Abstract
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.
Highlights
Life science laboratories are still dominated by partial and island automation
A further IoT sensor node was presented by Marques et al, which focused on the occupational health in laboratory environments regarding the air quality
The selected metal-oxide semiconductor gas sensors (MOX), BME688 and SGP30, are tiny digital solutions which already handle the heater control, calibration procedures, baseline and long-term correction, humidity compensation
Summary
Life science laboratories are still dominated by partial and island automation. The degree of automation can be increased by connecting different automation islands distributed in the laboratory building. LoRa, GPRS (general packet radio service), WiFi, and the NB-IoT (Narrowband IoT; lowpower wide-area (LPWA) technology) was implemented This allowed for the sensor-node integration via local networks or the Internet. A further IoT sensor node (named iAQ+) was presented by Marques et al, which focused on the occupational health in laboratory environments regarding the air quality. The developments presented show several compact gas detecting IoT-solutions for different application areas. These solutions are not adaptive enough, for example, regarding the fast integration of new sensors or the adaption of other power supplies, as required for the gas detection and expectation of other environmental data acquisitions in the automation infrastructure of a life science laboratory. The flexible integration of the sensor node into primary actors of the automation environment is required
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