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<p>Quartz Crystal Microbalance (QCM) is a device that allows non-destructive measurements of r in situ reaction activities. In this article, an array comprising of six 3MHz QCM sensors in an array were characterized using a vector network analyzer and OpenQCM, a portable measuring instrument that measures change in resonance frequency. Measurements of S21 transmission characteristics using the vector network analyzer provides the resonance frequency and can also be used to derive the RLC equivalent electrical circuit values of a resonant two-port network based on the Butterworth-Van Dyke model. In this work, R_m, L_m, C_m and C_owere obtained via curve-fitting of the measurement results to the simulated results. Measurements were done in triplicates to verify reproducibility for all 6 sensors. For comparison, measurements were also done using a portable, open-source instrument, OpenQCM. The OpenQCM instrument directly measures changes in resonance frequencies, making it ideal for biosensing experiments, which correlate changes in mass with changes in resonance frequencies. Comparison between resonance frequency measurements using VNA and OpenQCM exhibit low percentage difference 0.2%.  This QCM sensor array has the potential of conducting real-time, point-of-care analyses for detection of biological molecules. </p>

The growing demand of wireless sensor networks has created the necessity of miniature, portable, long lasting and easily recharged sources of power. Traditional, hazardous batteries are rendered unacceptable and the viability of 'green' MEMS energy harvesters has become even more dominant. This paper reviews the state-of-theart MEMS piezoelectric energy harvesters which promise a cleaner environment and eliminate the disposal issue of conventional batteries. Piezoelectric devices are the perfect candidate for implementation in micro generators as they are easily fabricated, are silicon compatible and demonstrate high efficiencies for mechanical to electrical energy conversion. The characteristic equations which govern the conversion of mechanical vibration to electrical power are described in this paper. The typical operating modes for MEMS piezoelectric energy cantilevers which are namely; d 31 and d 33 are also detailed. Criteria for optimum material suitable for MEMS energy scavengers to produce maximum power output are also outlined. Several MEMS energy harvesters which have been successfully fabricated and tested are also critically reviewed in this paper. Finally a comparison table highlighting the advantages and disadvantages of each work is presented.

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Lung cancer is the leading cause of death worldwide and has a significant impact on public health across society. Among all types of cancer, lung cancer is typically silent and it is commonly diagnosed at a later stage where treatment is rarely achievable. There is an urgent need for the development of the early diagnosis of lung cancer for an improved survival rate. Preliminary research shows that lung cancer is accompanied by increased oxidative stress which generates volatile organic compounds (VOCs). Hence, breath analysis offers the most promising solution for the early diagnosis of lung cancer as it is noninvasive and radiation free. Potential VOCs biomarkers in exhaled breath associated with oxidative stress and lipid peroxidation have been discussed to provide a quick approach to the diagnosis of lung cancer. Although gas chromatography-mass spectroscopy (GC-MS) able to analyze the VOCs biomarker, it is bulky, high cost, required expertise to handle and consumes a lot of time. Hence, the sensor-based technique provides the solution to overcome the limitation. Recently, acoustic wave sensors such as quartz crystal microbalance (QCM) and surface acoustic wave sensors (SAW) have been used to identify the presence of VOCs in various applications. This is due to its high selectivity, good reproducibility, and fast response sensing materials. The selection of vapour sensing materials plays a crucial role in developing a highly sensitive and selective and fast response acoustic wave sensors. For this purpose, various types of sensing layers from metal oxides, polymers, biopolymers and composites have been studied. We present a critical review of advanced vapour sensing materials that are primarily used in acoustic wave sensors in identifying the presence of various VOCs. Criteria to evaluate the performance of the acoustic wave sensors such as resonance frequency and sensitivity are also discussed.

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