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Crystalsense – electronic nose using novel gas sensing materials
07 July 2021

Crystalsense – electronic nose using novel gas sensing materials

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A new sensing solution for a bio-inspired electronic nose, able to “smell” mixtures of volatile organic compounds.

BACKGROUND

Gas sensors are used to measure the concentration of various gases in their surroundings. These devices are widely used across various end-use sectors, particularly in industries where various toxic and combustible gases need to be monitored and leaks prevented, such as industrial, petrochemical, and automotive industries. The overall gas sensors market expected to grow from USD 812,3 Million in 2016 to USD 1.297,6 Million by 2023, at a CAGR of 6,83% between 2017 and 2023. This growth is mainly attributed to the increase awareness on safety among end-users as well as stricter safety regulations imposed by government and regulatory bodies, particularly in North America and Europe. Gas sensors include fixed gas sensors as well as portable, non-invasive and wirelessly operated devices, which represent a fantastic window opportunity for growth.

TECHNOLOGY OVERVIEW

CrystalSense relates to gel-like materials that can sense gases, yielding optical and/or electrical signals in the presence of volatile organic compounds (VOCs) and also to a method to employ the gel-like materials as gas sensors in electronic noses (e-noses), including the in-house design and fabrication of optical and/or electrical signal acquisition devices, the development of dedicated signal processing and machine-learning algorithms. CrystalSense enables customizable solutions for different applications. It is possible to formulate libraries of gels with distinct chemical properties and rationally design gels with optimized chemical affinity towards target VOCs and the combination of signals generated from gels with distinct formulations forms patterns that can work as a fingerprint for distinct odours.

Figure 1: Gas-sensing gels and respective optical signals. (a) Schematic representation of the supramolecular organization within the gels. (b) Macroscopic appearance of a gel film (c) spread over untreated glass slides for electrical and optical sensing. (d) Pattern of optical responses of an array of 3 distinct gels subjected to cycles of 5 s exposure air saturated with one VOC (methanol, toluene or diethyl ether) alternated with 15 s exposure to ambient air. The gels respond very fast, within less than 1 s upon exposure to VOCs
Figure 1: Gas-sensing gels and respective optical signals. (a) Schematic representation of the supramolecular organization within the gels. (b) Macroscopic appearance of a gel film (c) spread over untreated glass slides for electrical and optical sensing. (d) Pattern of optical responses of an array of 3 distinct gels subjected to cycles of 5 s exposure air saturated with one VOC (methanol, toluene or diethyl ether) alternated with 15s exposure to ambient air. The gels respond very fast, within less than 1 s upon exposure to VOCs

 

Figure 2: Examples of the application of CrystalSense gels as optical and opto-electrical gas sensors. (a) Clustering of VOCs by principal component analysis of the relative amplitudes of the optical signals of 3 different sensors. (b) Identification of fish spoilage by the change in relative amplitude of a sensor’s optical signal and increase in bacterial counts (CFU/g). (c) Electrical and optical response of a sensor as a function of ethanol content in fuel mixtures (d) Confusion matrix representing the performance of VOCs classification using a Support Vector Machine algorithm fed with 12 features of the optical signal of a single sensor.
Figure 2: Examples of the application of CrystalSense gels as optical and opto-electrical gas sensors. (a) Clustering of VOCs by principal component analysis of the relative amplitudes of the optical signals of 3 different sensors. (b) Identification of fish spoilage by the change in relative amplitude of a sensor’s optical signal and increase in bacterial counts (CFU/g). (c) Electrical and optical response of a sensor as a function of ethanol content in fuel mixtures (d) Confusion matrix representing the performance of VOCs classification using a Support Vector Machine algorithm fed with 12 features of the optical signal of a single sensor.

FURTHER DETAILS

STAGE OF DEVELOPMENT

TRL: 4/5 – Proof-of-concept.

BENEFITS

  • Sensing Material – low cost and environmentally-friendly composition and production;
  • E-nose Technology – identification of fingerprints from simple or complex gas mixtures;
  • Customisible – tuneable selectivity and sensitivity for each application;
  • Real time – fast optical and electrical responses to VOCs;
  • Platform technology – custom-built for desired applications.

APPLICATIONS

This technology is useful for situations where information on the species present in a sample is required, particularly for samples in the vapor or gas phases. Examples include (but are not limited to):

  1. Manufacturing Industry (mainly in quality control, detection of hazardous agents), food processing, chemicals, refineries and petrochemical plants, pharmaceutical and biopharmaceutical industry, biotech companies and the wood industry, among others. The typical gas sensors for industrial applications include carbon dioxide gas sensors, oxygen gas sensors, hydrogen gas sensors and others;
  2. Security – entry ports (such as airports); military and national security;
  3. Environment – Risks and detection of pollutants, control of waste facilities and reservoirs of water treatment as well as in rivers and lakes;
  4. Non-invasive medical diagnostic and therapeutic devices for use in clinical settings and home care;
  5. Scientific research – Sensors in R&D useful for distinction of materials, botanical and ecological studies, as well as analytical methods in (bio)processing;
  6. Automotive – gas sensors used in automotives. The common gas sensors used in automotives include oxygen sensors and methane sensors;
  7. Building automation – gas sensors used in residential and commercial building automation applications;
  8. Food quality – assessment and sensorial assessment;
  9. Textiles – smart textiles;
  10. Well-being – monitoring human body smells (pleasant/unpleasant);
  11. Product control – pharmaceutical industry;

OPPORTUNITY

  • Seeking co-development partners;
  • Available for exclusive and non-exclusive licensing.

INTELLECTUAL PROPERTY

NOVA Inventors

Ana Cecília Roque

Abid Hussain

Academic Information | NOVA School of Science and Technology | FCT NOVA