Clean, safe water is essential for human health, food security, high-tech industry development, and sustainable urbanization. However, rapid and accurate pollution detection remains a major challenge in many parts of the world. Researchers at the National University of Singapore (NUS) have recently developed a new device that promises a breakthrough in water quality monitoring and management.
A research team led by Associate Professor Benjamin Tee from the Department of Materials Science and Engineering at the NUS College of Design and Engineering drew inspiration from the biological function of the skin's oily protective layer and translated this concept into a multifunctional material called ReSURF. Prepared via a rapid microphase separation method, the material spontaneously forms a waterproof interface and features self-healing and recyclability properties. The material design and the performance of the novel water quality sensor have been published in Nature Communications.
The researchers integrated ReSURF into a triboelectric nanogenerator (TENG) device to create the ReSURF sensor. This sensor functions as a water quality monitor capable of detecting multiple contaminants, including oil and fluorinated compounds – a capability beyond many existing sensors. It offers unique advantages: self-powered operation, self-healing, reusability, and recyclability, making it a sustainable solution for real-time, on-site, and long-term water quality monitoring.
Current water quality monitoring technologies – such as electrochemical sensors, optical detection systems, and biosensors – are effective for specific applications like heavy metals, phosphorus, and microbial contamination. However, they suffer from slow response times, high costs, reliance on external reagents or power sources, limited reusability, and the need for bulky laboratory equipment or specialized instruments. The ReSURF sensor developed by the NUS team effectively overcomes these challenges and excels in real-time, on-site water sensing. The self-powered device can detect contaminants in water in approximately 6 milliseconds – 40 times faster than a human blink.
The ReSURF sensor also possesses self-healing and recyclable properties, making it a sustainable, low-maintenance solution. The material is stretchable and transparent, allowing easy integration into flexible platforms such as soft robotics and wearable electronics – unlike traditional sensing materials. It also exhibits excellent solubility in solvents, enabling easy recycling and reuse in new devices without performance loss, thus providing an environmentally friendly solution.
The ReSURF sensor monitors water quality by analyzing the electrical signals generated when water droplets contact its surface. When a droplet containing analytes strikes the waterproof surface, it rapidly spreads and slides off, generating charge. The amplitude and characteristics of the signal vary with the analyte type and concentration. Real-time signal monitoring enables fast and accurate water quality assessment without an external power source.
To demonstrate its capabilities, the researchers tested the ReSURF sensor on a pufferfish-like soft robot, detecting oil and perfluorooctanoic acid (a common water pollutant). Both contaminants produced distinct voltage signals, proving its potential for early pollution monitoring.
The ReSURF sensor has broad application potential. It can be deployed in rivers, lakes, and reservoirs for early pollutant detection and rapid response to water contamination emergencies; in agriculture to monitor water safety in rice paddies and other areas; and in industrial settings and wastewater treatment plants to support wastewater management.
The team plans to further optimize the ReSURF sensor by enhancing pollutant detection specificity, integrating wireless data transmission, scaling the system for long-term or large-scale environmental monitoring, and exploring additional eco-friendly material alternatives to improve sustainability and regulatory compliance. Assoc Prof Tee stated that future iterations could incorporate other sensing modalities or machine-learning-based signal analysis for more precise pollutant identification and classification, positioning the platform as a foundation for smarter, faster-responding water quality monitoring systems.
