Particle-in-a-frame gold nanomaterials with interior nanogap-based sensor array for versatile analyte detection

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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Analyte detection using an ion-channel sensor array

2011 17th International Conference on Digital Signal Processing (DSP) ◽

10.1109/icdsp.2011.6004913 ◽

2011 ◽

Author(s):

P. S. Sattigeri ◽

K. N. Ramamurthy ◽

J. J. Thiagarajan ◽

M. Goryll ◽

A. Spanias ◽

...

Keyword(s):

Ion Channel ◽

Sensor Array ◽

Analyte Detection

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

10.26434/chemrxiv.8179244.v1 ◽

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.

Download Full-text

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.

Download Full-text