Detection and recognition of volatile organic compounds (VOCs) are crucial in many applications. While pure VOCs can be detected by various sensors, the discrimination of VOCs in mixtures, especially of similar molecules, is hindered by crosssensitivities. Isomer identification in mixtures is even harder. Metal−organic frameworks (MOFs) with their well-defined, nanoporous, and versatile structures have the potential to improve the VOC sensing performance by tailoring the adsorption affinities. Here, we detect and identify ternary xylene isomer mixtures by using an array of six gravimetric, quartz crystal microbalance (QCM)-based sensors coated with selected MOF films with different isomer affinities. We use classical molecular simulations to provide insights into the sensing mechanism. In addition to the attractive interaction between the analytes and the MOF film, the isomer discrimination is caused by the rigid crystalline framework sterically controlling the access of the isomers to different adsorption sites in the MOFs. The sensor array has a very low limit of detection of 1 ppm for each pure isomer and allows the isomer discrimination in mixtures. At 100 ppm, 16 different ternary o−p−m-xylene mixtures were identified with high classification accuracy (96.5%). This work shows the unprecedented performance of MOF-sensor arrays, also referred to as MOF-electronic nose (MOF-enose), for sensing VOC mixtures. Based on the study, guidelines for detecting and discriminating complex mixtures of volatile molecules are also provided.

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Traditional chemical sensing methodologies have typically relied on the specific chemistry of the analyte for detection. Modifications to the local environment surrounding the sensor represent an alternative pathway to impart selective differentiation. Here, we present the hybridization of a 2-D metal organic framework (Cu3(HHTP)2) with single-walled carbon nanotubes (SWCNTs) as a methodology for size discrimination of carbohydrates. Synthesis and the resulting conductive performance are modulated by both mass loading of SWCNTs and their relative oxidation. Liquid gated field-effect transistor (FET) devices demonstrate improved on/off characteristics and differentiation of carbohydrates based on molecular size. Glucose molecule detection is limited to the single micromolar concentration range. Molecular Dynamics (MD) calculations on model systems revealed decreases in ion diffusivity in the presence of different sugars as well as packing differences based on the size of a given carbohydrate molecule. The proposed sensing mechanism is a reduction in gate capacitance initiated by the filling of the pores with carbohydrate molecules. Restricting diffusion around a sensor in combination with FET measurements represents a new type of sensing mechanism for chemically similar analytes.

Gas sensor arrays, also known as electronic noses, leverage a diverse set of materials to identify the components of complex gas mixtures. Metal-organic frameworks (MOFs) have emerged as promising materials for electronic noses due to their high-surface areas and chemical as well as structural tunability. Using our recently reported genetic algorithm design approach, we examined a set of 50 MOFs and searched through over 1.125 × 10 15 unique array combinations to identify optimal arrays for the detection of CO 2 in air. We found that despite individual MOFs having lower selectivity for O 2 or N 2 relative to CO 2 , intelligently selecting the right combinations of MOFs enables accurate prediction of the concentrations of all components in the mixture (i.e., CO 2 , O 2 , N 2 ). We also analyzed the physical properties of the elements in the arrays to develop an intuition for improving array design. Notably, we found that an array whose MOFs have diversity in their volumetric surface areas has improved sensing. Consistent with this observation, we found that the best arrays consistently had greater structural diversity (e.g., pore sizes, void fractions, and surface areas) than the worst arrays.Sensors 2020, 20, 924 2 of 12 especially when tanks of CO 2 or dry ice are being used [19]. And even in areas where CO 2 is less likely to accumulate rapidly, there are concerns over its role as an indoor pollutant [8,20,21].Exposure to elevated levels of CO 2 in air poses a two-fold threat, acting as both an asphyxiant by displacing oxygen, and as a toxicant, both with potentially deadly consequences. At levels greater the 5%, it can result in the development of hypercapnia, a build-up of CO 2 in the bloodstream, and respiratory acidosis, an inability to clear excess CO 2 from the lungs [8,20,22]. At concentrations of 10% and greater, exposure can result in convulsions, coma, and even death. And at levels of 30% and greater, exposure can lead to a loss of consciousness in only a matter of seconds [8,20]. Clearly, rapid, sensitive, and portable (even wearable) CO 2 sensors would benefit many people.Recently, a number of studies have investigated the use of metal-organic frameworks (MOFs) for improving electronic noses [23][24][25][26][27]. MOFs are promising materials for gas adsorption applications due to their nanoporous nature, high internal surface areas, and ease of tunability [28][29][30][31][32][33][34]. Moreover, as crystalline materials, they lend themselves conveniently to accurate computational modeling not possible with their porous amorphous counterparts [35][36][37].Practically, however, there are still many significant obstacles to overcome in developing MOF-based electronic noses. While tens of thousands of MOFs have been reported in the literature, only a small fraction have simultaneously been amenable to thin-film deposition and also possess the stability required for a practical device. However, even if those material-property obstacles were overcome, an import design challenge has remained largely overloo...

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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.

Gas sensor arrays, also known as electronic noses, leverage a diverse set of materials to identify the components of complex gas mixtures. Metal-organic frameworks (MOFs) have emerged as promising materials for electronic noses due to their high-surface areas and chemical as well as structural tunability. Using our recently reported genetic algorithm design approach, we examined a set of 50 MOFs and searched through the over 1.125x1015 unique array combinations to identify optimal arrays for the detection of CO2 in air. We found that despite individual MOFs having lower selectivity for O2 or N2 relative to CO2, intelligently selecting the right combinations of MOFs enabled accurate prediction of the concentrations of all components in the mixture (i.e. CO2, O2, N2). We also analyzed the physical properties of the elements in the arrays to develop an intuition for improving array design. Notably, we find that diversity among the surface areas in the MOFs leads to improved sensing. Consistent with the observation, we found that, as one might expect, the best arrays consistently had more structural diversity than the worst arrays.

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