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研究生: 許家豪
Hsu, Chia Hao
論文名稱: 以微粒子影像流速儀發展微量血液黏度量測技術
Development of a Micro-Volume Viscometer for Blood Viscosity Measurements based on Micro-Particle Image Velocimetry (μPIV)
指導教授: 莊漢聲
Chuang, Han-Sheng
學位類別: 碩士
Master
系所名稱: 工學院 - 生物醫學工程學系
Department of BioMedical Engineering
論文出版年: 2014
畢業學年度: 102
語文別: 英文
論文頁數: 62
中文關鍵詞: 全血黏度牛頓流體非牛頓流體布朗運動微粒子影像流速分析儀
外文關鍵詞: Whole blood viscosity, Newtonian fluidics, Non-Newtonian fluidics, Brownian motion, μPIV diffusometry
相關次數: 點閱:100下載:1
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  • 全血黏度為一種可以提供身體訊息的病理資訊,例如:高血壓、多發性骨髓瘤、糖尿病等等。然而,目前市售或常用的液體黏度測量技術仍然具有許多無法突破的缺點,例如試驗溶液的需求量大、耗時、測量時易造成人為誤差、黏度計的工作範圍限制大或售價極高等缺點。由於這些缺陷,傳統黏度計很難應用在臨床的血液黏度檢測。為了解決以上問題,令全血黏度可以成為臨床使用的工具之一,此研究藉由微粒子影像流速技術,開發了一種新的微量黏度測量法。
    一般而言,藉由黏度,流體可以牛頓流體與非牛頓流體去做簡易的區分,其中牛頓流體是一種黏度並不會隨著流體的剪切率而改變的流體;而非牛頓流體則相反,非牛頓流體的黏度會因為不同的剪切率而發生改變,血液則屬於非牛頓流的一種。
    布朗運動是在流體中粒子的隨機擴散運動,其擴散量與液體黏度的關係可以用斯托克-愛因斯坦方程來表示,在此研究中,藉由微粒子影像流速分析技術可量化外加於樣本溶液內螢光乳膠粒子的布朗運動,並藉以求出樣本溶液的黏度。利用這項技術可以達到黏度的測量系統化、並且大幅降低測量時所需的試液量 (<2 μL),以及廣泛的測量範圍等等性質 (0.5 cP~ 1000 cP)。雖然在此時在血液黏度與疾病關係之資訊尚未被重視,本技術已可測量在靜態下不同血比容下之全血黏度,期盼基於圖像的微量黏度測量裝置可以在心血管及相關疾病診斷上提供有價值的資訊。

    Whole Blood viscosity can provide plenty of pathological information, such as hypertension, multiple myeloma, diabetes, and so forth. Conventionally, both capillary viscometers and rotary viscometers are most widely used in the clinical practices. However, capillary viscometers are easy to incur human errors while rotary viscometers are limited to working ranges, causing difficulty and hassle in cross-scale measurements. In addition, both instruments are sample consuming and wasteful. To solve the problems, a micro-volume viscosity measurement technique based on micro particle image velocimetry (μPIV) was therefore developed in this thesis.
    Generally, fluids can be divided into Newtonian and Non-Newtonian fluids based on viscosity. The viscosity of Newtonian fluid changes with the shear rate; whereas that of Non-Newtonian fluid varies with the shear rate. Accordingly, the whole blood is considered as Non-Newtonian fluid and should be carefully characterized.
    Brownian motion is known as random movement of particles suspended in a fluid. The relationship between the random movement and the liquid viscosity can be expressed by the Stoke-Einstein equation. In this thesis, the relationship was systematically investigated using the μPIV diffusometry. The liquid viscosity was eventually obtained by quantifying the Brownian motion of suspended particles dispersed in a medium. The technique was characterized by water-based solutions in dynamic and static measurement conditions. According to this technique, less than 2 μL samples are required in a static measurement and a broad viscosity range (0.5-1000 mPa•s) can be measured. Although, the relationships between the whole blood viscosity and diseases (diabetes, hypertension, multiple myeloma, and cancer) which receive less attention nowadays, the whole blood viscosity with different hematocrit can already be measured statically by this technique. The image based micro-volume viscometer can provide valuable information in cardiovascular diagnostics is expected.

    Abstract I 摘要 III 誌謝 IV List of figure VII List of table X Chapter 1 Introduction 1 1.1 Background 1 1.2 Literature review 2 1.2.1 Viscosity measurements 2 1.2.2 Micro particle image velocimetry (μPIV) 5 1.3 Motivation & Objective 6 Chapter 2 – Material & Methods 7 2.1 Brownian motion 7 2.2 Correlation algorithm 7 2.2.1 Peak Detection 9 2.2.2 Ensemble Cross-Correlation 10 2.2.3 Hindered diffusion 12 2.2.4 Derivation Viscosity 13 2.3 Phenomena of non-Newtonian liquids 15 2.3.1 Granular flow models for blood flow 15 2.4 Viscosity Measurement Based on a μPIV System 17 2.4.1 Chips design and fabrication 17 2.4.2 Experimental Setup 21 2.5 Sample Preparation 23 Chapter 3 – Results & Discussion 24 3.1 Characterization 24 3.1.1 Calibration of Measurements 24 3.1.2 Temperature effect 26 3.1.3 Error removal 28 3.2 Measurements of Newtonian Fluids 29 3.2.1 Static measurements 29 3.2.2 Verification of Newton viscosity law 30 3.3 Measurements of Non-Newtonian Fluids - Blood 33 3.3.1Time Stabilization 33 3.3.2 Hematocrit and Calibration factor for blood 34 3.3.3 Dynamic Measurements 38 Chapter 4 – Conclusion 41 Chapter 5 – Future Work 43 Reference 45 Appendix1 – Matlab code 48 Appendix2 – Award 62  

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