R. Walsh, K. Smith, P. S. Spuhler, J. P. Sullivan, R. J. Lee, D. T. Ting, X. Luo, A. T. Shaw, A. Bardia, L. V. Sequist, D. N. Louis, S. Maheswaran, R. Kapur, D. A. Haber and M. Toner, Science translational medicine, 2013, 5, 179ra147.
[14]. P. Woias, Sensors and Actuators B: Chemical, 2005, 105, 28-38.
[15]. J. Atencia and D. J. Beebe, Lab on a chip, 2006, 6, 567-574.
[16]. P. P. Austin Suthanthiraraj, M. E. Piyasena, T. A. Woods, M. A. Naivar, G. P. Lopez and S. W. Graves, Methods, 2012, 57, 259-271.
[17]. M. E. Piyasena, P. P. Austin Suthanthiraraj, R. W. Applegate Jr, A. M. Goumas, T. A. Woods, G. P. López and S. W. Graves, Analytical chemistry, 2012, 84, 1831-1839.
[18]. A. Lenshof, C. Magnusson and T. Laurell, Lab on a chip, 2012, 12, 1210-1223.
[19]. M. Li, S. Li, W. Cao, W. Li, W. Wen and G. Alici, Journal of Micromechanics and Microengineering, 2012, 22, 095001.
[20]. D. Holmes, H. Morgan and N. G. Green, Biosensors and Bioelectronics, 2006, 21, 1621-1630.
[21]. C. Yu, J. Vykoukal, D. M. Vykoukal, J. A. Schwartz, L. Shi and P. R. C. Gascoyne, Microelectromechanical Systems, Journal of, 2005, 14, 480-487.