Chemiresistive and Gravimetric Dual-Mode Gas Sensor toward Target Recognition and Differentiation

Intelligent MEMS Sensor Based on an Oxidized Medium-Entropy Alloy (FeCoNi) for H2 and CO Recognition

In this paper, we present the design and evaluation of an intelligent MEMS sensor employing the oxidized medium-entropy alloy (O-MEA) of FeCoNi as the gas-sensing material. Due to the specific catalytic exothermic reaction of the O-MEA on H2/CO, the sensor shows great selectivity for H2 and CO at 150 °C of the integrated microheater in the MEMS device, with the theoretical detection limit of 0.3 ppm for H2 and 0.29 ppm for CO. The MEMS heater, capable of instantaneous temperature changes in pulse operation mode, offers a novel approach for thermal modulation of the sensor, which is crucial for the adsorption and reaction of H2/CO molecules on the sensing layer surface. Consequently, we investigate the gas-sensing capabilities of the sensor under pulse heating modes (PHMs) of the MEMS heater and then propose the gas-sensing mechanism. The results indicate that PHMs significantly not only reduce the operating temperature and power consumption but also enhance the sensor's functionality by providing multivariable response signals, paving the way for intelligent gas detection. Based on the high selectivity to H2 and CO, transforming the transient sensing curves into two-dimensional images via Gramian Angular Field (GAF) model and subsequent modeling using a convolutional neural network (CNN) algorithm, we have realized efficient and intelligent recognition of H2 and CO. This work provides insight for the development of low-power, high-performance MEMS gas sensors and further intelligence of individual MEMS sensors.

A chemiresistive-potentiometric multivariate sensor for discriminative gas detection

Highly efficient gas sensors able to detect and identify hazardous gases are crucial for numerous applications. Array of conventional single-output sensors is currently limited by problems including drift, large size, and high cost. Here, we report a sensor with multiple chemiresistive and potentiometric outputs for discriminative gas detection. Such sensor is applicable to a wide range of semiconducting electrodes and solid electrolytes, which allows to tailor and optimize the sensing pattern by tuning the material combination and conditions. The sensor performance is boosted by equipping a mixed-conducting perovskite electrode with reverse potentiometric polarity. A conceptual sensor with dual sensitive electrodes achieves superior three-dimensional (sub)ppm sensing and discrimination of humidity and seven hazardous gases (2-Ethylhexanol, ethanol, acetone, toluene, ammonia, carbon monoxide, and nitrogen dioxide), and enables accurate and early warning of fire hazards. Our findings offer possibilities to design simple, compact, inexpensive, and highly efficient multivariate gas sensors.

Low-Power, Multi-Transduction Nanosensor Array for Accurate Sensing of Flammable and Toxic Gases

Toxic and flammable gases pose a major safety risk in industrial settings; thus, their portable sensing is desired, which requires sensors with fast response, low-power consumption, and accurate detection. Herein, a low-power, multi-transduction array is presented for the accurate sensing of flammable and toxic gases. Specifically, four different sensors are integrated on a micro-electro-mechanical-systems platform consisting of bridge-type microheaters. To produce distinct fingerprints for enhanced selectivity, the four sensors operate based on two different transduction mechanisms: chemiresistive and calorimetric sensing. Local, in situ synthesis routes are used to integrate nanostructured materials (ZnO, CuO, and Pt Black) for the sensors on the microheaters. The transient responses of the four sensors are fed to a convolutional neural network for real-time classification and regression of five different gases (H₂ , NO₂ , C₂ H₆ O, CO, and NH₃ ). An overall classification accuracy of 97.95%, an average regression error of 14%, and a power consumption of 7 mW per device are obtained. The combination of a versatile low-power platform, local integration of nanomaterials, different transduction mechanisms, and a real-time machine learning strategy presented herein helps advance the constant need to simultaneously achieve fast, low-power, and selective gas sensing of flammable and toxic gases.

Sensors for Volatile Organic Compounds

This paper provides an overview of recent developments in the field of volatile organic compound (VOC) sensors, which are finding uses in healthcare, safety, environmental monitoring, food and agriculture, oil industry, and other fields. It starts by briefly explaining the basics of VOC sensing and reviewing the currently available and quickly progressing VOC sensing approaches. It then discusses the main trends in materials' design with special attention to nanostructuring and nanohybridization. Emerging sensing materials and strategies are highlighted and their involvement in the different types of sensing technologies is discussed, including optical, electrical, and gravimetric sensors. The review also provides detailed discussions about the main limitations of the field and offers potential solutions. The status of the field and suggestions of promising directions for future development are summarized.

A Sensitivity-Enhanced Electrolyte-Gated Graphene Field-Effect Transistor Biosensor by Acoustic Tweezers

Low-abundance biomolecule detection is very crucial in many biological and medical applications. In this paper, we present a novel electrolyte-gated graphene field-effect transistor (EGFET) biosensor consisting of acoustic tweezers to increase the sensitivity. The acoustic tweezers are based on a high-frequency bulk acoustic resonator with thousands of MHz, which has excellent ability to concentrate nanoparticles. The operating principle of the acoustic tweezers to concentrate biomolecules is analyzed and verified by experiments. After the actuation of acoustic tweezers for 10 min, the IgG molecules are accumulated onto the graphene. The sensitivities of the EGFET biosensor with accumulation and without accumulation are compared. As a result, the sensitivity of the graphene-based biosensor is remarkably increased using SMR as the biomolecule concentrator. Since the device has advantages such as miniaturized size, low reagent consumption, high sensitivity, and rapid detection, we expect it to be readily applied to many biological and medical applications.

Highly sensitive gas sensing platforms based on field effect Transistor-A review

Highly selective, sensitive and fast gas sensing has attracted increasing attention in the fields of environmental protection, industrial production, personal safety as well as medical diagnostics. Field effect transistor (FET) sensors have been extensively investigated in gas sensing fields due to their small size, high sensitivity, high reliability and low energy consumption. This comprehensive review aims to discuss the recent advances in FET gas sensors based on materials such as carbon nanotubes, silicon carbide, silicon, metal oxides-, graphene-, transition metal dichalcogenides- and 2-dimensional black phosphorus. We first introduce different types of sensor structures and elaborate the gas-sensing mechanisms. Then, we describe the optimizing strategies for sensing performances, response parameters, FET based dual-mode sensors and FET based logic circuit sensors. Moreover, we present the key advances of the above materials in gas sensing performances. Meanwhile, shortcomings of such materials are also discussed and the future development of this field is proposed in this review.

Review of Gravimetric Sensing of Volatile Organic Compounds

Volatile organic compounds (VOCs) are pervasive in the environment. Since the early 1980s, substantial work has examined the detection of these materials, as they can indicate environmental changes that can affect human health. VOCs and similar compounds present a very specific sensing problem in that they are not reactive and often nonpolar, so it is difficult to find materials that selectively bind or adsorb them. A number of techniques are applied to vapor sensing. High resolution molecular separation approaches such as gas chromatography and mass spectrometry are well-characterized and offer high sensitivity, but are difficult to implement in portable, real-time monitors, whereas approaches such as chemiresistors are promising, but still in development. Gravimetric approaches, in which the mass of an adsorbed vapor is directly measured, have several potential advantages over other techniques but have so far lagged behind other approaches in performance and market penetration. This review aims to offer a comprehensive background on gravimetric sensing including underlying resonators and sensitizers, as well as a picture of applications and commercialization in the field.

Volatile organic compounds discrimination based on dual mode detection

We report on a volatile organic compound (VOC) sensor that can provide concentration-independent signals toward target gases. The device is based on a dual-mode detection mechanism that can simultaneously record the mechanical (resonant frequency, f _r) and electrical (current, I) responses of the same gas adsorption event. The two independent signals form a unique I-f _r trace for each target VOC as the concentration varies. The mechanical response (frequency shift, Δf _r) resulting from mass load on the device is directly related to the amount of surface adsorptions, while the electrical response (current variation, ΔI) is associated with charge transfer across the sensing interface and changes in carrier mobility. The two responses resulting from independent physical processes reflect intrinsic physical properties of each target gas. The ΔI-Δf _r trace combined with the concentration dependent frequency (or current) signals can therefore be used to achieve target both recognition and quantification. The dual-mode device is designed and fabricated using standard complementary metal oxide semiconductor (CMOS) compatible processes. It exhibits consistent and stable performance in our tests with six different VOCs including ethanol, methanol, acetone, formaldehyde, benzene and hexane.

Dual-Mode Gas Sensor Composed of a Silicon Nanoribbon Field Effect Transistor and a Bulk Acoustic Wave Resonator: A Case Study in Freons

In this paper, we develop a novel dual-mode gas sensor system which comprises a silicon nanoribbon field effect transistor (Si-NR FET) and a film bulk acoustic resonator (FBAR). We investigate their sensing characteristics using polar and nonpolar organic compounds, and demonstrate that polarity has a significant effect on the response of the Si-NR FET sensor, and only a minor effect on the FBAR sensor. In this dual-mode system, qualitative discrimination can be achieved by analyzing polarity with the Si-NR FET and quantitative concentration information can be obtained using a polymer-coated FBAR with a detection limit at the ppm level. The complementary performance of the sensing elements provides higher analytical efficiency. Additionally, a dual mixture of two types of freons (CFC-113 and HCFC-141b) is further analyzed with the dual-mode gas sensor. Owing to the small size and complementary metal-oxide semiconductor (CMOS)-compatibility of the system, the dual-mode gas sensor shows potential as a portable integrated sensing system for the analysis of gas mixtures in the future.