本研究敘述對臭氧感測具高靈敏度之氧化銦奈米磚合成，及手機操控可攜式半導體式氣體感測器建構。氧化銦奈米磚以低成本化學製程製作，在紫外光活化模式，對O_3感測電阻變化靈敏度達22.7 ppm^(-1)，最低偵測極限為1 ppb，對NO_2感測靈敏度約為8 ppm^(-1)，兩者比值約1：0.35，對其他氣體則極低。將氣體感測器架於空品監測站進行機器學習，以感測晶片電阻及環境溫濕度作為輸入值，以測站真實濃度作為目標值，並建構人工神經網路運算來預測濃度，在1 O_3 + 0.54 NO_2作為目標濃度值，決定係數R^2可達0.48。

This work describes the synthesis of highly O_3 sensitive indium oxide nanobricks and the construction of a smart phone operated portable semiconductor type gas sensor. The indium oxide nanobricks are synthesized by a low-cost chemical process. Under UV-activated sensing, a resistance change sensitivity of 22.7 ppm^(-1) and a lowest detection limit of 1 ppb for O_3 sensing are obtained. The sensitivity is around 8 ppm^(-1) for NO_2 and the sensitivity ratio is around 1:0.35. The sensitivities are significantly lower for other gases. The gas sensor is deployed in an air quality monitoring station for machine learning. The chip resistance, the temperature, and the relative humidity are taken as the input values, and the station concentrations the target values. An artificial neural network algorithm has been developed to predict the concentration. A coefficient of determination R^2 of 0.48 has been obtained when using 1 O_3 + 0.54 NO_2 as the target concentration.