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Radio Frequency Identification Temperature/CO 2 Sensor Using Carbon Nanotubes.

Ayesha HabibSafia AkramMohamed R AliTaseer MuhammadSajeela ZainabShafia Jehangir
Published in: Nanomaterials (Basel, Switzerland) (2023)
In the world of digitization, different objects cooperate with the Internet of Things (IoT); these objects also amplify using sensing and data processing structures. Radio frequency identification (RFID) has been identified as a key enabler technology for IoT. RFID technology has been used in different conventional applications for security, goods storage, transportation and asset management. In this paper, a fully inkjet-printed chipless radio frequency identification (RFID) sensor tag is presented for the wireless identification of tagged objects. The dual polarized tag consists of two resonating structures functioning wirelessly. One resonator works for encoding purpose and other resonator is used as a CO 2 /temperature sensor. The sensing behavior of the tag relies on the integration of a meandered structure comprising of multi-wall carbon nanotubes (MWCNT). The MWCNT is highly sensitive to CO 2 gas. The backscattered response of the square-shaped cascaded split ring resonators (SRR) is analyzed through a radar cross-section (RCS) curve. The overall tag dimension is 42.1 mm × 19.5 mm. The sensing performance of the tag is examined and optimized for two different flexible substrates, i.e., PET and Kapton ® HN. The flexible tag structure has the capability to transmit 5-bit data in the frequency bands of 2.36-3.9 GHz and 2.37-3.89 GHz, for PET and Kapton ® HN, respectively. The proposed chipless RFID sensor tag does not require any microchip or a power source, so it has a great potential for low-cost and automated temperature/CO 2 sensing applications.
Keyphrases
  • carbon nanotubes
  • low cost
  • computed tomography
  • bioinformatics analysis
  • high resolution
  • electronic health record
  • pet ct
  • machine learning
  • big data
  • deep learning
  • public health
  • pet imaging
  • human health
  • data analysis