A review of NDE techniques for hydrogels

The rapid advancements in the areas of biosensors, soft robotics, and tissue engineering increased the demand for hydrogels (gels) and their composites. This is aided by the ease of tailoring their properties. Consequently, insights into the physical, chemical, mechanical and thermal behaviour of gels become critical. While these insights are traditionally obtained using conventional mechanical tests, performing these becomes difficult owing to the low stiffness and slippery nature of gels. The former is typical of materials like rubber and foam, while the latter is specific to gels. Therefore, reliable non-destructive evaluation (NDE) methods became important to address these difficulties. A range of NDE techniques, based on different physical and mechanical principles, are currently available. This article summarises the established and upcoming NDE techniques to encourage novel research in NDE from a soft materials perspective. It reviews the use of techniques like visual inspection, ultrasonic testing, digital image correlation, optical coherence tomography, infrared thermography, vibration testing, terahertz testing and shearography for the mechanical evaluation of gels. The hardware, working principles, advantages, and disadvantages, and the major areas of application are discussed for each technique. Future directions with regards to conjugated deployment of techniques, interfacing with machine learning, and real-time monitoring capabilities.

Automated summary

The rapid advancements in biosensors, soft robotics, and tissue engineering have increased the demand for hydrogels and their composites. However, evaluating the physical, chemical, mechanical, and thermal properties of gels using traditional mechanical tests is difficult due to their low stiffness and slippery nature. Therefore, non-destructive evaluation (NDE) methods have become important. This article reviews various NDE techniques for mechanical evaluation of gels, discussing their advantages, disadvantages, and applications. The article also highlights future directions such as combining techniques, integrating machine learning, and real-time monitoring.