Abstract:Determination of the levels of heavy
metal ions would support assessment
of sources and pathways of water pollution. However, traditional spatial
assessment by manual sampling and off-site detection in the laboratory
is expensive and time-consuming and requires trained personnel. Aiming
to fill the gap between on-site automatic approaches and laboratory
techniques, we developed an autonomous sensing boat for on-site heavy
metal detection using square-wave anodic stripping voltammetry. A
fluidic sensing system … Show more
“…The main evaluation of the analytical performance of SPEs under laboratory conditions was already performed by the system developers and is reported in Yang et al ( 2021 ). In the context of this work, only the lifetime of the SPE involved in the integrated system and in use during the measuring campaigns was observed and documented.…”
Section: Resultsmentioning
confidence: 99%
“…Consequently, we focused on Pb and Cu for the purpose of this study. Any further details concerning the set-up and the sensing performance of the HM microfluidic detection device can be found in the publication from Yang et al ( 2021 ). Fig.…”
Smart monitoring has been studied and developed in recent years to create faster, cheaper, and more user-friendly on-site methods. The present study describes an innovative technology for investigative monitoring of heavy metal pollution (Cu and Pb) in surface water. It is composed of an autonomous surface vehicle capable of semiautonomous driving and equipped with a microfluidic device for detection of heavy metals. Detection is based on the method of square wave anodic stripping voltammetry using carbon-based screen-printed electrodes (SPEs). The focus of this work was to validate the ability of the integrated system to perform on-site detection of heavy metal pollution plumes in river catchments. This scenario was simulated in laboratory experiments. The main performance characteristics of the system, which was evaluated based on ISO 15839 were measurement bias (Pb 75%, Cu 65%), reproducibility (in terms of relative standard deviation: Pb 11–18%, Cu 6–10%) and the limit of detection (4 µg/L for Pb and 7 µg/L for Cu). The lowest detectable change (LDC), which is an important performance characteristic for this application, was estimated to be 4–5 µg/L for Pb and 6–7 µg/L for Cu. The life span of an SPE averaged 39 measurements per day, which is considered sufficient for intended monitoring campaigns. This work demonstrated the suitability of the integrated system for on-site detection of Pb and Cu emissions from large and medium urban areas discharging into small water bodies.
“…The main evaluation of the analytical performance of SPEs under laboratory conditions was already performed by the system developers and is reported in Yang et al ( 2021 ). In the context of this work, only the lifetime of the SPE involved in the integrated system and in use during the measuring campaigns was observed and documented.…”
Section: Resultsmentioning
confidence: 99%
“…Consequently, we focused on Pb and Cu for the purpose of this study. Any further details concerning the set-up and the sensing performance of the HM microfluidic detection device can be found in the publication from Yang et al ( 2021 ). Fig.…”
Smart monitoring has been studied and developed in recent years to create faster, cheaper, and more user-friendly on-site methods. The present study describes an innovative technology for investigative monitoring of heavy metal pollution (Cu and Pb) in surface water. It is composed of an autonomous surface vehicle capable of semiautonomous driving and equipped with a microfluidic device for detection of heavy metals. Detection is based on the method of square wave anodic stripping voltammetry using carbon-based screen-printed electrodes (SPEs). The focus of this work was to validate the ability of the integrated system to perform on-site detection of heavy metal pollution plumes in river catchments. This scenario was simulated in laboratory experiments. The main performance characteristics of the system, which was evaluated based on ISO 15839 were measurement bias (Pb 75%, Cu 65%), reproducibility (in terms of relative standard deviation: Pb 11–18%, Cu 6–10%) and the limit of detection (4 µg/L for Pb and 7 µg/L for Cu). The lowest detectable change (LDC), which is an important performance characteristic for this application, was estimated to be 4–5 µg/L for Pb and 6–7 µg/L for Cu. The life span of an SPE averaged 39 measurements per day, which is considered sufficient for intended monitoring campaigns. This work demonstrated the suitability of the integrated system for on-site detection of Pb and Cu emissions from large and medium urban areas discharging into small water bodies.
“…SPCEs were fabricated the same as reported in our previous study. 27 For better quality control, all the SPCEs were cleaned in 0.05 M HCl and deionized water, and then pre-tested by SWASV. According to the obtained stripping curves (charge vs. potential), the SPCEs were selected only within the charge range of 2–4 mC.…”
Section: Methodsmentioning
confidence: 99%
“…an automatic in situ sensing boat navigating in large natural waters) that rely on their reusability more than precision could be compatible with the sensing system in this study. 27 Reproducibility is pivotal for mass production and thus, five GSH-SPCEs and five bare SPCEs were tested in the same mixed solutions of Cd 2+ , Pb 2+ , and Cu 2+ with the ratio of 1 : 1 : 1. All the obtained sensitivities are shown in Table S6.…”
Section: Synthesis and Characterizations Of The Gshmentioning
confidence: 99%
“…23 Besides, owing to the working principle of metallic electrodes forming alloys with HM ions in the deposition step, the electrodes tend to lose their component substances in the formed alloys during stripping, thus resulting in only being able to be reused a relatively few times (∼10 times), [24][25][26] which could limit their application with the developments of in situ and automatic sensing tools used for water quality control in large lakes and rivers recently. 27 Furthermore, emerging 2D materials (e.g. Ti 3 C 2 ) and porous materials (e.g.…”
A metal-free thiol-modified graphene derivative introduces a reusable approach to alleviate mutual interference in electrochemical heavy metal detection.
Sensing systems necessitate automation to reduce human effort, increase reproducibility, and enable remote sensing. In this perspective, we highlight different types of sensing systems with elements of automation, which are based on flow injection and sequential injection analysis, microfluidics, robotics, and other prototypes addressing specific real-world problems. Finally, we discuss the role of computer technology in sensing systems. Automated flow injection and sequential injection techniques offer precise and efficient sample handling and dependable outcomes. They enable continuous analysis of numerous samples, boosting throughput, and saving time and resources. They enhance safety by minimizing contact with hazardous chemicals. Microfluidic systems are enhanced by automation to enable precise control of parameters and increase of analysis speed. Robotic sampling and sample preparation platforms excel in precise execution of intricate, repetitive tasks such as sample handling, dilution, and transfer. These platforms enhance efficiency by multitasking, use minimal sample volumes, and they seamlessly integrate with analytical instruments. Other sensor prototypes utilize mechanical devices and computer technology to address real-world issues, offering efficient, accurate, and economical real-time solutions for analyte identification and quantification in remote areas. Computer technology is crucial in modern sensing systems, enabling data acquisition, signal processing, real-time analysis, and data storage. Machine learning and artificial intelligence enhance predictions from the sensor data, supporting the Internet of Things with efficient data management.
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