Separating flow and state in segmented two-phase transport via time-of-flight triboelectric flow sensing and deep-learning-based state classification

초록

Flow sensing in flexible tubing and disposable cartridges is typically constrained by bulky hardware and the need for frequent calibration. Triboelectric nanogenerator (TENG) sensors offer self-powered, clip-on monitoring capabilities; however, most existing TENG flow meters rely on voltage amplitude measurements that are sensitive to the properties of both the tube and the fluid. In this study, a method based on a double-electrode time-of-flight (TOF) TENG was developed for monitoring gas-liquid segmented (two-phase) flow. In this scheme, two external single-electrode probes separated by a fixed distance detect interface transients, and the transit delay between the timing landmarks provides the segment velocity as the primary readout, along with the flow rate for a tube of known geometry. This TOF-based measurement reduces tube-and liquid-dependent amplitude variability, enabling calibration-free operation. The proposed approach shows strong agreement with optical reference measurements across different tube materials-such as PTFE and Tygon-and diverse liquid conditions, including NaCl solutions (0.5-2 M) and water-organic solvent mixtures. By shifting to a TOF-based readout, the method eliminates the need for amplitude calibration and significantly reduces variability arising from tube and fluid properties. In addition, a 1D convolutional neural network (CNN) classifier is established, which achieves over 90% accuracy in distinguishing liquid conditions, enabling simultaneous flow quantification and liquid-state identification. The proposed scalable clip-on configuration provides a practical and non-intrusive solution for self-powered flow monitoring in reconfigurable tubing and pipeline-like systems.

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