scholarly journals Evaluating the Fitness of Combinations of Adsorbents for Quantitative Gas Sensor Arrays

2020 ◽  
Author(s):  
Rachel Sousa ◽  
Cory Simon
Keyword(s):  
Inverse Problem ◽  
Gas Sensor ◽  
Sensor Array ◽  
Gas Sensing ◽  
Sensor Arrays ◽  
Industrial Process ◽  
Gas Composition ◽  
Monitoring And Control ◽  
Security Threat ◽  
Adsorption Model

Robust, high-performance gas sensing technology has applications in industrial process monitoring and control, air quality monitoring, food quality assessment, medical diagnosis, and security threat detection. Nanoporous materials (NPMs) could be utilized as recognition elements in a gas sensor because they selectively adsorb gas. Imitating mammalian olfaction, sensor arrays of NPMs use measurements of the adsorbed mass of gas in a set of distinct NPMs to infer the gas composition. Modular and adjustable NPMs, such as metal-organic frameworks (MOFs), offer a vast materials space to sample for combinations to comprise a sensor array that produces a response pattern rich with information about the gas composition.<br><br>Herein, we frame quantitative gas sensing, using arrays of NPMs, as an inverse problem, which equips us with a method to evaluate the fitness of a proposed combination of NPMs in a sensor array. While the (routine) forward problem is to use an adsorption model to predict the mass of gas adsorbed in the NPMs when immersed in a gas mixture of a given composition, the inverse problem is to predict the gas composition from the observed mass of adsorbed gas in each NPM. The fitness of a given combination of NPMs for gas sensing is then determined by the conditioning of its inverse problem: the prediction of the gas composition provided by a fit (unfit) combination of NPMs is insensitive (sensitive) to inevitable errors in the measurements of the mass of gas adsorbed in the NPMs. For illustration, we use experimentally measured adsorption data to analyze the conditioning of the inverse problem associated with a [IRMOF-1, HKUST-1] CH<sub>4</sub>/CO<sub>2</sub> sensor array.

scholarly journals Evaluating the Fitness of Combinations of Adsorbents for Quantitative Gas Sensor Arrays

2020 ◽  
Author(s):  
Rachel Sousa ◽  
Cory Simon
Keyword(s):  
Inverse Problem ◽  
Gas Sensor ◽  
Sensor Array ◽  
Gas Sensing ◽  
Sensor Arrays ◽  
Industrial Process ◽  
Gas Composition ◽  
Monitoring And Control ◽  
Security Threat ◽  
Adsorption Model

Robust, high-performance gas sensing technology has applications in industrial process monitoring and control, air quality monitoring, food quality assessment, medical diagnosis, and security threat detection. Nanoporous materials (NPMs) could be utilized as recognition elements in a gas sensor because they selectively adsorb gas. Imitating mammalian olfaction, sensor arrays of NPMs use measurements of the adsorbed mass of gas in a set of distinct NPMs to infer the gas composition. Modular and adjustable NPMs, such as metal-organic frameworks (MOFs), offer a vast materials space to sample for combinations to comprise a sensor array that produces a response pattern rich with information about the gas composition.<br><br>Herein, we frame quantitative gas sensing, using arrays of NPMs, as an inverse problem, which equips us with a method to evaluate the fitness of a proposed combination of NPMs in a sensor array. While the (routine) forward problem is to use an adsorption model to predict the mass of gas adsorbed in the NPMs when immersed in a gas mixture of a given composition, the inverse problem is to predict the gas composition from the observed mass of adsorbed gas in each NPM. The fitness of a given combination of NPMs for gas sensing is then determined by the conditioning of its inverse problem: the prediction of the gas composition provided by a fit (unfit) combination of NPMs is insensitive (sensitive) to inevitable errors in the measurements of the mass of gas adsorbed in the NPMs. For illustration, we use experimentally measured adsorption data to analyze the conditioning of the inverse problem associated with a [IRMOF-1, HKUST-1] CH<sub>4</sub>/CO<sub>2</sub> sensor array.

scholarly journals Evaluating the Fitness of Combinations of Adsorbents for Quantitative Gas Sensor Arrays

2020 ◽  
Author(s):  
Rachel Sousa ◽  
Cory Simon
Keyword(s):  
Inverse Problem ◽  
Gas Sensor ◽  
Sensor Array ◽  
Gas Sensing ◽  
Sensor Arrays ◽  
Industrial Process ◽  
Gas Composition ◽  
Monitoring And Control ◽  
Security Threat ◽  
Adsorption Model

Robust, high-performance gas sensing technology has applications in industrial process monitoring and control, air quality monitoring, food quality assessment, medical diagnosis, and security threat detection. Nanoporous materials (NPMs) could be utilized as recognition elements in a gas sensor because they selectively adsorb gas. Imitating mammalian olfaction, sensor arrays of NPMs use measurements of the adsorbed mass of gas in a set of distinct NPMs to infer the gas composition. Modular and adjustable NPMs, such as metal-organic frameworks (MOFs), offer a vast materials space to sample for combinations to comprise a sensor array that produces a response pattern rich with information about the gas composition.<br><br>Herein, we frame quantitative gas sensing, using arrays of NPMs, as an inverse problem, which equips us with a method to evaluate the fitness of a proposed combination of NPMs in a sensor array. While the (routine) forward problem is to use an adsorption model to predict the mass of gas adsorbed in the NPMs when immersed in a gas mixture of a given composition, the inverse problem is to predict the gas composition from the observed mass of adsorbed gas in each NPM. The fitness of a given combination of NPMs for gas sensing is then determined by the conditioning of its inverse problem: the prediction of the gas composition provided by a fit (unfit) combination of NPMs is insensitive (sensitive) to inevitable errors in the measurements of the mass of gas adsorbed in the NPMs. For illustration, we use experimentally measured adsorption data to analyze the conditioning of the inverse problem associated with a [IRMOF-1, HKUST-1] CH<sub>4</sub>/CO<sub>2</sub> sensor array.

scholarly journals Curating Metal-Organic Frameworks to Compose Robust Gas Sensor Arrays in Dilute Conditions

2019 ◽  
Author(s):  
Arni Sturluson ◽  
Rachel Sousa ◽  
Yujing Zhang ◽  
Melanie T. Huynh ◽  
Caleb Laird ◽  
...  
Keyword(s):  
Sensor Array ◽  
Gas Sensing ◽  
Metal Organic Frameworks ◽  
Sensor Arrays ◽  
Gas Composition ◽  
Composition Space ◽  
The Matrix ◽  
Metal Organic ◽  
Analyte Detection ◽  
Value Decomposition

Metal-organic frameworks (MOFs)-- tunable, nano-porous materials-- are alluring recognition elements for gas sensing. Mimicking human olfaction, an array of cross-sensitive, MOF-based sensors could enable analyte detection in complex, variable gas mixtures containing confounding gas species. Herein, we address the question: given a set of MOF candidates and their adsorption properties, how do we select the optimal subset to compose a sensor array that accurately and robustly predicts the gas composition via monitoring the adsorbed mass in each MOF? We first mathematically formulate the MOF-based sensor array problem under dilute conditions. Instructively, the sensor array can be viewed as a linear map from <i>gas composition space</i> to <i>sensor array response space</i> defined by the matrix <b>H</b> of Henry coefficients of the gases in the MOFs. Characterizing this mapping, the singular value decomposition of <b>H </b>is a useful tool for evaluating MOF subsets for sensor arrays, as it determines the sensitivity of the predicted gas composition to measurement error, quantifies the magnitude of the response to changes in composition, and recovers which direction in gas composition space elicits the largest/smallest response. To illustrate, on the basis of experimental adsorption data, we curate MOFs for a sensor array with the objective of determining the concentration of CO<sub>2</sub> and SO<sub>2</sub> in the gas phase.

Curating Metal-Organic Frameworks to Compose Robust Gas Sensor Arrays in Dilute Conditions

2019 ◽  
Author(s):  
Arni Sturluson ◽  
Rachel Sousa ◽  
Yujing Zhang ◽  
Melanie T. Huynh ◽  
Caleb Laird ◽  
...  
Keyword(s):  
Sensor Array ◽  
Gas Sensing ◽  
Metal Organic Frameworks ◽  
Sensor Arrays ◽  
Gas Composition ◽  
Composition Space ◽  
The Matrix ◽  
Metal Organic ◽  
Analyte Detection ◽  
Value Decomposition

Metal-organic frameworks (MOFs)-- tunable, nano-porous materials-- are alluring recognition elements for gas sensing. Mimicking human olfaction, an array of cross-sensitive, MOF-based sensors could enable analyte detection in complex, variable gas mixtures containing confounding gas species. Herein, we address the question: given a set of MOF candidates and their adsorption properties, how do we select the optimal subset to compose a sensor array that accurately and robustly predicts the gas composition via monitoring the adsorbed mass in each MOF? We first mathematically formulate the MOF-based sensor array problem under dilute conditions. Instructively, the sensor array can be viewed as a linear map from <i>gas composition space</i> to <i>sensor array response space</i> defined by the matrix <b>H</b> of Henry coefficients of the gases in the MOFs. Characterizing this mapping, the singular value decomposition of <b>H </b>is a useful tool for evaluating MOF subsets for sensor arrays, as it determines the sensitivity of the predicted gas composition to measurement error, quantifies the magnitude of the response to changes in composition, and recovers which direction in gas composition space elicits the largest/smallest response. To illustrate, on the basis of experimental adsorption data, we curate MOFs for a sensor array with the objective of determining the concentration of CO<sub>2</sub> and SO<sub>2</sub> in the gas phase.

Curating Metal-Organic Frameworks to Compose Robust Gas Sensor Arrays in Dilute Conditions

2019 ◽  
Author(s):  
Arni Sturluson ◽  
Rachel Sousa ◽  
Yujing Zhang ◽  
Melanie T. Huynh ◽  
Caleb Laird ◽  
...  
Keyword(s):  
Sensor Array ◽  
Gas Sensing ◽  
Metal Organic Frameworks ◽  
Sensor Arrays ◽  
Gas Composition ◽  
Composition Space ◽  
The Matrix ◽  
Metal Organic ◽  
Analyte Detection ◽  
Value Decomposition

Metal-organic frameworks (MOFs)-- tunable, nano-porous materials-- are alluring recognition elements for gas sensing. Mimicking human olfaction, an array of cross-sensitive, MOF-based sensors could enable analyte detection in complex, variable gas mixtures containing confounding gas species. Herein, we address the question: given a set of MOF candidates and their adsorption properties, how do we select the optimal subset to compose a sensor array that accurately and robustly predicts the gas composition via monitoring the adsorbed mass in each MOF? We first mathematically formulate the MOF-based sensor array problem under dilute conditions. Instructively, the sensor array can be viewed as a linear map from <i>gas composition space</i> to <i>sensor array response space</i> defined by the matrix <b>H</b> of Henry coefficients of the gases in the MOFs. Characterizing this mapping, the singular value decomposition of <b>H </b>is a useful tool for evaluating MOF subsets for sensor arrays, as it determines the sensitivity of the predicted gas composition to measurement error, quantifies the magnitude of the response to changes in composition, and recovers which direction in gas composition space elicits the largest/smallest response. To illustrate, on the basis of experimental adsorption data, we curate MOFs for a sensor array with the objective of determining the concentration of CO<sub>2</sub> and SO<sub>2</sub> in the gas phase.

scholarly journals 100 nm-Gap Fingers Dielectrophoresis Functionalized MOX Gas Sensor Array for Low Temperature VOCs Detection

Proceedings ◽  
2018 ◽  
Vol 2 (13) ◽  
pp. 1027
Author(s):  
Luca Francioso ◽  
Pasquale Creti ◽  
Maria Concetta Martucci ◽  
Simonetta Capone ◽  
Antonietta Taurino ◽  
...  
Keyword(s):  
Low Temperature ◽  
Gas Sensor ◽  
Sensor Array ◽  
Gas Sensing ◽  
Enhanced Sensitivity ◽  
Sno2 Nanocrystals ◽  
Gas Sensor Array ◽  
Order Of Magnitude ◽  
Nanogap Electrodes ◽  
Average Size

Present work reports the fabrication process and functional gas sensing tests of a 100 nm-gap fingers DiElectroPhoresis (DEP) functionalized MOX (Metal OXide) gas sensor array for VOCs detection at low temperature. The Internet of Things (IoT) scenario applications of the chemical sensing-enabled mobiles or connected devices are many ranging from indoor air quality to novel breath analyser for personal healthcare monitoring. However, the commercial MOX gas sensors operate at moderate temperatures (200–400 °C) [1], and this limits the mobile and wearable gadgets market penetration. Nanogap devices may represent the alternative devices with enhanced sensitivity even at low or room temperature. A nanogap electrodes MOX gas sensor array functionalized with 5 nm average size SnO2 nanocrystals with positive dielectrophoresis technique is presented. The single sensor active area is 4 × 4 µm2. The devices exhibited about 1 order of magnitude response at 100 °C to 150 ppm of acetone.

scholarly journals A Set of Platforms with Combinatorial and High-Throughput Technique for Gas Sensing, from Material to Device and to System

Micromachines ◽  
2018 ◽  
Vol 9 (11) ◽  
pp. 606 ◽  
Author(s):  
Zhenghao Mao ◽  
Jianchao Wang ◽  
Youjin Gong ◽  
Heng Yang ◽  
Shunping Zhang
Keyword(s):  
Gas Sensor ◽  
High Throughput ◽  
System Integration ◽  
Sensor Array ◽  
Gas Sensing ◽  
Parallel Synthesis ◽  
Testing Platform ◽  
Long Time ◽  
Sensitivity And Selectivity ◽  
Array Optimization

In a new E-nose development, the sensor array needs to be optimized to have enough sensitivity and selectivity for gas/odor classification in the application. The development process includes the preparation of gas sensitive materials, gas sensor fabrication, array optimization, sensor array package and E-nose system integration, which would take a long time to complete. A set of platforms including a gas sensing film parallel synthesis platform, high-throughput gas sensing unmanned testing platform and a handheld wireless E-nose system were presented in this paper to improve the efficiency of a new E-nose development. Inkjet printing was used to parallel synthesize sensor libraries (400 sensors can be prepared each time). For gas sensor selection and array optimization, a high-throughput unmanned testing platform was designed and fabricated for gas sensing measurements of more than 1000 materials synchronously. The structures of a handheld wireless E-nose system with low power were presented in detail. Using the proposed hardware platforms, a new E-nose development might only take one week.

Type discrimination and concentration prediction towards ethanol using a machine learning–enhanced gas sensor array with different morphology-tuning characteristics

2021 ◽  
Author(s):  
Tao Wang ◽  
Hongli Ma ◽  
Wenkai Jiang ◽  
Hexin Zhang ◽  
Min Zeng ◽  
...  
Keyword(s):  
Machine Learning ◽  
Gas Sensor ◽  
Sensor Array ◽  
Gas Sensing ◽  
Hierarchical Structures ◽  
Machine Learning Algorithms ◽  
Microwave Assisted ◽  
Gas Sensor Array ◽  
Microwave Assisted Method ◽  
Concentration Prediction

Microwave-assisted method has been developed to synthesize ZnO gas sensing nanomaterials with controllable hierarchical structures. Machine learning algorithms such as PCA, SVM, ELM, and BP further improve the selectivity and quantitation.

scholarly journals Identifying gas composition changes to which non-injective gas sensor arrays are unresponsive

2021 ◽  
Author(s):  
Cory Simon ◽  
Nickolas Gantzler ◽  
Adrian Henle ◽  
Praveen Thallapally ◽  
Xiaoli Fern
Keyword(s):  
Gas Sensor ◽  
Sensor Arrays ◽  
Gas Composition ◽  
Composition Changes ◽  
Gas Sensor Arrays

scholarly journals Review on Smart Gas Sensing Technology

Sensors ◽  
2019 ◽  
Vol 19 (17) ◽  
pp. 3760 ◽  
Author(s):  
Shaobin Feng ◽  
Fadi Farha ◽  
Qingjuan Li ◽  
Yueliang Wan ◽  
Yang Xu ◽  
...  
Keyword(s):  
Signal Processing ◽  
Gas Sensor ◽  
Gas Sensing ◽  
Sensor Arrays ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Sensing Technology ◽  
Drift Compensation ◽  
Artificial Neural Network Ann ◽  
Smart Mobile

With the development of the Internet-of-Things (IoT) technology, the applications of gas sensors in the fields of smart homes, wearable devices, and smart mobile terminals have developed by leaps and bounds. In such complex sensing scenarios, the gas sensor shows the defects of cross sensitivity and low selectivity. Therefore, smart gas sensing methods have been proposed to address these issues by adding sensor arrays, signal processing, and machine learning techniques to traditional gas sensing technologies. This review introduces the reader to the overall framework of smart gas sensing technology, including three key points; gas sensor arrays made of different materials, signal processing for drift compensation and feature extraction, and gas pattern recognition including Support Vector Machine (SVM), Artificial Neural Network (ANN), and other techniques. The implementation, evaluation, and comparison of the proposed solutions in each step have been summarized covering most of the relevant recently published studies. This review also highlights the challenges facing smart gas sensing technology represented by repeatability and reusability, circuit integration and miniaturization, and real-time sensing. Besides, the proposed solutions, which show the future directions of smart gas sensing, are explored. Finally, the recommendations for smart gas sensing based on brain-like sensing are provided in this paper.

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