本實驗旨在開發一種整合微氣閥與微流體元件的自動化細胞篩選系統，以分選活酵母菌和死酵母菌。系統中，螢光粒子模擬活酵母菌，標準粒子模擬死酵母菌，氣體作為連續相，粒子溶液作為分散相。利用氣體包覆生成液珠，將粒子包覆在液珠中進行隔離。通過影像辨識技術偵測液珠並將訊號回傳給微流體晶片中的微氣動閥，以控制液珠的移動並達到分類效果，從而分離含有螢光粒子和標準粒子的液珠，獲得高純度的螢光粒子溶液。
實驗結果顯示，在微氣動閥與Y字形分岔流道的幾何設計下，當微氣動閥氣壓為0.28 MPa時，液珠以0 %的分類異常率進入未啟動閥的流道出口，且經由測試發現此晶片可持續使用3.5小時以上。
實驗還發現，使用Pluronic F-127進行改質時，粒子與液珠不會黏附於流道，且粒子溶液能穩定生成液珠。
經PVA與Pluronic F-127改質後的偵測結果顯示，移動速度較慢的截斷式液珠相比滴落型液珠更易被辨識。雖需加入界面活性劑以穩定截斷式液珠的生成，但實驗結果顯示，截斷式液珠更適合用於自動化影像辨識分選技術中。
最終測試結果顯示，採用PVA改質的流道時，螢光粒子的分類準確率可達90.43%，目標區螢光粒子濃度達61.23%，提升31.45%，而廢棄區螢光粒子純度降至5.52%，下降24.26%。這些結果表明，該系統具有顯著的分類效果，並在細胞篩選中具有很大的應用潛力。

This experiment aims to develop an automated cell sorting system by integrating micro pneumatic valves with microfluidic components to sort live and dead yeast cells. In this system, fluorescent particles simulate live yeast cells, while standard particles represent dead yeast cells. The gas acts as the continuous phase, and the particle solution serves as the dispersed phase. By utilizing gas to encapsulate the particles within droplets, the system isolates them for sorting. Image recognition technology is employed to detect the droplets, and the signals are transmitted to the micro pneumatic valves on the microfluidic chip to control the movement of the droplets, thus separating the droplets containing fluorescent particles from those with standard particles and obtaining a high-purity fluorescent particle solution.
The experimental results show that with the geometric design of the micro pneumatic valve and Y-shaped bifurcation channel, when the valve pressure is set to 0.28 MPa, the droplets enter the non-actuated valve outlet with a 0% classification error rate. Additionally, the chip was found to operate continuously for over 3.5 hours.
The study also discovered that when the channels were modified with Pluronic F-127, particles and droplets did not adhere to the channel walls, and the particle solution stably generated droplets. Detection results after PVA and Pluronic F-127 modification indicated that the slower-moving pinch-off droplets were more easily recognized compared to dripping droplets. Although surfactants were required to stabilize the formation of pinch-off droplets, the experiment revealed that these droplets were more suitable for automated image recognition sorting technology.
Final testing demonstrated that when using PVA-modified channels, the classification accuracy of fluorescent particles reached up to 90.43%, with a target area fluorescent particle concentration of 61.23%, an increase of 31.45%. Meanwhile, the purity of fluorescent particles in the waste area decreased to 5.52%, a reduction of 24.26%. These findings indicate that the system has significant sorting effectiveness and considerable potential for applications in cell sorting.

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