Room temperature mixed-potential solid-electrolyte NO2 sensor for environmental monitoring

Zhang, Yueying; Gu, Tianyi; Liu, Fangmeng; Li, Jiang; Lv, Siyuan; Wang, Jing; Pan, Si; Jia, Xiaoteng; Sun, Peng; Gao, Yuan; Lu, Geyu

doi:10.1016/j.snb.2023.133943

Cited by 6 publications

(2 citation statements)

References 74 publications

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“…Choosing suitable gas sensors is paramount for designing an efficient sensor array. Various types of gas sensors have been developed, among which are electrochemical sensors [28], semiconductors [29], solid electrolytes [30], and catalytic combustion sensors [31]. Among these options, semiconductor gas sensors crafted from metal oxide materials demonstrate outstanding traits, such as high sensitivity, rapid response times, stability, and cost-effectiveness [32].…”

Section: Sensor Array Designmentioning

confidence: 99%

An E-nose system for identification and quantification of hazardous gas mixtures using a combined strategy of CNNs and attentional mechanisms

Yang,

Wang,

Zhao

et al. 2024

Phys. Scr.

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The chemical industry generates a broad spectrum of hazardous gases, presenting significant challenges for conventional detection methods due to their diverse chemical properties and low concentration levels. E-nose systems, employing sensor arrays, offer significant potential for the determination of gas mixtures. This study presents a novel E-nose algorithm, CNN-ECA, which integrated CNNs and attention mechanisms to improve the recognition accuracy of E-nose systems. By integrating the attention mechanism module into CNN's convolutional operations, the algorithm emphasizes critical feature information. Three hazardous gases (ammonia, methanol, and acetone) and their mixtures were chosen as target gases. CNNs were combined with various attention mechanism networks (SENet, ECA, and CBAM) to construct models, which were then employed to train and evaluate data collected from the sensor array. The results were compared with traditional network models (KNN, SVM, and CNN). Experimental findings indicated that the prediction performance of CNN models combined with attention mechanism networks surpassed that of traditional network models. Particularly, the CNN-ECA network model demonstrated the highest performance in both qualitative and quantitative analyses. This study presents a promising solution for mixed gas detection by synergizing CNN and attention mechanism networks, thereby enhancing the accuracy and reliability of mixed gas measurements. Moreover, capitalizing on the lightweight architecture of the CNN-ECA model, transfer learning techniques were employed to adapt it for deployment on the Raspberry Pi hardware platform. This facilitates the development of a real-time E-nose system for gas detection.

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Section: Sensor Array Designmentioning

confidence: 99%

An E-nose system for identification and quantification of hazardous gas mixtures using a combined strategy of CNNs and attentional mechanisms

Yang,

Wang,

Zhao

et al. 2024

Phys. Scr.

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“…This method is typically used for measurements in non-equilibrium gas mixtures containing H 2 , CO, CO 2 , hydrocarbons, SO 2 , NO x , NH 3 , and H 2 S [143]. Primarily portable and benchtop mixed potential sensors have been demonstrated [144][145][146][147][148]. An investigation towards wearable and room-temperature realizations of such gas-sensing devices could be a promising research direction.…”

Section: Electrochemicalmentioning

confidence: 99%

Wearable Nano-Based Gas Sensors for Environmental Monitoring and Encountered Challenges in Optimization

Hooshmand,

Kassanos,

Keshavarz

et al. 2023

Sensors

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With a rising emphasis on public safety and quality of life, there is an urgent need to ensure optimal air quality, both indoors and outdoors. Detecting toxic gaseous compounds plays a pivotal role in shaping our sustainable future. This review aims to elucidate the advancements in smart wearable (nano)sensors for monitoring harmful gaseous pollutants, such as ammonia (NH3), nitric oxide (NO), nitrous oxide (N2O), nitrogen dioxide (NO2), carbon monoxide (CO), carbon dioxide (CO2), hydrogen sulfide (H2S), sulfur dioxide (SO2), ozone (O3), hydrocarbons (CxHy), and hydrogen fluoride (HF). Differentiating this review from its predecessors, we shed light on the challenges faced in enhancing sensor performance and offer a deep dive into the evolution of sensing materials, wearable substrates, electrodes, and types of sensors. Noteworthy materials for robust detection systems encompass 2D nanostructures, carbon nanomaterials, conducting polymers, nanohybrids, and metal oxide semiconductors. A dedicated section dissects the significance of circuit integration, miniaturization, real-time sensing, repeatability, reusability, power efficiency, gas-sensitive material deposition, selectivity, sensitivity, stability, and response/recovery time, pinpointing gaps in the current knowledge and offering avenues for further research. To conclude, we provide insights and suggestions for the prospective trajectory of smart wearable nanosensors in addressing the extant challenges.

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Detection of Criegee intermediates (ozone oxidation of isoprene) by solid electrolyte sensor

Li,

Wang,

Yang

et al. 2024

J Solid State Electrochem

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Room temperature mixed-potential solid-electrolyte NO2 sensor for environmental monitoring

Cited by 6 publications

References 74 publications

An E-nose system for identification and quantification of hazardous gas mixtures using a combined strategy of CNNs and attentional mechanisms

An E-nose system for identification and quantification of hazardous gas mixtures using a combined strategy of CNNs and attentional mechanisms

Wearable Nano-Based Gas Sensors for Environmental Monitoring and Encountered Challenges in Optimization

Detection of Criegee intermediates (ozone oxidation of isoprene) by solid electrolyte sensor

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