Visible-Light-Driven Room Temperature NO2 Gas Sensor Based on Localized Surface Plasmon Resonance: The Case of Gold Nanoparticle Decorated Zinc Oxide Nanorods (ZnO NRs)

Self-assembled peptide microtubes (SPMTs)/SnO2 sensors for enhanced room-temperature gas detection under visible light illumination

Nitrogen dioxide (NO2) is an important contaminant that poses a severe threat to environmental sustainability. Traditional inorganic NO2 gas detectors are generally used under harsh operating conditions and employ environmentally unfriendly resources, thus preventing widespread practical applications. Herein, self-assembled peptide microtubes (SPMTs) are combined with SnO2 nanoparticles (NPs) to develop a bioinspired NO2 gas sensor. The sensor incorporated with SPMTs exhibits a lower resistance and a stronger response under visible light irradiation. Under exposure to 4.7-mW/cm2 white light irradiation, the device exhibits a response of 412 and a resistance of only 97 MΩ, contrast to 318 and 340 MΩ for the bare SnO2-based counterpart under the same test conditions. This work exemplifies the feasibility of using bioinspired approach employing peptides self-assembly strategy to engineer comprehensive pollution detectors, potentially enabling development in the environmentally friendly sensing field.

Plasmonic Particle Integration into Near-Infrared Photodetectors and Photoactivated Gas Sensors: Toward Sustainable Next-Generation Ubiquitous Sensing

Current challenges in environmental science, medicine, food chemistry as well as the emerging use of artificial intelligence for solving problems in these fields require distributed, local sensing. Such ubiquitous sensing requires components with 1) high sensitivity, 2) power efficiency, 3) miniaturizability, and 4) the ability to directly interface with electronic circuitry, i.e., electronic readout of sensing signals. Over the recent years, several nanoparticle-based approaches have found their way into this field and have demonstrated high performance. However, challenges remain, such as the toxicity of many of today's narrow bandgap semiconductors for NIR detection and the high energy consumption as well as low selectivity of state-of-the-art commercialized gas sensors. With their unique light-matter interaction and ink-based fabrication schemes, plasmonic nanostructures provide potential technological solutions to these challenges, leading also to better environmental performance. In this perspective recent approaches of using plasmonic nanoparticles are discussed for the fabrication of NIR photodetectors and light-activated, energy-efficient gas sensing devices. In addition, new strategies implying computational approaches are pointed out for miniaturizable spectrometers, exploiting the wide spectral tunability of plasmonic nanocomposites, and for selective gas sensors, utilizing dynamic light activation. The benefits of colloidal approaches for device fabrication are discussed with regard to technological advantages and environmental aspects, which are barely considered so far.

Ultra-Low-Power E-Nose System Based on Multi-Micro-LED-Integrated, Nanostructured Gas Sensors and Deep Learning

As interests in air quality monitoring related to environmental pollution and industrial safety increase, demands for gas sensors are rapidly increasing. Among various gas sensor types, the semiconductor metal oxide (SMO)-type sensor has advantages of high sensitivity, low cost, mass production, and small size but suffers from poor selectivity. To solve this problem, electronic nose (e-nose) systems using a gas sensor array and pattern recognition are widely used. However, as the number of sensors in the e-nose system increases, total power consumption also increases. In this study, an ultra-low-power e-nose system was developed using ultraviolet (UV) micro-LED (μLED) gas sensors and a convolutional neural network (CNN). A monolithic photoactivated gas sensor was developed by depositing a nanocolumnar In₂O₃ film coated with plasmonic metal nanoparticles (NPs) directly on the μLED. The e-nose system consists of two different μLED sensors with silver and gold NP coating, and the total power consumption was measured as 0.38 mW, which is one-hundredth of the conventional heater-based e-nose system. Responses to various target gases measured by multi-μLED gas sensors were analyzed by pattern recognition and used as the training data for the CNN algorithm. As a result, a real-time, highly selective e-nose system with a gas classification accuracy of 99.32% and a gas concentration regression error (mean absolute) of 13.82% for five different gases (air, ethanol, NO₂, acetone, methanol) was developed. The μLED-based e-nose system can be stably battery-driven for a long period and is expected to be widely used in environmental internet of things (IoT) applications.

Influence of dopant concentration on the ammonia sensing performance of citric acid-doped polyvinyl acetate nanofibers

The chemical modification of polymer nanofiber-based ammonia sensors by introducing dopants into the active layers has been proven as one of the low-cost routes to enhance their sensing performance. Herein, we investigate the influence of different citric acid (CA) concentrations on electrospun polyvinyl acetate (PVAc) nanofibers coated on quartz crystal microbalance (QCM) transducers as gravimetric ammonia sensors. The developed CA-doped PVAc nanofiber sensors are tested against various concentrations of ammonia vapors, in which their key sensing performance parameters (i.e., sensitivity, limit of detection (LOD), limit of quantification (LOQ), and repeatability) are studied in detail. The sensitivity and LOD values of 1.34 Hz ppm^-1 and 1 ppm, respectively, can be obtained during ammonia exposure assessment. Adding CA dopants with a higher concentration not only increases the sensor sensitivity linearly, but also prolongs both response and recovery times. This finding allows us to better understand the dopant concentration effect, which subsequently can result in an appropriate strategy for manufacturing high-performance portable nanofiber-based sensing devices.

Fast and noninvasive electronic nose for sniffing out COVID-19 based on exhaled breath-print recognition

The reverse transcription-quantitative polymerase chain reaction (RT-qPCR) approach has been widely used to detect the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, instead of using it alone, clinicians often prefer to diagnose the coronavirus disease 2019 (COVID-19) by utilizing a combination of clinical signs and symptoms, laboratory test, imaging measurement (e.g., chest computed tomography scan), and multivariable clinical prediction models, including the electronic nose. Here, we report on the development and use of a low cost, noninvasive method to rapidly sniff out COVID-19 based on a portable electronic nose (GeNose C19) integrating an array of metal oxide semiconductor gas sensors, optimized feature extraction, and machine learning models. This approach was evaluated in profiling tests involving a total of 615 breath samples composed of 333 positive and 282 negative samples. The samples were obtained from 43 positive and 40 negative COVID-19 patients, respectively, and confirmed with RT-qPCR at two hospitals located in the Special Region of Yogyakarta, Indonesia. Four different machine learning algorithms (i.e., linear discriminant analysis, support vector machine, stacked multilayer perceptron, and deep neural network) were utilized to identify the top-performing pattern recognition methods and to obtain a high system detection accuracy (88–95%), sensitivity (86–94%), and specificity (88–95%) levels from the testing datasets. Our results suggest that GeNose C19 can be considered a highly potential breathalyzer for fast COVID-19 screening.

High-Performance Room-Temperature Conductometric Gas Sensors: Materials and Strategies

Chemiresistive sensors have gained increasing interest in recent years due to the necessity of low-cost, effective, high-performance gas sensors to detect volatile organic compounds (VOC) and other harmful pollutants. While most of the gas sensing technologies rely on the use of high operation temperatures, which increase usage cost and decrease efficiency due to high power consumption, a particular subset of gas sensors can operate at room temperature (RT). Current approaches are aimed at the development of high-sensitivity and multiple-selectivity room-temperature sensors, where substantial research efforts have been conducted. However, fewer studies presents the specific mechanism of action on why those particular materials can work at room temperature and how to both enhance and optimize their RT performance. Herein, we present strategies to achieve RT gas sensing for various materials, such as metals and metal oxides (MOs), as well as some of the most promising candidates, such as polymers and hybrid composites. Finally, the future promising outlook on this technology is discussed.