Olfactory Sensor Light-Induced Adsorption Kinetics
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Solution Overview
Problem
Existing odorant sensing devices require a large number of sensors to effectively discriminate among multiple odorants, leading to increased complexity, cost, and difficulty in data acquisition and processing, limiting their widespread adoption in various applications.
Innovation Solution
The use of a single or small number of sensors irradiated with different light sources, such as infrared (IR) and ultraviolet (UV), to alter odorant adsorption kinetics, allowing for the identification of odorants through changes in physical properties measured during irradiation, and employing machine learning models to determine characteristics of the odorants.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a large number of sensors are deployed in parallel to discriminate among multiple odorants, then odorant discrimination capability is improved, but device complexity, size, and cost increase
Solution Approach 1:
The patent applies parameter changes by irradiating a single sensor with light sources of different wavelengths (e.g., UV, visible, IR) to dynamically alter the sensor's adsorption characteristics. This allows one sensor to exhibit multiple sensing states, effectively replacing the need for multiple fixed-sensitivity sensors and reducing device complexity while maintaining odorant discrimination capability
Solution Approach 2:
The invention introduces dynamic control of sensor properties through light irradiation. By switching between different wavelengths and intensities of light, the sensor's adsorption behavior is dynamically adjusted, enabling a single sensor to perform the function of multiple static sensors with different sensitivities
2Measurement precision
If a large number of sensors are deployed in parallel to discriminate among multiple odorants, then odorant discrimination capability is improved, but device cost increases
Solution Approach 1:
The patent makes a single sensor universal by enabling it to respond to multiple odorants through light-controlled parameter changes. The same sensor can be tuned to detect different odorant types by adjusting the irradiation wavelength, eliminating the need to manufacture and assemble multiple specialized sensors, thereby reducing device cost
3Measurement precision
If a large number of sensors are deployed in parallel to discriminate among multiple odorants, then odorant discrimination capability is improved, but data acquisition and processing difficulty increases
Solution Approach 1:
The patent segments the sensing process into multiple stages by using sequential light irradiation at different wavelengths. Each irradiation stage produces a distinct response pattern for different odorants, creating a segmented data acquisition process that is simpler to process than simultaneous multi-sensor data, as the temporal separation of measurements reduces data correlation complexity
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach reduces the number of sensors needed, decreases device size and cost, while maintaining high sensitivity and accuracy in odorant identification, enabling broader practical applications by using a single sensor to perform like an array of sensors.
Implementation Method 1
exposing a sensor to one or more odorants to adsorb molecules of the one or more odorants onto the sensor surface
Implementation Method 2
irradiating the sensor with a light sequence using one or more light sources to alter the adsorption kinetics of the molecules of the one or more odorants onto the sensor surface
Data Source
AI summary
Disclosed herein are devices and related methods for identifying odorants using a combination of light sources which enable discrimination between different odorants and/or determination of odorant concentration. The response of a chemical sensor to one or more odorants is observed under a combination and/or sequence of light sources, and the resulting data is subsequently analyzed using machine learning methods to identify one or more odorants and/or to determine the concentration of one or more odorants.