New Digital Metal-Oxide (MOx) Sensor Platform

Rüffer, Daniel; Hoehne, Felix; Bühler, Johannes

doi:10.3390/s18041052

Cited by 83 publications

(65 citation statements)

References 45 publications

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“…In addition, materials analysis of the BME680 and CCS811 sensors was performed indicating that the sensing MOx films were predominantly comprised of SnO 2 . The SGP30 sensor resistive MOx layer has been previously described as SnO 2 doped with between 0.1%-0.5% palladium resistive layer coated onto nanoparticles [13], confirming that the sensors compared all contained the same class (n-type) of MOx sensing elements.…”

Section: Discussionmentioning

confidence: 63%

“…It is manufactured by Austria Mikro Systeme (ams) and is designed to synthesize the raw sensor data and output a time count, temperature, TVOC in parts per billion (ppb) and approximate CO 2 equivalence in ppm. The SGP30 sensor is made by Sensirion and is the first meta-oxide gas sensor with multiple sensors on the same chip [13]. It outputs a time count, TVOC in parts per billion (ppb) and an equivalent CO 2 concentration in parts per million.…”

Section: Mox Sensors Testedmentioning

confidence: 99%

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Real-Time Sensor Response Characteristics of 3 Commercial Metal Oxide Sensors for Detection of BTEX and Chlorinated Aliphatic Hydrocarbon Organic Vapors

et al. 2019

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The objective of this study was to examine the sensor response characteristics of three commercial Internet of Things (IoT) compatible metal oxide (MOx) sensors in preparation for the development of field-scale sensor networks for the real-time monitoring of volatile organic compounds (VOCs) in indoor environments located in proximity to brownfield sites. Currently, there is limited examination of such sensor responses to relevant mixtures of target VOCs, such as the common petrochemicals benzene, toluene, ethylbenzene, and xylene (BTEX), as well as chlorinated aliphatic hydrocarbon (CAH) contaminants such as tetrachloroethylene (PCE) and trichloroethylene (TCE) which are frequently associated with deterioration of indoor air quality. To address this, a study of three commercial metal oxide (MOx) sensors (SGP30, BME680, and CCS811) was undertaken to examine the sensor response characteristics of individual components as well as mixtures of each of the target BTEX and CAH chemicals over relevant indoor air concentrations within the operating range of the MOx sensors (0–6000 ppb). Our investigation revealed similar response patterns to those previously reported for the thick film MOx sensor to most individual target VOCs, however, response trends for mixtures were more difficult to discern. In general, the MOx sensors we examined demonstrated similar magnitude responses to the CAHs as BTEX compounds indicating reliable detection of CAHs.

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Section: Discussionmentioning

confidence: 63%

Section: Mox Sensors Testedmentioning

confidence: 99%

Real-Time Sensor Response Characteristics of 3 Commercial Metal Oxide Sensors for Detection of BTEX and Chlorinated Aliphatic Hydrocarbon Organic Vapors

et al. 2019

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“…However, nowadays, there are still few studies in the bibliography that include them. Some of these sensors have previously been used to monitoring plants activity [19], to study their individual response to BTEX (Benzene, Toluene, Ethylbenzene and Xylenes) compounds [20], or to investigate their architecture and operation (specifically Sensirion's SGP30 model) [21].…”

Section: Introductionmentioning

confidence: 99%

Electronic Nose with Digital Gas Sensors Connected via Bluetooth to a Smartphone for Air Quality Measurements

Arroyo

Meléndez

Suárez

et al. 2020

Sensors

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This paper introduces a miniaturized personal electronic nose (39 mm × 33 mm), which is managed through an app developed on a smartphone. The electronic nose (e-nose) incorporates four new generation digital gas sensors. These MOx-type sensors incorporate a microcontroller in the same package, being also smaller than the previous generation. This makes it easier to integrate them into the electronics and improves their performance. In this research, the application of the device is focused on the detection of atmospheric pollutants in order to complement the information provided by the reference stations. To validate the system, it has been tested with different concentrations of NOx including some tests specifically developed to study the behavior of the device in different humidity conditions. Finally, a mobile application has been developed to provide classification services. In this regard, a neural network has been developed, trained, and integrated into a smartphone to process the information retrieved from e-nose devices.

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“…Here, the products of three different vendors were considered: Sensirion, Bosch, and AMS. Each of these devices is based on a metal–oxide semiconductor gas sensor technology [13]. They are sensor systems in which the gas sensor is integrated with read-out electronics, preprocessing, and digital output.…”

Section: Methodsmentioning

confidence: 99%

Development of a Sensor Node for Remote Monitoring of Plants

Catini

Papale

Capuano

et al. 2019

Sensors

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The appraisal of stress in plants is of great relevance in agriculture and any time the transport of living plants is involved. Wireless sensor networks (WSNs) are an optimal solution to simultaneously monitor a large number of plants in a mostly automatic way. A number of sensors are readily available to monitor indicators that are likely related to stress. The most common of them include the levels of total volatile compounds and CO2 together with common physical parameters such as temperature, relative humidity, and illumination, which are known to affect plants’ behavior. Recent progress in microsensors and communication technologies, such as the LoRa protocol, makes it possible to design sensor nodes of high sensitivity where power consumption, transmitting distances, and costs are optimized. In this paper, the design of a WSN dedicated to plant stress monitoring is described. The nodes have been tested on European privet (Ligustrum Jonandrum) kept in completely different conditions in order to induce opposite level of stress. The results confirmed the relationship between the release of total Volatile Organic Compounds (VOCs) and the environmental conditions. A machine learning model based on recursive neural networks demonstrates that total VOCs can be estimated from the measure of the environmental parameters.

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New Digital Metal-Oxide (MOx) Sensor Platform

Cited by 83 publications

References 45 publications

Real-Time Sensor Response Characteristics of 3 Commercial Metal Oxide Sensors for Detection of BTEX and Chlorinated Aliphatic Hydrocarbon Organic Vapors

Real-Time Sensor Response Characteristics of 3 Commercial Metal Oxide Sensors for Detection of BTEX and Chlorinated Aliphatic Hydrocarbon Organic Vapors

Electronic Nose with Digital Gas Sensors Connected via Bluetooth to a Smartphone for Air Quality Measurements

Development of a Sensor Node for Remote Monitoring of Plants

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