Advances in 2D Materials Based Gas Sensors for Industrial Machine Olfactory Applications.

The escalating development and improvement of gas sensing ability in industrial equipment, or "machine olfactory", propels the evolution of gas sensors toward enhanced sensitivity, selectivity, stability, power efficiency, cost-effectiveness, and longevity. Two-dimensional (2D) materials, distinguished by their atomic-thin profile, expansive specific surface area, remarkable mechanical strength, and surface tunability, hold significant potential for addressing the intricate challenges in gas sensing. However, a comprehensive review of 2D materials-based gas sensors for specific industrial applications is absent. This review delves into the recent advances in this field and highlights the potential applications in industrial machine olfaction. The main content encompasses industrial scenario characteristics, fundamental classification, enhancement methods, underlying mechanisms, and diverse gas sensing applications. Additionally, the challenges associated with transitioning 2D material gas sensors from laboratory development to industrialization and commercialization are addressed, and future-looking viewpoints on the evolution of next-generation intelligent gas sensory systems in the industrial sector are prospected.

Real-Time Monitoring of Air Discharge in a Switchgear by an Intelligent NO2 Sensor Module.

An air-insulated power equipment adopts air as the insulating medium and is widely implemented in power systems. When discharge faults occur, the air produces decomposition products represented by NO2. The efficient NO2 sensor enables the identification of electrical equipment faults. However, single-sensor-dependent NO2 detection is vulnerable to interfering gases. Implementing the sensor array could reduce the interference and improve detection efficiency. In the field of NO2 detection, In2O3 sensors have exhibited tremendous advantages. In our work, four composites based on In2O3 are integrated into sensor arrays, which could detect 250 ppb of NO2 and exhibit excellent selectivity when simultaneously exposed to CO. To further reduce the impact of humidity on gas-sensing performance, a convolutional neural network and a long short-term memory model equipped with an attention mechanism are proposed to evaluate NO2 concentration within 1 ppm, and the detection error is 63.69 ppb. In addition, the NO2 concentration estimation platform based on a microgas sensor is established to detect air discharge faults. The average concentration of NO2 generated by 10 consecutive discharge faults at 15 kV is 726.58 ppb, which indicates severe discharge in the switchgear. Our NO2 estimation method has great potential for large-scale deployment in low- and medium-voltage switchgears.

Research progress and prospects on gas-sensitive mechanisms of semiconductor sensors.

Semiconductor materials with wide bandgaps are extensively employed for gas detection due to their advantages of low cost, high sensitivity, fast speed, excellent stability, and distinctive selectivity. Previous studies have reported on different kinds of semiconductor materials and their complex synthesis procedures. However, the research progress on gas-sensitive mechanisms seriously lags behind the performance improvement. The research route of the gas-sensing mechanism is not clear, resulting in an unclear development direction of novel sensitive materials. This review aims to summarize existing approaches and their progress on the interpretation of gas-sensing mechanisms in semiconductors, such as the calculations based on density functional theory, semiconductor physics, and in situ experiments. Ultimately, a reasonable route for the mechanism investigation has been proposed. It guides the development direction of novel materials and reduces the cost of screening highly selective materials. Overall, this review can provide helpful guidance concerning the gas-sensitive mechanism for scholars.

Multicomponent SF6 decomposition product sensing with a gas-sensing microchip.

A difficult issue restricting the development of gas sensors is multicomponent recognition. Herein, a gas-sensing (GS) microchip loaded with three gas-sensitive materials was fabricated via a micromachining technique. Then, a portable gas detection system was built to collect the signals of the chip under various decomposition products of sulfur hexafluoride (SF6). Through a stacked denoising autoencoder (SDAE), a total of five high-level features could be extracted from the original signals. Combined with machine learning algorithms, the accurate classification of 47 simulants was realized, and 5-fold cross-validation proved the reliability. To investigate the generalization ability, 30 sets of examinations for testing unknown gases were performed. The results indicated that SDAE-based models exhibit better generalization performance than PCA-based models, regardless of the magnitude of noise. In addition, hypothesis testing was introduced to check the significant differences of various models, and the bagging-based back propagation neural network with SDAE exhibits superior performance at 95% confidence.

Multivariate Evaluation Method for Screening Optimum Gas-Sensitive Materials for Detecting SF6 Decomposition Products.

In previous studies, the selection of optimal gas-sensing materials for detecting target gases mainly relied on their response value, but other indices, such as the recovery capability of materials, have usually been overlooked. Here, we propose a new method for evaluating sensor effectiveness that includes a broader range of performance indices. In this study, four gas sensors based on metal-oxide semiconductors (WO3, CeO2, In2O3, and SnO2) were used as examples, and their performance in the detection of four decomposition products of sulfur hexafluoride (SF6) was investigated. After gas-sensing experiments, values for working temperature, response value, and recovery capability were obtained. A multivariate evaluation method of mixing principal component analysis, information entropy, and variation coefficient was developed to calculate the weights of various indices, and the sensors' optimal working temperatures could be identified quantitatively. Using five variables (working temperature, response value, recovery capability, fluctuation rate, and detection limit), we continued to apply this multivariate evaluation method to calculate the weights and acquire comprehensive scores for the four sensors. Finally, these scores were used to identify the optimal materials for detecting SF6 decomposition products. This procedure has the potential for selecting the best sensors for other gases.

Short period sinusoidal thermal modulation for quantitative identification of gas species.

The field of chemical (gas) sensing has witnessed an unprecedented increase in device sensitivity with single molecule detection now becoming a reality. In contrast to this, the ability to distinguish or discriminate between gas species has lagged behind. This is problematic and results in a high rate of false alarms. Here, we demonstrate a short period sinusoidal thermal modulation strategy to quantitatively and rapidly identify two industrially relevant gases (hydrogen sulfide (H2S) and sulfur dioxide (SO2)) by using a single semiconducting metal oxide sensor device. By applying sinusoidal heating voltages with a fixed short period, we were able to simultaneously obtain distinct patterns of dynamic responses. These characteristic patterns were adopted to build and validate a gas recognition library. Our approach does not rely on large-scale sensor arrays and complex algorithms and is amenable for real-time and low-power gas monitoring.