3 research outputs found

    High-Performance Airflow Sensors Based on Suspended Ultralong Carbon Nanotube Crossed Networks

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    Airflow sensors are in huge demand in many fields such as the aerospace industry, weather forecasting, environmental monitoring, chemical and biological engineering, health monitoring, wearable smart devices, etc. However, traditional airflow sensors can hardly meet the requirements of these applications in the aspects of sensitivity, response speed, detection threshold, detection range, and power consumption. Herein, this work reports high-performance airflow sensors based on suspended ultralong carbon nanotube (CNT) crossed networks (SCNT-CNs). The unique topologies of SCNT-CNs with abundant X junctions can fully exhibit the extraordinary intrinsic properties of ultralong CNTs and significantly improve the sensing performance and robustness of SCNT-CNs-based airflow sensors, which simultaneously achieved high sensitivity, fast response speed, low detection threshold, and wide detection range. Moreover, the capability for encapsulation also guaranteed the practicality of SCNT-CNs, enabling their applications in respiratory monitoring, flow rate display and transient response analysis. Simulations were used to unveil the sensing mechanisms of SCNT-CNs, showing that the piezoresistive responses were mainly attributed to the variation of junction resistances. This work shows that SCNT-CNs have many superiorities in the fabrication of advanced airflow sensors as well as other related applications

    Scalable Structural Coloration of Carbon Nanotube Fibers via a Facile Silica Photonic Crystal Self-Assembly Strategy

    No full text
    The coloration of carbon nanotube (CNT) fibers (CNTFs) is a long-lasting challenge because of the intrinsic black color and chemically inert surfaces of CNTs, which cannot satisfy the aesthetic and fashion requirements and thus significantly restrict their performance in many cutting-edge fields. Recently, a structural coloration method of CNTFs was developed by our group using atomic layer deposition (ALD) technology. However, the ALD-based structural coloration method of CNTFs is expensive, time-consuming, and not suitable for the large-scale production of colorful CNTFs. Herein, we developed a very simple and scalable liquid-phase method to realize the structural coloration of CNTFs. A SiO2/ethanol dispersion containing SiO2 nanospheres with controllable sizes was synthesized. The SiO2 nanospheres could self-assemble into photonic crystal layers on the surface of CNTFs and exhibited brilliant colors. The colors of SiO2 nanoparticle-coated CNTFs could be easily changed by tuning the sizes of SiO2 nanospheres. This method provides a simple, effective, and promising way for the large-scale production of colorful CNTFs

    Scalable Structural Coloration of Carbon Nanotube Fibers via a Facile Silica Photonic Crystal Self-Assembly Strategy

    No full text
    The coloration of carbon nanotube (CNT) fibers (CNTFs) is a long-lasting challenge because of the intrinsic black color and chemically inert surfaces of CNTs, which cannot satisfy the aesthetic and fashion requirements and thus significantly restrict their performance in many cutting-edge fields. Recently, a structural coloration method of CNTFs was developed by our group using atomic layer deposition (ALD) technology. However, the ALD-based structural coloration method of CNTFs is expensive, time-consuming, and not suitable for the large-scale production of colorful CNTFs. Herein, we developed a very simple and scalable liquid-phase method to realize the structural coloration of CNTFs. A SiO2/ethanol dispersion containing SiO2 nanospheres with controllable sizes was synthesized. The SiO2 nanospheres could self-assemble into photonic crystal layers on the surface of CNTFs and exhibited brilliant colors. The colors of SiO2 nanoparticle-coated CNTFs could be easily changed by tuning the sizes of SiO2 nanospheres. This method provides a simple, effective, and promising way for the large-scale production of colorful CNTFs
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