Genetic Algorithm Design of MOF-based Gas Sensor Arrays for CO2-in-Air Sensing

High-precision Helicobacter pylori infection diagnosis using a dual-element multimodal gas sensor array

Helicobacter pylori (H. pylori) is a globally widespread bacterial infection. Early diagnosis of this infection is vital for public and individual health. Prevalent diagnosis methods like the isotope 13C or 14C labelled urea breath test (UBT) are not convenient and may do harm to the human body. The use of cross-response gas sensor arrays (GSAs) is an alternative way for label-free detection of metabolite changes in exhaled breath (EB). However, conventional GSAs are complex to prepare, lack reliability, and fail to discriminate subtle changes in EB due to the use of numerous sensing elements and single dimensional signal. This work presents a dual-element multimodal GSA empowered with multimodal sensing signals including conductance (G), capacitance (C), and dissipation factor (DF) to improve the ability for gas recognition and H. pylori-infection diagnosis. Sensitized by poly(diallyldimethylammonium chloride) (PDDA) and the metal-organic framework material NH2-UiO66, the dual-element graphene oxide (GO)-composite GSAs exhibited a high specific surface area and abundant adsorption sites, resulting in high sensitivity, repeatability, and fast response/recovery speed in all three signals. The multimodal sensing signals with rich sensing features allowed the GSA to detect various physicochemical properties of gas analytes, such as charge transfer and polarization ability, enhancing the sensing capabilities for gas discrimination. The dual-element GSA could differentiate different typical standard gases and non-dehumidified EB samples, demonstrating the advantages in EB analysis. In a case-control clinical study on 52 clinical EB samples, the diagnosis model based on the multimodal GSA achieved an accuracy of 94.1%, a sensitivity of 100%, and a specificity of 90.9% for diagnosing H. pylori infection, offering a promising strategy for developing an accurate, non-invasive and label-free method for disease diagnosis.

Multipressure Sampling for Improving the Performance of MOF-based Electronic Noses

Metal-organic frameworks (MOFs) are a promising class of porous materials for the design of gas sensing arrays, which are often called electronic noses. Due to their chemical and structural tunability, MOFs are a highly diverse class of materials that align well with the similarly diverse class of volatile organic compounds (VOCs) of interest in many gas detection applications. In principle, by choosing the right combination of cross-sensitive MOFs, layered on appropriate signal transducers, one can design an array that yields detailed information about the composition of a complex gas mixture. However, despite the vast number of MOFs from which one can choose, gas sensing arrays that rely too heavily on distinct chemistries can be impractical from the cost and complexity perspective. On the other hand, it is difficult for small arrays to have the desired selectivity and sensitivity for challenging sensing applications, such as detecting weakly adsorbing gases with weak signals, or conversely, strongly adsorbing gases that readily saturate MOF pores. In this work, we employed gas adsorption simulations to explore the use of a variable pressure sensing array as a means of improving both sensitivity and selectivity as well as increasing the information content provided by each array. We studied nine different MOFs (HKUST-1, IRMOF-1, MgMOF-74, MOF-177, MOF-801, NU-100, NU-125, UiO-66, and ZIF-8) and four different gas mixtures, each containing nitrogen, oxygen, carbon dioxide, and exactly one of the hydrogen, methane, hydrogen sulfide, or benzene. We found that by lowering the pressure, we can limit the saturation of MOFs, and by raising the pressure, we can concentrate weakly adsorbing gases, in both cases, improving gas detection with the resulting arrays. In many cases, changing the system pressure yielded a better improvement in performance (as measured by the Kullback-Liebler divergence of gas composition probability distributions) than including additional MOFs. We thus demonstrated and quantified how sensing at multiple pressures can increase information content and cross-sensitivity in MOF-based arrays while limiting the number of unique materials needed in the device.

Determining best practices for using genetic algorithms in molecular discovery

Genetic algorithms (GAs) are a powerful tool to search large chemical spaces for inverse molecular design. However, GAs have multiple hyperparameters that have not been thoroughly investigated for chemical space searches. In this tutorial, we examine the general effects of a number of hyperparameters, such as population size, elitism rate, selection method, mutation rate, and convergence criteria, on key GA performance metrics. We show that using a self-termination method with a minimum Spearman's rank correlation coefficient of 0.8 between generations maintained for 50 consecutive generations along with a population size of 32, a 50% elitism rate, three-way tournament selection, and a 40% mutation rate provides the best balance of finding the overall champion, maintaining good coverage of elite targets, and improving relative speedup for general use in molecular design GAs.

Gas sensing based on metal-organic frameworks: Concepts, functions, and developments

Effective detection of pollutant gases is vital for protection of natural environment and human health. There is an increasing demand for sensing devices that are equipped with high sensitivity, fast response/recovery speed, and remarkable selectivity. Particularly, attention is given to the designability of sensing materials with porous structures. Among diverse kinds of porous materials, metal-organic frameworks (MOFs) exhibit high porosity, high degree of crystallinity and exceptional chemical activity. Their strong host-guest interactions with guest molecules facilitate the application of MOFs in adsorption, catalysis and sensing systems. In particular, the tailorable framework/composition and potential for post-synthetic modification of MOFs endow them with widely promising application in gas sensing devices. In this review, we outlined the fundamental aspects and applications of MOFs for gas sensors, and discussed various techniques of monitoring gases based on MOFs as functional materials. Insights and perspectives for further challenges faced by MOFs are discussed in the end.

Computational Design of MOF-Based Electronic Noses for Dilute Gas Species Detection: Application to Kidney Disease Detection

The diverse chemical composition of exhaled human breath contains a vast amount of information about the health of the body, and yet this is seldom taken advantage of for diagnostic purposes due to the lack of appropriate gas-sensing technologies. In this work, we apply computational methods to design mass-based gas sensor arrays, often called electronic noses, that are optimized for detecting kidney disease from breath, for which ammonia is a known biomarker. We define combined linear adsorption coefficients (CLACs), which are closely related to Henry's law coefficients, for calculating gas adsorption in metal-organic frameworks (MOFs) of gases commonly found in breath (i.e., carbon dioxide, argon, and ammonia). These CLACs were determined computationally using classical atomistic molecular simulation techniques and subsequently used to design and evaluate gas sensor arrays. We also describe a novel numerical algorithm for determining the composition of a breath sample given a set of sensor outputs and a library of CLACs. After identifying an optimal array of five MOFs, we screened a set of 100 simplified computer-generated, water-free breath samples for kidney disease and were able to successfully quantify the amount of ammonia in all samples within the tolerances needed to classify them as either healthy or diseased, demonstrating the promise of such devices for disease detection applications.

Material Screening for Gas Sensing Using an Electronic Nose: Gas Sorption Thermodynamic and Kinetic Considerations

To detect multiple gases in a mixture, one must employ an electronic nose or sensor array, composed of several materials, as a single material cannot resolve all the gases in a mixture accurately. Given the many candidate materials, choosing the right combination of materials to be used in an array is a challenging task. In a sensor whose sensing mechanism depends on a change in mass upon gas adsorption, both the equilibrium and kinetic characteristics of the gas-material system dictate the performance of the array. The overarching goal of this work is twofold. First, we aim to highlight the impact of thermodynamic characteristics of gas-material combination on array performance and to develop a graphical approach to rapidly screen materials. Second, we aim to highlight the need to incorporate the gas sorption kinetic characteristics to provide an accurate picture of the performance of a sensor array. To address these goals, we have developed a computational test bench that incorporates a sensor model and a gas composition estimator. To provide a generic study, we have chosen, as candidate materials, hypothetical materials that exhibit equilibrium characteristics similar to those of metal-organic frameworks. Our computational studies led to key learnings, namely, (1) exploit the shape of the sensor response as a function of gas composition for material screening purposes for gravimetric arrays; (2) incorporate both equilibrium and kinetics for gas composition estimation in a dynamic system; and (3) engineer the array by accounting for the kinetics of the materials, the feed gas flow rate, and the size of the device.

Non-injective gas sensor arrays: identifying undetectable composition changes

Metal-organic frameworks (MOFs) are nanoporous materials with good prospects as recognition elements for gas sensors owing to their adsorptive sensitivity and selectivity. A gravimetric, MOF-based sensor functions by measuring the mass of gas adsorbed in a MOF. Changes in the gas composition are expected to produce detectable changes in the mass of gas adsorbed in the MOF. In practical settings, multiple components of the gas adsorb into the MOF and contribute to the sensor response. As a result, there are typically many distinct gas compositions that produce the same single-sensor response. The response vector of a gas sensor array places multiple constraints on the gas composition. Still, if the number of degrees of freedom in the gas composition is greater than the number of MOFs in the sensor array, the map from gas compositions to response vectors will be non-injective (many-to-one). Here, we outline a mathematical method to determine undetectable changes in gas composition to which non-injective gas sensor arrays are unresponsive. This is important for understanding their limitations and vulnerabilities. We focus on gravimetric, MOF-based gas sensor arrays. Our method relies on a mixed-gas adsorption model in the MOFs comprising the sensor array, which gives the mass of gas adsorbed in each MOF as a function of the gas composition. The singular value decomposition of the Jacobian matrix of the adsorption model uncovers (i) the unresponsive directions and (ii) the responsive directions, ranked by sensitivity, in gas composition space. We illustrate the identification of unresponsive subspaces and ranked responsive directions for gas sensor arrays based on Co-MOF-74 and HKUST-1 aimed at quantitative sensing of CH₄/N₂/CO₂/C₂H₆mixtures relevant to natural gas sensing.

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

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 material 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 each NPM when immersed in a gas mixture of a given composition, the inverse problem is to predict the gas composition from the observed masses of adsorbed gas in the NPMs of the array. 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 and HKUST-1) CH₄/CO₂ sensor array.