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Room-temperature NO<sub>2</sub> sensor based on electrochemically etched porous silicon
Choi, Myung Sik,Na, Han Gil,Mirzaei, Ali,Bang, Jae Hoon,Oum, Wansik,Han, Seungmin,Choi, Sun-Woo,Kim, Mooshob,Jin, Changhyun,Kim, Sang Sub,Kim, Hyoun Woo Elsevier 2019 Journal of Alloys and Compounds Vol.811 No.-
<P><B>Abstract</B></P> <P>With high-performance room-temperature gas sensors being in great demand from an energy-saving standpoint, in this study, we fabricated porous silicon (PS) sensors by electrochemically etching at different times (30, 60, and 90 min). The porous nature of the etched PSs was studied using scanning electron microscopy, and subsequently gas sensors were fabricated. NO<SUB>2</SUB> sensing studies showed that the highest gas performance can be obtained at room temperature (30 °C). Furthermore, the PS sensor etched for 60 min had the best performance among the sensors, which is related to its higher surface area and high enough initial resistance. In particular for the PS sensor etched for 60 min, the response (R<SUB>a</SUB>/R<SUB>g</SUB>) to 10 ppm NO<SUB>2</SUB> was 9.56, which was much higher than other interfering gases, demonstrating its high selectivity towards NO<SUB>2</SUB> gas. This study reveals the need for optimization of electrochemical etching to realize gas sensors based on PS working at room temperature.</P> <P><B>Highlights</B></P> <P> <UL> <LI> We fabricated porous silicon (PS) sensors for room-temperature NO<SUB>2</SUB> sensing, by electrochemically etching at different times. </LI> <LI> The PS sensor etched for 60 min had the best performance among the sensors. </LI> <LI> The response of PS sensor to 10 ppm NO<SUB>2</SUB> was 9.56, demonstrating its high selectivity towards NO<SUB>2</SUB> gas. </LI> </UL> </P>
최완식(Wansik Choi),안창선(Changsun Ahn) 대한기계학회 2020 대한기계학회 춘추학술대회 Vol.2020 No.12
Because there are only small space between vehicles in lateral direction, the lateral maneuvers should be considered for safety. Especially, the lane change is most frequent lateral maneuver. Therefore, we focus on the lane change. To predict the future trajectory, we proposed the deep learning based prediction model. The model has a cascade structure with one classification model to predict the lane change, one regression model to predict time to lane change and two regression models to predict longitudinal and lateral deviation of the vehicle. The performance of the proposed model is validated by simulation with the test set. The results show better performance compare with the kinematics-based prediction.