Chemo-Resistive Gas Sensor with Temperature Modulation
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Solution Overview
Problem
Existing gas sensing technologies face challenges in accurately estimating gas concentrations in mixtures due to the intrinsic instability of chemo-resistive gas sensors, calibration inaccuracies, and cross-sensitivities, especially in real-world scenarios with varying gas concentrations and noisy data.
Innovation Solution
A gas sensing device utilizing chemo-resistive gas sensors with controlled temperature oscillations, preprocessing for noise suppression, and a decision-making block with trained machine learning models that leverage dynamic sensor characteristics and auxiliary sensor data for improved accuracy and stability, enabling robust classification and regression of gas concentrations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If chemo-resistive gas sensors are used for gas concentration sensing, then the device can detect gas concentrations, but the sensors exhibit intrinsic instability and calibration inaccuracies
Solution Approach 1:
The patent applies parameter changes by dynamically adjusting the temperature of the chemo-resistive gas sensors during operation. The sensors are heated to different temperatures (e.g., 200°C, 300°C, 400°C) at different time points to capture temperature-dependent resistance responses. This temperature variation creates multiple measurement conditions that help distinguish between sensor instability and actual gas concentration changes, thereby improving measurement accuracy while accounting for reliability issues.
Solution Approach 2:
The patent implements periodic action by alternating between measurement phases at different temperatures. The sensor operates in cycles, switching between higher and lower temperatures periodically. This periodic temperature modulation allows the system to capture dynamic response patterns that characterize both the sensor's inherent instability and its response to target gases, enabling differentiation through pattern recognition algorithms.
2Measurement precision
If simple models are used for sensor functionality proof, then the device complexity is low, but the gas concentration estimation accuracy is insufficient
Solution Approach 1:
The patent replaces complex physical/chemical sensing mechanisms with a simplified electrical measurement system. Instead of using multiple complex sensor types or sophisticated measurement setups, the system uses a single chemo-resistive sensor with temperature modulation and pattern recognition algorithms. The complexity is shifted from hardware to software, using machine learning algorithms to process the temperature-dependent resistance patterns and extract accurate gas concentration information.
3Measurement precision
If geographically distributed sensor systems are used, then gas concentration estimation can be performed, but the system complexity and data processing requirements increase
Solution Approach 1:
The patent merges the functionality of multiple sensors or distributed measurement points into a single sensor system. By using one chemo-resistive sensor with temperature modulation, the system consolidates what would otherwise require multiple sensors operating simultaneously. The temperature dimension provides additional measurement degrees of freedom, allowing a single sensor to capture the information that would traditionally require spatial distribution of multiple sensors.
4Measurement precision
If large amounts of data from distributed sensors are processed, then pattern analysis can be performed, but the processing time and computational resources increase
Solution Approach 1:
The patent extracts only the essential features from the sensor data for pattern analysis. Instead of processing complete raw data sets from multiple sensors, the system extracts temperature-dependent resistance patterns and key characteristics from a single sensor's time-series measurements. This selective extraction of critical information maintains pattern analysis accuracy while dramatically reducing computational requirements and processing time.
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 solution provides a versatile, accurate, and efficient method for gas concentration estimation in complex scenarios, addressing instability and calibration issues, and is suitable for various applications, including air quality monitoring with low material costs and minimal memory requirements.
Implementation Method 1
one or more heating elements for heating each of the gas sensors, wherein the one or more heating elements are brought to a first temperature during the recovery phases and to a second temperature during the sense phases
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 heating elements for heating each of the gas sensors; a preprocessing block for filtering signal samples in order to generate filtered signal samples for each of the gas sensors; an information extraction block for generating representations for the filtered signal samples for each of the gas sensors based on dynamic characteristics of the received filtered signal samples of the respective gas sensor; and a decision making block for receiving the representations, wherein the decision making block includes a trained model based algorithm stage having an input layer and an output layer, wherein the decision making block includes trained models, wherein the decision making block creates sensing results based on output values of the output layer of the algorithm stage, and wherein the output values are created by using the trained models.