Simultaneous Measurement of Surface Tension and Viscosity Using a Liquid Dynamics Sensor.

IF 10.7 2区 材料科学 Q1 CHEMISTRY, PHYSICAL
Naruhito Seimiya, Kuniharu Takei
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引用次数: 0

Abstract

The dynamics of liquids upon impact with an object exhibit distinctive behaviors influenced by physical parameters such as surface tension and viscosity, which can be determined by analyzing a liquid's dynamic response. However, measuring these parameters typically requires different tools, a complicated setup, increased space, and higher costs. To streamline this process, a liquid dynamic sensor capable of simultaneously extracting surface tension and viscosity via a single-step measurement is proposed. The proposed measurement method uses a superhydrophobic sensor comprising three electrode pairs, which are fabricated using laser-induced graphene on polydimethylsiloxane. The sensor monitors time-series resistance changes triggered by liquid impact dynamics. The results show that time-series liquid dynamics on the sensor surface vary with the liquid's surface tension and viscosity, allowing for the differentiation of these properties. By implementing an echo state network algorithm, surface tension and viscosity are successfully estimated simultaneously. In addition, the system demonstrates reliable generalization capability, accurately estimating the properties of unknown liquids, which confirms the proposed sensor's robustness for simultaneous measurement of liquid physical parameters.

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来源期刊
Small Methods
Small Methods Materials Science-General Materials Science
CiteScore
17.40
自引率
1.60%
发文量
347
期刊介绍: Small Methods is a multidisciplinary journal that publishes groundbreaking research on methods relevant to nano- and microscale research. It welcomes contributions from the fields of materials science, biomedical science, chemistry, and physics, showcasing the latest advancements in experimental techniques. With a notable 2022 Impact Factor of 12.4 (Journal Citation Reports, Clarivate Analytics, 2023), Small Methods is recognized for its significant impact on the scientific community. The online ISSN for Small Methods is 2366-9608.
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