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

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 gas composition space to sensor array response space defined by the matrix H of Henry coefficients of the gases in the MOFs. Characterizing this mapping, the singular value decomposition of H 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 CO2 and SO2 in the gas phase.

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

10.26434/chemrxiv.8179244 ◽

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 gas composition space to sensor array response space defined by the matrix H of Henry coefficients of the gases in the MOFs. Characterizing this mapping, the singular value decomposition of H 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 CO2 and SO2 in the gas phase.

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

10.26434/chemrxiv.12971171 ◽

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. 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] CH4/CO2 sensor array.

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

10.26434/chemrxiv.12971171.v1 ◽

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. 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] CH4/CO2 sensor array.

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

10.26434/chemrxiv.12971171.v2 ◽

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. 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] CH4/CO2 sensor array.

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On the Use of MOFs and ALD Layers as Nanomembranes for the Enhancement of Gas Sensors Selectivity

Nanomaterials ◽

10.3390/nano9111552 ◽

2019 ◽

Vol 9 (11) ◽

pp. 1552 ◽

Cited By ~ 3

Author(s):

Weber ◽

Graniel ◽

Balme ◽

Miele ◽

Bechelany

Keyword(s):

Thin Films ◽

Gas Sensors ◽

Gas Sensing ◽

Metal Organic Frameworks ◽

Atomic Layer ◽

Operational Performance ◽

Layer Deposition ◽

Metal Organic ◽

Further Development ◽

Different Gases

Improving the selectivity of gas sensors is crucial for their further development. One effective route to enhance this key property of sensors is the use of selective nanomembrane materials. This work aims to present how metal-organic frameworks (MOFs) and thin films prepared by atomic layer deposition (ALD) can be applied as nanomembranes to separate different gases, and hence improve the selectivity of gas sensing devices. First, the fundamentals of the mechanisms and configuration of gas sensors will be given. A selected list of studies will then be presented to illustrate how MOFs and ALD materials can be implemented as nanomembranes and how they can be implemented to improve the operational performance of gas sensing devices. This review comprehensively shows the benefits of these novel selective nanomaterials and opens prospects for the sensing community.

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Hierarchical hollow ZnO cubes constructed using self-sacrificial ZIF-8 frameworks and their enhanced benzene gas-sensing properties

New Journal of Chemistry ◽

10.1039/c5nj00549c ◽

2015 ◽

Vol 39 (9) ◽

pp. 7060-7065 ◽

Cited By ~ 31

Author(s):

Wenhui Li ◽

Xiaofeng Wu ◽

Haidi Liu ◽

Jiayuan Chen ◽

Wenxiang Tang ◽

...

Keyword(s):

Gas Sensing ◽

Metal Organic Frameworks ◽

Chemical Sensitivity ◽

Sensing Properties ◽

Low Concentration ◽

Gas Sensing Properties ◽

Metal Organic ◽

Gaseous Benzene

Hierarchical ZnO hollow cubes constructed using Zn-based metal–organic frameworks enhance significantly chemical sensitivity towards low-concentration gaseous benzene.

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