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

In recent years the degree of automation in life science laboratories increased considerably by introducing stationary and mobile robots. This trend requires intensified considerations of the occupational safety for cooperating humans, since the robots operate with low volatile compounds that partially emit hazardous vapors, which especially do arise if accidents or leakages occur. For the fast detection of such or similar situations a modular IoT-sensor node was developed. The sensor node consists of four hardware layers, which can be configured individually regarding basic functionality and measured parameters for varying application focuses. In this paper the sensor node is equipped with two gas sensors (BME688, SGP30) for a continuous TVOC measurement. In investigations under controlled laboratory conditions the general sensors’ behavior regarding different VOCs and varying installation conditions are performed. In practical investigations the sensor node’s integration into simple laboratory applications using stationary and mobile robots is shown and examined. The investigation results show that the selected sensors are suitable for the early detection of solvent vapors in life science laboratories. The sensor response and thus the system’s applicability depends on the used compounds, the distance between sensor node and vapor source as well as the speed of the automation systems.

Section: Sensor Boardmentioning

confidence: 99%

Flexible IoT Gas Sensor Node for Automated Life Science Environments Using Stationary and Mobile Robots

Neubert

Roddelkopf

Al-Okby

et al. 2021

“…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

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.

“…However, in order to give the user of the consumer-grade 3D printer a timely, binary indication of whether a threshold concentration value of BTEX compounds has been exceeded, the proposed solution has to be available for the fraction of the price of the 3D printer itself. To this end, chemical gas sensors could be used, as demonstrated in previous studies in which metal oxide semiconductor (MOS) and surface acoustic wave sensors were employed with good results to detect BTEX compounds in the air [ 17 , 18 ]. While the commercially available sensors lack the sensitivity and specificity necessary to directly measure the concentration of benzene in 3D printing fumes, their response signals to the complex mixture of VOCs could be indirectly linked to a reference concentration of a target analyte determined using other methods by means of machine learning techniques.…”

Section: Introductionmentioning

confidence: 99%

Monitoring the BTEX Volatiles during 3D Printing with Acrylonitrile Butadiene Styrene (ABS) Using Electronic Nose and Proton Transfer Reaction Mass Spectrometry

Wojnowski

Kalinowska

Gębicki

et al. 2020

We describe a concept study in which the changes of concentration of benzene, toluene, ethylbenzene, and xylene (BTEX) compounds and styrene within a 3D printer enclosure during printing with different acrylonitrile butadiene styrene (ABS) filaments were monitored in real-time using a proton transfer reaction mass spectrometer and an electronic nose. The quantitative data on the concentration of the BTEX compounds, in particular the concentration of carcinogenic benzene, were then used as reference values for assessing the applicability of an array of low-cost electrochemical sensors in monitoring the exposure of the users of consumer-grade fused deposition modelling 3D printers to potentially harmful volatiles. Using multivariate statistical analysis and machine learning, it was possible to determine whether a set threshold limit value for the concentration of BTEX was exceeded with a 0.96 classification accuracy and within a timeframe of 5 min based on the responses of the chemical sensors.