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Citation: Al-Okby, M.F.R.; Neubert,
S.; Roddelkopf, T.; Fleischer, H.;
Thurow, K. Evaluating of IAQ-Index
and TVOC Parameter-Based Sensors
for Hazardous Gases Detection and
Alarming Systems. Sensors 2022,22,
1473. https://doi.org/10.3390/
s22041473
Academic Editor: Jesus Lozano
Received: 13 January 2022
Accepted: 11 February 2022
Published: 14 February 2022
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4.0/).
sensors
Article
Evaluating of IAQ-Index and TVOC Parameter-Based Sensors
for Hazardous Gases Detection and Alarming Systems
Mohammed Faeik Ruzaij Al-Okby 1, 2, * , Sebastian Neubert 3, Thomas Roddelkopf 4, Heidi Fleischer 4
and Kerstin Thurow 2
1Technical Institute of Babylon, Al-Furat Al-Awsat Technical University (ATU), Kufa 54003, Iraq
2Center for Life Science Automation (Celisca), University of Rostock, 18119 Rostock, Germany;
kerstin.thurow@celisca.de
3Leibniz Institute for Baltic Sea Research Warnemünde, 18119 Rostock, Germany;
sebastian.neubert@io-warnemuende.de
4
Institute of Automation, University of Rostock, 18119 Rostock, Germany; thomas.roddelkopf@celisca.de (T.R.);
heidi.fleischer@uni-rostock.de (H.F.)
*
Correspondence: mohammed.al_okby@atu.edu.iq or mohammed.al-okby@celisca.de; Tel.: +49-381-498-7806
Abstract:
The measurement of air quality parameters for indoor environments is of increasing impor-
tance to provide sufficient safety conditions for workers, especially in places including dangerous
chemicals and materials such as laboratories, factories, and industrial locations. Indoor air quality
index (IAQ-index) and total volatile organic Compounds (TVOC) are two important parameters to
measure air impurities or air pollution. Both parameters are widely used in gases sensing applica-
tions. In this paper, the IAQ-index and TVOCs have been investigated to identify the best and most
flexible solution for air quality threshold selection of hazardous/toxic gases detection and alarming
systems. The TVOCs from the SGP30 gas sensor and the IAQ-index from the SGP40 gas sensor
were tested with 12 different organic solvents. The two gas sensors are combined with an IoT-based
microcontroller for data acquisition and data transfer to an IoT-cloud for further processing, storing,
and monitoring purposes. Extensive tests of both sensors were carried out to determine the minimum
detectable volume depending on the distance between the sensor node and the leakage source. The
test scenarios included static tests in a classical chemical hood, as well as tests with a mobile robot in
an automated sample preparation laboratory with different positions.
Keywords:
gas sensors; hazardous gases; alarming system; indoor air quality index (IAQ-index);
internet of things (IoT); total volatile organic materials (TVOCs)
1. Introduction
The measurement of the indoor air quality parameters is one of the important factors
for improvements of health condition and life quality in our world. Many commercial
electronic products include sensors for estimating values for the indoor air quality such as
IAQ-index/VOC index, TVOCs, equivalent CO
2
, equivalent Ethanol, equivalent Hydrogen,
and others. In addition to the monitoring of air contamination in living environments,
the measurements of the indoor air quality can be used effectively in occupational safety
applications, especially in chemical laboratories, factories, and any locations that may use
or store dangerous chemicals that can produce toxic/hazardous gases, and chemical vapors.
The IAQ-index can be used as a reference or a threshold for triggering an alarm in case
of any abnormal levels of air pollution. The early detection and alarming of toxic and
hazardous gases can avoid dangerous situations with negative impact on workers and the
environment.
Several methods, communication modules, sensor types, and hosted environments
and processors have been used to build hazardous gases detection and alarming systems.
Neubert et al. [
1
], proposed a flexible IoT-based sensor node for hazardous gas detection
Sensors 2022,22, 1473. https://doi.org/10.3390/s22041473 https://www.mdpi.com/journal/sensors
Sensors 2022,22, 1473 2 of 26
and monitoring. The system consists of two MOX (Metal Oxide Semiconductor) gas sensors
which are BME688 (Bosch Sensortec, Reutlingen, Germany) and SGP30 (Sensirion AG, Stafa,
Switzerland) combined with NXP ARM microcontroller. The system was tested with several
VOCs as a standalone unit as well as hosted by a stationary and mobile robot. The acquired
data were sent to an IoT-cloud for data monitoring and storing using a WROOM (Espressif
Systems, Shanghai, China) Wi-Fi module.
Palacín et al. [
2
], proposed an application of MOX gas sensor array hosted by a robotic
system for early gas leakage detection. In this case, 16 gas sensors from four types TGS
2600, 2602, 2611, 2620 (Figaro Engineering Inc., Osaka, Japan) have been assembled at
one portable unit. The system has been tested with ethanol, and acetone. The system
has been controlled using the ARM-based STM32F407VGT6 microcontroller. APR-02
personal robot was used for gas detection unit hosting and providing the power supply
and communication with the control station.
Burgués et al. [
3
], presented a quadcopter-based hazardous gas localization and
mapping system. The TGS 8100 (Figaro Engineering Inc., Osaka, Japan) MOX gas sensor
was used and tested with ethanol. The system is controlled by a 32-bit STM32F405 ARM
microcontroller (STMicroelectronics, Geneva, Switzerland). The acquired data can be
transferred to the ground control station using a 2.4 GHz RF communication module.
Optical sensors technologies were used by several researchers for hazardous gases
detection. This type of gas sensor is less influenced by temperature and humidity changes in
the tested environments, and safer in case of flammable gases detection. Esfahani et al. [
4
],
developed a tunable optical electronic nose for hazardous gases and vapors detection. The
sensing element consists of four tunable non-dispersive infrared (NDIR) optical sensors
with 3.1 to 10.5
µ
m effective wavelengths. Each optical sensor has an emitter-detector pair,
which is encapsulated with the others’ sensors pair in one heated chamber. The system was
tested with several gases and VOCs such as carbon dioxide, methane, ethanol, isopropanol,
acetone, but also cola, orange juice, and coffee. The test results revealed that the system
was able to detect and distinguish between the odors of the tested chemicals.
Shi et al. [
5
], proposed a low-cost, and simple design fiber-optic gas sensor for the
detection of volatile organic compounds. The sensor is based on a polydimethylsiloxane
(PDMS) self-assembled Fabry-Perot interferometer. The tested VOCs were injected into
the testing gas chambers and a sensor probe was used to take the gas sample and send it
to a spectrometer with a 0.02 nm wavelength. The sensor was tested with several VOCs
such as ethanol, isopropanol, toluene, and ethyl acetate. The tests results show good sensor
performance, high sensitivity, good stability, and rapid response.
Arroyo et al. [
6
], presented an air quality measurement system based on electrochemi-
cal and optical particle matter (PM) gas sensors. The system is a portable device that can
provide various values for gas and environmental measurements such as the concentration
of different gases/chemicals vapors, PM
2.5
, PM
10
, temperature, humidity, and location data.
A 32-bit STM32L476 ultra-low-power microcontroller (STMicroelectronics, Plan-les-Ouates,
Switzerland) was used for data processing and system management. The system was
connected to an IoT cloud platform using GSM and Ethernet modules, and all acquired
real-time data can be transferred directly to the IoT cloud via the internet. Two system
prototypes were tested with several chemicals such as NO
2
, NO, CO, O
3
, PM
2.5
, and PM
10
,
and the results show good performance.
In this paper, we investigate two MOX gas sensors SPG30, and SPG40 (Sensirion AG,
Stäfa, Switzerland) for measuring the indoor air quality parameters, which are the IAQ-
index and the total volatile organic compound TVOC. The sensors were tested with different
VOCs materials in two different test environments. Various sensor positions on a fixed
stand and mobile robot were used to compare the performance of the two sensors based on
the measured parameters (IAQ-index and TVOC). The main goal of the proposed work is
to evaluate, which parameter should be used in future design for hazardous gases detection
and alarming systems. A combination of two different gas measurement parameters such
Sensors 2022,22, 1473 3 of 26
as the IAQ-index and TVOC will allow the system to minimize false-positive as well as
false-negative gas detection errors [7–13].
2. Materials and Methods
The presented sensor module consists of three main layers. The sensing layer is re-
sponsible for sensing different parameters. This layer includes three sensors SGP30, SGP40,
and SHTC3 (Sensirion AG, Stäfa, Switzerland) to measure IAQ-index, TVOC, and ambient
environmental parameters (temperature, humidity), respectively. The second layer is the
processing layer, which is responsible for receiving and processing the measured data from
the sensing layer. The processing layer prepares the required gas and environmental mea-
surements and send via a Wi-Fi module to the IoT cloud (third layer). It is a server system
responsible for transfer, processing, storing, and provision of the data. A communication
server on the third layer handles the communication to the second layer using UDP and
HTTP POST protocol. The IoT-cloud stores the received and processed data in a database
using an MS-SQL database server. The data can be accessed by the users from any location
using a web server. Figure 1explains the system structures. A detailed description of the
systems modules is given in the following subsections.
Figure 1. System structure.
2.1. SGP30 Gas Sensor
The SGP30 is a metal oxide semiconductor (MOX) gas sensor that is used for the
measurement of air quality TVOC and equivalent CO
2
(eCO
2
) parameters. It is a complete
system on chip (SoC) that can easily be adapted with hosted devices using I
2
C commu-
nication protocol with I
2
C address (0x58). The sensor has a compact size design with
2.45 ×2.45 ×0.9 mm3
, and has a low-power consumption (48 mA at 1.8 V) which makes
it suitable for wearable and battery-powered based application and systems. The sensor
can operate with a 1 Hz sample rate for both TVOC and eCO
2
with an output range of
0–60 ppm for TVOC and 400–1000 for eCO
2
. In the proposed system, the Adafruit SGP30
module has been used (see Figure 2) [14].
Sensors 2022,22, 1473 4 of 26
Figure 2. The used Adafruit SGP30 module board.
2.2. SGP40 Gas Sensor
The SGP40 is a metal oxide semiconductor (MOX) gas sensor used for indoor air
quality index IAQ-index (also called VOC index) measurements. It is a small (2.44
×
2.44
×
0.85 mm
3
) system on chip sensor, including a 6-pin DFN package. The sensor has a
low-power consumption (2.6 mA at 3.3 V) and can be easily adapted with any hosting
system using an I
2
C bus with (0x59) I2C address. The sensor sample rate for IAQ-Index
is 1 Hz and the IAQ-Index ranges from 0–500. The compact size, lightweight, and low-
power consumption make the sensor very suitable for battery power-based and wearable
applications. The Adafruit SGP40 module board is used in the proposed system (see
Figure 3) [15].
Figure 3. The used Adafruit SGP40 module board.
2.3. SHTC3 Environmental Sensor
The SHTC3 is a fully calibrated digital environmental sensor used for temperature
(T) and relative humidity (RH) measurement. It is used for SGP30 and SGP40 calibration
algorithms to reduce the measurement errors that can result from changes in the ambient
humidity and temperature. It consists of a band gap temperature sensor, a capacitive
humidity sensor, an A/D converter, and memory for calibration data. The sensor com-
municates with the hosted system using an I
2
C communication bus. The sensors are
manufactured in a small DFN package of dimensions 2
×
2
×
0.75 mm
3
, and it is classified
as ultra-low-power consumption sensor. The sensor can be powered by any source with
a voltage range between 1.62 and 3.6 V). The operation measurement ranges are (1–100%
RH) for relative humidity and (
−
40 to 125
◦
C). The sensor response time is 1 ms with an
accuracy range of
±
2% RH and
±
0.2
◦
C for humidity and temperature, respectively. In the
proposed system, the Adafruit SHTC3 module board has been used (see Figure 4) [16].
Sensors 2022,22, 1473 5 of 26
Figure 4. The used Adafruit SHTC3 module board.
2.4. WeMos D1 Mini Development Board
The WeMos D1 Mini is a Wi-Fi-based microcontroller for IoT applications, including
the chip ESP8266 (Espressif Systems, Shanghai, China). The ESP8266 is driven by the
32-bit Tensilica L106 processor (Tensilica Inc., San Jose, CA, USA) with a built-in TCP/IP
networking software for a simple Wi-Fi connection. It has a 4 MB memory and can run with
up to 160 MHz max speed. The WeMos D1 Mini provides all the required communication
protocols, which are mainly the I
2
C bus to communicate with the sensing layer, the Wi-
Fi protocol to communicate with the IoT-cloud, and the CH340 driver integrated circuit
for establishing a simple communication between the WeMos board and computers. In
addition to all the previous features, the WeMos D1 Mini provides a low cost (
≈€
4), small
size (2.5
×
3 cm), and low-power consumption that make it a good choice for mobile
devices, wearable electronics, and IoT applications. Figure 5shows the used WeMos D1
Mini Development board [17].
Figure 5. The used WeMos D1 Mini Development board.
3. Work Description
This work aims to evaluate two parameters used widely to measure air pollution/air
impurity: indoor air quality index (IAQ-Index) and the total volatile organic compounds
concentration (TVOC). The result of this study will help to choose the best parameter that
can be used in hazardous gases detection and alarming system. The selected parameter
will be used as thresholds for triggering alarms to alert people from hazardous and toxic
gas leakages in a work environment. The IAQ-Index refers to the air quality/air pollution
within and around the work areas. The IAQ-index does not have a measurement unit. The
IAQ-Index range is bounded between 0–500 steps from the best to the worst air quality
level (see Table 1for IAQ-Index values, and it is categories) [18].
The second selected parameter is the TVOC. The TVOC refers to the concentration of
volatile organic compounds, which are a group of organic chemicals that vaporize easily
at room temperature. The abbreviation VOC is used for a large group of chemicals such
as ethanol, acetone, hexane, benzene, etc. The abbreviation TVOC refers to the presence
of several VOCs in the air sample. TVOC can be measured in milligrams per cubic meter
(mg/m
3
) or in parts per million (ppm) which we used in the presented work. Table 1
explains an approximation of TVOC levels compared to the IAQ-Index levels [19–23].
Sensors 2022,22, 1473 6 of 26
Table 1. IAQ-index and TVOC categories concerning the IAQ levels
IAQ Levels Category IAQ-Index Values TVOC [ppm]
1 Good 0–50 0–0.065
2 Moderate 51–100 0.066–0.22
3 Unhealthy for sensitive peoples 101–150 0.23–0.66
4 Unhealthy 151–200 0.67–2.2
5 Very unhealthy 201–300 2.3–5.5
6 Hazardous 301–500 >5.5
The SGP30 and SGP40 gas sensors measure the TVOC and IAQ-index, respectively.
The vendors of the gas sensors recommend using an environmental sensor for measuring
the temperature (T) and relative humidity (RH) of the environment. Thus, the SHCT3
environmental sensor (Sensirion AG, Stäfa, Switzerland) has been used for measuring the
T and, RH and feed them to the SGP30, and SGP40 algorithm for calibrating the calculation
of the IAQ-index and TVOC values. The calibration process required approximately 5 min
before each time the sensor was turned on. All used sensors communicate with the WeMos
D1 Mini microcontroller (MCU) using the I2C bus, which allows the MCU to communicate
with all sensors at the same time using a unique I2C address for each sensor. After the
calibration process is completed, the system starts to measure the TVOC and IAQ-index
baseline at the system location. The system will measure both IAQ-index, and TVOC in
parallel and in a normal clean environment. The system measurements should be kept
in levels 1 and 2 for both gas sensors as one of the test conditions. Figure 6explains the
flowchart of the measurements process.
Figure 6. Flowchart of measurements process.
Sensors 2022,22, 1473 7 of 26
The system is designed to contentiously operate in the target environment. The
measured parameters are sent to the IoT-cloud for monitoring and data storing. In the
presented work, two test scenarios were used. The first test scenario was set up inside
a typical hood (Waldner Holding GmbH and Co. KG, Wangen im Allgäu, Germany)
designed for chemical and analytical laboratories. The sensor node was fixed on a movable
stand with manual adjustable height. The testing VOCs were injected inside a petri dish
using pipettes from Eppendorf (Eppendorf AG, Hamburg, Germany). The second test
environment was an automated chemical laboratory (Center for Life Science Automation,
University of Rostock, Germany) using a humanoid robot as a host for the sensor node.
4. Results
The aim of the tests in all scenarios and positions is to determine the minimum VOC
amount that can be detected by the used sensors from the testing distance (distance between
the VOC leakage source and the sensors). The system has been tested with 12 different
VOC solvents, according to Table 2.
Table 2. The Tested VOC solvents.
Name Molecular Formula Boiling Point in ◦C
Acetone C3H6O 56
Acetonitrile C2H3N 82
Benzene C6H680.1
Dichloromethane CH2Cl239.6
Diethyl ether C4H10O 34.6
Ethanol C2H6O 78.37
Formic acid CH2O2100.8
Heptane C7H16 98.42
Hexane C6H14 69
Isopropanol C3H8O 82.5
Methanol CH3OH 64.7
Toluene C7H8110.6
The tests were carried out at two chemical laboratories with different tests procedures.
The first laboratory was a classical laboratory for manual sample preparation, whereas
the second laboratory was used for automated sample preparation with stationary and
humanoid robots. Both laboratories have an air conditioning system which stabilizes the
laboratory temperature at approximately 22
±
0.5
◦
C. Furthermore, both sensors have
been provided by the temperature and humidity live measurements using the SHCT3
environmental sensor (included in the sensor node) to overcome any drift on the gas
measurements due to unstable temperature and relative humidity. The following section
describes the tests procedure and tests results in each test environment.
4.1. System Testing in Chemicals Preparation Hood
These testes were carried out in a hood in a classical manual chemical laboratory.
The hood has a special ventilation system for suction of chemical vapors during sample
preparations. The ventilation system was turned off during the system testing to prevent
any interference due to air ventilation. Two positions for the VOC source were used, one
directly 1 m below the sensor. For the second test position, the sensor node was shifted one
meter horizontally from the first position (see Figure 7).
Sensors 2022,22, 1473 8 of 26
Figure 7.
Testing positions for VOC source; (
a
)-position 1 m below the sensor, (
b
)-position 1 m away
from the sensor.
4.2. H20 Tests
In the second testing scenario, the sensor node was mounted on an H20 humanoid
robot (Dr. Robot Inc., Markham, ON, Canada). The H20 robot provides the required power
supply voltage for operating the sensor node and for storing the acquired test results. The
sensor node was fixed on the shoulder level of the robot body to allow the detection of any
leakages of hazardous gases during sample preparation and transportation in automated
chemical laboratories. In this scenario, the robot starts from a specific charging station
and moves for one minute to reach the sample preparation table. The VOC material was
injected inside the petri dish for the test. The robot was programmed to stop for 60 s directly
at the leakage source (petri dish) which was located at 22 cm directly under the sensor
node. After completion of the measurement time, the robot returns to the charging station.
The total time from the starting until returning to the end point is approximately 3.5 min.
Figure 8shows the sensor node hosted by the H20 robot and the sensor node location
regarding gas leakage source.
The main goal of the tests is the determination of the minimum volume of leakages
(VOCs) that can be detected by the sensor node. Thus, the tests have been carried out with
different volumes. Depending on the sensor’s response, the volume was increased. The
used volumes were 2
µ
L, 5
µ
L, 10
µ
L, 50
µ
L, and 100
µ
L. Once the minimum detectable
volume for a selected position and distance was determined, the test volume was not
increased any further in order to avoid overloading the sensor. Figures 9–20 below show
the test results of the selected VOCs inside the hood for two locations of the petri dish and
for sensor node with H20 robot. Each figure has 6 charts (a–f) presenting (a) the SGP30 tests
results from 1 m directly under the leakage source, (b) the SGP40 tests results from 1 m
directly under the leakage source, (c) SGP30 tests results after shifting the leakage source
1 m to the side of the first position, (d) the SGP40 tests results after shifted the leakage
source 1 m to the side of the first position, (e) the H20 robot-SGP30 tests results from 22 cm
from the VOC leakage source, and (f) the H20 robot-SGP40 tests results from 22 cm from
the VOC leakage source.
Sensors 2022,22, 1473 9 of 26
Figure 8.
The sensor node hosted by the H20 robot. (
a
,
b
) front and side views of the used H20
robot with the sensor node. (
c
) The robot position for the measurements process. (
d
) The sensors are
positioned directly above the VOCs leakage source for the testing process.
Sensors 2022,22, 1473 10 of 26
Figure 9.
TVOC for SGP30, and IAQ-index for SGP40 tests results for acetone. (
a
) the SGP30 tests
results from 1 m directly under the leakage source, (
b
) the SGP40 tests results from 1 m directly under
the leakage source, (
c
) SGP30 tests results after shifting the leakage source 1 m to the side of the
first position, (
d
) the SGP40 tests results after shifted the leakage source 1 m to the side of the first
position, (
e
) the H20 robot-SGP30 tests results from 22 cm from the VOC leakage source, and (
f
) the
H20 robot-SGP40 tests results from 22 cm from the VOC leakage source.
Sensors 2022,22, 1473 11 of 26
Figure 10.
TVOC for SGP30, and IAQ-index for SGP40 tests results for acetonitrile. (
a
) the SGP30
tests results from 1 m directly under the leakage source, (
b
) the SGP40 tests results from 1 m directly
under the leakage source, (
c
) SGP30 tests results after shifting the leakage source 1 m to the side of
the first position, (
d
) the SGP40 tests results after shifted the leakage source 1 m to the side of the first
position, (
e
) the H20 robot-SGP30 tests results from 22 cm from the VOC leakage source, and (
f
) the
H20 robot-SGP40 tests results from 22 cm from the VOC leakage source.
Sensors 2022,22, 1473 12 of 26
Figure 11.
TVOC for SGP30, and IAQ-index for SGP40 tests results for benzene. (
a
) the SGP30 tests
results from 1 m directly under the leakage source, (
b
) the SGP40 tests results from 1 m directly under
the leakage source, (
c
) SGP30 tests results after shifting the leakage source 1 m to the side of the
first position, (
d
) the SGP40 tests results after shifted the leakage source 1 m to the side of the first
position, (
e
) the H20 robot-SGP30 tests results from 22 cm from the VOC leakage source, and (
f
) the
H20 robot-SGP40 tests results from 22 cm from the VOC leakage source.
Sensors 2022,22, 1473 13 of 26
Figure 12.
TVOC for SGP30, and IAQ-index for SGP40 tests results for Dichloromethane. (
a
) the
SGP30 tests results from 1 m directly under the leakage source, (
b
) the SGP40 tests results from 1 m
directly under the leakage source, (
c
) SGP30 tests results after shifting the leakage source 1 m to the
side of the first position, (
d
) the SGP40 tests results after shifted the leakage source 1 m to the side of
the first position, (
e
) the H20 robot-SGP30 tests results from 22 cm from the VOC leakage source, and
(f) the H20 robot-SGP40 tests results from 22 cm from the VOC leakage source.
Sensors 2022,22, 1473 14 of 26
Figure 13.
TVOC for SGP30, and IAQ-index for SGP40 tests results for Diethyl ether. (
a
) the SGP30
tests results from 1 m directly under the leakage source, (
b
) the SGP40 tests results from 1 m directly
under the leakage source, (
c
) SGP30 tests results after shifting the leakage source 1 m to the side of
the first position, (
d
) the SGP40 tests results after shifted the leakage source 1 m to the side of the first
position, (
e
) the H20 robot-SGP30 tests results from 22 cm from the VOC leakage source, and (
f
) the
H20 robot-SGP40 tests results from 22 cm from the VOC leakage source.
Sensors 2022,22, 1473 15 of 26
Figure 14.
TVOC for SGP30, and IAQ-index for SGP40 tests results for ethanol. (
a
) the SGP30 tests
results from 1 m directly under the leakage source, (
b
) the SGP40 tests results from 1 m directly under
the leakage source, (
c
) SGP30 tests results after shifting the leakage source 1 m to the side of the
first position, (
d
) the SGP40 tests results after shifted the leakage source 1 m to the side of the first
position, (
e
) the H20 robot-SGP30 tests results from 22 cm from the VOC leakage source, and (
f
) the
H20 robot-SGP40 tests results from 22 cm from the VOC leakage source.
Sensors 2022,22, 1473 16 of 26
Figure 15.
TVOC for SGP30, and IAQ-index for SGP40 tests results for formic acid. (
a
) the SGP30
tests results from 1 m directly under the leakage source, (
b
) the SGP40 tests results from 1 m directly
under the leakage source, (
c
) SGP30 tests results after shifting the leakage source 1 m to the side of
the first position, (
d
) the SGP40 tests results after shifted the leakage source 1 m to the side of the first
position, (
e
) the H20 robot-SGP30 tests results from 22 cm from the VOC leakage source, and (
f
) the
H20 robot-SGP40 tests results from 22 cm from the VOC leakage source.
Sensors 2022,22, 1473 17 of 26
Figure 16.
TVOC for SGP30, and IAQ-index for SGP40 tests results for heptane. (
a
) the SGP30 tests
results from 1 m directly under the leakage source, (
b
) the SGP40 tests results from 1 m directly under
the leakage source, (
c
) SGP30 tests results after shifting the leakage source 1 m to the side of the
first position, (
d
) the SGP40 tests results after shifted the leakage source 1 m to the side of the first
position, (
e
) the H20 robot-SGP30 tests results from 22 cm from the VOC leakage source, and (
f
) the
H20 robot-SGP40 tests results from 22 cm from the VOC leakage source.
Sensors 2022,22, 1473 18 of 26
Figure 17.
TVOC for SGP30, and IAQ-index for SGP40 tests results for hexane. (
a
) the SGP30 tests
results from 1 m directly under the leakage source, (
b
) the SGP40 tests results from 1 m directly under
the leakage source, (
c
) SGP30 tests results after shifting the leakage source 1 m to the side of the
first position, (
d
) the SGP40 tests results after shifted the leakage source 1 m to the side of the first
position, (
e
) the H20 robot-SGP30 tests results from 22 cm from the VOC leakage source, and (
f
) the
H20 robot-SGP40 tests results from 22 cm from the VOC leakage source.
Sensors 2022,22, 1473 19 of 26
Figure 18.
TVOC for SGP30, and IAQ-index for SGP40 tests results for isopropanol. (
a
) the SGP30
tests results from 1 m directly under the leakage source, (
b
) the SGP40 tests results from 1 m directly
under the leakage source, (
c
) SGP30 tests results after shifting the leakage source 1 m to the side of
the first position, (
d
) the SGP40 tests results after shifted the leakage source 1 m to the side of the first
position, (
e
) the H20 robot-SGP30 tests results from 22 cm from the VOC leakage source, and (
f
) the
H20 robot-SGP40 tests results from 22 cm from the VOC leakage source.
Sensors 2022,22, 1473 20 of 26
Figure 19.
TVOC for SGP30, and IAQ-index for SGP40 tests results for methanol. (
a
) the SGP30 tests
results from 1 m directly under the leakage source, (
b
) the SGP40 tests results from 1 m directly under
the leakage source, (
c
) SGP30 tests results after shifting the leakage source 1 m to the side of the
first position, (
d
) the SGP40 tests results after shifted the leakage source 1 m to the side of the first
position, (
e
) the H20 robot-SGP30 tests results from 22 cm from the VOC leakage source, and (
f
) the
H20 robot-SGP40 tests results from 22 cm from the VOC leakage source.
Sensors 2022,22, 1473 21 of 26
Figure 20.
TVOC for SGP30, and IAQ-index for SGP40 tests results for toluene. (
a
) the SGP30 tests
results from 1 m directly under the leakage source, (
b
) the SGP40 tests results from 1 m directly under
the leakage source, (
c
) SGP30 tests results after shifting the leakage source 1 m to the side of the
first position, (
d
) the SGP40 tests results after shifted the leakage source 1 m to the side of the first
position, (
e
) the H20 robot-SGP30 tests results from 22 cm from the VOC leakage source, and (
f
) the
H20 robot-SGP40 tests results from 22 cm from the VOC leakage source.
Sensors 2022,22, 1473 22 of 26
In the experimental testing, each gas sensor was tested 36 times with all the 12 tested
VOCs. The SGP40 successfully detects the tested VOCs 33/36 while the SGP30 sensor
success in only 21/36 attempts. Figure 21 summarized the percentage of successful gas
detection attempts for both sensors.
Figure 21. Percentage of successful gas detection attempts for both sensors.
5. Discussion
The SGP30 and SGP40 tests result give us a clear view of individual sensor performance
and the sensor detection range for each solvent for two positions inside the hood, as well as
attached with the humanoid H20 robot. The two sensors reacted differently for each tested
VOC material, as shown in Figures 9–20. We can summarize the acquired result for each
tested material as follows:
•
Acetone: the tests show that the TVOC SGP30 sensor can detect volumes
≥
10
µ
L
inside the hood for both positions (directly under the sensor and shifted one meter),
while the IAQ-index of SGP40 can detect volumes
≥
5
µ
L directly under the sensor
and
≥
10
µ
L for shifted 1 m horizontally. The H20 robot tests show a weak sensor
signal of the SGP30 sensor for 2
µ
L and 10
µ
L. A sufficient signal that can be used as a
threshold for the alarm systems can only be achieved for volumes
≥
100
µ
L. The H20
robot tests for SGP40 show better performance and the sensor can clearly react and
detect volumes ≥10 µL.
•
Acetonitrile: the tests show that the TVOC of the SGP30 sensor can detect
volumes ≥10 µL
inside the hood for the first position, and failed to detect the
2
µ
L, 5
µ
L, 10
µ
L for the second position. The IAQ-index of SGP40 can detect volumes
≥
5
µ
L directly under the sensor and failed to detect the 2
µ
L, 5
µ
L, 10
µ
L for the
second position. The H20 robot tests show that the SGP30 can only detect the volume
≥
100
µ
L. The H20 robot tests for SGP40 show better performance and the sensor can
clearly react and detect volumes ≥10 µL.
•
Benzene: the tests show that the TVOC of the SGP30 sensor can detect volumes
≥
100
µ
L inside the hood for the first position but failed for the detection of volumes up
to 100
µ
L for the second position. The IAQ-index of SGP40 can detect volumes
≥
10
µ
L
directly under the sensor, as well as volumes
≥
100
µ
L for the second position. The
H20 robot tests show only a weak signal for the SGP30 and reacts weakly for a volume
of 100
µ
L. The H20 robot tests for the SGP40 show better performance; the sensor can
clearly react and detect volumes ≥10 µL.
Sensors 2022,22, 1473 23 of 26
•
Dichloromethane: the tests show that the TVOC of the SGP30 sensor can detect
volumes
≥
100
µ
L inside the hood for both positions. The IAQ-index of the SGP40
can detect the volumes
≥
50
µ
L directly under the sensor and volumes
≥
100
µ
L for
the second position. In the H20 robot tests, the SGP30 can detect volumes
≥
100
µ
L,
whereas no detection was possible with the SGP40.
•
Diethyl ether: the tests show a weak reaction of the TVOC of the SGP30 for all tested
volumes at both positions. The IAQ-index of the SGP40 enables the detection of
volumes
≥
50
µ
L for both positions. The H20 robot tests again show a weak reaction
of the SGP30 sensor for all tested volumes. The H20 robot tests for SGP40 show better
performance and enable detection of volumes ≥2µL.
•
Ethanol: inside the hood, the TVOC of the SGP30 sensor can detect volumes
≥
5
µ
L
inside the hood for the first position. A slightly weaker response can be found for the
second position. The IAQ-index of SGP40 can detect volumes
≥
2
µ
L for both positions.
The H20 robot tests show that the SGP30 can detect volumes
≥
100
µ
L, whereas the
SGP40 shows better performance and enables the detection of volumes ≥2µL.
•
Formic acid: the TVOC of the SGP30 sensor enables the detection of volumes
≥
2
µ
L
inside the hood for the first position, and
≥
5
µ
L for the second position, respectively.
The IAQ-index of SGP40 can detect volumes
≥
2
µ
L for both positions. The H20 robot
tests show that the SGP30 and SGP40 can detect volumes ≥100 µL.
•
Heptane: for all tested volumes, no sensor signals were detected for the TVOC of
the SGP30 in both positions. The IAQ-index of SGP40 can detect volumes
≥
5
µ
L for
both positions. The H20 robot tests again show that the SGP30 cannot detect volumes
below 100
µ
L. In contrast, the tests for the SGP40 resulted in minimum detectable
volumes ≥100 µL.
•
Hexane: the tests show that the TVOC of the SGP30 sensor cannot detect the tested
volumes for both positions. The IAQ-index of SGP40 can detect volumes
≥
5
µ
L for
the first position but failed for the second position. The H20 robot tests show that
the SGP30 can detect volumes
≥
100
µ
L, whereas the SGP40 enables the detection of
volumes ≥10 µL.
•
Isopropanol: the tests show a weak response that the TVOC of the SGP30 sensor
reacts weak for all the 2
µ
L, 10
µ
L, and 100
µ
L for both positions. The IAQ-index of
SGP40 can detect the volumes
≥
10
µ
L for both positions. The H20 robot tests show
that the SGP30 reacts weak for all tested volumes, whereas the SGP40 shows a better
performance and can clearly detect volumes ≥10 µL.
•
Methanol: the tests inside the hood show that the TVOC of the SGP30 sensor can detect
volumes
≥
100
µ
L for both positions. The IAQ-index of the SGP40 showed a good
signal for volumes
≥
2
µ
L for both positions. In the H20 robot tests,
volumes ≥100 µL
can be detected with the SGP 30. The SGP40 again shows better performance and
enables the detection of volumes ≥2µL.
•
Toluene: the tests inside the hood show that the TVOC of the SGP30 sensor can detect
volumes
≥
100
µ
L for the first position, whereas only a weak signal could be found
for all tested volumes for the second position. The IAQ-index of the SGP40 can detect
volumes
≥
2
µ
L directly under the sensor and volumes
≥
100
µ
L for the second
position. In the H20 robot tests, no signals were detected for all tested volumes for the
SGP30. The SGP40 enables the detection of volumes ≥10 µL.
Table 3explain the SGP30 and SGP40 gas sensors tests result for the minimum detected
useful volumes of the tested VOC solvents in all test environments and positions where P1
refer to the test inside the hood directly one meter under the sensor node, and P2 refer to
the same test inside the hood with shafting the leakage source one meter.
Sensors 2022,22, 1473 24 of 26
Table 3. System Tests Results inside the Hood and when Attached to the H20 Robot.
VOC SGP30-P1 SGP30-P2 SGP30-H20 SGP40-P1 SGP40-P2 SGP40-H20
Acetone ≥10 µL≥10 µL≥100 µL≥5µL≥10 µL≥10 µL
Acetonitrile ≥10 µL - ≥100 µL≥5µL - ≥10 µL
Benzene ≥100 µL - - ≥10 µL≥100 µL≥10 µL
Dichloromethane ≥100 µL≥100 µL≥100 µL≥50 µL≥100 µL -
Diethyl ether - - - ≥50 µL≥50 µL≥2µL
Ethanol ≥5µL≥10 µL≥100 µL≥2µL≥2µL≥2µL
Formic acid ≥2µL≥5µL≥100 µL≥2µL≥2µL≥100 µL
Heptane - - ≥100 µL≥5µL≥5µL≥100 µL
Hexane - - ≥100 µL≥5µL - ≥10 µL
Isopropanol - - - ≥10 µL≥10 µL≥10 µL
Methanol ≥100 µL≥100 µL≥100 µL≥2µL≥2µL≥2µL
Toluene ≥100 µL - - ≥2µL≥100 µL≥10 µL
The sensor’s response time (the time required for the sensor output signal to reach
90% of the maximum measured value from its previous settled state) for each tested VOC
material was calculated for position 1 (inside the hood directly 1 m under the tested
material). Figure 22 shows the sensor response time for both SGP30 and SGP40 gas sensors.
Figure 22. Response time for SGP30 and SGP40 gas sensors for several tested VOCs.
6. Conclusions
In this work, we investigate the performance of two novel gas sensors SPG30, and
SPG40 that use different parameters for detecting hazardous and toxic gases/chemical
vapors which are the TVOC, and IAQ-index. One of the main goals of this study is to
help us to select the best parameter between TVOC and IAQ-index for future design
and implementation of hazardous gases detection and alarming systems. The tests were
implemented in two procedures in different test environments the first inside a chemical
preparation hood and the second in an open automated laboratory by attaching the sensor
node to a Mobil robot. The tests result show that the IAQ-index of the SGP40 reacts
better compared to the TVOC of SGP30 for 11 of 12 tested VOCs materials (except the
Sensors 2022,22, 1473 25 of 26
dichloromethane). The SGP40 sensor showed better test results and successfully detects
the gas leakage in 91.6% of the test attempts while the SGP30 was only successful in 58.3%
of the test attempts. The sensor response time for all tested VOCs material was calculated
for both sensors, and the results show that the response time of the SGP40 sensor is longer
for all the tested VOCs. Based on these results, the TVOC based SGP30 gas sensor will be
replaced with IAQ-index based SGP40 or a newer version that uses the IAQ-index for air
quality and gas impurity levels detection for new sensor nodes.
Author Contributions:
Conceptualization, M.F.R.A.-O., H.F. and K.T.; methodology, M.F.R.A.-O. and
K.T.; formal analysis, M.F.R.A.-O.; writing—original draft preparation, M.F.R.A.-O.; writing—review
and editing, M.F.R.A.-O., S.N. and K.T.; visualization, M.F.R.A.-O.; supervision, K.T.; project adminis-
tration, T.R. and K.T. All authors have read and agreed to the published version of the manuscript.
Funding:
This research was carried out 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 work are available on request from the
corresponding author.
Acknowledgments:
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 1st author partial scholar
funding. Moreover, the authors would like to thank Steffen Junginger, Anna Bach, Anne Reichelt,
Sybille Horn, and Heiko Engelhardt for help and technical support.
Conflicts of Interest: The authors declare no conflict of interest.
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