Berlin Green Framework-Based Gas Sensor for Room-Temperature and High-Selectivity Detection of Ammonia

Recent advances in 2D MXene-based heterostructures for gas sensing: mechanisms and applications in environmental and biomedical fields

MXenes, a unique class of 2D transition metal carbides, have gained attention for gas sensing applications due to their distinctive properties. Since the synthesis of Ti3C2Tx MXene in 2011, significant progress has been made in using MXenes as chemiresistive sensors. Their layered structure, abundant surface groups, hydrophilicity, tunable conductivity, and excellent thermal properties make MXenes ideal for low-power, flexible, room temperature gas sensors, fostering scalable and reproducible applications in portable devices. This review evaluates the latest advancements in MXene-based gas sensors, beginning with an overview of the elemental compositions, structures, and typical fabrication process of MXenes. We subsequently examine their applications in gas sensing domains, evaluating the proposed mechanisms for detecting common volatile organic compounds such as acetone, formaldehyde, ethanol, ammonia, and nitrogen oxides. To set this apart from similar reviews, our focus centered on the mechanistic interactions between MXene sensing materials and analytes (particularly for chemiresistive gas sensors), leveraging the distinct functionalities of MXene chemistries, which can be finely tuned for specific applications. Ultimately, we examine the current limitations and prospective research avenues concerning the utilization of MXenes in environmental and biomedical applications.

High-performance ammonia sensor at room temperature based on 2D conductive MOF Cu3(HITP)2

Sensitive detection of ammonia in the environment is crucial due to its potential danger to human ecology and health. In gas detection technology, resistive sensors utilizing golden cross finger electrodes combined with gas-sensitive materials are commonly employed. In this study, we demonstrated a room-temperature sensor for ambient ammonia detection. The sensor is composed of two-dimensional layer-stacked metal-organic framework (MOF) Cu3(HITP)2 nanomaterials drop-coated onto gold-forked finger electrodes. Density-functional theory simulation (DFT) and sensor gas-sensitive performance testing were conducted for characterization. The sensor exhibited high sensitivity, selectivity, low detection limit, excellent reproducibility, and stability. This can be attributed to the abundant Cu active sites exposed in the hexagonal ring and layer-stacked framework structure of Cu3(HITP)2 nanomaterials. Ammonia adsorption leads to electron transfer into the Cu3(HITP)2 framework, resulting in decreased sensor resistance. Real-time monitoring of sensor resistance changes enabled quantitative analysis. Results showed a 91.4 % response of the Cu3(HITP)2 sensor to 100 ppm NH3, with response and recovery times of 26 s and 20 s, respectively. The sensor's limit of detection (LOD) was approximately 15 ppb. The sensor exhibited a relatively high response to NH3 at 25 °C, as demonstrated by dynamic gradient test curves. These findings suggest that constituting a room-temperature ammonia sensor by uniformly drop-coating Cu3(HITP)2 onto a gold-forked finger electrode is a feasible strategy.

Overcoming the Limits of Cross-Sensitivity: Pattern Recognition Methods for Chemiresistive Gas Sensor Array

As information acquisition terminals for artificial olfaction, chemiresistive gas sensors are often troubled by their cross-sensitivity, and reducing their cross-response to ambient gases has always been a difficult and important point in the gas sensing area. Pattern recognition based on sensor array is the most conspicuous way to overcome the cross-sensitivity of gas sensors. It is crucial to choose an appropriate pattern recognition method for enhancing data analysis, reducing errors and improving system reliability, obtaining better classification or gas concentration prediction results. In this review, we analyze the sensing mechanism of cross-sensitivity for chemiresistive gas sensors. We further examine the types, working principles, characteristics, and applicable gas detection range of pattern recognition algorithms utilized in gas-sensing arrays. Additionally, we report, summarize, and evaluate the outstanding and novel advancements in pattern recognition methods for gas identification. At the same time, this work showcases the recent advancements in utilizing these methods for gas identification, particularly within three crucial domains: ensuring food safety, monitoring the environment, and aiding in medical diagnosis. In conclusion, this study anticipates future research prospects by considering the existing landscape and challenges. It is hoped that this work will make a positive contribution towards mitigating cross-sensitivity in gas-sensitive devices and offer valuable insights for algorithm selection in gas recognition applications.

Comparative Effects of Hydrazine and Thermal Reduction Methods on Electromagnetic Interference Shielding Characteristics in Foamed Titanium Carbonitride MXene Films

The urgent need for the development of micro-thin shields against electromagnetic interference (EMI) has sparked interest in MXene materials owing to their metallic electrical conductivity and ease of film processing. Meanwhile, postprocessing treatments can potentially exert profound impacts on their shielding effectiveness (SE). This work comprehensively compares two reduction methods, hydrazine versus thermal, to fabricate foamed titanium carbonitride (Ti3CNTx) MXene films for efficient EMI shielding. Upon treatment of ≈ 100 µm-thick MXene films, gaseous transformations of oxygen-containing surface groups induce highly porous structures (up to ≈ 74.0% porosity). The controlled application of hydrazine and heat allows precise regulation of the reduction processes, enabling tailored control over the morphology, thickness, chemistry, and electrical properties of the MXene films. Accordingly, the EMI SE values are theoretically and experimentally determined. The treated MXene films exhibit significantly enhanced SE values compared to the pristine MXene film (≈ 52.2 dB), with ≈ 38% and ≈ 83% maximum improvements for the hydrazine and heat-treated samples, respectively. Particularly, heat treatment is more effective in terms of this enhancement such that an SE of 118.4 dB is achieved at 14.3 GHz, unprecedented for synthetic materials. Overall, the findings of this work hold significant practical implications for advancing high-performance, non-metallic EMI shielding materials.

MOF-Based Chemiresistive Gas Sensors: Toward New Functionalities

The sensing performances of gas sensors must be improved and diversified to enhance quality of life by ensuring health, safety, and convenience. Metal-organic frameworks (MOFs), which exhibit an extremely high surface area, abundant porosity, and unique surface chemistry, provide a promising framework for facilitating gas-sensor innovations. Enhanced understanding of conduction mechanisms of MOFs has facilitated their use as gas-sensing materials, and various types of MOFs have been developed by examining the compositional and morphological dependences and implementing catalyst incorporation and light activation. Owing to their inherent separation and absorption properties and catalytic activity, MOFs are applied as molecular sieves, absorptive filtering layers, and heterogeneous catalysts. In addition, oxide- or carbon-based sensing materials with complex structures or catalytic composites can be derived by the appropriate post-treatment of MOFs. This review discusses the effective techniques to design optimal MOFs, in terms of computational screening and synthesis methods. Moreover, the mechanisms through which the distinctive functionalities of MOFs as sensing materials, heterostructures, and derivatives can be incorporated in gas-sensor applications are presented.

Chemiresistive gas sensor based on Mo0.5W0.5S2 alloy nanoparticles with good selectivity and ppb-level limit of detection to ammonia

Transition metal dichalcogenides (TMDs) are promising materials for chemiresistive gas sensor, while TMD alloys (two chalcogenide or/and metal elements) with tunable electronic structures have drawn little attention in gas sensing. Herein, Mo_0.5W_0.5S₂ alloy nanoparticles (NPs) were prepared by a facile sonication exfoliation method and then tested for ammonia sensing. The crystal structure, geometric morphology, and elemental composition of Mo_0.5W_0.5S₂ NPs were investigated. The gas sensing measurements demonstrated Mo_0.5W_0.5S₂ NPs with good response to ammonia at 80 °C with a limit of detection down to 500 part per billion (ppb). The sensor also displayed good stability as well as superb selectivity to ammonia in the presence of interferences, such as methanol, acetone, benzene, and cyclohexane. The theoretical calculations revealed Mo and W atoms at edges (such as Mo_0.5W_0.5S₂ (010)) of sheet-like NPs as the active sites for ammonia adsorption. Electrons donated by the adsorbed ammonia were combined with holes in p-type Mo_0.5W_0.5S₂ NPs, and the concentration of the main charge carrier was reduced, resulting in resistance enhancement.

Metal-organic frameworks: A promising option for the diagnosis and treatment of Alzheimer's disease

Beta-amyloid (Aβ) peptide is one of the main characteristic biomarkers of Alzheimer's disease (AD). Previous clinical investigations have proposed that unusual concentrations of this biomarker in cerebrospinal fluid, blood, and brain tissue are closely associated with the AD progression. Therefore, the critical point of early diagnosis, prevention, and treatment of AD is to monitor the levels of Aβ. In view of the potential of metal-organic frameworks (MOFs) for diagnosing and treating the AD, much attention has been focused in recent years. This review discusses the latest advances in the applications of MOFs for the early diagnosis of AD via fluorescence and electrochemiluminescence (ECL) detection of AD biomarkers, fluorescence detection of the main metal ions in the brain (Zn2+, Cu2+, Mn2+, Fe3+, and Al3+) in addition to magnetic resonance imaging (MRI) of the Aβ plaques. The current challenges and future strategies for translating the in vitro applications of MOFs into in vivo diagnosis of the AD are discussed.

Gas-Sensitive Cellulosic Triboelectric Materials for Self-Powered Ammonia Sensing

Gas-sensitive materials are capable of dynamic identification and content monitoring of specific gases in the environment, and their applications in the field of gas sensing are promising. However, weak adsorption properties are the main challenge limiting the application of gas-sensitive materials. A highly adsorbent gas-sensitive cellulose nanofibril (CNF)-based triboelectric material with a layered structure is prepared here and it is applied to self-powered gas sensing. The layered structure of the triethoxy-1H,1H,2H,2H-tridecafluoro-n-octylsilane cellulose nanofiber (PFOTES-CNF)-based gas-sensitive material further enhances the adsorption of the material due to electrostatic adsorption in the electrostatic field induced by triboelectricity. It is found that the ammonia-sensitive material obtained by loading Ti3 C2 Tx in PFOTES-CNF has a fast response/recovery (12/14 s), high sensitivity response (Vair /Vgas  = 2.1), high selectivity response (37.6%), and low detection limit (10 ppm) for 100 ppm of ammonia gas. In addition, the ammonia-sensitive CNF-based triboelectric material can accurately identify NH3 concentration changes in the range of 10-120 ppm and transmit the signal wirelessly to the user interface, facilitating real-time online monitoring of NH3 in the environment. A novel strategy is provided here for designing and preparing high-performance gas-sensitive composites and the analysis of self-powered gas sensing is guided.

Multi-interface Assembled N-Doped MXene/HCFG/AgNW Films for Wearable Electromagnetic Shielding Devices with Multimodal Energy Conversion and Healthcare Monitoring Performances

With the progressive requirements of modern electronics, outstanding electromagnetic interference (EMI) shielding materials are extensively desirable to protect intelligent electronic equipment against EMI radiation under various conditions, while integrating functional applications. So far, it remains a great challenge to effectively construct thin films with diversiform frameworks as integrated shielding devices. To simultaneously promote electromagnetic waves (EMWs) attenuation and construct integrated multifunction, an alternating-layered deposition strategy is designed to fabricate polydimethylsiloxane packaged N-doped MXene (Ti₃CNT_x)/graphene oxide wrapped hollow carbon fiber/silver nanowire films (p-LMHA) followed by annealing and encapsulation approaches. Contributed by the synergistic effect of consecutively conductive networks and porous architectures, LMHA films exhibit satisfying EMI shielding effectiveness of 73.2 dB at a thickness of 11 μm, with a specific EMI shielding effectiveness of 31 150.1 dB·cm²·g^-1. Benefiting from the encapsulation, p-LMHA films further impart hydrophobicity and reliability against harsh environments. Besides, p-LMHA devices integrate a rapid-response behavior of the electro/photothermal and, meanwhile, function as a healthcare monitoring sensor. Therefore, it is believed that the p-LMHA films assembled by independent conductive networks with reliability offer a facile solution for practical multimodular protection of devices with integration characteristics.

Imbalanced data fault diagnosis of hydrogen sensors using deep convolutional generative adversarial network with convolutional neural network

The fault diagnosis of hydrogen sensors is of great significance. However, it is difficult to collect data samples for some modes of hydrogen sensor signals, so the data samples may be unbalanced, which can seriously affect the fault diagnosis results. In this paper, we present a novel convolutional neural network (CNN)-based deep convolutional generative adversarial network (DCG) method (DCG-CNN) for gas sensor fault diagnosis. First, we transform the 1D fault signals of the gas sensor into 2D gray images for end-to-end conversion with no signal data information loss. Second, we use the DCG to enrich the 2D gray images of small fault data samples, which results in balanced sensor fault datasets. Third, we use the CNN method to improve the accuracy of fault diagnosis. In order to understand the internal mechanism of the CNN, we further visualize the learned feature maps of fault data samples in each layer of the CNN and try to analyze the reasons for the method's high performance. The fault diagnosis accuracy of the DCG-CNN is shown to be higher than that of other traditional methods.