Next Generation Sensors and Systems

New nanostructured Schottky junctions based on graphene/platinum grown on different substrates are fabricated and investigated for sensing applications. The integration of graphene layer (s) on regular M-S junctions was only possible by using an ALD-grown platinum thin film (~40 nm) and then growing graphene in PECVD at temperatures lower than platinum silicide formation temperature (i.e., <700 °C). The electrochemical and radiation sensing behaviors were investigated using two different substrate types. The first is a moderately-doped n-type (e⁻ conc. ≈ 2 × 10¹⁵ cm⁻³) silicon substrate in which a Schottky rectifier response with different threshold voltages was observed. An order of magnitude increase in generated current was observed with the use of high-resistivity silicon substrates (ρ ≥ 10,000 Ωcm). By adding a dielectric layer to form a graphene-metal-insulator-semiconductor junction (Graphene–MIS) a linear ohmic response was observed. The obtained responses were explained by studying the band diagrams for the different processes with the aid of XPS and Raman analyses that clearly indicated the p-doping of the graphene layer in response to γ-ray radiations, which resulted in a strong reversed current tunneled through the ultrathin platinum layer. In the case of high-resistivity silicon substrates, the reversed current is much stronger due to the weak forward current from the substrate, which resulted in a stronger reverse response and, ultimately, higher sensitivity. The uniqueness of the research is based on the process for growing the graphene layer on the M-S junction. Exfoliated graphene results in increased contact resistance and low e− mobility, which will not yield the desired effects.

In this work, we present the design of a planar interdigital microsensor for the characterization of biological mediums by impedance spectroscopy. We propose a theoretical optimization of the geometrical parameters of this sensor. The optimization allows to extend the measurement frequency range by reducing the polarization effect which is manifested by a double layer (DL). We present also a new method for a planar interdigital transducer to determine the parameters (relative permittivity, capacitance) of the double layer (DL) on the surface of the electrode loaded by a biological medium. The CoventorWare^® software was used to simulate the model design of interdigital transducer in three dimensions (3D). The simulation results are coherent with the method proposed. Therefore, this method can be used to determine the parameters of double layers of a planar interdigital sensor in order to match its frequency band to the intented application.

Phthalates are the most ubiquitous chemicals that are used as plasticizer in almost every plastic product manufacturing including food packaging, toys, and pharmaceutical consumables. The carcinogenic and teratogenic nature of these synthetic chemicals has been well cited in published research as a potential health threat for all living beings on earth. Contemporary phthalates detection techniques require high level of skills, expensive equipment and extended analysis time as compared to the rapid assay method presented in this chapter. A real-time, non-invasive detection of phthalates was performed by employing electrochemical impedance spectroscopy technique incorporating an enhanced penetration depth interdigital capacitive transducer. A research was conducted to investigate the application of pre-concentration extraction polymer with molecular imprinted recognition sites as an analyte sensitive coating for the sensor to introduce molecular selectivity for di (2-ethylhexyl) phthalate molecules. Self-assembled monolayer with embedded imprinted polymer was used to functionalize the sensing surface in order to capture phthalate molecules spiked in the water samples and commercial drinks. Equilibrium rebinding experiments were conducted to demonstrate the adsorption capabilities of the molecular imprinted polymer coating. Impedance spectra were obtained over a frequency range by applying frequency response analyzer (FRA) algorithm to observe impedance change that occurred for various concentrations of the analyte. Complex nonlinear least square (CNLS) curve fitting spectrum algorithm interpreted the experimentally obtained impedance spectra into electrochemical equivalent electric circuit and corresponding parameters. Analysis of the equivalent circuit was used to explain the kinetics of chemical bindings taking place at the electrode-electrolyte interface in the electrochemical cell in terms of electrical component parameters. The results from the proposed detection system were validated by high-performance liquid chromatography (HPLC-DAD).

We report on magnetic, transport and structural properties of Co_x-Cu_100−x (5 ≤ x ≤ 30) and Fe₃₇Cu₆₃ glass-coated microwires prepared by the Taylor-Ulitovsky method. The objective of the reported work is to develop a novel functional materials exhibiting giant magnetoresistance (GMR). For Co-Cu microwires with x = 5 we observed the resistivity minimum at 40 K associated with the Kondo-like behaviour but magnetoresistance is small. For x ≥ 10 magnetoresistance reaches 9 % at low temperatures. Temperature dependence of susceptibility shows considerable difference for x > 10 and x ≤ 10 attributed to the presence of small Co grains embedded in the Cu matrix for x ≥ 10. By X-ray diffraction we found, that the structure of Co_x-Cu_100−x microwires for x ≥ 10 is granular consisting of two phases: fcc Cu appearing in all the samples and fcc α-Co presented only in microwires with higher Co content. Structure of Fe₃₇Cu₆₃ microwires consists of Cu nanograins with average grain size of around 40 nm and α-Fe nanocrystals with average grain size ranging between 6 and 45 nm depending on samples geometry. These microwires also exhibit GMR (up to 7.5 % at 5 K).

In this chapter we are reporting on correlation of Giant magnetoimpedance (GMI) effect and magnetic properties of amorphous and nanocrystalline Co-Fe rich glass-coated microwires. We measured the GMI magnetic field, frequency dependences and hysteresis loops of composite microwires produced by the Taylor-Ulitovsky technique. We observed that GMI effect and magnetic softness of glass-coated microwires produced by the Taylor-Ulitovsky technique can be tailored either controlling magnetoelastic anisotropy of as-prepared microwires or controlling their internal stresses and structure by heat treatment. High GMI effect has been observed in as-prepared and annealed Co-rich microwires. In the case of Fe-rich Finemet-type microwires we observed considerable magnetic softening of studied microwires after annealing. This magnetic softening correlates with the devitrification of amorphous samples. Amorphous Fe-rich microwires generally exhibited low GMI effect (GMI ratio below 5 %). Considerable enhancement of the GMI effect (GMI ratio up to 100 %) has been observed in heat treated microwires with nanocrystalline structure. The objective of this reported work is to develop magnetically soft thin wires for applications in magnetic field sensors.

Zirconia electrochemical oxygen sensor is widely used today in different industrial power and chemicals production applications (O₂-Analyzer) and transportation (lambda sensor). Non-Faradaic Electrochemical Modification of Catalytic Activity (NEMCA) was invented in 80th and shown high efficiency in the catalytic activity and selectivity increase of the gas exposed electrodes. For the first time NEMCA was investigated for the activation and re-activation of the industrial potentiometric oxygen sensor based on stabilized zirconia solid electrolyte. Electrochemical promotion of O₂-sensors with Pt-cermet electrodes aged in the field was significant with up to 5 times in the sensor’s impedance reduction. Stability tests on the NEMCA activated O₂-sensors were showing just a minor impedance increase after 2 weeks. Sensor response time was also reduced using NEMCA by ~5–10 % with up to 3 times the sensor’s signal noise reduction on the aged zirconia oxygen electrochemical cells.

Monitoring respiration rate in everyday life enables an early detection of the diseases and disorders that can suddenly appear as a life threatening episode. Respiratory Rate (RR) is defined as the number of breaths per minute and is a very important physiological parameter to be monitored in people both in healthy and critical condition, as it gives meaningful information regarding their respiratory system performance as well as condition. A typical RR for adult human being at rest is 12–20 and its corresponding frequency is 0.2 Hz approximately. During recovery from surgical anesthesia, a μ-opioid agonists used for pain control can slow down RR leading to bradypnea (RR < 12) or even apnea (cessation of respiration for an indeterminate period), while airway obstructions like asthma, emphysema and COPD. In all these cases long term monitoring can extend the capabilities of healthcare providers but only constraint lies with the performance reliability along with the economic barrier. In this chapter, a MEMS based capacitive nasal sensor system for measuring Respiration Rate (RR) of human being is developed. In order to develop such system, two identical arrays of diaphragms based MEMS capacitive nasal sensors are designed and virtually fabricated. A proposed schematic of the system consists of signal conditioning circuitry alongwith the sensors, is described here. In this proposed scheme, the two identical sensor arrays are mounted below Right Nostril (RN) and Left Nostril (LN), in such a way that the nasal airflow during inspiration and expiration impinge on the sensor diaphragms. Due to nasal airflow, the designed square diaphragm of the sensor is being deflected and thus induces a corresponding change in the original capacitance value. This change in capacitance value is be detected by a CMOS based clocked capacitance-to-voltage converter. The capacitive type MEMS sensors often suffer from stray and standing capacitive effect, in order to nullify this precision interface with MEMS capacitive pressure sensor, followed by an amplifier and a differential cyclic ADC is implemented to digitize the pressure information. The designed MEMS based capacitive nasal sensors is capable of identifying normal RR (18.5 ± 1.5 bpm) of human being. The design of sensors and its characteristics analysis are performed on a FEA/BEA based virtual simulation platform.

It is very essential to visualise the internal condition of human body not only for studying the anatomy and physiology, but also for diagnosing a disease. Physicians always try to analyze an organ or body part in order to study its physiological and anatomical status for understanding and/or treating its illness. Thus, it is always requisite to introduce the diagnostic tool called medical imaging. The period of medical imaging started in 1895, when Roentgen discovered the powerful invisible rays called X-rays. Gradually the medical imaging introduced X-Ray CT, Gamma Camera, Positron emission tomography (PET), Single-Photon Emission Computed Tomography (SPECT), Magnetic Resonance Imaging (MRI), and Ultra SonoGraphy (USG). Recently, medical imaging field is more advanced with comparatively newer tomographic imaging modalities like Electrical Impedance Tomography (EIT), Diffuse Optical Tomography (DOT), Optical Coherence Tomography (OCT), and Photoacaustic Tomography (PAT). The EIT has been extensively researched in different fields of science and engineering due to its several advantages. In correlation with the application of the EIT in the medical field, thoracic electrical impedance tomography (EIT) is used to diagnose patients suffering from the acute respiratory distress syndrome (ARDS) for monitoring their conditions ranging from dynamic shifting of body fluids to lung aeration, right at the bedside. Moreover, EIT-derived numeric parameters would help the physician to evaluate the state of the lung of a patient under observation. Thus, here we have performed a Finite Element Method based simulation study for monitoring the condition of lungs and heart of ARDS patients. Therefore, a finite element method (FEM) model of human thorax in three dimensional platform with FEM Multiphysics software is created and tested with new ventilation indices regarding their ability to quantitatively describe structural changes in the lung due to the gravitationally dependent lung collapse. Additionally, analysis is made to find the electrode pairs capable of separating the lung and heart activity when a particular amount of constant current is injected through them, are also carried out. Finally, a real time EIT system using 16 Ag-AgCl electrodes are developed to get the image of human thorax. The data are collected using the adjacent current injection technique and are plotted using FEM Multiphysics software. The reconstructed FEM images using the forward solver of EIT method shows the approximate area of the thorax (lungs, heart etc.) under observation. This chapter will present a brief overview on application of EIT for monitoring of the lung fluid movement and estimation of lung area in a human being alongwith physical and mathematical aspect with a goal to achieve a system having higher potential to cater medical challenges in lung oriented diseases.

Activity recognition is a popular research area with a number of applications, particularly in the smart home environment. The unique features of smart home sensors have challenged traditional data analysis methods. However, the recognition of anomalous activities is still immature in the smart home when compared with other domains such as computer security, manufacturing defect detection, medical image processing, etc. This chapter reviews smart home’s dense sensing approaches, an extensive review from sensors, data, analysis, algorithms, prompting reminder system, to the recent development of anomaly activity detection.

There are currently limited options for the meat producers for monitoring the water content of their products as they are processed or cured. Most existing methodologies are destructive, or require the use of probes which touch or penetrate the meat and lead to issues of contamination and damage. Thus, the aim of this investigation is to use an electromagnetic (EM) wave sensor to monitor the meat drying process and determine its suitability as a non-destructive and non-contact technique. The sensor has been modelled using High Frequency Structure Simulation Software (HFSS) and then constructed. Experimental work was conducted involving measurement of meat weight and EM signature (namely the S₁₁ parameter in the frequency range 1–6 GHz) over a period of approximately 1 week, with measurements recorded every hour. The change in EM signature and weight loss has been analysed and correlations drawn from the resultant data. The results demonstrate a strong relationship between the S₁₁ measurement and weight loss of the meat sample (R² = 0.8973), and it is proposed that this could be used as the basis for future industrial application for measuring meat products during drying processes, such as those used in curing.

In recent times, number of researchers have investigated vehicle tracking applications by fusing the measurements done by accelerometers (as part of Inertial Navigation System-INS) and Global Positioning System (GPS). Since smartphones contain both the set of sensors, there exists a high degree of interest in utilizing personal phones for such tracking applications. However, mobile phone sensors have limitations in measurement accuracy and reliability. Usually, sudden changes in vehicle speed are not always captured well by GPS. Accelerometers, on the other hand, suffer from multiple noise sources. In this chapter, we investigate the noise performance of a few smartphone based accelerometers. Then, we apply the said noise analysis for improving the estimation of the speed of moving vehicle, as captured by GPS. A number of experiments were carried out to capture the vehicle’s position and speed from OBD2 (On Board Diagnosis V2), GPS as well as 3-axes accelerometer. We also demonstrate a method by which the phone’s orientation is compensated for while calculating speed from the measured acceleration. Further, a new method of INS/GPS fusion is proposed which enhances the accuracy of speed estimation. It is envisaged that with increasing estimation accuracy, the application of multi-sensor fusion in autonomous vehicles will be greatly enhanced.

This chapter reviewed and explained the importance of water quality monitoring and its technology from generation to the distribution. The authors are reporting a SCADA based intelligence sensing system which is utilising the Internet of Things to improve easy access to monitor water quality, treatment process and plant health in real time. Internet of Things, a new era of computing technology and a smart connect, machine to machine, machine to infrastructure, machine to environment an intelligent system which can talk each other, make operational functions in universal network in the cloud. Continuous surveillance of potable water quality during production and delivery, what is happening in a plant and information required to apply needs in the water distribution network requires huge connection infrastructure and its availability with time. IoT is the solution of these problems of platform. In North 24 parganas Arsenic area water supply scheme uses Internet of Things 1st time in India to access water quality information, plant information, and water supply information globally anywhere in the earth having the network of internet. This paper describes how IoT works in a water treatment process, production and distribution in a scalable way. This will be the convergence of consumer, business and industrial internet considering environmental impacts. This will be the way to get the information in your pocket. This chapter also describes how SCADA works with IoT to assist in the production of potable safe drinking water. The case study describes the functionalities of Supervisory Control and Data Acquisition System (SCADA) and Internet of Things (IoT) system installed which proves its sustainability for last 10 years. It helps to minimize the cost of production, smooth delivery and maximize water quality to help rural people from menace of various water contaminations like Arsenic threat. This chapter also described the features of future water monitoring through indigenously developed sensors.

In this work a novel way for the recognition and classification of objects inside a scene, inspired by the well-known frame theory, is described. First of all, three-dimensional (3D) point clouds have been obtained using a laser triangulation system of high resolution, in order to achieve low noise datasets for the method validation. Once specific areas are extracted following a novel autonomous algorithm, a fit of the second order is defined on these acquired samples to find local curvatures. Such information is compared to the words of a precomputed dictionary made of curvatures, which constitutes the frame basis. The results of the comparison give evidence to the presence of particular objects in the scene under investigation. As a matter of fact, introducing a threshold value ζ, similarities can be found and thus objects can be recognized. Specifically, the final results state the effectiveness of the method to distinguish objects having different surface properties. Moreover, the agreement of results is proven despite of the contribution of the measurement noise which produces outlier points.

A design of self-generating component powered by magnetic energy harvesting is presented. In order to demonstrate the devices, a magnetic field alarm is developed. It consists of an energy harvesting module, Cockcroft-Walton circuit and piezo buzzer. The energy harvesting module is composed of coil and magnetic flux concentration core. It can generate 200 µW from an environmental magnetic field of 200 µT at 60 Hz. The Cockcroft-Walton circuit can converts the AC voltage to a suitable DC voltage for the piezo buzzer. This alarm can notice not only the magnetic field level defined by ICNIRP2010 but also the existence of magnetic field energy to be harvested. For the further developments, theoretical estimations for harvesting energy, effective permeability and optimum load condition are also discussed.