| dc.description.abstract | In the past decade, graphene and layered material such as Transition metal dichalcogenides (TMDs) and Transition metal oxides (TMOs) have been investigated for their importance in several applications. For example, these materials can be used in several fields such as energy storage and sensing device production; additionally, some of these important devices need to be scaled up for mass production. In this work, production of 2D material has been performed by liquid phase exfoliation (LPE) and the nanosheet study has been followed by strain sensor fabrication, incorporating the nanosheet in a polymeric matrix. Afterwards, a statistical computer program has been used to build a scheme able to predict the interactions between the composite variables. These predictions describe when and how the results are dependent on different variables.
Liquid phase exfoliation is a scalable method to produce large quantities of few-layer 2D materials. Here, we report on the scale-up production of WS2 in water and surfactant using shear exfoliation. Liquid exfoliation was performed changing processing parameters such as WS2 concentration, surfactant (Sodium Cholate, NaC) concentration, total volume, shear time, and shear rate. The extinction spectra for LPE WS2 was measured and empirical metrics allowed us to calculate the mean thickness and concentration of the dispersed nanosheets. The scaling equation of WS2 exfoliation in water and NaC was found and WS2 concentration and production rate were optimized, reaching a concentration of WS2 nanoflakes of 1.82 g/L and a production rate of 0.95 g/h.
With the development of sensor technology, the growing demand of strain sensors has taken place to realize devices such as wearable electronics necessary for biomonitoring, or devices able to detect pressure or vibration changes. For this reason, the necessity to improve mechanical and electrical properties led to the use of nanocomposites as strain sensors. Nanocomposites exploit the combined properties of both filler and matrix. A strain sensor is a material that changes its resistance when a strain is applied and, commonly, the resistance increases during the tensile deformation. The strain gauge is related to the change of resistance as a function of strain variation. The higher the gauge factor, the better the performance of the strain sensor. Considering that the strain gauge tells how fast the resistance changing as the composite is stretched, if the resistance increases, the gauge factor has positive values and for nanocomposites; this value can have a range from 2 to 500. Here, TMDs properties have been investigated including the nanomaterials in a poly (ethylene oxide) (PEO) matrix. The doping of TMDs (i.e. WS2 and MoS2) by the PEO yields conductive nanocomposites which act like sensors while stress transfer leads to nanosheets deformation. As a result, negative gauge factor has been found for these materials. This behaviour can be related to the band gap change of the material; in fact, spectroscopic and theoretical studies showed that the band gap of MoS2 can be changed under strain, implying a negative piezoresistivity. However, MoS2-PEO composite gauge factors are approximately −25, but fall to −12 for WS2-PEO composites and roughly −2 for PEO filled with MoSe2 or WSe2. In this work, electromechanical properties of these nanocomposites have been studied. We also develop a simple model which describes all these observations and show that these composites can be used as dynamic strain sensors. Different parameters describe how performant a sensor is and gauge factor and hysteresis are two of them. The gauge factor describes the electrical properties of a material and determines how the resistance changes as a function of strain. The hysteresis tells more about the mechanical properties of a polymeric composite. In particular, it describes the degree of material elasticity. So far, many attempts have been made to find a nanocomposite with both high gauge factor and low hysteresis proving this challenge particularly difficult. One solution could be combining several polymeric matrixes with nanosheets in order to obtain characteristics of all materials that would provide us with the desired properties. In the last part of this work, a statistical program called Design of Experiment (DOE) has been used to predict the best ensemble of variables that would provide the optimization of both gauge factor and hysteresis. Here, different ratios of two different silicon-based polymeric matrixes have been used such as Sylgard 170® and silicone oil with different oil viscosities. Finally, graphene nanosheets have been included into the matrix and the relationship between these parameters has been studied. | en |
