研究目的
Investigating the capability of an optical fiber dynamic light scattering sensor to simultaneously assess concentration and flow speed of silica nanofluids under static and dynamic conditions.
研究成果
The optical fiber sensor successfully monitored silica nanofluids under static and dynamic conditions, providing precise measurements of concentration and flow speed. The sensor's performance was enhanced by artificial neural networks, achieving low average errors. This system offers a simple, minimally invasive, and nondestructive approach for assessing particle dispersions, applicable to various industries and laboratories.
研究不足
The study is limited by the simplicity and low cost of the system, which does not allow the evaluation of particle size distribution. Additionally, the high stability of the SiO2 nanofluid results in very slow sedimentation, requiring long observation times.
1：Experimental Design and Method Selection:
The study employed an optical fiber dynamic light scattering sensor to evaluate silica nanofluids. The methodology included static and dynamic condition tests to assess concentration and flow speed.
2：Sample Selection and Data Sources:
Silica nanoparticles were synthesized and dispersed in deionized water to create nanofluids. The sensor's response was evaluated in environments with different concentration zones and under flow conditions.
3：List of Experimental Equipment and Materials:
Equipment included a scanning electron microscope (SEM, EVO MA 15, Zeiss), a Malvern Zetasizer Nano Zn-Zen 3600 for size distribution, and an optical fiber sensor setup with a 1310 nm laser diode and single-mode fiber.
4：Experimental Procedures and Operational Workflow:
The sensor probe was immersed in different concentration zones of sedimented nanofluids and in flowing nanofluids to collect optical data. Data were processed using MATLAB routines.
5：Data Analysis Methods:
The autocorrelation function of the backscattered light intensity was analyzed to determine decay rates, which were correlated to concentration and flow speed using artificial neural networks.
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