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Gravimetric Humidity Sensors
Published in Ghenadii Korotcenkov, Handbook of Humidity Measurement, 2019
As shown before, a decrease in the thickness of the piezoelectric layer gives a noticeable increase in sensitivity. But, at the same time as the thickness decreases, the mechanical strength of the self-appliance decreases significantly. To combine high sensitivity and acceptable mechanical strength, QCM devices are made in the form of a membrane on a silicon substrate (see Figure 12.5), using for this purpose various approaches developed for micromachining technology (Zhang et al. 2010). In this case, the resonator can operate up to approximately 1 GHz (O’Toole et al. 1992). In the literature, sensors manufactured using such technology have abbreviations TFR (a thin-film resonator) or TFBAR (thin-film, bulk acoustic resonator). Compared with QCMs, film bulk acoustic resonators (FBARs) are much more compact.
Ultra-Low-Power RF Transceivers
Published in Krzysztof Iniewski, Wireless Technologies, 2017
Emanuele Lopelli, Johan D. van der Tang, Arthur H. M. van Roermund
Different universities are involved in pioneering research on ultra-low-power devices and networks. At Berkeley University, an ultra-low-power microelectromechanical system (MEMS)-based transceiver has been developed [7]. Despite using a 1.9 GHz carrier frequency and only two channels, the receiver power consumption is 3 mA from a 1.2 V power supply. The data rate is 40 kbps at 1.6 dBm output power. The low receiver power consumption is mainly obtained by using a high quality-factor (Q) MEMS resonator implemented as a thin-film bulk acoustic resonator (FBAR). If more channels are needed, such as in the case of an FHSS transceiver, the hardware requirement increases linearly with the number of channels, making this choice impractical from a low-power point of view. The transmitter part adopts direct modulation of the oscillator and MEMS technology, eliminating power-hungry blocks such as phase-locked loops (PLLs) and mixers, therefore reducing the overall power consumption. Two major drawbacks can be foreseen in the proposed architecture. While reducing the circuit and technological gap toward a micro-Watt node, it relies on nonstandard components (MEMS), which increase the cost and require higher driving voltage. Furthermore, it lacks in robustness due to the use of only two channels, while requiring a linear increase of the power consumption with the channel number if a more robust frequency diversity scheme has to be implemented.
Zno Thin Films and Nanostructures for Acoustic Wave-Based Microfluidic and Sensing Applications
Published in Jiabao Yi, Sean Li, Functional Materials and Electronics, 2018
Hua-feng pang, j. K. Luo, Y. Q. Fu
A sensor is a device or instrument that converts the physical/chemical/ electrical/mechanical quantities into visible or readable signals. It normally consists of three units, including the input port, sensing unit, and output port, with a functional relationship between the input and output quantities in a form of electrical or optical signals. The sensor always appears as a probe device that widely exists around our world, covering natural sensors in living organisms such as eye, nose, and ear, and the artificial sensors including biochemical sensors, gas sensors, physical sensors such as humidity and temperature sensors, pressure sensor, and viscometers [20,21]. The sensor technologies have made a remarkable leap in the last a few decades owing to the development of micro-electromechanical systems (MEMS) in microelectronic engineering [20]. This allows multiple sensors to be manufactured at micro or nanoscale as microsensors or nanosensor, which can reach a significantly higher selectivity and sensitivity compared with the macroscopic sensors. Take thin-film bulk acoustic resonator (TFBAR) for instance, it operates with a frequency in the range of GHz and offers a high sensitivity to the variations of mass load [22]. Tremendous advances and latest technologies of the sensor structure, manufacturing technology, and signal-processing algorithms have been incorporated into micro and nanosensors and wireless sensor networks [23]. The sensors have now been broadly applied as an integral part in medical diagnostics, chemical, and biological recognition systems, health care, automobile and industrial manufacturing, and environmental monitoring. Among the various sensing materials, ZnO thin films and nanostructures have been widely used for designing and developing of the sensors due to their high sensitivity to the physical, chemical, and biological environment [21].
Shear horizontal wave propagation in multilayered magneto-electro-elastic nanoplates with consideration of surface/interface effects and nonlocal effects
Published in Waves in Random and Complex Media, 2022
The SH wave propagating along the x1 axis of a MEE multilayered nanoplate consisting of alternative PE and PM sublayers are taken into account, such as shown in Figure 1, in which both the surface/interface effect and nonlocal effects are included. Here, sub-layers are numbered from the bottom to the top, with the thickness of each layer represented by hj, and all of them are polarized along the x3 direction. Generally, the multilayered structure shown in Figure 1 is adopted on wave filters and nondestructive testing technologies [32–35]. The thin-film bulk acoustic resonator based on the Bragg scattering effect is the typical application of multilayered structures. With the development of modern communication techniques, these devices are becoming increasingly smaller and sometimes are fabricated with specified functions on the nanometer scale. Under this condition, the working performance analysis in nano-scale cannot be neglected anymore. For instance, due to interface stresses, the components related to stress, electric displacement, and magnetic induction are not continuous across the interface [18,35], which are totally different from the typical macro-structures. Our goal is to investigate surface/interface effects as well as nonlocal effects on SH wave propagation in the structure depicted in Figure 1.
Multi-objective optimisation model and hybrid optimization algorithm for Electric Vehicle Charge Scheduling
Published in Journal of Experimental & Theoretical Artificial Intelligence, 2023
Durga Mahato, Vikas Kumar Aharwal, Apurba Sinha
An imperative part for drivers and operators of the power grid is charging EVs for optimal management. Several studies have been devised for EV charging and smart charging methods. The majority of techniques focus on both controlled and uncontrolled EV charging. Uncontrolled EV charging may maximise the grid issue that involves losses in power, deviations in voltage, and substations overloads. A thin-film bulk acoustic resonator (FBAR or TFBAR) (Matoug et al., 2018) and Bulk-Acoustic-Wave (BAW) (Asderah & Kalkur, 2017) is a device which consists of a piezoelectric material which are produced by thin film methods. Piezoelectric materials produce an electric current when placed under mechanical stress which is called piezoelectricity. Due to this some current is produced but the current cannot be harnessed directly in an EV since batteries can store only DC power. The synchronised charging methods (Deng et al., 2022) are devised for alleviating the issues with various methods (Sobhanzadeh et al., 2021), like centralised and distributed control and time of use (TOU) (Yagcitekin & Uzunoglu, 2016). In Cao, Tang, et al., (2012), TOU price process was developed in the regulated market for charging optimisation in EVs to reduce charging costs by adapting battery SOC and charging power. Subsequently, they compared the outcomes of non-optimised charging cost and optimised (Aydin & Cetinkale, 2022) energy desire. A dual tariff charging method is devised for shifting charging requests using peak time to off-peak time (Ra et al., 2013). In Dallinger et al., (2013), TOU-based tariffs and uncontrolled charging are employed to discover grid-based vehicles’ irregular renewable energy combination rate impacts. In Nyns et al., (2010), an improved particle swarm optimisation technique is employed for optimising EV charging, but the slow charge optimisation technique of EV at night may affect performance. An optimisation technique with minimal charging cost is presented in Richardson et al., (2012). This model is addressed by Non-dominated Sorting Genetic Algorithm II (NSGA-II) algorithm and failed to adapt load fluctuation. In Wang et al., (2017), the EV level is optimised and coordinated wind power and charging scheduling is analysed. However, the model considers the node and transmission power voltage for EV charging (Jiang et al., 2017). Multi-objective optimisation is defined as the process of finding optimal solution for a desired goals.The purpose of EV’s CS is to provide a balance among different objectives, which includes reduction the electricity cost, battery degradation minimisation etc. There are various muti-objective optimisation techniques (Ridha et al., 2021) namely, monarch butterfly optimisation (MBO) (Singh et al., 2020), slime mould algorithm (SMA) (Zheng et al., 2021), moth search algorithm (MSA) (Yigit & Celik, 2020), Runge Kutta method (RUN) (Rawa et al., 2022), and Harris hawks optimisation (HHO) (Nalcaci et al., 2020).