Smart Sensors for Health and Environment Monitoring

In this chapter, the CMOS process-based biosensor platform composed of the carbon nanotube network (CNN) decorated with the gold nanoparticles (GNP) as the immobilization site for the probe molecules is introduced. Some issues are considered for applying integrated CMOS chip as a biosensor. We review several attempts from various research groups for immobilization of the probe molecules on the CNN channel surface and mechanisms for modulation of the channel resistance by the bound target molecules in the affinity-based sensors. We suggest the simple method using conventional CMOS gold bumps as sensing electrodes which reduces additional complex post-process. Additionally “padless” chip overcoming the problem of selective CNN coating electrically shorting the sensor electrode and the chip pad area is proposed as a real medical application.

This chapter reports a comprehensive review of the state-of-the-art in research on daily health monitoring and early diagnosis of specific diseases via the analysis of exhaled breath biomarkers. Different types of breath analyzing techniques including gas chromatography/mass spectroscopy (GC/MS), selected-ion flow-tube mass spectroscopy (SIFT-MS), and proton transfer reaction-mass spectrometry (PTR-MS) are compared to evaluate the unique strengths of each method. Recently, as an emerging breathsensing technique, we highlight chemiresistive-type gas sensors with characteristics of portability, cost effectiveness, and real-time analysis. Among various diseases, we focused on studies related to the diagnosis of diabetes and lung cancer. A number of studies have demonstrated a strong correlation between exhaled breath components and specific diseases, thus offering strong potential for clinical diagnostic application using exhaled breath sensors. In addition, we also summarized recent progress on daily healthcare such as fat-burning and halitosis through breath analysis. Finally, future perspectives on clinical applications using breath analyzing techniques are discussed.

Implantable neural sensors are the enabling devices for neural prosthesis that has the potential to restore lost functions in neurologically impaired humans. Movement loss due to a neurological disability is devastating, and has produced a large patient population for whom there are limited available therapeutic options. Permanently implanted cortical microelectrodes measure signals from regions of the brain that are directly related to intended movements. In conjunction with newly developed decoding techniques using probabilistic analysis, good correlation has been achieved for the arm movement of a non-human primate between the signals recorded directly from the brain and the real physical action by the animal. A recent culmination of research into brain sensor development has been human clinical trials where intention-driven neuronal ensemble activity has been converted into a control signal that enables a patient with tetraplegia to perform useful tasks. In this chapter, we describe the recent progress in the development of advanced brain implantable microsystems for the brain machine interface (BMI) that includes not only the optimal system architecture with ultracompact low power electronics, but also new technologies for a broad-band neural data transmission as well as an efficient power delivery to the brain implantable active electronics.

During the past decades, the use of intracortical microelectrode arrays for brain–computer interface has increased due to the high spatial and temporal resolutions compared with the noninvasive methods such as electroencephalogram (EEG), functional magnetic resonance imaging (fMRI), and near-infrared spectroscopy (NIRS). Recently, it was also reported that the intracortical microelectrode was implanted to the human brain for the purpose of controlling a robot arm. Although the invasive method with the microelectrode may have the safety and the ethical issues, it is undoubtable that the microelectrode array can provide the most precise and effective means to directly record and modulate the neural activity. To date, a variety of multichannel microelectrodes penetrating mammalian nerve tissues have been proposed with respect to shapes, materials, fabrication methods, and so forth. Among the various types, the silicon-based microelectrodes array has gained the biggest technical advances as well as the clinical applications. Despite the large amount of advance in research, however, the clinical use of the intracortical microelectrode arrays has not been realized mostly due to the failure of functionality for long-term applications. It is believed that the major failure mode of the microelectrode arrays is the brain tissue reaction against the implanted electrodes. Since the glial encapsulation acts as an electrical insulation layer around the electrodes, the neuronal signals cannot be recorded via the electrodes. In order to overcome this problem, various strategies have been attempted including the electrode design optimization, the flexible microelectrodes and the drug delivery to suppress the tissue responses. In this chapter, the technical advances for the high-density microelectrode arrays are reviewed and the various strategies are discussed to enable the clinical use of the intracortical microelectrode arrays for brain–computer interface as well as the treatment of brain disorders.

Optical coherence tomography (OCT) allows cross-sectional imaging of biological tissues at spatial resolutions on the order of several to tens of microns showing potential of detecting or screening for diseases. Until recently, however, OCT has been too slow for large volume imaging that greatly limits its clinical utility. The second-generation OCT technology has recently been developed that solves this problem by providing images at much higher frame rates with high sensitivity. In this chapter, we discuss the emergence and the recent advances of the second-generation OCT technology, and show the new applications and changes that this new technology has brought to the clinical field.

The importance of air quality monitoring is rapidly increasing. Although state-of-the-art air quality monitoring systems based on sophisticated optical systems or gas chromatography provide high accuracy measurement of air quality parameters, they cannot provide highly portable and/or personalized platform due to high cost, difficult maintenance and poor portability. With the advent of mobile electronic systems such as smartphones or wearable gadgets, people are more interested in obtaining personalized and highly localized environmental information rather than averaged and global information. To meet this need, highly integrated, ultra-compact, and low-power gas sensors that can be put into small electronic systems should be developed. The best approach to enable this is to use microfabricated (i.e., MEMS) sensing platform and nanoengineered sensing materials. In this chapter, we review the design and fabrication MEMS-based gas sensors and their applications to portable air quality monitoring. In addition, the principles, designs and the usage of functional nanomaterials to highly sensitive, highly selective and quickly responding air quality sensors are explained. Finally, we explain our recent development on the controlled synthesis of nanomaterials on microfabricated platform and its application to advanced gas sensing devices.

With the increased use of chemicals for laundry and cleaning, artificial adhesives, paints, and space heaters indoors, the concentration of volatile organic compounds (VOCs) increases, thus threatening human health. It is known that VOCs are the possible causes of atopic disease and asthma. Some of the VOC species such as formaldehyde and benzene are carcinogenic. The first step to avoid damage from VOCs is to determine their concentration in air. In typical approaches to detect VOCs, an adsorbent to capture the gas molecules of the VOCs is placed in the area of interest and taken to a laboratory, and subsequently, the VOCs are thermally desorbed and directed to large-sized detectors; the entire process is time-consuming and costly. With the increased public awareness of the hazards of VOCs, research on the cost-effective detection of VOCs using small personal devices has become more active. In this chapter, fundamental technologies to detect VOCs are introduced, and current research works are discussed on low-cost sensing of VOCs using miniaturized devices to evaluate indoor air quality.

Developing simple, low-cost, sensitive, and reliable gas sensors has been attracting great attraction due to promising applications to handheld devices such as smartphones, tablet computers, and remote air quality control. Simplicity in operation, high sensitivity, low cost, flexibility in production, and small size make chemoresistive gas sensors based on semiconducting materials including metal oxides and carbon nanomaterials as the prime candidate for the use in handheld devices over other types of gas sensors. In this chapter, we introduce the heaterless operation of chemoresistive gas sensors based on nanostructured metal oxide thin films to lower power consumption. Furthermore, we propose a route to develop high-performance chemoresistive gas sensors using 2-dimensional nanomaterials such as graphene, transition metal disulfide, and metal oxide nanosheets for room temperature operation which can dramatically reduce the power consumption.

Considering the increasing environmental threat posed by heavy metals and harmful chemicals, measures to remove and control these toxicants before their emission in significant levels into the ecosystem are urgently required. Concurrently, rapid, accurate measuring techniques and devices must be developed to monitor the progress; that is, it is essential to build a pollution prevention system wherein we can accurately and quickly assess the extent of heavy metals or harmful chemical contamination, either in the field where these pollutants are released or in the laboratory. Recently, a variety of molecular recognition technologies aimed at the highly selective and sensitive detection of target heavy metal ions at relatively low cost have been under development. In this chapter, recent research and development results for these inexpensive front-line heavy metal ion determination systems including optical, electrochemical, and chemical-mechanical sensors will be reviewed.

This chapter presents a nonlinear modulation based fatigue crack detection methodology. It is estimated that up to 90 % of failures of in-service metallic structure are the result of fatigue cracks. The conventional techniques, which rely on the linear property modifications of ultrasonic waves, are reported to be not sensitive enough to detect the fatigue cracks until they become visibly large. On the other hand, the sensitivity of the nonlinear ultrasonic techniques to defects has been shown to be far better than that of the linear ones. When a structure is damaged, it produces local nonlinearity, and the nonlinear components such as harmonics, subharmonics, modulations, and frequency shifting occur due to the damage. Among various nonlinear ultrasonic techniques, this chapter presents a fatigue crack detection technique based on nonlinear modulation. A fatigue crack detection algorithm using outlier analysis is introduced, and the algorithm is validated using an aluminum plate and real aircraft fitting-lug structure under various temperature and loading conditions.

One of the useful applications of wireless sensor networks is structural health monitoring, where sensors are distributed to monitor buildings, bridges, large dams, etc. Out of a large number of application domains we focus on the fatigue crack detection of a structure, e.g., bridge. In this chapter, we summarize the required components for data networking for autonomous fatigue crack detection and explore the design choices there. We first discuss the unique characteristics in delivering data stemming from autonomous fatigue crack detection such as data traffic pattern, and network topology, and the necessary degree of performance metrics, e.g., energy efficiency and latency. From the data networking perspective, we present and compare the strength and weakness of various design choices in wireless sensor networks, covering multiple layers in networking protocol stack.

Sensor networks are becoming extensively used to effectively and autonomously monitor our environment, where examples include environmental and habitat monitoring, structural health monitoring and condition-based equipment maintenance, and disaster management and emergency response. One of the popular and primary mechanisms for achieving low energy consumption in energy-constrained wireless sensor networks (WSNs) is duty cycling where each node periodically alternates the states of awake and dormant, motivated by the fact that a non-negligible portion of energy is consumed when in the idle listening state. In this chapter, under the framework of duty cycling, we survey the four key components for energy-efficient delivery of sensing data: (i) medium access control (MAC), (ii) routing, (iii) wake-up scheduling, and (iv) time synchronization. These four components are often coupled in many cases, where they have to be collaboratively optimized for better energy efficiency in operating WSNs. We survey the recent advances in those four components and conclude with the discussion of the future directions in this area.

In this chapter, we show how network-wide coding can be used to construct reliable wireless sensor networks that are power and bandwidth efficient. There are many challenges in designing efficient wireless sensor networks. Performance of wireless communication systems is severely affected by high path loss, fading, and interference. Furthermore, change in network topology due to mobility complicates neighbor discovery, routing, and scheduling. Our proposed network-wide coding, a coding strategy over networks, is a technique that can provide both reliability and efficiency when applied to a large-scale wireless sensor network in the presence of such obstacles.