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  • 學位論文

奈米液滴汽化與後續穴蝕效應之數值模擬

Numerical modeling of nanodroplet vaporization and concomitant cavitation

指導教授 : 李百祺
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摘要


聲穿孔作用是指利用超音波與超音波對比劑的相互作用使細胞膜產生穿孔現象,已被廣泛研究於輸送藥物、蛋白質和基因。目前已知可引發聲穿孔作用的主要機制之一為慣性穴蝕效應,但慣性穴蝕效應引發時造成的微氣泡破裂,可能會帶來非必要性的細胞死亡,因此期望找到其他方法來引發聲穿孔作用。在我們過去以聲學激發液滴汽化同步光學激發液滴汽化為基礎的聲穿孔研究中,我們已展示液滴反覆汽化凝結的機制,可產生一定劑量的聲穿孔效應,此顯示重複性汽化可引發聲穿孔作用的潛在性。為了近一步瞭解重複性汽化的機制,適當的數值分析方法是必要的。本篇研究將結合流體力學及超音波原理,以數值分析方法建立液滴重複性汽化模型,並在模型中發現可重複性汽化較容易發生於聲壓小週期長的參數條件。此外,為改善傅立葉分析在判斷可重複性汽化發生率的限制,本研究亦採用有限基函式之小波轉換進行具有不同波形之訊號分析,發現當其在高頻區間的能量超過設定之閥值時,能有大於 90 %之正確率找出液滴相變之發生,並可加入每微秒之相變次數提高可重複性汽化之判別。此外,我們也發現小波轉換於低頻高頻間的能量比值可為另一個判定汽化及穴蝕效應的方法。最後,以機器學習進行自動化分析時,發現傅立葉轉換在汽化及慣性穴蝕效應的分析上仍為較佳的指標,而小波轉換則可協助分析重複性汽化之發生機率及次數,因此未來可結合小波與傅立葉轉換用於汽化、重複性汽化與穴蝕效應之分析,協助臨床上更有效率的制定治療策略。

並列摘要


Sonoporation involves the use of ultrasound and exogenous agents to enhance cell permeabilization, and it has been widely investigated in the delivery of drugs, proteins, and genes into the cells. It is known that inertial cavitation, which is related to microbubbles destruction, is an effective mechanism to enhance sonoporation. However, cell damage may also occur during inertial cavitation. In previous studies, we have also demonstrated that repeatable vaporization and condensation between nanodroplets and microbubbles can also facilitate sonoporation. To further understand the underlying mechanism of vaporization and condensation and define measurable physical parameters for their effects on sonoporation, a numerical model needed to be developed and compared with the experimental results. With our numerical model, we found that repeatable vaporization has a higher possibility of occurrence under low acoustic pressure with long ultrasound cycles. In addition to the Fourier-based analysis, we also proposed to use the wavelet transform for signal analysis. The results show that we can achieve a 90% accuracy in predicting the vaporization events. Repeated vaporization can also be evaluated by using the number of phase transitions within a specific time window. In addition, the energy ratio between different scales of the wavelet transform can be used as an index to determine the vaporization and inertial cavitation effects. Finally, with machine learning model training, it is found that the Fourier analysis can be a better indicator to analyze vaporization and inertial cavitation effects, and the wavelet transform is more suitable for the analysis of the probability and occurring times of repeatable vaporization. These methodologies have the potential to improve efficiency when planning therapeutic strategies.

參考文獻


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