A research team at Sheffield university has created a novel acoustoelastic imaging technique to enable noninvasive probing of local mechanical stresses in soft materials.
The method, which monitors the speeds of shear waves created by specially-programmed acoustic radiation force, was recently reported by researchers in a study just published in the journal Science Advances.
The approach represents a significant advancement in both the analysis of biological systems’ workings and the creation of soft machinery and gadgets.
Importance of Mechanical Stress in Biological Systems
Mechanical stress refers to the forces or pressures that act on materials, causing them to deform or undergo strain.
Mechanical stresses are crucial for the growth, upkeep, and repair of tissues and organs in biological systems; also in engineering soft machines and devices.Â
However, if the mechanical qualities are unknown, it is frequently difficult to assess these stresses in situ.
This problem is addressed by the new method the researchers have suggested, an approach based on acoustoelastic imaging is one of the most recent innovations.
Explanation of Acoustoelastic Imaging-Based Method
The researchers used a method from a rail that measures stress along railway lines using sound waves.
The method, which is used for both rail and medical ultrasound, is based on a straightforward tenet: the higher the tension, the faster the sound waves travel.
The researchers created a technique that transmits two sound waves in various directions using this idea and their mathematical hypotheses are then used to relate the tension to the wave speed.
The acoustoelastic imaging-based technique includes inducing shear waves in soft materials using a specially-programmed acoustic radiation force[1].
Without having to know the constitutive properties of the materials, the speed of the shear waves is remotely detected with an ultrasound transducer to infer local mechanical stresses.
By measuring the velocity of these waves, researchers may determine the local stresses in soft materials without understanding their fundamental properties.
As there is no intervention or deterioration of the materials being measured, the approach is noninvasive.
This method has been utilized to image the passive uniaxial stress in a skeletal muscle as well as the uniaxial and bending stresses in an isotropic hydrogel [2].
These measurements were done without knowing the constitutive parameters of the materials, demonstrating how well the method works for detecting local mechanical stresses in soft materials.
Potential Applications of the Technique
The acoustoelastic imaging-based approach, according to the researchers, has a wide range of possible applications, from healthcare to engineering.
The method can be applied in healthcare to monitor soft objects and machinery and to identify illnesses that change the stresses in soft tissues.
The technique can be used in engineering to create and improve soft machines and gadgets for a variety of uses, including wearable technology and soft robots.
The technology is particularly appealing for monitoring soft structures and tissues because it doesn’t require intrusive procedures or interventions.
It will be tested and improved upon by the researchers in an effort to make it widely applicable in a wide range of industries.
References
- Zhaoyi Zhang et al., ‘Noninvasive measurement of local stress inside soft materials with programmed shear waves‘, Science Advances, 8 March 2023, “We propose an acoustoelastic imaging-based method to infer the local stresses in soft materials by measuring the speeds of shear waves induced by custom-programmed acoustic radiation force.”, https://www.science.org/doi/10.1126/sciadv.add4082[↩]
- Zhaoyi Zhang et al., ‘Noninvasive measurement of local stress inside soft materials with programmed shear waves‘, Science Advances, 8 March 2023, “We demonstrate the application of the method by imaging uniaxial and bending stresses in an isotropic hydrogel and the passive uniaxial stress in a skeletal muscle.”, https://www.science.org/doi/10.1126/sciadv.add4082[↩]
