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

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.

Full text

sensors
Article
Flexible IoT Gas Sensor Node for Automated Life Science
Environments Using Stationary and Mobile Robots
Sebastian Neubert 1, *, Thomas Roddelkopf 1, Mohammed Faeik Ruzaij Al-Okby 2,3 , Steffen Junginger 1
and Kerstin Thurow 2


Citation: Neubert, S.; Roddelkopf, T.;
Al-Okby, M.F.R.; Junginger, S.;
Thurow, K. Flexible IoT Gas Sensor
Node for Automated Life Science
Environments Using Stationary and
Mobile Robots. Sensors 2021,21, 7347.
https://doi.org/10.3390/s21217347
Academic Editor: Chris Blackman
Received: 30 September 2021
Accepted: 2 November 2021
Published: 4 November 2021
Publisher’s Note: MDPI stays neutral
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Copyright: © 2021 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
1Institute of Automation, University of Rostock, 18119 Rostock, Germany;
thomas.roddelkopf@celisca.de (T.R.); steffen.junginger@uni-rostock.de (S.J.)
2Center of Life Science Automation (celisca), 18119 Rostock, Germany;
mohammed.al_okby@atu.edu.iq (M.F.R.A.-O.); kerstin.thurow@celisca.de (K.T.)
3Technical Institute of Babylon, Al-Furat Al-Awsat Technical University (ATU), Kufa 51015, Iraq
*Correspondence: sebastian.neubert@uni-rostock.de; Tel.: +49(0)-381-498-7806
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.
Keywords: IoT; sensor node; life science; automation; laboratory; mobile robot; gas detection
1. Introduction
Life science laboratories are still dominated by partial and island automation. The de-
gree of automation can be increased by connecting different automation islands distributed
in the laboratory building. The use of mobile robots will influence this development. Sev-
eral applications using mobile robots in life science laboratories have been
described [1–3]
.
The connection of originally separated automation plants which now needs to pass lab-
oratories and corridors which in parallel are used by human operators leads to novel
challenges regarding occupational safety. One main issue is the transfer of compounds,
which partially emits hazardous or toxic gases. The labware for transporting such materials
needs to be closed to avoid the compounds or resulting gases from escaping. However
arising leakages, undetected contaminations or simple accidents, for example by collisions
on the automation island (by local robots) or between mobile robots and obstacles, require
a fast detection to avoid hazardous situations for the human operators.
A wide range of gases, which affect the health risk for humans belong to volatile
organic compounds (VOC). This group of chemicals evaporates easily from fluids or
solids at room temperature. VOCs can have natural or anthropogenic origins and occur
for example in agriculture, in many industry branches, in traffic and also in common
household products, for example, in paints, adhesives, cosmetics and cleaning agents [
4
,
5
].
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Sensors 2021,21, 7347 2 of 22
Consequently, they are all around and can partially lead to serious health problems since
a number of them exhibit toxic, neurotoxic, carcinogenic or mutagenic properties [
6
].
Furthermore, the concentration of many VOCs are consistently up to ten times higher
indoors than outdoors [
7
] because of the often higher emission and missing air exchange.
In life science industries/laboratories many VOCs, for example, C6H14 (hexane), CH3OH
(methanol), C
2
H
5
OH (ethanol), CH
2
Cl
2
(dichloromethane), C
3
H
6
O (acetone), C
2
H
3
N
(acetonitrile), C
2
H
6
O (dimethyl ether), C
3
H
8
O (2-propanol), C
7
H
8
(toluene), are widely
used as the starting material, as solvents, as refrigerants or for dilutions and many further
VOCs in partially high concentrations can be found there. Consequently, the risk for
health impairments [
8
] in such environments can be significantly higher if they are not
handled correctly. Thus, it is necessary to monitor the often autonomously acting systems,
especially if the automation systems can leave the laboratories equipped with appropriately
safety features.
Many research groups deal with the field of gas detection and monitoring. This high
interest is on the one hand attributable to novel innovations and possibilities in sensor
design [
9
,
10
] and on the other hand to the increasing demand of technical solutions, for
example, the air pollution monitoring in urban environments [
11
–
13
], the supporting mea-
sures against the COVID-19 pandemic [
14
,
15
] and the protection of working environments
against hazardous situations by the exposure of pollutants [16–18].
Benammar et al. presented a solution for gas monitoring in the private sector. There-
fore, a modular IoT platform for the real-time monitoring of the indoor air quality regarding
CO
2
(carbon dioxide), CO (carbon monoxide), SO
2
(sulfur dioxide), NO
2
(nitrogen dioxide),
O
3
(trioxygen) and Cl
2
(chlorine) was developed. The platform is based on the low-power
Waspmote hardware (Libelium, Zaragoza, Spain) architecture in combination with a XBee
PRO Series 2 radio module for data transmission. By using analog front-end (AFE) modules
the gas sensors are arranged on a sensor interface board, which is connected to the Wasp-
mote board. A gateway device based on the Raspberry Pi 2 B (Raspberry Pi Foundation,
Cambridge, UK) minicomputer allows the adaption to Internet standards like WiFi and
Ethernet for HTTP data transmission to an IoT server [19].
Addabbo et al. developed a low-power gas sensor node for detecting CO, O
2
(oxygen)
and NO
x
(nitrogen oxides) in industrial plants and public buildings (including temperature
and humidity). For the integration of the battery-driven sensor nodes a multi-layer network
architecture on the basis of ZigBee (low-power near field protocol) and LoRa (Long Range;
low-power wide-range protocol) communication was used. The ZigBee sensor nodes on
the lower layer build mesh sub-networks and transfer data via gateways directly to the
Internet or to the next higher layer, which uses LoRa. On this layer LoRa-based sensor
nodes and ZigBee-LoRa gateways submit data via the LoRa-Internet gateway to the upper
layer of the Internet backbone. The data were transferred via HTTP (hypertext transfer
protocol) and stored in a MySQL-database; the functionality of the solution regarding data
acquisition and transfer was tested by establishing a network in the institute’s building
(7000 m2with five floors) [20].
A further gas sensor node was presented by Zhao et al., which focused on monitoring
the indoor air quality (IAQ) by detecting CO
2
, PM
2.5
(particulate matter with a diameter
of 2.5
µ
m or less [
21
]) and HCOH (formaldehyde). Concerning the data transmission
a multi-protocol approach supporting Modbus (via RS485; wired communication) and
LoRa, GPRS (general packet radio service), WiFi, and the NB-IoT (Narrowband IoT; low-
power wide-area (LPWA) technology) was implemented. This allowed for the sensor-node
integration via local networks or the Internet. The data were transferred into a cloud
platform where they were stored in a database and published by a web server [22].
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 iAQ+ prototype sampled the IAQ index (rating system for indoor air quality) by
using the smart sensor BME680 from Bosch (Gerlingen, Germany; including temperature,
humidity and air pressure measurement). As a basis the WiFi-ready development board

Sensors 2021,21, 7347 3 of 22
Fire Beetle ESP8266 was used. A web service collected, analyzed and stored the data in a
SQL Server database and notified the user via SMS (short message service) or e-mail if the
limit values were exceeded [23].
Wall et al. introduced a gas monitoring IoT solution for promoting wellness and
safeguarding social interaction. In that development the BME680 from Bosch was also
used to detect the IAQ index as seen in Marques et al. The data were transmitted to a
server (hosted on a Raspberry Pi) via HTTP POST request (WiFi) and stored in a MySQL
database. The solution was evaluated by two 2-week data collection periods in a kitchen
and a study [24].
An electronic nose using four smart sensors was presented in Arroyo et al. The
sensors IAQ-Core and CCS811 from AMS (Premstätten, Austria) as well as the SGP30 from
Sensirion (Staefa, Switzerland) and the BME680 from Bosch were integrated for measuring
VOC and peripheral parameters on one compact circuit board. The data were transferred
via Bluetooth low energy to a mobile device where they were interpreted by a neural
network integration [
25
]. A similar sensor combination was also used by Jose et al., using
LoRa for data transmission [26].
The developments presented show several compact gas detecting IoT-solutions for
different application areas. These solutions chiefly 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. Due to that, in this paper
a flexible IoT (Internet of Things) gas-sensor node, using a modular functional concept,
is presented. In the pursued application scenario very critical situations with fast and
serious consequences for laboratory assistants can arise, which consequently require a fast
detection of small leakages wherever they occur. So the flexible integration of the sensor
node into primary actors of the automation environment is required.
2. System Concept and Implementation
The basis of the considerations in this paper is a modular sensor node, which has
a major demand to be easily adapted or extended to different measurement scenarios
in life science automation. This includes, for example, the option for wired and wire-
less communication, the fast exchange of sensors and the adaption of different power
supplies. One first measurement aim is the reliable and fast detection of VOC emissions
in laboratory environments especially in cases where the sensors are in motion. As de-
tection range for the sensors, the level 4 (>3–10 mg/m
3
—660—2200 ppb) and level 5
(>10–25 mg/m3—2200—5500 ppb
) of the recommended guide values from the German
Federal Environment Agency [
27
] and concentrations beyond that, which are commonly
occur in laboratory environments, need to be covered by the target IoT-solution. However,
a highly precise concentration determination is not required and the differentiation of the
detected VOCs is not yet focused. Accordingly, a continuous TVOC-monitoring is used for
the current target application. A further important demand is the continuous data transfer
to the automation infrastructure for the immediate data interpretation. Small size and long
battery life of the sensor node are secondary demands in this paper.
2.1. Gas Sensor Node
The developed sensor node consists of four modules with different functionalities.
These modules can be individually combined to a stack depending on the specific require-
ments of the applications. The size of one module is 50
×
35
×
12 mm
3
and the resulting
stack reaches a height of 50 mm (excluding battery, see Section 2.1.4). In Figure 1the
components of the sensor node are briefly introduced.

Sensors 2021,21, 7347 4 of 22
Figure 1.
Modules of the sensor node including a brief conclusion of the main components and the
assembled sensor-node stack (right).
2.1.1. Microcontroller Board
The central module of the sensor node is the microcontroller board primary for data
sampling and processing and for managing the data transfer and the system configura-
tion via the USB-C-interface. It consists of an ARM Cortex M0+ microcontroller (32-bit,
32 MHz) including I
2
C—inter-integrated circuit, SPI—serial peripheral interface, and
UART—universal asynchronous receiver transmitter. The USB interface is also used for
the power supply if the battery board is not available. As the central unit of the node the
microcontroller is connected to all pins for inter-board connection (see Section 2.1.5). The
configuration via the USB interface allows the adjustment of the calibration settings for
each implemented sensor or the configuration of the settings for WiFi-communication if
any changes from the default settings are necessary. Further, this board has a real-time clock
(MCP7941-I/MS, Microchip Technology, Chandler, AZ, USA) included to enable absolute
time stamps for the acquired data and to avoid postponements during data sampling,
especially during the beginning of the measurement.
2.1.2. Sensor Board
The second required module is the sensor board, which includes the sensors and the
required voltage adaption. The sensor board’s design follows a simple structure and can
be designed individually with different sensors, depending on the application whereby
it can be exchanged or extended (adding more than one sensor module into the node
stack) if required. Currently the sensor board is equipped with three sensors as shown in
Table 1and has space still to be extended depending on the individual sensor’s size and
peripheral requirements.
Table 1. Integrated sensor solutions and their basic specifications.
BME688 [28] SGP30 [29] MS5803-05BA [30]
manufacturer Bosch Sensirion TE connectivity
power supply 1.71–3.6 V 1.62–1.98 V 1.8–3.6 V
acquired parameter (only
major parameters included)
IAQ, CO2eq., ambient
temperature, relative
humidity, atmospheric pressure
TVOC, CO2eq. (H2-based)
atmospheric pressure
(high resolution),
ambient temperature
interfaces SPI, I2C I2C (1.8 V) SPI, I2C
size (in mm3)3.0 ×3.0 ×0.93 2.45 ×2.45 ×0.9 6.4 ×6.2 ×2.88

Sensors 2021,21, 7347 5 of 22
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 (for BME688 partially supported
by a related processing library [
28
]). They also offer comfortable interfaces as SPI or I
2
C.
In investigations from Yurko et al. about BME680 and SGP30 it can be seen that the
sensors show suitable characteristics for the pursued application regarding consistence and
reproducibility [
31
]. The BME688 is an enhancement of the BME680, which includes all
features of the BME680 as well as some additional features, for example, options for using
artificial intelligence [
32
]. In addition to gas sensors, other sensors can also be integrated
on the board to extend the sensor node’s monitoring range. This applies to temperature
and humidity sensors, optical or acoustic sensors. One example is the high-resolution
atmospheric pressure sensor MS5803-05BA (TE connectivity, Schaffhausen, Switzerland)
for the detection of changings in the height of the sensor node’s position (required in
combination with the mobile robot)), which is also used in the current configuration.
The sensor board exhibits a unique shape in the form of two extension arms for the gas
sensors. This allows a better contact with the target gases and avoids a mutual temperature
influence of the sensors due to their heating procedures. Both sensors are currently used
with the lowest supported sampling rate of 1 Hz. The sampling rate can be increased, but
then the data interpretation needs to be performed by the user themselves.
In contrast to the SGP30 the BME688 data interpretation is not part of the sensor itself,
here, the already mentioned library is required, for example, to convert the measured IAQ
(index of air quality)-value into a TVOC-concentration or to compensate the influence of
ambient temperature and humidity, which affects the emission rate of VOCs [
33
]. Corre-
spondingly, the ambient parameters of the BME688, also including atmospheric pressure,
are provided to the library for the compensation of such influences. In the presented
development the Bosch library bsec_2-0-6-1_generic_release_04302021 is used. The SGP30
also accepts the relative humidity as an input parameter for compensation purposes, which
is taken in the current configuration from the BME688.
2.1.3. Communication Board
The communication board is optional and allows, besides the USB communication
(microcontroller board), the wireless communication for the sensor node. Currently the
ESP-WROOM-02D WiFi-module (Espressif Systems, Shanghai, China) and the ACN52840
Bluetooth 5 module (Aconno, Düsseldorf, Germany) are integrated. The WiFi-module
is used for data transmission while the BLE-module will be used for indoor localization
matters, which will not be studied further in this paper.
The WiFi-module includes an own microcontroller which deals with the wireless
communication to the infrastructure cloud. It is connected via SPI to the microcontroller
board to set the real-time clock by the servers’ clock time, to request the configuration
settings (for example, available sensors, WiFi connection data) and to transfer the acquired
sensor data including the absolute time stamp. Further a FRAM (ferroelectric random
access memory)-storage (512 kByte) is considered on this board for buffering data in case
of an interrupted network connection.
2.1.4. Power/Battery Board
A further optional module is the power/battery board. It offers, additionally to USB,
two further options for the sensor node’s power supply. A direct supply of 24 V can be
connected by a terminal block (two poles). This enables the connection with devices such
as mobile robots, which do not support USB for the power supply of peripheral devices.
The second option is the supply from a single cell battery (3.7 V, 53
×
32
×
9 mm
3
, currently
with 1000 mAh), which can be recharged via the USB interface of the microcontroller board
or via the 24 V if necessary.

Sensors 2021,21, 7347 6 of 22
2.1.5. Inter-Board Connection
To support a higher flexibility regarding the integration and combination of the single
boards, every board consists of two board-to-board connector lines on each side, which
allow a firm attachment between them and permit access to the required pins to every
board, independent of the boards’ stack order. In Figure 2the pin-assignment for the
inter-board connection is shown. Extending boards can simply be attached and integrated
via the available interface solutions. If additional communication interfaces on one of the
boards are required then this does not necessarily need to be connected to the inter-board
connection. This is, for example, performed for the FRAM-storage integration on the
communication-board since this is only required for the WiFi communication.
Figure 2. Concluded pin-assignment of the inter-board connection using the microcontroller board as example.
2.2. Communication
If the basic sensor node, including microcontroller and sensor board, is used in
combination with the communication-board, the wireless data transfer via WiFi to the
infrastructure cloud can be supported. In the sensor node the microcontroller board estab-
lishes the internal communication to the available sensors and waits for data requests from
the communication board. The communication between these boards is realized by SPI in
which the communication board takes on the master role. Before the communication board
starts with the data transfer it inquires communication parameters, such as target network,
IP-addresses and ports from the microcontroller board. Since the microcontroller board
is the central module all important configuration parameters are stored on it. In case the
communication board needs to be exchanged the sensor node keeps the individual config-
uration parameters. If the communication board has all required parameters, it listens for
data requests from the communication server of the infrastructure cloud. The communica-
tion server sends a UDP broadcast in a configurable interval including server identification,
current time stamp, requested data (e.g., all data including ambient temperature and hu-
midity or gas data only) and the URL for HTTP-data transmission. The advantage of this
communication strategy is that the sensor nodes do not need to be registered at the server.
A sensor node can be started directly with the necessary network-configurations and all
further information is provided via the broadcast. The supplied current time stamp also
permits the synchronization of the measurement data if communication errors occur and
data need to be buffered in the FRAM.
If the configuration procedure is finished, the repetitive data transfer is started as seen
in the sequence diagram of Figure 3. The microcontroller board collects the data from the
sensors and transfers them to the communication board, where all data are directly stored

Sensors 2021,21, 7347 7 of 22
in the FRAM. If the communication board receives the UDP-broadcast the required data
are selected and bundled in a structured JSON-protocol which includes base information
and a list of the data sets (see Figure 4). Every data set consists of the requested data for one
measurement period (currently 1 s), separated for the individual sensors. Appropriately,
for every data set one time stamp is considered. For every single value the name and the
unit is supplied additionally.
Figure 3.
Simplified sequence diagram of the data transfer process between the sensor node components and the communi-
cation server.
On the server side the received data packets are assigned and stored into the relational
database structure of the infrastructure cloud. One advantage of using higher structured
protocols is that new, unknown sensor parameters or sensor nodes can be automatically
included without administration support. The data are available via the provided IoT-Web-
App including a visualizing interface that allows viewing certain periods of the acquired
data or a live view of incoming data (see Figure 5).

Sensors 2021,21, 7347 8 of 22
Figure 4. Simplified presentation of the transfer protocol’s structure in JSON-format.
Figure 5.
Visualizing tool of the IoT-Web-App showing the responses of the BME688 and the SGP30 to a small
VOC emission.
3. Experimental Methods and Results
The aim of the experiments in this paper is to prove the usability of the selected
sensors and to define application conditions for using the developed sensor node solution
in an automated environment. Therefore, investigations under laboratory conditions
and application-related experiments were executed in a classical laboratory environment.
For all experiments the sensor node was given a 30 min warm-up time to achieve stable
sensitivity level of MOX-sensors [34].
3.1. Investigations under Laboratory Conditions
The investigations under laboratory conditions were executed inside a Secuflow fume
hood (Waldner Holding GmbH & Co. KG, Wangen im Allgäu, Germany). The fume hood

Sensors 2021,21, 7347 9 of 22
was required to avoid the distribution of the arising fumes and to avoid strong influences
by circulations of the ambient air. During the experiments the Secuflow was closed and the
exhaust ventilation was temporarily turned off. In the experimental design the sensor node
was attached to a stand of corresponding height above a petri dish (see Figure 6a). The
dispensing of the VOCs was performed by different pipettes from Eppendorf (Hamburg,
Germany) depending on the respectively required amount. The laboratory was equipped
with an air conditioning system which kept the temperature at 22.0
±
0.5
◦
C and the
relative humidity at 50.0 ±2.0%.
Figure 6.
(
a
) Experimental setup in fume cupboard including the stand, petri dish and a sensor node; (
b
) sensor-node
orientation: facing upwards; (
c
) sensor-node orientation: facing sideways; (
d
) sensor-node orientation: facing downwards.
Both sensors have different baselines in neutral environments. The SGP30 fluctu-
ates between 0–0.05 ppm in the fume hood whereas the BME688 baseline is between
0.49–0.51 ppm
whereby the minimal value of the sensor is given with 0.5 ppm by the
Bosch-library.
3.1.1. Sensor Orientation
A first key issue is to identify the preferred orientation of the sensor node in relation
to the emission source. Starting from the fact that in our applications the emission is
primarily located below the sensor node, the assumption is that in this case a higher
detection sensitivity can be reached if the sensor is faced downwards. In the experiment
setting, the sensors were fixed at a height of 25 cm above the petri dish. The sensors’
orientations (facing upwards, sideways and downwards; see Figure 6) were investigated
with two VOCs, ethanol and hexane, which had varying effects on the sensors regarding
the sensitivity. For every experiment an amount of 1 mL (for hexane) and 10
µ
L (for ethanol)
were dispensed into the petri dish and positioned directly below the sensors. The chosen
amounts were defined in preliminary investigations, so that the sensors show clear results
but do not reach their saturation.
In Figure 7the results of the orientation experiment are presented. From the data it can
be seen that the facing-downwards orientation always shows a strong response compared
to the facing-upwards orientation, as to be expected. The facing-sideways orientation
shows partially stronger reactions than the downwards orientations, but this behavior is

Sensors 2021,21, 7347 10 of 22
not reliable since in some cases its reaction can be weaker than the face upwards orientation,
as it can be seen in Figure 7for the BME688 sensor and hexane.
Figure 7.
Measurement results for different orientations (facing upwards, sideways and downwards) for the BME688 sensor
with (a) 10 µL ethanol and with (b) 1 mL hexane and the SGP30 sensor with (c) 10 µL ethanol and with (d) 1 mL hexane.
3.1.2. Reactivity of Sensors
In a second consideration the reactivity of the used sensors regarding arising VOC
emissions are compared. For this experiment the same setting was used as described in
Section 3.1.1. The height of the sensors was 25 cm above the petri dish and the sensor was
facing downwards. In Figure 8the sensors’ reactivity is exemplarily shown for hexane, as
VOC with a weaker response, and ethanol, as VOC with a stronger response to the sensors.
Figure 8. Reactivity of BME688 and SGP30 for (a) ethanol and (b) hexane.
The presented data show that both sensors react almost parallel to hexane. The
amplitudes differ only insignificantly. For ethanol the SGP30 shows a fast rising and decay
behavior in contrast to the BME688. For abruptly increasing concentrations the BME688
shows a strong deceleration for increase and decrease, whereby the decrease case down to
the baseline may take several minutes, depending on the amplitude.
3.1.3. Sensors’ Reaction Related to Different VOCs
In this consideration the sensors’ behavior on five different VOCs depending on the
distance between sensor and petri dish (height) and the amount of the compounds were

Sensors 2021,21, 7347 11 of 22
investigated. The environmental conditions here were the same as in the investigations
before. The sensor node was faced downwards oriented and the petri dish was directly
underneath. The following commonly used solvents and compounds were tested:
•C2H5OH—ethanol (70%, technical grade)
•CH2O2—formic acid (≥98%)
•CH2Cl2—dichloromethane
•C2H3N—acetonitrile
•C6H14—hexane
In the investigation setting two different heights, 25 and 40 cm, and four different
amounts for each compound were used. For dichloromethane, acetonitrile and hexane
the amounts 1, 5, 10 and 20 mL were selected. In comparison to the other compounds,
ethanol and formic acid showed significantly stronger responses to the sensors in the
investigation setting. Very low amounts of these compounds led very fast to the saturation
of the sensors, especially for the BME688. Due to this behavior, the test amounts for ethanol
and formic acid were equally reduced to 10, 100, 500 and 1000
µ
L. In Figures 9–13 the
sensors’ responses to different compounds were shown, separated for both sensors and
both heights. For higher concentrations the responses for ethanol and formic acid still
reached the sensors saturation and were correspondingly not shown in Figures 9and 10.
Figure 9.
Response of BME688 and SGP30 for different amounts of ethanol and for 25 and 40 cm height above the emission
source (consider the second ordinate axis in c) for 10
µ
L). In (
a
) a measurement height of 25 cm for BME688 and (
b
) for
SGP30 is used. In (c) the measurement height was changed to 40 cm for BME688 and (d) for SGP30.

Sensors 2021,21, 7347 12 of 22
Figure 10.
Response of BME688 and SGP30 for different amounts of formic acid and for 25 and 40 cm height above the
emission source. In (
a
) a measurement height of 25 cm for BME688 and (
b
) for SGP30 is used. In (
c
) the measurement height
was changed to 40 cm for BME688 and (d) for SGP30.
Figure 11.
Response of BME688 and SGP30 for different amounts of dichloromethane and for 25 and 40 cm height above
the emission source. In (
a
) a measurement height of 25 cm for BME688 and (
b
) for SGP30 is used. In (
c
) the measurement
height was changed to 40 cm for BME688 and (d) for SGP30.

Sensors 2021,21, 7347 13 of 22
Figure 12.
Response of BME688 and SGP30 for different amounts of hexane and for 25 and 40 cm height above the emission
source. In (
a
) a measurement height of 25 cm for BME688 and (
b
) for SGP30 is used. In (
c
) the measurement height was
changed to 40 cm for BME688 and (d) for SGP30.
Figure 13.
Response of BME688 and SGP30 for different amounts of acetonitrile and for 25 and 40 cm height above the
emission source. In (
a
) a measurement height of 25 cm for BME688 and (
b
) for SGP30 is used. In (
c
) the measurement height
was changed to 40 cm for BME688 and (d) for SGP30.
For ethanol and formic acid both sensors showed, as expected, a strong sensitiv-
ity by reaching the maximum values (saturation) of the sensors (BME688: 1000 ppm;

Sensors 2021,21, 7347 14 of 22
SGP30: 60 ppm
). For lower amounts it can either be observed that the measured concen-
tration also decreases or that the value decreases earlier from the maximum, since the
smaller amounts of the compounds were already evaporated. Further, the results show
that the range of the measured amounts, which fall within the scope between baseline and
saturation, is mostly less than 100
µ
L, depending on the sensor. The influence regarding
the different heights here can only be observed for formic acid where a small change of
height strongly effects the measured concentration, which proves the sensors sensitivity.
Despite higher amounts for dichloromethane, hexane and acetonitrile, significantly
smaller responses can be measured. For most measurements both sensors show a similar
behavior. A clear exception is acetonitrile, here the BME688 responds first with a decreasing
measurement value, which after differently long periods slowly increases. In contrast to
that the SGP30 shows values that correlate with the amount of the compound. Regarding
the different distances between the sensors and the petri dish only hexane shows a stronger
response for 25 cm than for 40 cm.
3.2. Application-Related Investigations
These investigations were executed in an automated laboratory environment. In
small application scenarios using one stationary and one mobile robot, the behavior of the
sensor node was investigated. Additional to the laboratory investigations the gas exposure
time onto the sensor node has an important influence, depending on the robot’s speed.
Moreover, the environments are not free of other VOC influences, which partially effects
the baselines of the sensors.
3.2.1. Stationary Transport Robot: TS60 (Stäubli)
In this experiment the sensor node’s behavior in an application-related scenario using
the SCARA TS60 from Stäubli was investigated. The sensor node was mounted to one side
of the robots concentric three-finger gripper (see Figure 14a). In that simple pick-and-place
application the robot arm moved from a distant position (distance: 94 cm) to a rack, which
included one 15 mL tube. The arm had to grab the tube and to bring it back to the start
position. The experiment was performed at four different speed levels, which can be set in
fixed percentage steps (used steps: 2%, 5%, 10% and 25%) from the programmed maximum
speed. SCARAs inherently offer a high-speed level depending on the mechanical structure
of the joints, which is why the speed did not exceed 25%. For this task the TS60 primarily
required two joints, the main rotatory joint (joint 1) and the translatory joint (joint 3) with
the gripper on its bottom end (see Figure 14b). The nominal speeds of the axes were 385
◦
/s
for joint 1 and 2000 mm/s for joint 3. Consequently in the different speed levels the tasks
require the following mean durations for execution (in min:sec:msec):
•2% speed: 1:16:52
•5% speed: 00:31:15
•10% speed: 00:15:93
•25% speed: 00:06:84
In this experiment again hexane and ethanol were used as VOCs, where hexane has
a rather low and ethanol a rather high response to the integrated sensors. A petri dish
(diameter: 10 cm) was positioned close to the tube on the opposite side, where the sensor
node is mounted to the arm, when it is above the tube (see Figure 14a). In this way the
worst-case scenario for detecting emissions around the target point was selected. In each
experiment 1 mL hexane and 100
µ
L ethanol were used. Since the environment was not
as encapsulated as in the fume hood, the baselines’ ranges partially dilated and reached
0–0.05 ppm for the SGP30 and 0.47–0.53 ppm for the BME688.

Sensors 2021,21, 7347 15 of 22
Figure 14.
Experimental setting using the SCARA TS60. (
a
) Sensor node attached to the gripper which is above the tube.
The petri dish is positioned opposite to the sensor node. (
b
) Joint 1 and 2 are rotatory joints and act around the z-axis. Joint
3 is a translatory joint and is acting in z-direction (up and down). Further the distance between the start position (also the
end position) and the position of the tube to be picked up is shown.
The data for 1 mL hexane show a different behavior between BME688 and SGP30
(see Figure 15). While the SGP30 has comparably slight responses for all speed levels, the
BME688 clearly detects hexane for the speed levels 2% and 5% but not for the higher speed
levels. The responses in these levels are too close to the baseline to use them for detection
and the same applies to the SGP30 results. A reason for the failed detection is that the
BME688 requires considerably more time for such VOCs with a weaker effect. Further, due
to the fast movement an airflow arises, which in first order influences the measurement for
the slower reacting sensor, BME688.
In contrast to that, 100
µ
L ethanol could be clearly detected by both sensors for all
speed levels (see Figure 16). Also here the dependency of the BME688 from the speed level
becomes apparent by the strong decreasing concentration with a higher speed. The SGP30
shows a distinctly more stable behavior regarding the amplitude over the speed levels and
also a faster decay behavior.
3.2.2. Mobile Robot: H20 (Dr. Robot)
In the last experiment the mobile robot H20 from Dr Robot (Ontario, ON, Canada)
was used (see Figure 17a) to prove the sensors’ behavior by passing the VOC emission
sources on the laboratory floor. For that the sensor node was once mounted to the side
and once mounted to the front of the robot with each 25 cm above the floor. In case that
the sensor node was side-mounted it directly passes above the VOC source while in case
of the front-mounted sensor node additionally a lateral shift of around 40 cm arose (see
Figure 17b,c
). The investigation was executed with the three speed levels 0.14, 0.21 and
0.28 m/s, whereby 0.28 m/s corresponds with the standard transfer speed typically used.

Sensors 2021,21, 7347 16 of 22
Figure 15.
Results for the detection of 1 mL hexane during a pick and place procedure using TS60 in different speed levels,
(a) for 2%, (b) for 5%, (c) for 10% and (d) for 25%.
Figure 16.
Results for the detection of 100
µ
L ethanol during a pick and place procedure using a TS60 at different speed
levels, (a) for 2%, (b) for 5%, (c) for 10% and (d) for 25%.

Sensors 2021,21, 7347 17 of 22
Figure 17.
(
a
) H20 robot and the petri dish in a celisca laboratory; (
b
) Experiment setting for the side-mounted sensor node;
(c) Experiment setting for the front-mounted sensor node.
As investigation compounds again, hexane and ethanol were used, which were pro-
vided in a petri dish (diameter: 15 cm). While 5 mL hexane was dosed into the petri dish,
only 1 mL ethanol was used. Using smaller amounts in this experiment were unsuitable
due to the fast evaporation of the compounds. The petri dish was placed a short distance
from the H20 path so that the robot just passed it, as seen in Figure 17. For the investigation
execution a laboratory was used, which did not include technical equipment with ventila-
tors since these can influence the baseline. The baseline in the investigation laboratory was
for the BME688 in the range of 0.45–0.55 ppm and for the SGP30 between 0–0.07 ppm.
In Figures 18 and 19 the results of the VOC-concentration measurements for the
prescribed speed levels are presented. The results are separated for the selected sensors
and the method of mounting. In all scenarios a clear response from the sensors can be
observed. As is to be expected, the results from the side-mounting for both sensors and
for both compounds show stronger responses than from the front-mounting. For ethanol
in combination with the side-mounting both sensors reach their saturation for all speed
levels. Thus, only one speed level is presented in Figure 19a,b. The different speed levels
only show insignificant differences or ambiguous responses between the sensors. This may
depend on the relatively low difference of the speed levels and additionally it is assumed
that the plain front surface of the robot’s travel unit (lower robot part) pushes the air and
causes swirling effects, which influence the results. This effect can be clearly observed
in Figure 19b where the SGP30 regarding its faster reactivity compared to the BME688
(Figure 19a) shows this behavior.

Sensors 2021,21, 7347 18 of 22
Figure 18.
Results for the detection of 5 mL hexane during passing by with the H20-robot in different speed levels and
with front- and side-mounted sensor node. (
a
) Response of the side-mounted BME688 and (
b
) SGP30. (
c
) Response of the
front-mounted BME688 and (d) SGP30.
Figure 19.
Results for the detection of 1 mL ethanol during passing by with the H20-robot in different speed levels and
with front- and side-mounted sensor node. (
a
) Response of the side-mounted BME688 and (
b
) SGP30. (
c
) Response of the
front-mounted BME688 and (d) SGP30.
Since this experiment was executed in a real laboratory air drafts by the air condi-
tioning or opening doors could have also slightly influenced the experimental results.

Sensors 2021,21, 7347 19 of 22
Especially the presence of humans could influence the measurement significantly, because
of the perspirations of cosmetics and the human body itself. Thus, these disruptive factors
were avoided as much as possible.
4. Discussion and Conclusions
The developed IoT sensor node follows a modular approach, which can be adapted to
various application scenarios regarding measured parameters and the functional features.
Currently the sensor node consists of four modules from which two modules (microcon-
troller and sensor board) are necessary for the base functionality (sensor data acquisition,
data processing, system configuration etc.) and two further modules (communication and
battery board) are available to provide additional features, such as wireless communication
and battery operation. Unnecessary modules can simply be excluded and the required
ones can be attached to any position due to the multi-interface connection between the
modules. Thus, a flexible IoT-platform is provided, which can be simply adapted and
extended. In that way the sensor node can be used independently from the sensor configu-
ration as a local sensor-node (connected to a PC), as an IoT-sensor node and as a mobile
IoT-sensor node. The disadvantage of this modular approach is that the sensor node is
comparably large and that by the divided processing units, a higher power consumption
and consequently a lower battery life arises.
The data transfer to the infrastructure cloud is realized in a JSON format via HTTP,
which is always initiated by a UDP-broadcast from the communication server. The strategy
using a broadcast allows the server to control the data transfer (dividing to different
target systems, adjustment of transfer cycle and transferred data) and to synchronize all
connected sensor nodes. The JSON protocol is structured in a way that an automatic data
assignment on the server side can be performed. Consequently, the protocol is comparably
comprehensive, but for any system extensions regarding new parameters or new sensor
nodes no manual adaptions on the server side are required.
The presented configuration of the sensor node focus the continuous early detection
of hazardous gas situations by two integrated MOX gas sensors, which allow a TVOC-
monitoring. To improve the fast detection of critical situations in machine-dominated areas
the integration of such sensor nodes into the mechanic actors of automated environments
were investigated.
In the executed investigations the suitability of the sensor node for detecting criti-
cal gas emissions in automated laboratory environments was examined. First of all the
general characteristic of the selected gas sensors (SGP30 and BME688) was ascertained
in laboratory investigations. This comprises the preferred sensors’ orientation if the ap-
proximate direction of the emission is known (here primarily below), the reactivity of the
sensors and their behavior towards different frequently used VOCs in celisca (ethanol,
formic acid, acetonitrile, dichloromethane and hexane). Regarding the orientation both
sensors clearly show a stronger response if they are facing downward, into the direction of
emission. In combination with a robot this orientation may possibly hold some dangers
for the sensors if, for example, very high concentrations affect the sensors or if accidents
induced by the robots pollute the sensors with any substances. In all cases a suitable casing
is recommended.
Concerning the reactivity of the sensors the BME688 shows, compared to the SGP30,
for high concentration differences a significantly higher delay, which also can be observed
in the decay range. The reason for the delay is the interposed library and the therein
included filtering. If the IAQ as a result is sufficient and stable ambient conditions are
given, the Bosch library is quite possibly not mandatorily necessary and a faster response
could be observed.
The investigations regarding the different VOCs have shown that the sensors react
remarkably parallel, neglecting the delay of the BME688. One exception is acetonitrile
where here the results obviously differ. The BME688 responds with a negative amplitude
before it turns to slight positive amplitudes with a significant delay. As expected, between

Sensors 2021,21, 7347 20 of 22
the VOCs very different responses could be noticed, whereby in the presented investigation
ethanol and formic acid show significantly higher amplitudes than the other compounds.
In the practice-related investigation the sensor-nodes integration into the laboratory
equipment was evaluated. Therefore, two kinds of robots were used, the SCARA TS60
(Stäubli) and the mobile robot H20 (Dr Robot). Both robots moved the sensor node and in
the case of the TS60 partially very high speeds are reached. These high speeds show a clear
influence on the gas detection, especially for compounds with rather low response to the
sensors. A reason for this behavior is the considerable shorter time near the VOC emission
source and the comparably slow sampling rate of the sensors (1 s). In combination with the
delay of the BME688 in the fastest trials, hexane could not be detected. The results of the
SGP30 certainly show corresponding results, but the amplitude only minimally exceeds
the thresholds of the baseline. For 1 mL hexane a clear detection is only possible using
lower movement speeds of the TS60. In case of 100
µ
L ethanol the detection can be clearly
observed whereby the measured concentration strongly decreases with the increasing
speed level. For all trials the fast movements of the TS60 gripper inducts an air draft
and turbulences, which partially can be seen in the data by delayed or short, stronger
sensor responses. In the case of fast moving robots it can be necessary to consider specific
short-stop positions in the robots work flow, especially in critical areas, where the robot is
passing very fast without waiting phases. Furthermore, regular robot waiting phases can
be used to let the robot patrol in the safe area for unexpected emissions.
By using mobile robots the higher speed levels play a subordinated role since these
robot types usually work cooperating in the same area as human operators. In contrast to
stationary robots, which are fixed on tables and are partially encapsulated by a housing,
the mobile type passes many different areas, which have no constant baseline and which
are stronger influenced by environmental effects, such as humans or arising air drafts.
In this investigation the robots passing an emission source with different speeds and the
influence of two mounting positions was tested. It could be observed that especially for
the side-mounting, a clearer detection was validated by passing the emission source. In
practice this means that one sensor node for the right and one for the left side could be
more efficient to cover the robot’s surrounding area. By using the front-mounting only
one sensor node is required, with the restriction that the detection range is significantly
smaller. Thus, it has to be expected that if the robot drops the compounds itself, detection
is improbable. An appropriate alternative approach is to mount the sensor node to the back
side of the robot. This is, in case of the H20 robot, not feasible since the charging port is at
the back side and allows no installation. Depending on the requirements both mounting
concepts can be used.
Summarizing, both sensors have shown that they are generally suitable for the detec-
tion of VOC leakages in laboratories. While the BME688 reacts comparably slow and shows
varying behavior here, for example with acetonitrile the SGP30 sometimes responded
comparably low. In combination the sensors gave stable feedback about the gas concen-
tration of the near environment also if the sensors were moved with adequate speeds.
Consequently, for optimizing the interpretation of the gas sensor results, a cooperative
sensor fusion or artificial-intelligence solutions [
35
] can be helpful to avoid false negative
and false positive errors. Especially, machine learning can help to distinguish different
VOCs to precisely identify the real hazard for human operators. The IoT structure supports
this approach and enables a central combination of data from the distributed sensor-node
network. Further increasing the sampling rate of the sensors can also help to detect VOCs
during faster movements of the robot. For both sensors the sampling time of one-second
was recommended by the manufacturer and is supported by the sensors pre-calibration as
well as the BME library. Using more than two different gas sensors can be helpful, if they
are alternatingly triggered. Moreover other sensors, for example, the SGP40 are currently
evaluated as candidates for alternative sensor solutions in the presented application.
The application shown is only one example for using the presented sensor node.
Further applications for an individual environment-parameter monitoring of transferred

Sensors 2021,21, 7347 21 of 22
samples or human operators are possible. In future works the focus for refining the sensor
node is the implementation of the indoor localization to record the position of detection.
This enables the addition of the sensor node to all movable elements in the laboratory, for
example, roller carriages with laboratory equipment on it, which needs to be monitored
and located.
Author Contributions:
Idea and general concept, K.T., T.R., S.J. and S.N.; design and implementation
of sensor node, T.R. and S.N.; practical investigations and measurements, M.F.R.A.-O. and S.N.;
writing—review and editing, S.N. and K.T.; funding acquisition, K.T. All authors have read and
agreed to the published version of the manuscript.
Funding:
This research was performed within the Synergy Project ADAM (Autonomous Discovery
of Advanced Materials) funded by the European Research Council (grant number 856405).
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement:
The data presented in this study are available on request from the
corresponding author.
Acknowledgments:
The authors thank Heidi Fleischer, Anne Reichelt and Sybille Horn for their
support in executing the investigations in the laboratory and Heiko Engelhardt for his support
regarding the circuit board assembly. Further the authors would like to thank the Ministry of Higher
Education and Scientific Research/Iraq, Al-Furat Al-Awsat Technical University (ATU, Iraq), for the
second author’s partial scholar funding.
Conflicts of Interest: The authors declare no conflict of interest.
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