High accuracy measurement of mechanical strain is critical and broadly practiced in several application areas including structural health monitoring, industrial process control, manufacturing, avionics and the automotive industry, to name a few. Strain sensors, otherwise known as strain gauges, are fueled by various nanomaterials, among which graphene has attracted great interest in recent years, due to its unique electro-mechanical characteristics. Graphene shows not only exceptional physical properties but also has remarkable mechanical properties, such as piezoresistivity, which makes it a perfect candidate for strain sensing applications. In the present review, we provide an in-depth overview of the latest studies focusing on graphene and its strain sensing mechanism along with various applications. We start by providing a description of the fundamental properties, synthesis techniques and characterization methods of graphene, and then build forward to the discussion of numerous types of graphene-based strain sensors with side-by-side tabular comparison in terms of figures-of-merit, including strain range and sensitivity, otherwise referred to as the gauge factor. We demonstrate the material synthesis, device fabrication and integration challenges for researchers to achieve both wide strain range and high sensitivity in graphene-based strain sensors. Last of all, several applications of graphene-based strain sensors for different purposes are described. All in all, the evolutionary process of graphene-based strain sensors in recent years, as well as the upcoming challenges and future directions for emerging studies are highlighted.

Coronavirus (COVID-19) is a highly infectious viral disease and first appeared in Wuhan, China. Within a short time, it has become a global health issue. The sudden emergence of COVID-19 has been accompanied by numerous uncertainties about its impact in many perspectives. One of major challenges is understanding the underlying mechanisms in the spread of this outbreak. COVID-19 is spread similar to the majority of infectious diseases through transmission via relatively large respiratory droplets. The awareness of the dispersal of these droplets is crucial in not only improving methods for controlling the dispersion of COVID-19 droplets, but also in discovering fundamental mechanisms of its transmission. In this study, a numerical model is developed to study the motion of droplets expelled through the respiratory system. Based on the source of these droplets, different sizes of droplets such as large ones and aerosols, which behave differently in the environment, can be generated. In this regard, diverse sources of droplets, namely breathing, coughing, and sneezing, are considered in this analysis. Besides, the time for a single droplet to fall from a height of 1.8 m is also obtained. The results reveal that the traditional distances suggested by different sources for keeping the social distance are not enough, which is linked to different nature of the droplet generation.

During the last decade, hydrodynamic cavitation has been implemented in various applications such as energy harvesting and biomedical applications. Facile hydrodynamic cavitation methods are required for fulfilling the requirements in these applications. In this study, a new generation microfluidic device containing eight parallel micro-orifices with a new design was fabricated and tested with the purpose of intensifying the cavitating flows and early cavitation inception. The roughness elements in the micro-orifices facilitated cavitation inception. This study presents a general perspective of occurrence of different cavitating flow patterns in microscale and addresses the ambiguities about the conditions for the formation of a specific flow pattern. Cavitation inception occurred with the appearance of small bubbles emerging from roughness elements at a rather low upstream pressure in the open loop experimental setup. A reduction in the cavitation number resulted in the formation of different flow patterns such as cavitation clouds, twin cavities, sheet cavities, and bubbly flows. Having several flow patterns with different intensities all together within a single microfluidic device is the main advantage of the proposed device over the state of the art microfluidic devices. Generation of flow patterns with various released energy levels makes this proposed device a unique multi-functional platform, which can be implemented to a lab on a chip platform for applications such as nanoparticle synthesis and wound healing.

Hydrodynamic cavitation is considered an effective tool to be used in different applications, such as surface cleaning, ones in the food industry, energy harvesting, water treatment, biomedical applications, and heat transfer enhancement. Thus, both characterization and intensification of cavitation phenomenon are of great importance. This study involves design and optimization of cavitation on chip devices by utilizing wall roughness elements and working fluid alteration. Seven different microfluidic devices were fabricated and tested. In order to harvest more energy from cavitating flows, different roughness elements were used to decrease the inlet pressure (input to the system), at which cavitation inception occurs. The implemented wall roughness elements were engineered structures in the shape of equilateral triangles embedded in the design of the microfluidic devices. The cavitation phenomena were also studied using ethanol as the working fluid, so that the fluid behavior differences in the tested cavitation on chip devices were explained and compared. The employment of the wall roughness elements was an effective approach to optimize the performances of the devices. The experimental results exhibited entirely different flow patterns for ethanol compared to water, which suggests the dominant effect of the surface tension on hydrodynamic cavitation in microfluidic channels.

During the last decade, hydrodynamic cavitation has been implemented in various applications such as energy harvesting and biomedical applications. Facile hydrodynamic cavitation methods are required for fulfilling the requirements in these applications. In this study, a new generation microfluidic device containing eight parallel micro-orifices with a new design was fabricated and tested with the purpose of intensifying the cavitating flows and early cavitation inception. The roughness elements in the micro-orifices facilitated cavitation inception. This study presents a general perspective of occurrence of different cavitating flow patterns in microscale and addresses the ambiguities about the conditions for the formation of a specific flow pattern. Cavitation inception occurred with the appearance of small bubbles emerging from roughness elements at a rather low upstream pressure in the open loop experimental setup. A reduction in the cavitation number resulted in the formation of different flow patterns such as cavitation clouds, twin cavities, sheet cavities, and bubbly flows. Having several flow patterns with different intensities all together within a single microfluidic device is the main advantage of the proposed device over the state of the art microfluidic devices. Generation of flow patterns with various released energy levels makes this proposed device a unique multi-functional platform, which can be implemented to a lab on a chip platform for applications such as nanoparticle synthesis and wound healing.

Using Finite Difference Time Domain (FDTD) simulations, we demonstrate the effectiveness of a photonic nanojet emerging from an SiO2 sphere semi-immersed in SU-8 material under 365 nm plane wave illumination for scanning skin malignancies.