Oscillatory Testing
Oscillatory Testing - Related Videos
11:26
Assessing Cerebral Autoregulation via Oscillatory Lower Body Negative Pressure and Projection Pursuit Regression
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Cited by 7 •
2014
Cerebral perfusion is maintained across a range of pressures via cerebral autoregulation. However, characterizing autoregulation requires prominent pressure fluctuations at regulated frequencies. The described protocol will show how oscillatory lower body negative pressure can generate pressure fluctuations to provide data for projection pursuit regression for quantification of the autoregulatory curve.
08:32
Assembly and Characterization of an External Driver for the Generation of Sub-Kilohertz Oscillatory Flow in Microchannels
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2022
The protocol demonstrates a convenient method to produce harmonic oscillatory flow from 10-1000 Hz in microchannels. This is performed by interfacing a computer-controlled speaker diaphragm to the microchannel in a modular manner.
07:56
Preparation of Liquid Crystal Networks for Macroscopic Oscillatory Motion Induced by Light
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Cited by 11 •
2017
The goal of the protocol is to create liquid crystalline polymer films that can mechanically oscillate under continuous light irradiation. We describe in great detail the conception of free-standing films, from the liquid crystal alignment method to the photo-actuation. The experimental protocol applied to prepare this material is broadly applicable.
07:28
Psychophysically-anchored, Robust Thresholding in Studying Pain-related Lateralization of Oscillatory Prestimulus Activity
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Cited by 8 •
2017
Psychophysical methods such as the QUEST estimation procedure can efficiently yield robust estimates of the stimulation intensity at which nonpainful sensations transition into painful sensations. By stimulating repeatedly at the threshold intensity, the variability in rating responses can directly be attributed to perceptual classifications in subsequent analyses.
FAQs
The phase shift shows how the sample’s response is timed relative to the imposed deformation or stress. Analyzing this relationship allows researchers to quantify viscoelastic behavior rather than describing stiffness alone. In bioengineering materials, the result helps distinguish responses associated with elastic energy storage from those associated with delayed, dissipative behavior during repeated loading.
Storage modulus quantifies the material’s elastic contribution, reflecting how much deformation energy is stored during oscillation. Loss modulus quantifies the dissipative contribution, indicating how much energy is lost as the sample responds. Considering both values provides a more complete description of hydrogels, scaffolds, tissues, and other soft materials than either measure alone.
Researchers can vary oscillation frequency, deformation or stress amplitude, and temperature. These changes probe how the sample’s measured response shifts under different loading rates or thermal conditions. Comparing the resulting phase shifts and moduli reveals changes in viscoelastic behavior and can expose structural differences that might not appear under a single testing condition.
Soft biological tissues and biomedical materials may experience repeated loading rather than one-time deformation. Applying oscillatory conditions that resemble physiological loading helps researchers evaluate stiffness, elasticity, energy dissipation, and structural changes in a more relevant context. This information supports assessment of whether an implant, scaffold, hydrogel, or related material has suitable mechanical behavior.
A sample is subjected to repeated deformation or stress, commonly in a sinusoidal pattern, while its response is recorded over time. Researchers can repeat measurements at selected frequencies, amplitudes, or temperatures, then analyze the phase shift between input and response. The analysis produces storage and loss moduli for comparing material behavior across conditions.
The method can characterize hydrogels, polymer scaffolds, biomaterials, and soft biological tissues. Testing these different sample types reveals how their stiffness, elasticity, energy dissipation, and structural behavior respond to repeated loading or changing conditions. Such comparisons help guide the development of tissue-engineering matrices, drug-delivery systems, implants, and other biomedical materials.