MOx Sensor Array Quantification of TVOCs, Ozone, and NO2
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Solution Overview
Problem
Existing metal-oxide (MOx) sensors struggle with cross-interference and inaccurate quantification of individual gases such as Ozone (O3) and Nitrogen Dioxide (NO2), leading to baseline estimation issues and inaccurate TVOC measurements.
Innovation Solution
A system comprising sensor arrays with TVOC/O3 and O3/NO2 sensors, combined with humidity and temperature sensors, uses multi-temperature sweeps and neural networks for real-time detection, identification, and quantification, mitigating cross-interference and providing accurate gas concentration measurements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If MOx sensors are used for gas detection, then cost and size are reduced, but measurement precision deteriorates due to cross-interference between gases
Solution Approach 1:
The patent segments the gas detection task by using multiple MOx sensors with different selectivities arranged in an array. Each sensor responds differently to various gases, and by analyzing the pattern of responses across multiple sensors, the system can distinguish between different gases (TVOC, O3, NO2) and accurately quantify their concentrations despite individual sensor cross-interference.
Solution Approach 2:
The patent applies parameter changes by heating MOx sensors to different temperatures (ranging from 150°C to 400°C) to optimize their response characteristics for different gases. By varying the operating temperature parameter, the sensors' selectivity and sensitivity to specific gases (TVOC, O3, NO2) are enhanced, improving measurement precision while maintaining the low-cost advantage of MOx sensors.
2Adaptability or versatility
If multiple gases are detected using MOx sensors, then gas detection versatility is improved, but measurement precision deteriorates due to baseline estimation issues
Solution Approach 1:
The patent implements feedback through a machine learning algorithm that continuously analyzes resistance data from multiple MOx sensors and adjusts baseline estimates in real-time. The system receives feedback from humidity and temperature sensors to compensate for environmental effects on sensor readings, dynamically updating baseline values to maintain accurate quantification of TVOC, O3, and NO2 concentrations even when multiple gases are present.
Solution Approach 2:
The patent uses a composite sensing approach by combining multiple MOx sensors with different metal oxide compositions (e.g., SnO2, ZnO, In2O3) that have different selectivities. This composite sensor array, combined with humidity and temperature sensors, creates a multi-parameter detection system that can distinguish between different gases and maintain measurement precision across diverse gas detection scenarios.
3Measurement precision
If sensor arrays with multiple sensor types are used, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent applies universality by designing a integrated sensor array where multiple MOx sensors and environmental sensors (humidity and temperature) share common signal processing and control circuitry. The machine learning algorithm serves as a universal processing unit that handles data from all sensors, performing baseline estimation, cross-interference compensation, and gas quantification for TVOC, O3, and NO2 simultaneously, thereby managing device complexity through functional integration.
Solution Approach 2:
The system implements self-service through automated baseline estimation and calibration procedures. The machine learning algorithm automatically adjusts baseline values based on real-time sensor readings and environmental conditions without requiring manual intervention. The system self-calibrates by analyzing patterns in resistance data from multiple sensors and compensating for cross-interference effects, reducing the operational complexity despite the multi-sensor configuration.
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
Enables selective detection and quantification of O3, NO2, and TVOC, while compensating for humidity and temperature effects, ensuring accurate and reliable gas analysis.
Implementation Method 1
MOx sensors typically consist of a substrate, heater, and a metal oxide surface... To obtain readable and useful resistance signals out of MOx sensors, they are typically heated at temperatures between 150° C. and 400° C.
Implementation Method 2
To obtain readable and useful resistance signals out of MOx sensors, they are typically heated at temperatures between 150° C. and 400° C.
Data Source
AI summary
An apparatus is provided that includes one or more first MOx sensors, one or more second MOx sensors, and one or more humidity and/or temperature sensors configured to detect, identify, and quantify a first gas, a second gas, and a third gas, where the first gas is a TVOC, the second gas is NO2, and the third gas is O3. At least one processor can be configured to receive resistance data, humidity data and temperature data from the one or more first MOx sensors, the one or more second MOx sensors, and the one or more humidity and/or temperature sensors. The at least one process can run a pre-trained or continuously learning neural network, or other machine learning models, to detect, identify, and quantify the first gas, the second gas, and the third gas, while mitigating humidity, temperature, and O3 influence on the overall output performance.