In this paper, a highly sensitive piezoresistive differential pressure Microsensor is proposed. This Microsensor is consisted of a silicon microcantilever (Length=145 μ m; Width=100 μ m; Thickness=0. 29 μ m) and two piezoresistors were mounted (via proper connections) on the Microsensor for measuring the created pressure difference. Applying pressure to the microcantilever induces longitudinal and transverse stresses in the piezoresistors, changing their electric resistance and, consequently, the output voltage in the reading circuit of the Microsensor. Longitudinal and transverse stresses, different relative sensor resistances resulting from different pressures, voltage variations along the piezoresistors, and microcantilever deflection resulting from different pressures were investigated. To improve the sensor sensitivity, effect of doping concentration, piezoresistors width, and the width of the structure placed under the piezoresistors were studied. In addition, we studied how increasing the width and length of the beam influenced the sensitivity of the sensor. Based on analysis results, the sensor sensitivity was increased from 0. 26  /Pa to 15. 78  /Pa ( 60 times). To evaluate the behavior and performance of the proposed Microsensor, the following characteristics were analyzed: maximum microcantilever displacement, von Mises stress distribution along the beam and Microsensor resistance variations.

In this paper, the two-layer micro sensor is modeled as a two-layer clamped-clamped microbeam and it is optimized by using the genetic algorithm. Using the results of this research, clamped-clamped microbeams can be designed in such a way that the performance of Microsensors whose structure includes these microbeams will be improved. The quality factor, the sensitivity, and the maximum stress are selected as objective functions. The sensitivity and the quality factor are the functions of the natural frequency. The natural frequency is calculated based on Rayleigh’s method. The quality factor is calculated by approximation established on the one layer’s quality factor formula. To calculate the maximum stress, the system is assumed as a mass-spring system that has a harmonic displacement and the maximum deflection is the static deflection. The thickness of each layer, the width of the microbeam, and the length of the microbeam are selected as design variables. The optimization is done based on classical and non-classical theory by the genetic algorithm. The results based on both theories are approximately equal. The length of the microbeam is the most important variable and very changes (approximately 190%). The thickness of the silicon layer has the least effect on the results and changes just lower than  (approximately 20%). The results show that when the maximum stress decreases and the sensitivity increases, the quality factor decreases which is undesirable. Maximum sensitivity is obtained when the microbeam is very small.

The vibration analysis is an important step in the design and optimization of Microsensors. In most of the cases, COMSOL software is employed to consider the size-dependency on the dynamic behavior in the MEMS sensors. In this paper, the Modified Couple Stress Theory (MCST) is used to capture the size effect on dynamic behavior in a Microsensor with two layers of the silicon and piezoelectric. The governing equations of the system and also associated boundary conditions are derived based on the MCST and using Hamilton’ s principle by obtaining the total kinetic and potential energies of the system. Then, the obtained governing equations are solved using an analytical approach to determine the natural frequencies of the system. The first, second and third natural frequencies of the Microsensor are determined using an analytical approach. Finally, the natural frequency variations of the system are presented with respect to different values of the system parameters such as dimensionless parameters of the sensor geometric, the thickness of the silicon and piezoelectric layers and also the dimensionless material length scale parameter. The obtained results show that the material length scale parameter values and also the length, width, and thickness of each layer of the sensor are extremely effective on the vibration characteristics of the piezoelectric cantilever-based Micro Electro Mechanical System (MEMS) sensors. Also, the results show that the first natural frequency of the Microsensor will decrease with either increasing dimensionless material length scale parameter or decreasing the thickness of silicon and piezoelectric. This analytical approach presents an efficient method to predict the dynamic behavior of Microsensors and consequent optimization in their design procedure.

In this paper, new optimizations of the two-layer microbeams based on the classical and non-classical theory are presented. In the first step, the natural frequency is obtained based on the modified couple stress theories. Afterwards, three important functions of the microbeams which are used as Microsensors, sensitivity, quality factor, and maximum stress are defined. In the subsequent stage, two and three objective optimizations are carried out by using the genetic algorithm. At the two-objective optimization, sensitivity and quality factor are selected as objective functions. At the three objective optimizations, the maximum stress adds to the objective functions. The geometric parameters are design variables and there are some constraints and limits for those. The results are presented based on the classical and non-classical theory and optimal points are obtained for each optimization by using MATLAB.

Introduction: Early diagnosis of gastric cancer is the best solution for reversing cancer and curing patients. So far, gastric cancer is diagnosed in late stages due to misinterpretation of symptoms or absence of specific symptoms. Highly sensitive and selective detection methods for biomarkers are needed for early detection. Methods: This paper proposed a new fast screening method, based on the utilization of a new stochastic Microsensor, able to reliably perform the molecular recognition and quantitative determination of three biomarkers including CA19-9, CEA, and p53 in biological samples such as whole blood, saliva, urine, and tumoral tissue. Results: The limits of determination were lower than those used for the standard assay of these biomarkers in the specialized laboratories (e. g., ELISA, chemiluminescence) and this can facilitate the determination of CA19-9, CEA, and p53 at very low levels in biological fluids. The validation of the method was done; using biological samples (urine, saliva, whole blood, and tumoral tissues) from confirmed patients. Conclusions: The proposed tools and method proposed in this paper can be used as a screening test for early diagnosis of gastric cancer as well as to follow up on the efficiency of the proposed treatment.