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Method for evaluating fluid surface tension can aid with clinical diagnoses

MAY 22, 2020
Because certain health conditions change the viscosity of biological fluids, characterizing these changes is a promising diagnostic technique.
Method for evaluating fluid surface tension can aid with clinical diagnoses internal name

Method for evaluating fluid surface tension can aid with clinical diagnoses lead image

Biological fluids are made of a combination of high and low molecular weight proteins, lipids and surfactants, which, together, control the surface tension. Various health conditions affect the composition of these fluids, increasing the fluid viscosity and impacting its surface tension. Because the surface tension can be an important biological marker, the ability to measure it can help in the diagnosis of conditions like respiratory distress syndrome and chronic bronchitis. Liu et al. report a new method for studying this in biological fluids.

The authors generated capillary waves in porcine whole blood by an acoustic radiation force (ARF). They used optical coherence tomography (OCT) – which has been widely applied to study the mechanical properties of solids but rarely liquids – to evaluate the phase velocity of capillary waves, which relates to the surface tension. Comparing their experimental determination of the surface tension with theoretical calculations yielded very close agreement.

“We are excited to say that this method can be applied to differentiate actual pathologic fluids or blood from healthy subjects in future biomedical applications,” said author Hsiao-Chuan Liu.

Blood, plasma and mucus are the biological fluids used for clinical and laboratory analysis. Liu believes their method of using ARF-OCT can be applied to generate and record capillary waves in these fluids and be a promising noncontact method for evaluating surface tension and evaluating health conditions.

Source: “Fluid surface tension evaluation using capillary wave measurement with optical coherence tomography,” by Hsiao-Chuan Liu, Piotr Kijanka, and Matthew W. Urban, AIP Advances (2020). The article can be accessed at https://doi.org/10.1063/1.5143935 .

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