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Multi‐Parametric Sensing Platforms Based on Nanoparticles
Author(s) -
SegevBar Meital,
Bachar Nadav,
Wolf Yaniv,
Ukrainsky Ben,
Sarraf Lior,
Haick Hossam
Publication year - 2017
Publication title -
advanced materials technologies
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.184
H-Index - 42
ISSN - 2365-709X
DOI - 10.1002/admt.201600206
Subject(s) - parametric statistics , computer science , wearable computer , component (thermodynamics) , structural health monitoring , parametric model , artificial intelligence , real time computing , engineering , embedded system , electrical engineering , statistics , physics , mathematics , thermodynamics
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.