請用此 Handle URI 來引用此文件:
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/2392
標題: | 應用奈米金粒子布朗運動結合粒子追蹤測速之溫度量測參數最佳化 Parametric Optimization of PTV Based Temperature Measurement by Brownian Motion of Gold Nanoparticles |
作者: | Jun-Yang Lin 林均洋 |
指導教授: | 孫珍理 |
關鍵字: | 溫度量測,奈米金粒子,布朗運動,粒子追蹤測速, temperature measurements,gold nanoparticles,Brownian motion,particle tracking velocimetry, |
出版年 : | 2017 |
學位: | 碩士 |
摘要: | 本研究中探討利用奈米金粒子布朗運動位移推估周圍流體溫度之技術中,各項分析參數對於誤差之影響,以獲得最佳之參數及分析策略。我們使用內含直徑150 nm奈米金粒子之溶液,在不同的流體溫度與粒子密度下,拍攝並記錄粒子之布朗運動影像,影像中粒子之直徑約為11 px,並使用粒子追蹤測速法分析其位移以估算流體溫度,進而探討觀察時間、時間間隔與方均位移量等參數對溫度估算系統誤差與隨機誤差之影響,並找出最佳參數。
實驗之結果顯示,粒子密度對於溫度估算之系統誤差與隨機誤差並沒有明顯影響。但當粒子密度小於108 ml-1,觀察時間為10 s時,粒子運動較不易產生影像重疊的情況,較適合進行溫度估算。當方均根位移量 (RMSD) 小於1.5 px時,pixel locking現象會造成較大的溫度估算系統誤差與隨機誤差,但若增加RMSD或影像數量,則可有效降低pixel locking現象之影響;當RMSD介於1.5 至 2 px時,溫度估算之系統誤差有最小值。當RMSD 大於2 px時,在固定觀察時間條件下,RMSD越大即代表時間間隔較大,影像數量減少,誤差主要受到影像數量減少之影響而變大。但若在固定影像數量條件下,則RMSD小於1.3 px時,隨著RMSD增加,pixel locking之影響變小,使溫度估算之系統誤差減少。固定觀察時間下,當時間間隔為0.06 s時,溫度估算之總不確定性有最小值;固定影像數量下,當時間間隔為0.08 s時,溫度估算之總不確定性有最小值。 從實驗結果可歸納出,當觀察時間為10 s時,最佳RMSD介於1.5 至 2 px之間,可使pixel locking之影響降到最低,同時獲得足夠的影像數量,而當影像數量大於166時,溫度估算有較小之隨機誤差。最佳粒子密度介於105 ml-1與108 ml-1間,此時粒子運動不易產生影像重疊,且影像中有足夠的粒子可供分析。 In this study, we explore the accuracy of using Brownian motion of gold nanoparticles to quantify the temperature of surrounding fluid. By employing the particle tracking velocimetry (PTV), the displacement of gold nanoparticles is measured and used to estimate the temperature through Einstein’s theory. In the image, the diameter of each particle is approximately 8 px. Influences of the tracking time, time interval, particle density and number of frames are investigated to obtain the optimal parameters, which minimize the total error. The experimental results show that particle density plays a minor role in the temperature estimation. Nonetheless, particle density lower than 10-8 ml-1 is recommended in order to avoid overlap of particles. When the root mean squared displacement (RMSD) is smaller than 1.5 pixels, pixel locking is more severe, which leads to higher systematic errors and random errors. However, random error can be reduced by increasing the number of frames at small RMSD. When RMSD falls between 1.5 and 2 pixels, temperature estimation has the lowest systematic error. Once RMSD exceeds 2 pixels, error is majorly influenced by number of frames. For a given tracking time, longer time interval is required to obtain larger RMSD, which results in fewer images. Hence, both systematic and random error raise with the increase of RMSD. For a given number of frames, on the other hands, systematic errors are nearly independent of RMSD if RMSD is larger than 1.3 pixel. Due to influence of pixel locking, systematic errors decreases with increasing RMSD if RMSD is smaller than 1.3 pixel. When tracking time is fixed, the optimal time interval is 0.06 s. When the number of frames is fixed, the optimal time interval is 0.08 s. In summary, the optimal RMSD falls between 1.5 and 2 pixels for a tracking time of 10s, so that number of frames are sufficient and the effect of pixel locking can be minimized. To reduced random errors, more than 166 frame should be taken with a RMSD larger than 1.5 pixel and smaller than 2 pixel. |
URI: | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/2392 |
DOI: | 10.6342/NTU201701737 |
全文授權: | 同意授權(全球公開) |
顯示於系所單位: | 機械工程學系 |
文件中的檔案:
檔案 | 大小 | 格式 | |
---|---|---|---|
ntu-106-1.pdf | 2.85 MB | Adobe PDF | 檢視/開啟 |
系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。