Graphene as a Piezoresistive Material in Strain Sensing Applications

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.

A Practical Strain Sensor Based on Ecoflex/Overlapping Graphene/Ecoflex Sandwich Structures for Vocal Fold Vibration and Body Motion Monitoring

Graphene-based stretchable and flexible strain sensors are one of the promising “bridges” to the biomedical realm. However, enhancing graphene-based wearable strain sensors to meet the demand of high sensitivity, broad sensing range, and recoverable structure deformation simultaneously is still a great challenge. In this work, through structural design, we fabricated a simple Ecoflex/Overlapping Graphene/Ecoflex (EOGE) strain sensor by encapsulating a graphene sensing element on polymer Ecoflex substrates using a drop-casting method. The EOGE strain sensor can detect stretching with high sensitivity, a maximum gauge factor of 715 with a wide strain range up to 57%, and adequate reliability and stability over 1,000 cycles for stretching. Moreover, the EOGE strain sensor shows recoverable structure deformation, and the sensor has a steady response in the frequency disturbance test. The good property of the strain sensor is attributed to the resistance variation induced by the overlap and crack structure of graphene by structural design. The vibrations caused by sound and various body movements have been thoroughly detected, which exhibited that the EOGE strain sensor is a promising candidate for wearable biomedical electronic applications.

Shear strain-induced structure relaxation of Ni Σ17 [110](223) grain boundary: A molecular dynamics simulation

The stabilization of grain boundaries (GBs) is beneficial for improving the stability and mechanical properties of nanocrystalline (NC) metals. Molecular dynamics (MD) calculations were performed to investigate the shear response of Ni [Formula: see text]17 [110](223) symmetrical tilt GB. It was found that under the action of shear, the nucleation and evolution of the GB source Shockley partial dislocations ultimately result in the low-energy-state transformation of the GB structure units (SUs). However, the Ag atom contained in the GB increases the shear stress and strain required for the GB relaxation, and the strain range for the GB relaxation is expanded, indicating the inhibitory effect of the Ag atom on the structural relaxation of Ni [Formula: see text]17 [110](223) GB. As the temperature increases from 10 K to 250 K, the structural relaxation of Ni [Formula: see text]17 [110](223) GB becomes easier to proceed. In addition to segregation-induced GB stabilization, strain-induced GB relaxation and the roles of foreign atom and temperature clarified in this work could provide several new entry points for stabilizing high-energy GBs.

High Effective Sensitivity, Wide Working Range of Melt-blown Nonwoven Conductive Substrates for Smart Wearable Strain Sensors

High sensitivity, wide working range and flexible portability of strain sensors are crucial for smart wearable applications. To obtain these performances, several elastic melt-blown nonwoven substrates with excellent flexibility and high conductivity were developed by loading with polypyrrole through a double-dipping and double-rolling finishing method. The structure and conductivity are characterized by scanning electron microscope, infrared spectrometer, digital source meter and so on. The results indicate that the conductivity of prepared substrates is affected by the pyrrole concentration and polypyrrole amount deposited in nonwovens. Obviously, the conductivity and sensitivity of substrates as strain sensors are highly and positively correlated to the fiber orientation in nonwovens, and the effective working range and corresponding sensitivity of sensors are determined by the elastic deformation interval of melt-blown substrate. When the pyrrole concentration is 5.5%, the nonwoven substrate with 45.30% porosity possesses the anisotropic optimal conductivity with 23.491 S m-1 along winding direction and 15.063 S m-1 along width direction. Moreover, the as-prepared flexible conductive substrate exhibits the characteristics of wide working strain range (0-24.2%), high sensitivity with 1.94 gauge factor at the range, fast response (0.023 s), tiny hysteresis (0.011s), high durability and stability after 1000 cycles. Furthermore, the as-prepared sensor provides an effective method to prepare smart wearable strain sensors used as the monitor of finger bending in details and the precise recognition of human voice changes.

Strain Sensing Elastic Core-Spun Yarns Based on Long Silver Nanowires

Strain sensing is one of the important functions of intelligent fabric, which can transform the external stress (or strain) into visible electrical signals and monitor the characteristics of human physiology and motion. At present, the flexible strain sensor has low sensitivity, small strain range and unstable performance after repeated stretching. In this work, core-spun yarns with polyurethane (PU) filament as core and long silver nanowires (AgNWs) loaded cotton fiber as shell was fabricated by spinning technology. The results showed that when the loading of AgNWs was 10 wt%, the strain range of the PU/cotton@AgNWs core-spun yarn was 0-60%, the gauge factor of 12.6 was linear, and the strain sensing and mechanical properties were stable after repeated stretching. This strain sensing elastic core-spun yarns constructed by spinning technology could be used as one of the important materials for intelligent wearable devices.

A Miniaturized Thin-Plate Low Cycle Fatigue Test Method at Elevated Temperature

This study aims to develop a high temperature LCF test method using a non-standard miniature thin-plate (MTP) specimen in order to characterize cyclic visco-plasticity behavior of component materials. For demonstration, fully reversed strain-range controlled LCF and creep-fatigue (CF) tests at 600 °C have been performed for a martensitic steel using both standard-sized full-scale (SSFS) and MTP specimens. A scaling factor is determined using cyclic visco-plastic finite element (FE) for geometry constraint evaluation and data conversion based on the reference strain approach. The equivalent energy principal is proposed to assess the geometry constraint effect that non-standard MTP specimen has. The high temperature LCF results from the MTP specimen based on the proposed testing methodology have shown a good agreement with SSFS specimen data under equivalent conditions. The methodology can therefore be used to conduct accurate transferability to achieve equivalent LCF behavior between the conventional standard specimen and the MTP specimen.

Fabrication of Piezo-Resistance Composites Containing Thermoplastic Polyurethane/Hybrid Filler Using 3D Printing

In this study, 3D-printable flexible piezoresistive composites containing various amounts of cilia-like hybrid fillers were developed. In the hybrid fillers, micro-scale Cu particles with a 0D structure may allow them to easily disperse into the flexible TPU matrix. Furthermore, nanoscale multi-walled carbon nanotubes (MWCNTs) with a high aspect ratio, present on the surface of the Cu particles, form an electrical network when the polymer matrix is strained, thus providing good piezoresistive performance as well as good flowability of the composite materials. With an optimal hybrid filler content (17.5 vol.%), the 3D-printed piezoresistive composite exhibits a gauge factor of 6.04, strain range of over 20%, and durability of over 100 cycles. These results highlight the potential applications of piezoresistive pressure sensors for health monitoring, touch sensors, and electronic skin.

High-temperature Cyclic Deformation Behavior and Damage-coupled Unified Viscoplastic Constitutive Model for a Ductile Cast Iron QTRSi4Mo1

Isothermal low cycle fatigue tests for a ductile cast iron QTRSi4Mo1 were carried out at 500°C and 760°C. The results showed that it exhibited initial cyclic hardening followed by saturation at 500°C, while gradual cyclic softening occurred at 760°C due to a more pronounced creep effect. A damage-coupled unified viscoplastic constitutive model incorporating two nonlinear and one linear strain range-dependent drag stress components was developed to model the distinct strain range-dependent deformation behaviors. The piecewise damage evolution law was introduced to reflect the slow linear and the rapid nonlinear evolution characteristics during the damage development. Furthermore, the parameter identification approach for the unified viscoplastic model was proposed, including the initial estimates combined with the genetic algorithm-based global optimization procedure. The results showed that the proposed damage-coupled viscoplastic model can simulate the cyclic deformation behaviors and predict the LCF failure life of the ductile cast iron QTRSi4Mo1.