Novel material enables real-time water quality monitoring

Clean, safe water is vital for human health and wellbeing, but detecting contamination quickly and accurately remains a major challenge in many parts of the world. A new device developed at the National University of Singapore (NUS) has the potential to significantly advance water quality monitoring and management, as revealed in the journal Nature Communications.

Taking inspiration from the biological function of the oily protective layer found on human skin, NUS researchers translated this concept into a versatile material, named ReSURF, capable of spontaneously forming a water-repellent interface. The researchers incorporated this material — which can be prepared through a rapid micro-phase separation approach — into a device known as a triboelectric nanogenerator (TENG), which uses the energy from the movement of water droplets to create an electric charge. The resulting device — the ReSURF sensor — can be applied as a water quality monitor.

“The ReSURF sensor can detect various pollutants, such as oils and fluorinated compounds, which are challenging for many existing sensors,” said team leader Associate Professor Benjamin Tee. “This capability, together with unique features such as [being] self-powered, self-healing, reusability and recyclability, positions ReSURF as a sustainable solution for real-time, onsite and sustainable water quality monitoring.”

Existing water quality monitoring technologies such as electrochemical sensors, optical detection systems and biosensors are effective in certain specific applications, such as detecting heavy metals, phosphorus and microbial pollution. However, these technologies often face limitations including slow response, high costs, reliance on external reagents or power sources, limited reusability, and the need for bulky laboratory equipment or specialised instrumentation. The ReSURF sensor effectively overcomes these challenges — particularly in terms of onsite real-time sensing, as the device has demonstrated the ability to detect water contaminants in approximately 6 ms.

The sensor monitors water quality by analysing the electrical signals generated when analytes — such as salts, oils or pollutants — in the water droplets, contact its surface. When water droplets containing analytes strike the water-repellent surface of the sensor, they spread out and slide off quickly, generating electric charges within milliseconds. The magnitude and characteristics of the signal generated would vary according to the composition and concentration of the analytes present. By monitoring these signals in real time, the ReSURF sensor can rapidly and accurately assess water quality without the need for external power sources.

Being stretchable and transparent, the ReSURF material can be easily integrated into flexible platforms including soft robotics and wearable electronics, setting it apart from conventional sensing materials. Furthermore, the material can be easily recycled due to its solubility in solvents, enabling it to be reused in new devices without a loss in performance.

To demonstrate its capabilities, the researchers tested the ReSURF sensor on a pufferfish-like soft robot in detecting oil in water and perfluorooctanoic acid — a common contaminant found in water sources. The test produced promising results with both contaminants producing different voltage signals, providing a proof of concept that the ReSURF sensor can be used in early surveillance of possible contamination.

The ReSURF sensor could be deployed in rivers, lakes and reservoirs to enable early surveillance of pollutants, allowing for quick response to water contamination emergencies. In agriculture, it is capable of monitoring water safety in areas like rice fields. In industrial settings and sewage treatment plants, the sensor could provide valuable insights for wastewater management.

The research team now hopes to optimise the sensor by enhancing the specificity of pollutant detection, integrating wireless data transmission capabilities, and scaling the system for long-term or large-scale environmental monitoring. Additionally, the researchers plan to explore more eco-friendly material alternatives to enhance sustainability and align with evolving environmental regulations.

“Future iterations could integrate additional sensing modalities or machine learning-based signal analysis to enable more precise identification and classification of pollutants,” Tee said. “We envision this platform as a foundation for the development of more intelligent and responsive water quality monitoring systems.”

Image courtesy National University of Singapore