Physiological loading of knee cartilage is highly dynamic and may contribute to the progression of osteoarthritis. Thus, an understanding of cartilage's dynamic mechanical properties is crucial in cartilage research. In this study, vibrometry was used as a fast (2 h), noncontact and novel alternative to the slower (30 h), traditional mechanical and biochemical assays for characterization of cartilage from the condyle, patella, trochlear groove and meniscus. Finite-element models predicted tissue resonant frequencies and bending modes, which strongly correlated with experiments ( = 0.93). Vibrometry-based viscoelastic properties significantly correlated with moduli from stress relaxation and creep tests, with correlation strengths reaching up to 0.78. Loss modulus also strongly correlated with glycosoaminoglycan (GAG) content. Dynamic properties measured by vibrometry significantly differed among various knee cartilages, ranging between 6.1 and 56.4 MPa. Interestingly, meniscus viscoelastic properties suggest that contrary to common belief, it may lack shock absorption abilities; instead, condylar hyaline cartilage may be a better shock absorber. These data demonstrate for the first time that vibrometry is a noncontact approach to dynamic mechanical characterization of hyaline and fibrocartilage cartilage with concrete relationships to standard quasi-static mechanical testing and biochemical composition. Thus, with a single tool, vibrometry greatly facilitates meeting multiple regulatory recommendations for mechanical characterization of cartilage replacements.
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http://dx.doi.org/10.1098/rsif.2021.0765 | DOI Listing |
J Biomed Opt
July 2024
Stevens Institute of Technology, Center for Quantum Science and Engineering, Department of Physics, Hoboken, New Jersey, United States.
Significance: Tissues' biomechanical properties, such as elasticity, are related to tissue health. Optical coherence elastography produces images of tissues based on their elasticity, but its performance is constrained by the laser power used, working distance, and excitation methods.
Aim: We develop a new method to reconstruct the elasticity contrast image over a long working distance, with only low-intensity illumination, and by non-contact acoustic wave excitation.
Soft Matter
January 2024
Department of Mechanical Engineering, University of Wisconsin-Madison, Madison, 53706, WI, USA.
Sensors (Basel)
January 2023
Faculty of Electrical and Computer Engineering, Cracow University of Technology, Warszawska 24, 31-155 Cracow, Poland.
In the current economic situation of many companies, the need to reduce production time is a critical element. However, this cannot usually be carried out with a decrease in the quality of the final product. This article presents a possible solution for reducing the time needed for quality management.
View Article and Find Full Text PDFSensors (Basel)
December 2022
Centre for Audio, Acoustics and Vibration, University of Technology Sydney, Ultimo, NSW 2007, Australia.
Laser Doppler vibrometers (LDVs) have been widely adopted due to their large number of benefits in comparison to traditional contacting vibration transducers. Their high sensitivity, among other unique characteristics, has also led to their use as optical microphones, where the measurement of object vibration in the vicinity of a sound source can act as a microphone. Recent work enabling full correction of LDV measurement in the presence of sensor head vibration unlocks new potential applications, including integration within autonomous vehicles (AVs).
View Article and Find Full Text PDFMicromachines (Basel)
October 2022
Harvey Mudd College, Claremont, CA 91711, USA.
This work presents a behavioral model for a microelectromechanical (MEM) relay for use in circuit simulation. Models require calibration, and other published relay models require over a dozen parameters for calibration, many of which are difficult to extract or are only available after finite element analysis. This model improves on prior work by taking advantage of model normalization, which often results in models that require fewer parameters than un-normalized models.
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