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Multi‐Parametric Sensing Platforms Based on Nanoparticles

Meital Segev‐Bar, Nadav Bachar, Yaniv Wolf, Ben Ukrainsky, Lior Sarraf, Hossam Haick

Year
2016
Citations
48

Abstract

Multi‐parametric sensing platforms offer the possibility to measure simultaneously several stimuli, and potentially to differentiate between the different signals. They have advantages in fields that include wearable systems, humanoid robotics, structural health monitoring and precision agriculture, since a complex stimuli from the environment is usually an integrated component in these examples. In the current progress report, we present and discuss new avenues in nanoparticle‐based multi‐parametric sensing platforms for the detection, classification and separation of common stimuli, e.g., temperature, humidity, strain/pressure and volatile organic compounds (VOCs). New data involving multi‐parametric sensing with nanoparticle‐based sensors are given for each topic. Future prospects are discussed.

Keywords

Parametric statisticsComputer scienceWearable computerComponent (thermodynamics)Structural health monitoringParametric modelArtificial intelligenceReal-time computingEngineeringEmbedded system

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