Innovative Electrochemical Nano-Robot: Integrating Printed Nanoelectronics with a Remote-Controlled Robotic for On-Site Underwater Electroanalysis
Saba Mohammadlou, Shayan Angizi, Amir Hatamie
- 发表年份
- 2025
- 引用次数
- 4
摘要
Smart and remote sensing technologies offer significant advantages across various applications. This study introduces an innovative approach integrating printed electrochemical nanosensors with a remotely controllable underwater robot, creating a “Robo-sensor” for underwater and surface water sensing. Unlike conventional underwater sensors, the Robo-sensor operates in three dimensions, both on the surface and underwater, targeting specific locations such as coastal areas and deep-sea environments while transmitting data to an external analytical unit. To achieve this, we developed a conductive nanoink by combining graphite, silver nanorods (AgNRs; diameter: 30 ± 20 nm, length: 2 ± 0.2 μm), nail polish as a cohesive agent, and an organic solvent. This ink was used to fabricate an electroanalytical system on the mini-robot’s body. The Robo-sensor, connected to a portable potentiostat, demonstrated linear responses to hydroquinone (HQ) and nitrite ions, with detection ranges of 5.0–1356.0 and 3.0–1200.0 μM, respectively, under artificial seawater conditions (High salinity). Its repeatability (RSD <6%), stability (up to 40 continuous applications with an error < ± 10%), and sensitivity were thoroughly evaluated. The Robo-sensor’s practical applications included detecting chemical leaks in underwater pipelines containing HQ, a hazardous chemical relevant to coastal industries such as petrochemicals. Additionally, it analyzed surface water contaminated with NO2– near and far from a wastewater discharge pipeline, providing valuable insights for environmental and ecosystem investigations. In conclusion, the developed Robo-sensor enables on-site analysis both on the surface and underwater, reducing time, costs, and risks in high-hazard environments like deep waters. With further modifications, this strategy could be adapted for diverse applications, including offshore oil and petrochemical operations, corrosion studies, and underwater environmental monitoring, thus expanding the real-world impact of electrochemical science.
关键词
相关论文
Statistical Learning Theory
Yuhai Wu, Vladimir Vapnik
1999
Artificial intelligence: a modern approach
1995
Fractional Differential Equations
Igor Podlubný
2025
Applied Nonlinear Control
Jean-Jacques Slotine, Weiping Li
1991