Discriminating BTX Molecules by the Nonselective Metal Oxide Sensor-Based Smart Sensing System

Metal Oxide-Metal Organic Framework Layers for Discrimination of Multiple Gases Employing Machine Learning Algorithms

The increasing demand for gas molecule detection emphasizes the need for portable sensor devices possessing selectivity, a low limit of detection (LOD), and a large dynamic range. Despite substantial progress in developing nanostructured sensor materials with heightened sensitivity, achieving sufficient selectivity remains a challenge. Here, we introduce a strategy to enhance the performance of chemiresistive gas sensors by combining an advanced sensor design with machine learning (ML). Our sensor architecture consists of a tungsten oxide (WO3) nanoparticle network, as the primary sensing layer, with an integrated zeolitic imidazolate framework (ZIF-8) membrane layer, used to induce a gas-specific delay to the diffusion of analytes, sharing conceptual similarities to gas chromatography. However, the miniaturized design and chemical activity of the ZIF-8 results in a nontrivial impact of the ZIF-8 membrane on the target analyte diffusivity and sensor response. An ML method was developed to evaluate the response dynamics with a panel of relevant analytes including acetone, ethanol, propane, and ethylbenzene. Our advanced sensor design and ML algorithm led to an excellent capability to determine the gas molecule type and its concentration, achieving accuracies of 97.22 and 86.11%, respectively, using a virtual array of 4 sensors. The proposed ML method can also reduce the necessary sensing time to only 5 s while maintaining an accuracy of 70.83%. When compared with other ML methods in the literature, our approach also gave superior performance in terms of sensitivity, specificity, precision, and F1-score. These findings show a promising approach to overcome a longstanding challenge of the highly miniaturized but poorly selective semiconductor sensor technology, with impact ranging from environmental monitoring to explosive detection and health care.

Heterothermic Cataluminescence Sensor System for Efficient Determination of Aldehyde Molecules

A simple and stable cataluminescence (CTL) sensing platform based on a single sensing material for effective and rapid detection of aldehydes is an urgent need due to growing concerns for the environment, security, and health. Here, an effective and user-friendly identification method is successfully proposed to determine six common aldehydes of homologous compounds via a heterothermic CTL sensor system. Using Gd2O3 with excellent catalytic activity as a sensing material, thermodynamic and kinetic insights into the interactions between Gd2O3 and aldehydes at different temperatures were extracted and integrated to generate a unique constellation profile for each tested aldehyde, whereby achieving their effective and prompt determination. Moreover, the sensor system allowed the quantitative analysis of aldehydes with detection limits of 0.001, 0.009, 0.011, 0.011, 0.007, and 0.003 μg mL-1. Significantly, the sensor system had an excellent stability of up to 30 days. The CTL sensing platform was constructed based on a thermal regulation strategy that can provide a new approach to chemical agent identification.

Prompt Electronic Discrimination of Gas Molecules by Self-Heating Temperature Modulation

Though considerable progress has been achieved on gas molecule recognition by electronic nose (e-nose) comprised of nonselective (metal oxide) semiconductor chemiresistors, extracting adequate molecular features within short time (<1 s) remains a big obstacle, which hinders the emerging e-nose applications in lethal or explosive gas warning. Herein, by virtue of the ultrafast (∼20 μs) thermal relaxation time of self-heated WO3-based chemiresistors fabricated via oblique angle deposition, instead of external heating, self-heating temperature modulation has been proposed to generate sufficient electrical response features. Accurate discrimination of 12 gases (including 3 xylene isomers with the same function group and molecular weight) has been readily achieved within 0.5-1 s, which is one order faster than the state-of-the-art e-noses. A smart wireless e-nose, capable of instantaneously discriminating target gas in ambient air background, has been developed, paving the way for the practical applications of e-nose in the area of homeland security and public health.

Classification and concentration estimation of CO and NO2 mixtures under humidity using neural network-assisted pattern recognition analysis

This study addresses the concerns regarding the cross-sensitivity of metal oxide sensors by building an array of sensors and subsequently utilizing machine earning techniques to analyze the data from the sensor arrays. Sensors were built using In2O3, Au-ZnO, Au-SnO2, and Pt-SnO2 and they were operated simultaneously in the presence of 25 different concentrations of nitrogen dioxide (NO2), carbon monoxide (CO), and their mixtures. To investigate the effects of humidity, experiments were conducted to detect 13 distinct CO and NO2 gas combinations in atmospheres with 40% and 90% relative humidity. Principal component analysis was performed for the normalized resistance variation collected for a particular gas atmosphere over a certain period, and the results were used to train deep neural network-based models. The dynamic curves produced by the sensor array were treated as pixelated images and a convolutional neural network was adopted for classification. An accuracy of 100% was achieved using both models during cross-validation and testing. The results indicate that this novel approach can eliminate the time-consuming feature extraction process.

Simultaneous Sensing of Multiplex Volatile Organic Compounds by Adsorption and Plasmon Dual-Induced Raman Enhancement Technique

Developing highly efficient gas sensors with excellent performance for rapid and sensitive detection of volatile organic compounds (VOCs) is of critical importance for the protection of human health, ecological environment, and other factors. Here, a robust gas sensor based on Raman technology was constructed by an in situ grown 2D covalent organic framework (COF) on Au nanoparticles' surface in the microchannel. Dual enhancement effects are included for the as-prepared microfluidic sensor. First, acting as a gas confinement chamber, the 2D COF could effectively capture gas molecules with high adsorption capacity and fast adsorption kinetics, resulting in VOCs' preconcentration at a high level in the COF layer. At the same time, after being stacked in the microchannel, abundant hot spots were generated among the nanogaps of Au@COF NPs. The local surface plasmon resonance effect could effectively enhance the Raman intensity. Both factors contribute to the improved detection sensitivity of VOCs. As a demonstration, several representative VOCs with different functional groups were tested. The resultant Raman spectra were subjected to the statistical principal component analysis. Varied VOCs can be successfully detected with a detection limit as low as ppb level and distinguished with 95% confidence interval. The present microfluidic platform provides a simple, sensitive, and fast method for VOCs' sensing and distinguishing, which is expected to hold potential applications in the fields of health, agricultural, and environmental research.

Electrical and Gas Sensor Properties of Nb(V) Doped Nanocrystalline β-Ga2O3

A flame spray pyrolysis (FSP) technique was applied to obtain pure and Nb(V)-doped nanocrystalline β-Ga₂O₃, which were further studied as gas sensor materials. The obtained samples were characterized with XRD, XPS, TEM, Raman spectroscopy and BET method. Formation of GaNbO₄ phase is observed at high annealing temperatures. Transition of Ga(III) into Ga(I) state during Nb(V) doping prevents donor charge carriers generation and hinders considerable improvement of electrical and gas sensor properties of β-Ga₂O₃. Superior gas sensor performance of obtained ultrafine materials at lower operating temperatures compared to previously reported thin film Ga₂O₃ materials is shown.

A review on MXene and its nanocomposites for the detection of toxic inorganic gases

In the search of the viable candidate for the sensing of pollutant gases, two-dimensional (2D) material transition metal carbides (MXenes) have attracted immense attention due to their outstanding physical and chemical properties for sensing purposes. The formation of unique 2D layered structure with high conductivity, large mechanical strength, and high adsorption properties furnish their strong interactions with gaseous molecules, which holds a promising place for developing ideal gas sensing devices. This review looks at recent achievements in diversified MXenes, with a focus gaining on in-depth understanding of MXene-based materials in room temperature inorganic gas sensors through both theoretical and experimental studies. In the first part of the review, the properties and advantages of sensing material (MXene) in comparison with other 2D materials are discussed. In the second part, the unique advantages of chemiresistive based sensors and the demerits of other detection methods are summarized in detail. This section is followed by the unique structural design of MXene bases materials for improving the sensing performance towards detection of inorganic gases. The interaction between MXene and the adsorbed gases on its surface is discussed, with a possible sensing mechanism. Finally, an overview of the current progress and opportunities for the demand of MXene is emphasized and perspectives for future improvement of the design of MXene in gas sensors are highlighted. Therefore, this review highlights the opportunities and the advancement in 2D material-based gas sensors which could provide a new avenue for rapid detection of toxic gases in the environment.

Chemical Discrimination of Benzene Series and Molecular Recognition of the Sensing Process over Ti-Doped Co3O4

This work achieved the chemical discrimination of benzene series (toluene, xylene isomers, and ethylbenzene gases) based on the Ti-doped Co₃O₄ sensor. Benzene series gases presented different gas-response features due to the differences in redox rate on the surface of the Ti-doped Co₃O₄ sensor, which created an opportunity to discriminate benzene series via the algorithm analysis. Excellent groupings were obtained via the principal component analysis. High prediction accuracies were acquired via k-nearest neighbors, linear discrimination analysis (LDA), and support vector machine classifiers. With the confusion matrix for the data set using the LDA classifier, the benzene series have been well classified with 100% accuracy. Furthermore, in situ diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) and density functional theory calculations were conducted to investigate the molecular gas-solid interfacial sensing mechanism. Ti-doped Co₃O₄ showed strong Lewis acid sites and adsorption capability toward reaction species, which benefited the toluene gas-sensing reaction and resulted in the highly boosted gas-sensing performance. Our research proposed a facile distinction methodology to recognize similar gases and provided new insights into the recognition of gas-solid interfacial sensing mechanisms.