Chemo-Resistive Gas Sensor Temperature Modulation
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Solution Overview
Problem
Existing gas sensing technologies face challenges in accurately distinguishing between different gases in a mixture using chemo-resistive sensors, often requiring costly and impractical solutions, such as selective physical filters or additional non-chemo-resistive sensors, which increase product size and cost, and struggle with cross-sensitivity and instability.
Innovation Solution
A gas sensing device employing multiple chemo-resistive gas sensors with controlled heating profiles, preprocessing, feature extraction, and machine learning algorithms to classify and quantify gas concentrations, utilizing trained models to reduce cross-sensitivity and improve accuracy, and incorporating temperature modulation to enhance repeatability and stability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If selective physical gas filters or additional non-chemo-resistive gas sensors are used to distinguish between different gases, then gas identification accuracy is improved, but product size and cost increase significantly
Solution Approach 1:
The patent applies parameter changes by varying the heating temperature of chemo-resistive gas sensors to different values. By controlling the heater to heat the sensor element to different temperatures, the sensor's sensitivity and response characteristics change, enabling differentiation between various gases without adding physical filters or additional sensor types. This resolves the contradiction by maintaining measurement precision while avoiding increased device complexity.
2Measurement precision
If selective physical gas filters or additional non-chemo-resistive gas sensors are used to distinguish between different gases, then gas identification accuracy is improved, but manufacturing cost increases significantly
Solution Approach 1:
The invention uses parameter changes by implementing multiple heating temperature levels for the chemo-resistive gas sensors. This approach enables accurate gas identification through software-based pattern recognition at different temperatures, eliminating the need for expensive selective physical filters or additional specialized sensors. The solution reduces manufacturing cost while maintaining measurement precision by utilizing a single sensor type operated under varying thermal conditions.
3Device complexity
If chemo-resistive gas sensors are used for gas sensing, then device complexity is reduced, but cross-sensitivity and instability increase
Solution Approach 1:
The patent implements periodic action by alternately heating the chemo-resistive gas sensor to different temperatures in a cyclic manner. The heater controller periodically varies the heating temperature, causing the sensor to operate at multiple temperature points sequentially. This periodic temperature variation enables the system to capture different response characteristics of the sensor to various gases, allowing for accurate gas identification and compensation of drift effects, thereby improving reliability while maintaining device simplicity.
Solution Approach 2:
The system employs feedback by continuously monitoring the sensor output signals at different temperatures and using this information to identify gases and compensate for sensor drift. The processor analyzes the pattern of resistance changes across multiple temperatures and uses this feedback to improve measurement accuracy and stability over time, resolving the reliability issue while keeping the device simple.
4Device complexity
If chemo-resistive gas sensors are used for gas sensing, then device complexity is reduced, but measurement precision deteriorates due to cross-sensitivity
Solution Approach 1:
The patent resolves the measurement precision issue by changing the operational parameter of the sensor - specifically, the heating temperature. By measuring the sensor's resistance at multiple different temperatures, the system captures a unique response fingerprint for each gas type. The processor analyzes these multi-temperature response patterns to accurately determine gas concentration and identity, eliminating cross-sensitivity problems while maintaining a simple single-sensor device architecture.
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
The device achieves improved detection accuracy and reduced cross-sensitivity, enabling reliable gas identification and quantification in complex mixtures, such as distinguishing between NO2 and O3, while addressing intrinsic sensor instability and drift, and can be applied in various scenarios including air quality monitoring.
Implementation Method 1
one or more heat sources, wherein the one or more heat sources are controlled in such way that the gas sensors are each heated according to one or more temperature profiles
Implementation Method 2
one or more chemo-resistive gas sensors, wherein each of the gas sensors is configured for generating signal samples corresponding to a concentration of one of the one or more gases
Implementation Method 3
the interaction between graphene sheets and absorbed gas analytes influences the electronic structure of the material depending on the mixture of gases, resulting in altered charge carrier concentration and changed electrical conductance
Data Source
AI summary
A gas sensing device includes one or more chemo-resistive gas sensors; one or more heat sources, wherein the gas sensors are heated according to one or more first temperature profiles during the recovery phases and according to one or more second temperature profiles during the sense phases; a preprocessing processor for generating preprocessed signal samples; a feature extraction processor for extracting one or more feature values from the received preprocessed signal samples; and a gas concentration processor for creating a sensing result, wherein the gas concentration processor includes a classification processor for outputting a class decision value, wherein the classification processor is configured for outputting a confidence value, wherein the classification processor includes a first trained model based algorithm processor, wherein the gas concentration processor comprises a quantification processor for creating an estimation value, and wherein the quantification processor comprises a second trained model based algorithm processor.