A research team led by Professor JIANG Changlong from the Hefei Institutes of Physical Science of the Chinese Academy of Sciences has developed a highly sensitive, real-time sensor for detecting trace water, addressing key challenges in modern industrial quality control and environmental monitoring.

The results were published in Chemical Engineering Journal.

Accurate measurement of trace water is crucial for environmental monitoring and quality control in modern industry. However, detecting water at extremely low concentrations (parts per billion [ppb]) is challenging. Signals are weak, water's strong polarity slows sensor response, and performance is easily affected by temperature changes and complex backgrounds. The high cost and the complexity of sensitive materials also limit the widespread use of high-performance sensors.

In this study, the researchers used a one-step hydrothermal synthesis to prepare a MIL-101-NH₂(Eu) metal-organic framework (MOF) with dual-color intrinsic fluorescence. This material avoids interference from other organic solvents and exhibits a fluorescence color change when exposed to trace water, enabling visual detection. Europium ions (Eu³⁺) and BDC-NH₂ ligands in the MOF enhance red fluorescence via the "antenna effect." When water interacts with the ligand, intramolecular charge transfer weakens this effect, revealing the ligand's blue fluorescence and allowing detection at remarkably low concentrations.

The researchers then combined the MOF with carboxymethyl cellulose to construct an MOF@cellulose nanofiber membrane sensor via in situ growth. The resulting sensor demonstrates a rapid response time, high sensitivity, and visual fluorescence detection of moisture.

To further improve performance, the researchers integrated a deep learning algorithm, enhancing detection accuracy and sensitivity.

This work provides a reliable, cost-effective, and environmentally friendly approach to developing functionalized nanofiber sensors with tunable optical properties that have promising applications in environmental monitoring and smart wearable devices.