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

Infection Diagnostics Enabled by Selective Adsorption of Breath-Based Biomarkers in Zr-Based Metal-Organic Frameworks

Exhaled breath contains trace levels of volatile organic compounds (VOCs) that can reveal information about metabolic processes or pathogens in the body. These molecules can be used for medical diagnosis, but capturing and accurately measuring them is a significant challenge in chemical separations. A highly selective nanoporous sorbent can be used to capture target molecules from a breath sample and preconcentrate them for use in a detector. In this work, we present a combined predictive modeling-experimental validation study in which five Zr-based metal-organic frameworks (MOFs) were identified and tested. These MOFs display good selectivity for a variety of VOCs known to be indicators of viral infections such as influenza and COVID-19. We first used molecular simulation to identify promising MOF candidates that were subsequently synthesized and tested for recovery of a variety of VOCs (toluene, propanal, butanone, octane, acetaldehyde) at concentrations of 20 ppm in humid nitrogen. We show that MOF-818, PCN-777, and UiO-66 have particularly good selectivity for the target molecules in the presence of humidity. These three MOFs each recover around 40-60% of the targets (with the exception of acetaldehyde) at up to 95% relative humidity. MOF-818 recovers 63% of butanone and 60% of toluene at 80% relative humidity. Recovery for acetaldehyde is lower across all MOFs at high humidity, but notably, MOF-808 recovers 90% of acetaldehyde at 60% humidity.

Environmental Stability Determines the Cytotoxicity of Metal-Organic Frameworks to a Nitrogen-Fixing Bacterium Azotobacter vinelandii

During widespread applications of metal-organic frameworks (MOFs), the environmental hazards and risks of MOFs have aroused great concerns. In this study, we aimed to reveal the importance of the environmental stability of MOFs on their toxicity. Two Zn-MOFs, namely, ZIF-8 with high aqueous stability and Zn-BDC with low aqueous stability, were compared directly in the toxicological evaluations of a nitrogen-fixing bacterium Azotobacter vinelandii. Zn-BDC showed strong cytotoxicity at 100 mg/L and higher, inducing growth inhibition, cell apoptosis, structural changes, oxidative damage, and, consequently, loss of nitrogen fixation ability. In contrast, ZIF-8 was nearly nontoxic to A. vinelandii. The transcriptome analysis showed that Zn-BDC directly disturbed the ribosome pathway and lowered the expression level of nitrogen-fixing nif cluster genes. On the other hand, ZIF-8 stress could regulate the flagellar assembly, siderophore group nonribosomal peptide biosynthesis, bacterial chemotaxis, and amino sugar and nucleotide sugar metabolism pathways to promote the cell growth of A. vinelandii. Beyond that, the toxicity of Zn-MOFs to A. vinelandii was associated with the release of Zn2+, but Zn-MOFs were less toxic than the mixtures of their starting materials. Overall, our results suggested that the environmental stability of Zn-MOFs determined their environmental toxicity through different molecular pathways. Designing stable MOFs is preferred due to environment-friendly considerations.

Triboelectric Nanogenerator for Self-Powered Gas Sensing

With the continuous acceleration of industrialization, gas sensors are evolving to become portable, wearable and environmentally friendly. However, traditional gas sensors rely on external power supply, which severely limits their applications in various industries. As an innovative and environmentally adaptable power generation technology, triboelectric nanogenerators (TENGs) can be integrated with gas sensors to leverage the benefits of both technologies for efficient and environmentally friendly self-powered gas sensing. This paper delves into the basic principles and current research frontiers of the TENG-based self-powered gas sensor, focusing particularly on innovative applications in environmental safety monitoring, healthcare, as well as emerging fields such as food safety assurance and smart agriculture. It emphasizes the significant advantages of TENG-based self-powered gas sensor systems in promoting environmental sustainability, achieving efficient sensing at room temperature, and driving technological innovations in wearable devices. It also objectively analyzes the technical challenges, including issues related to performance enhancement, theoretical refinement, and application expansion, and provides targeted strategies and future research directions aimed at paving the way for continuous progress and widespread applications in the field of self-powered gas sensors.

A photoprogrammable electronic nose with switchable selectivity for VOCs using MOF films

Advanced analytical applications require smart materials and sensor systems that are able to adapt or be configured to specific tasks. Based on reversible photochemistry in nanoporous materials, we present a sensor array with a selectivity that is reversibly controlled by light irradiation. The active material of the sensor array, or electronic nose (e-nose), is based on metal-organic frameworks (MOFs) with photoresponsive fluorinated azobenzene groups that can be optically switched between their trans and cis state. By irradiation with light of different wavelengths, the trans-cis ratio can be modulated. Here we use four trans-cis values as defined states and employ a four-channel quartz-crystal microbalance for gravimetrically monitoring the molecular uptake by the MOF films. We apply the photoprogrammable e-nose to the sensing of different volatile organic compounds (VOCs) and analyze the sensor array data with simple machine-learning algorithms. When the sensor array is in a state with all sensors either in the same trans- or cis-rich state, cross-sensitivity between the analytes occurs and the classification accuracy is not ideal. Remarkably, the VOC molecules between which the sensor array shows cross-sensitivity vary by switching the entire sensor array from trans to cis. By selectively programming the e-nose with light of different colors, each sensor exhibits a different isomer ratio and thus a different VOC affinity, based on the polarity difference between the trans- and cis-azobenzenes. In such photoprogrammed state, the cross-sensitivity is reduced and the selectivity is enhanced, so that the e-nose can perfectly identify the tested VOCs. This work demonstrates for the first time the potential of photoswitchable and thus optically configurable materials as active sensing material in an e-nose for intelligent molecular sensing. The concept is not limited to QCM-based azobenzene-MOF sensors and can also be applied to diverse sensing materials and photoswitches.

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.

Metal-organic Framework Materials Coupled to Optical Fibers for Chemical Sensing: A Review

Metal-organic frameworks (MOFs), a newer class of crystalline nanoporous materials, have been in the limelight owing to their exceptional tunability for structures and physicochemical properties, and have found successful applications in gas storage, gas separation, and catalysis. The mesmerizing properties of MOFs, especially the extensive and tunable porosity and chemical selectivity, also make them an excellent candidate class as chemo-sensory materials. Moreover, MOF-based sensors have attracted considerable attention in the past decade. Recent literature reviews focused on the progress of MOF-based electronic sensors and luminescent MOF sensors, while sensors exploiting the dielectric properties (refractive index) of MOFs were also demonstrated and discussed very recently. The motivation of this report is to provide, for the first time, a general review on such MOF sensors with a particular focus on miniature optical fiber (OF) based MOF sensors and to demonstrate the promising potential of MOFs as dielectric coatings on OF for highly sensitive chemical sensing. The fundamental principle of OF-MOF sensors relies on the tunability of the refractive index of a MOF, which is dependent on the amount and type of adsorbed guest molecules in the MOF pores. MOF sensors based on different optical sensing principles are reviewed; challenges and perspectives on further research into the field of OF-MOF sensors are also discussed.

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.