Oxygen vacancy‐rich ZnO nanorods‐based MEMS sensors for swift trace ethanol recognition

Abstract

Ethanol vapor plays a significant role in the aspects of human health and industrial production, thus necessitating a swift, sensitive, and low‐power ethanol detection in the field of future gas sensors. In this work, we prepared micro–electro–mechanical system ethanol sensors based on ZnO nanorods (NRs) and nanoparticles (NPs) for trace ethanol detection. Both ZnO samples were synthesized by a facile hydrothermal method. The comparison results exhibited that ZnO NRs based sensors prevailed over NPs‐based counterparts in terms of sensitivity, optimal operation temperature, and reaction speeds. Briefly, that ZnO NRs‐based sensors presented a large response (11.5 toward 5 ppm), fast response/recovery times (5 s/5 s), ultralow detection limit (400 ppb), and tiny power consumption (30 mW) at 245°C, surpassing most of recently reported ethanol sensors and commercial products based metal oxides. The abundant oxygen vacancies, large specific surface area, and porous structure were primarily responsible for the excellent sensor performance. This work also offers a facile and competitive approach to realize a sensitive and swift trace ethanol recognition with minimal power consumption, catering for the demanding requirements of future gas sensors in the fields of wearable devices and Internet of Things.

Microextrusion Printing of Hierarchically Structured Thick V2O5 Film with Independent from Humidity Sensing Response to Benzene

“…Another factor influencing the sensing properties is the presence and number of oxygen vacancies since they can facilitate the formation of oxygen species on the film surface, which would then react with organic gas and release additional electrons to the material conduction band, resulting in increased conductivity change and response [ 79 ]. These processes can be described by the following equations [ 79 ]: O V + O 2 (gas) ↔ O 2 − ad + O V * 2O V + O 2 (gas) ↔ O 2 2− ad + 2O V * ↔ 2O − ad + 2O V * …”

Black Phosphorus Nanosheet/Tin Oxide Quantum Dot Heterostructures for Highly Sensitive and Selective Trace Hydrogen Sulfide Sensing

“…Metal oxide QDs can efficiently detect hazardous gases due to ample reactive sites and enormous material utility for gas–solid interactions. , Benefiting from the abundant and nonconformal interfacial contacts between both components and the monolayer SnO 2 QDs on the BP template, it was highly anticipated that the optimized BP/SnO 2 sensors would achieve superior H 2 S detection over pure SnO 2 counterparts. In addition, the miniaturized sensor structure favored a much lower energy consumption than that of traditional ceramic-tube devices (400–1000 mW) . To validate these hypotheses, the temperature- and concentration-dependent response, sensitivity, response/recovery speeds, selectivity, repeatability, long-term stability, and humidity tolerance were investigated in sequence.…”

“…In addition, the miniaturized sensor structure favored a much lower energy consumption than that of traditional ceramic-tube devices (400−1000 mW). 31 To validate these hypotheses, the temperature-and concentration-dependent response, sensitivity, response/recovery speeds, selectivity, repeatability, long-term stability, and humidity tolerance were investigated in sequence. Then, control sensors based on rGO-or MoS 2 -templated SnO 2 composites were prepared to confirm the exclusive advantages of BP over rGO and MoS 2 in H 2 S detection.…”

Rapid Estimation of Ethanol Concentration using Acousto‐Optic Diffraction

“…Its concentration has also been found to be crucial for tissue penetration [20] and biodiversity studies. [21] Common analytical methods to determine ethanol concentrations in aqueous solutions include gas chromatography, [22,23] gas chromatography-mass spectroscopy, [24] high performance liquid chromatography, [25] Raman spectroscopy, [26] Fourier transform infrared spectroscopy, [27] nuclear magnetic resonance (NMR) spectroscopy, [28] mass spectrometry, [29] electrochemical sensing, [30] use of various metal oxide nanostructure based sensors [31,32] among others. These methods usually involve use of expensive equipment, are often time consuming and need special expertise to analyze the experimental data.…”