Can it be detected? A computational protocol for evaluating MOF-metal oxide chemiresistive sensors for early disease detection
Maryam Nurhuda, Ken-ichi Otake, Susumu Kitagawa, Daniel M. Packwood

TL;DR
This paper introduces a computational protocol to evaluate MOF-metal oxide sensors' ability to detect disease-related compounds in human breath, aiding early diagnosis through high-throughput screening.
Contribution
It presents a novel high-throughput computational method combining structure relaxation and binding energy analysis for sensor evaluation, validated on a specific MOF-metal oxide system.
Findings
Protocol aligns with experimental results for NM125-TiO2
Identifies promising breath compounds for disease detection
Enables targeted experimental validation
Abstract
Human breath contains over 3000 volatile organic compounds, abnormal concentrations of which can indicate the presence of certain diseases. Recently, metal-organic framework (MOF)-metal oxide composite materials have been explored for chemiresistive sensor applications, however their ability to detect breath compounds associated with specific diseases remains unknown. In this work, we present a new high-throughput computational protocol for evaluating the sensing ability of MOF-metal oxide towards small organic compounds. This protocol uses a cluster-based method for accelerated structure relaxation, and a combination of binding energies and density-of-states analysis to evaluate sensing ability, the latter measured using Wasserstein distances. We apply this protocol to the case of the MOF-metal oxide composite material NM125-TiO2 and show that it is consistent with previously reported…
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Taxonomy
TopicsAdvanced Nanomaterials in Catalysis · Gas Sensing Nanomaterials and Sensors · Electrochemical sensors and biosensors
