Next Article in Journal
Machine Learning for Enhanced Operation of Underperforming Sensors in Humid Conditions
Previous Article in Journal
From Gas Sensors to Artificial Neural Networks: A New Precision Farming Approach for Crop Coefficient Determination
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Abstract

From a Memory Sensor to a Sensor without Memory: Trigger Mechanism †

1
Center for Sensors and Devices, Fondazione Bruno Kessler, 38123 Trento, Italy
2
Department of Information Engineering and Computer Science, University of Trento, 38123 Trento, Italy
3
Department of Civil Environmental and Mechanical Engineering, University of Trento, 38123 Trento, Italy
*
Author to whom correspondence should be addressed.
Presented at the XXXV EUROSENSORS Conference, Lecce, Italy, 10–13 September 2023.
Proceedings 2024, 97(1), 92; https://doi.org/10.3390/proceedings2024097092
Published: 25 March 2024

Abstract

:
In the context of environmental monitoring, maximum levels of nitrogen dioxide (NO2) are internationally regulated since long exposure impacts health. In general, very low concentrations in the sub-ppm range are found for NO2, which implies the need for very sensitive detection systems. Here, we demonstrate a chipless RFID sensing cell with both threshold detection and re-usability capabilities.

1. Introduction

Chipless solutions are advantageous with respect to their chipped counterparts for the absence of integrated electronics on the tag. The tag is composed of conductive resonators that generate resonance peaks in the frequency or time domains. This feature makes chipless tags very attractive due to their low price, completely planar profile, mechanical/functional stability, which is especially convenient in the case of environmental monitoring in harsh environments. Moreover, the gas sensing functionality can be added simply by working on the effective permittivity properties of the tag, where a sensing material is selected for NO2 detection. In this study, a layer of tin-dioxide (SnO2) is deposited on the conductive resonator to demonstrate a chipless sensing cell with NO2 detection properties. The sensor behaves like a non-volatile memory sensor capable of detecting an NO2 event. The purpose of this paper is to demonstrate a trigger mechanism to turn a memory sensor into a sensor without memory, i.e., capable of detecting a new event. This is clearly advantageous for its reuse and in the context of a sustainable sensing network.

2. Materials and Methods

A typical frequency-domain chipless RFID tag is composed of a series of conductive resonators on a dielectric substrate, and the information is encoded in the resonance peaks that they generate in the frequency domain. If a sensitive layer is added in correspondence to the resonators, it interacts with a specific physical parameter or chemical substance present in the environment and modifies the intensity and the frequency of the encoding resonance peak accordingly. In this study, we investigate a single resonant cell to demonstrate its feasibility for NO2 detection. We select an electric-field-coupled (ELC) resonator, already used in our previous research on humidity detection [1,2,3], and we scale it to resonate at 1.78 GHz (final size 20 × 20 mm2). It is produced by a microlithography and wet-etching process on the 17 µm copper layer of a commercially available 168 µm thick Rogers RO4350 flexible substrate. A thin layer of SnO2 is produced using sol–gel, and adequate oxygen vacancies are created in this way. The paste is then deposited over the resonator through spin coating.
The sensor cell is tested for the detection of nitrogen dioxide in the sub-ppm range, specifically 0.5 ppm of NO2. A gas-flow chamber prototype (as shown in Figure 1a) is arranged for the purpose of having a holder for the sensor and LED source. We acquire the sensor response at room temperature and under a fixed, controlled humidity (50% RH) considering the frequency range 1.5–2 GHz and using an Agilent ENA series vector network analyzer (VNA) connected to a Signal Hound near-field magnetic probe, which is placed outside the chamber. The sensor is therefore read wirelessly and is completely passive.

3. Results and Final Discussion

The response and recovery trends for the 0.5 ppm NO2 flux are plotted in Figure 1b, where the resonance peak intensity of the |S11|-parameter is tracked for the entire period. In particular, the sensor recovery was monitored for more than 6 h without reaching the full recovery condition. The sensor can therefore be classified as a non-volatile memory sensor which can detect the occurrence of a NO2 event. To trigger sensor recovery, we exploited the irradiation of the sensor with an ultra-violet LED (wavelength λ = 405 nm, power 1 mW), which speeds up the complete reset of the sensor. As shown in Figure 1c, the UV LED is switched on as the recovery starts and the sensor resets in less than 7 minutes for the specific case of 0.5 ppm and an intensity difference of 0.15 dB.

Author Contributions

All authors contributed equally. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data is contained within the article.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Marchi, G.; Mulloni, V.; Acerbi, F.; Donelli, M.; Lorenzelli, L. Tailoring the Performance of a Nafion 117 Humidity Chipless RFID Sensor: The Choice of the Substrate. Sensors 2023, 23, 1430. [Google Scholar] [CrossRef] [PubMed]
  2. Marchi, G.; Zanazzi, E.; Mulloni, V.; Donelli, M.; Lorenzelli, L. Electromagnetic Modeling Strategy Supporting the Fabrication of Inkjet-Printed Chipless RFID Sensors. IEEE J. Flex. Electron. 2023, 2, 145–152. [Google Scholar] [CrossRef]
  3. Marchi, G.; Mulloni, V.; Manekiya, M.; Donelli, M.; Lorenzelli, L. A Preliminary Microwave Frequency Characterization of a Nafion-Based Chipless Sensor for Humidity Monitoring. In Proceedings of the 2020 IEEE SENSORS, Rotterdam, The Netherlands, 25–28 October 2020; pp. 1–4. [Google Scholar]
Figure 1. A non-volatile NO2 chipless memory sensor turned into a reusable sensor. (a) Measurement setup; (b) response and recovery trend for the non-volatile memory sensor; (c) response and recovery trend when the sensor is turned into a sensor without memory using a UV LED source.
Figure 1. A non-volatile NO2 chipless memory sensor turned into a reusable sensor. (a) Measurement setup; (b) response and recovery trend for the non-volatile memory sensor; (c) response and recovery trend when the sensor is turned into a sensor without memory using a UV LED source.
Proceedings 97 00092 g001
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Marchi, G.; Mulloni, V.; Gaiardo, A.; Valt, M.; Donelli, M.; Lorenzelli, L. From a Memory Sensor to a Sensor without Memory: Trigger Mechanism. Proceedings 2024, 97, 92. https://doi.org/10.3390/proceedings2024097092

AMA Style

Marchi G, Mulloni V, Gaiardo A, Valt M, Donelli M, Lorenzelli L. From a Memory Sensor to a Sensor without Memory: Trigger Mechanism. Proceedings. 2024; 97(1):92. https://doi.org/10.3390/proceedings2024097092

Chicago/Turabian Style

Marchi, Giada, Viviana Mulloni, Andrea Gaiardo, Matteo Valt, Massimo Donelli, and Leandro Lorenzelli. 2024. "From a Memory Sensor to a Sensor without Memory: Trigger Mechanism" Proceedings 97, no. 1: 92. https://doi.org/10.3390/proceedings2024097092

Article Metrics

Back to TopTop