RESUMO
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.
RESUMO
The release of metal particles and ions due to wear and corrosion is one of the main underlying reasons for the long-term complications of implantable metallic implants. The rather short-term focus of the established in-vitro biocompatibility tests cannot take into account such effects. Corrosion behavior of metallic implants mostly investigated in in-vitro body-like environments for long time periods and their coupling with long-term in-vitro experiments are not practical. Mathematical modeling and modeling the corrosion mechanisms of metals and alloys is receiving a considerable attention to make predictions in particular for long term applications by decreasing the required experimental duration. By using such in-silico approaches, the corrosion conditions for later stages can be mimicked immediately in in-vitro experiments. For this end, we have developed a mathematical model for multi-pit corrosion based on Cellular Automata (CA). The model consists of two sub-models, corrosion initialization and corrosion progression, each driven by a set of rules. The model takes into account several environmental factors (pH, temperature, potential difference, etc.), as well as stochastic component, present in phenomena such as corrosion. The selection of NiTi was based on the risk of Ni release from the implant surface as it leads to immune reactions. We have also performed experiments with Nickel Titanium (NiTi) shape memory alloys. The images both from simulation and experiments can be analyzed using a set of statistical methods, also investigated in this paper (mean corrosion, standard deviation, entropy etc.). For more widespread implementation, both simulation model, as well as analysis of output images are implemented as a web tool. Described methodology could be applied to any metal provided that the parameters for the model are available. Such tool can help biomedical researchers to test their new metallic implant systems at different time points with respect to ion release and corrosion and couple the obtained information directly with in-vitro tests.
