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Fabrication, Operation and Flow Visualization in Surface-acoustic-wave-driven Acoustic-counterflow Microfluidics

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A Deep-Learned Monolithic Nanoparticle Asymmetric Thermal Flow Sensor for Flow Vector Estimation.

Huijae Park¹, Sangjin Yoon¹, Junhyuk Bang¹

¹Wearable Soft Electronics Lab, Department of Mechanical Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Korea.

|August 12, 2025

Summary

This summary is machine-generated.

A novel deep-learned monolithic asymmetric thermal flow sensor offers accurate fluid dynamics measurement. This thin-film device minimizes flow disturbance and integrates hardware with AI for precise flow vector estimation.

Keywords:

asymmetric thermal flow sensor deep learning heater laser processing reduction sintering

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Area of Science:

Materials Science
Sensor Technology
Artificial Intelligence

Background:

Accurate flow sensing is critical across industrial, environmental, and biomedical fields.
Conventional flow sensors often suffer from bulkiness, complexity, and flow disturbance.
Existing calorimetric sensors require intricate multi-electrode setups.

Purpose of the Study:

To develop a novel, compact, and accurate flow sensor.
To overcome limitations of traditional bulky and complex flow sensors.
To integrate advanced materials processing with deep learning for enhanced flow estimation.

Main Methods:

Fabrication of a monolithic asymmetric thermal flow sensor using laser-induced selective sintering of nickel oxide nanoparticles.
Integration of a microheater and asymmetric spiral temperature sensors into a thin-film device.
Application of deep learning and reinforcement learning algorithms for flow vector estimation based on sensor resistance changes.

Main Results:

The thin-film sensor design minimizes flow disturbance and enhances measurement accuracy.
The asymmetric sensor configuration simplifies design and enables AI-driven flow estimation.
Real-time data monitoring via embedded wireless communication ensures reliable flow assessment.

Conclusions:

The developed deep-learned monolithic asymmetric thermal flow sensor provides a versatile and accurate solution for fluid dynamics measurement.
This integrated hardware-software approach offers a significant advancement over conventional flow sensing technologies.
The sensor is suitable for diverse applications requiring precise and non-intrusive flow estimation.