T
Tie Li
Researcher at Chinese Academy of Sciences
Publications - 200
Citations - 3639
Tie Li is an academic researcher from Chinese Academy of Sciences. The author has contributed to research in topics: Etching (microfabrication) & Silicon. The author has an hindex of 24, co-authored 173 publications receiving 2563 citations. Previous affiliations of Tie Li include University of Science and Technology of China.
Papers
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Journal ArticleDOI
Flexible Capacitive Tactile Sensor Based on Micropatterned Dielectric Layer.
Tie Li,Hui Luo,Lin Qin,Xuewen Wang,Xuewen Wang,Xiong Zuoping,Ding Haiyan,Yang Gu,Zheng Liu,Ting Zhang +9 more
TL;DR: The utilization of bionic microstructures on natural lotus leaves is demonstrated to design and fabricate new-type of high-performance flexible capacitive tactile sensors that present stable and high sensing performance, such as high sensitivity and wide dynamic response range.
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Silicon-nanowire-based CMOS-compatible field-effect transistor nanosensors for ultrasensitive electrical detection of nucleic acids.
TL;DR: A novel semiconducting silicon nanowire field-effect transistor (SiNW-FET) biosensor array for ultrasensitive label-free and real-time detection of nucleic acids and ultrahigh sensitivity for rapid and reliable detection of 1 fM of target DNA and high specificity single-nucleotide polymorphism discrimination is reported.
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Enhanced sensing of nucleic acids with silicon nanowire field effect transistor biosensors.
TL;DR: Enhanced sensing of biological species by optimization of operating parameters and fundamental understanding for SiNW FET detection limit was obtained.
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Porous Ionic Membrane Based Flexible Humidity Sensor and its Multifunctional Applications.
TL;DR: An attachable smart label using PIM‐based sensor is explored to measure the water contents of human skin, which shows a great linear relationship between the sensitivity of the sensor and the facial water contents measured by a commercial reference device.
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Fingerprint-Inspired Flexible Tactile Sensor for Accurately Discerning Surface Texture.
TL;DR: To demonstrate the texture discrimination capability, the sensors are tested for accurately discerning various surface textures, such as the textures of different fabrics, Braille characters, the inverted pyramid patterns, which will have great potential in robot skins and haptic perception, etc.