A low cost and high performance NH3 detection system for a harsh agricultural environment

Yang, Boxuan; Li, Xian; Hua, Zhongqiu; Li, Zhemin; He, Xiangnan; Yan, Rui; Li, Yanhao; Zhi, Zinan; Tian, Chen

doi:10.1016/j.snb.2022.131675

Cited by 9 publications

(5 citation statements)

References 21 publications

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“…Contrary to bulky precision sensors, the implantable and printable nanosensors have shown unprecedented resolution and reproducibility in detecting the pollutants created from the agricultural production activities (e.g. atmospheric ammonia), 9 moisture, soil pH and fertilizer level. 10 The high sensitivity, specificity, stability and accuracy followed by the rapid response rate make nanosensors more useful for smart agriculture over conventional sensors due to their higher surface to volume ratio, and faster electron transfer kinetics.…”

Section: Current Statusmentioning

confidence: 99%

Review—Recent Advances in Nanosensors for Precision Agriculture

Tong,

Goh,

Jiang

2023

J. Electrochem. Soc.

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Accurate assessment of plant health conditions across thousands of crops is a challenging undertaking in high density indoor farming as the environmental conditions experienced by individual plants can be very different. Manually inspecting visible symptoms of plant diseases is also not a feasible method because the process is time-consuming and human evaluations are subjective. Compared with traditional bulky sensors, nanosensor-based array can be seamlessly attached onto the plants to identify the onset and type of stress in-vivo via the detection of the plant signaling molecules triggered by plant stress. Most review articles about nanosensors are focused on the working mechanisms, fabrication processes, and device architectures. This review aims at highlighting how nanotechnology can introduce additional value to sensing applications for precision farming, together with the adoption of nanosensors in the current agricultural sector. Further efforts in understanding the applications of nanosensors in a safe and sustainable agricultural environment is also addressed.

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Section: Current Statusmentioning

confidence: 99%

Review—Recent Advances in Nanosensors for Precision Agriculture

Tong,

Goh,

Jiang

2023

J. Electrochem. Soc.

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“…Yang et al have designed a humidity compensation method by operating two sensors in differential mode to eliminate the influence of fluctuating humidity levels in the environment. 16 As for software methods, 17,18 a mathematical model of humidity error can be established to correct sensor responses to targeted VOC vapors. However, these techniques only consider the influence of humidity as a systematic error that can be eliminated in the recording process by compensation approaches.…”

Section: ■ Introductionmentioning

confidence: 99%

“…To improve the sensing stability and reliability of VOC sensors in high-humidity backgrounds, extensive research has been dedicated, including humidity compensation, humidity management, and hydrophobic modification. Yang et al have designed a humidity compensation method by operating two sensors in differential mode to eliminate the influence of fluctuating humidity levels in the environment . As for software methods, , a mathematical model of humidity error can be established to correct sensor responses to targeted VOC vapors.…”

Section: Introductionmentioning

confidence: 99%

Hydrophobic MOF/PDMS-Based QCM Sensors for VOCs Identification and Quantitative Detection in High-Humidity Environments

Cao,

Fu,

Fan

et al. 2024

ACS Appl. Mater. Interfaces

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Metal−organic frameworks (MOFs) have great potential in quartz crystal microbalance (QCM) platforms for volatile organic compound (VOCs) detection and recognition due to their unique properties. However, the MOFs' hydrophilicity degrades performance in high-humidity environments, limiting reliable VOC sensing in complex environments. Herein, we propose a novel VOC virtual sensor array (VSA) using a single QCM sensor with an adsorption layer composed of MIL-101(Cr) MOF and polydimethylsiloxane (PDMS), realizing stable sensing and accurate identification for different VOCs under various relative humidity (RH) conditions. The hydrophobic PDMS layer improves the moisture resistance of the sensor to 4 and 14 times in terms of shifts in resonant frequency and scattering parameters, respectively. In addition, performance is maintained over 2 days of water treatment, demonstrating superior water resistance. The highest sensitivity of 2.68 mdB ppm −1 is achieved for isopropanol detection, with the lowest limit of detection of 20.06 ppm for acetone. Combining resonant signals and lumped parameters, the proposed VSA technique effectively discriminates four VOCs (ethanol, 2-propanol, acetone, and acetonitrile) with a high accuracy of 95.3% under both 60% and 90% RH backgrounds. The studies provide a promising solution for reliable low-concentration VOC detection using QCM sensors in high-humidity environments such as underground spaces.

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“…Various ammonia detection methods exist, including calorimetry [ 11 , 12 , 13 ], electrochemistry [ 14 , 15 , 16 ], ion-selective electrodes [ 17 , 18 , 19 , 20 ], gas-sensitive tubes [ 21 , 22 , 23 ], spectroscopy [ 24 , 25 , 26 ], and sampling with laboratory analysis [ 27 , 28 , 29 ]. Among them, artificial olfaction systems, or electronic noses, have emerged as a mainstream technology in recent years, particularly for ammonia detection in breeding farms.…”

Section: Introductionmentioning

confidence: 99%

Structure Optimization and Data Processing Method of Electronic Nose Bionic Chamber for Detecting Ammonia Emissions from Livestock Excrement Fermentation

Shi,

Niu

et al. 2024

Sensors

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In areas where livestock are bred, there is a demand for accurate, real-time, and stable monitoring of ammonia concentration in the breeding environment. However, existing electronic nose systems have slow response times and limited detection accuracy. In this study, we introduce a novel solution: the bionic chamber construction of the electronic nose is optimized, and the sensor response data in the chamber are analyzed using an intelligent algorithm. We analyze the structure of the biomimetic chamber and the surface airflow of the sensor array to determine the sensing units of the system. The system employs an electronic nose to detect ammonia and ethanol gases in a circulating airflow within a closed box. The captured signals are processed, followed by the application of classification and regression models for data prediction. Our results suggest that the system, leveraging the biomimetic chamber, offers rapid gas detection response times. A high classification prediction accuracy, with a determination coefficient R2 value of 0.99 for single-output regression and over 0.98 for multi-output regression predictions, is achieved by incorporating a backpropagation (BP) neural network algorithm. These outcomes demonstrate the effectiveness of the electronic nose, based on an optimized bionic chamber combined with a BP neural network algorithm, in accurately detecting ammonia emitted during livestock excreta fermentation, satisfying the ammonia detection requirements of breeding farms.

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A low cost and high performance NH3 detection system for a harsh agricultural environment

Cited by 9 publications

References 21 publications

Review—Recent Advances in Nanosensors for Precision Agriculture

Review—Recent Advances in Nanosensors for Precision Agriculture

Hydrophobic MOF/PDMS-Based QCM Sensors for VOCs Identification and Quantitative Detection in High-Humidity Environments

Structure Optimization and Data Processing Method of Electronic Nose Bionic Chamber for Detecting Ammonia Emissions from Livestock Excrement Fermentation

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