New MEMS Sensor Uses Pulsed Heating and Machine Learning to Identify Gases with Precision—All from a Single Sensing Element

A team of researchers has developed a MEMS-based gas sensor that leverages pulsed heating and machine learning to accurately identify gases like hydrogen, carbon monoxide, and ammonia, using just one sensing element instead of a full array.

Silicon Wafers with microchips used in electronics.
Study: Pulse-driven MEMS gas sensor combined with machine learning for selective gas identification. Image Credit: asharkyu/Shutterstock.com

Published in Microsystems & Nanoengineering, the study outlines how this compact, low-cost “electronic nose” achieves high selectivity and energy efficiency by combining smart thermal modulation with advanced data analysis. Built using standard wafer-level fabrication methods, the sensor offers a simplified architecture without sacrificing performance.

Why This Matters

Gas sensors based on metal oxide semiconductors (MOS) are widely used across various industries, but they have a long-standing weakness: poor selectivity. Because their operation hinges on temperature-sensitive chemisorption, they often struggle to distinguish between different gases. Cross-sensitivity is common, and overlapping responses are a known issue.

To get around this, engineers have relied on arrays of different sensors and heavy-duty data processing. While effective, these approaches add bulk, complexity, and power consumption—less than ideal for portable or scalable applications.

This new approach flips that script. Instead of more sensors or more power, the researchers used pulsed heating—applying quick, periodic bursts of heat to generate dynamic response patterns that reveal the unique kinetics of each gas. This transient data holds more subtle, information-rich cues than conventional steady-state readings.

A Closer Look at the Sensor Design

The sensor was built using standard microfabrication techniques. The base is a silicon wafer with a suspended support layer. On top of this, both the microheater and sensing electrodes were patterned on the same metal layer—but cleverly arranged: the heater sits on the outside, while the electrodes are inside. This layout eliminates the need for additional insulating layers, simplifying production.

The microheater’s small thermal mass allows it to heat up quickly, within about 20 milliseconds, making it well-suited for pulsed thermal cycling.

For the sensing material, the team used SnO2 nanosheets, which are known for their high surface reactivity and large surface area. These were prepared via sonication and drop-casting, then thermally aged to stabilize their properties before testing.

How Testing Was Done

Testing took place in a controlled chamber, where the sensor was exposed to known concentrations of hydrogen (H2), carbon monoxide (CO), and ammonia (NH3). Gases were introduced using mass flow controllers, and the sensor was heated using square-wave voltage pulses with varying duty cycles—ratios like 1/3, 1/2, and 2/3—which controlled the heating and cooling phases.

During operation, the sensor’s conductance was measured continuously, capturing both the steady-state and the fast-changing transient responses induced by the pulsed heat.

The Role of Machine Learning

To make sense of the rich signal data, the team used principal component analysis (PCA) to reduce the data dimensions and visualize patterns. Then, they applied several classification algorithms—linear discriminant analysis (LDA), k-nearest neighbors (KNN), support vector machines (SVM), and random forests (RF)—to recognize and categorize the gases.

These models weren’t just good—they were spot on. Classification accuracy hit 100 %, showing that even subtle variations in the transient response patterns were enough to reliably distinguish between gases.

Importantly, the pulsed heating didn’t just add variety; it unlocked information that isn’t available in traditional isothermal operation. Each gas had its own signature response curve during both the heating and cooling phases, shaped by its unique adsorption and diffusion behavior.

The team also experimented with different pulse parameters, such as duty cycle and pulse length. Fine-tuning these settings led to better contrast in the response signals and improved classification accuracy, without adding hardware complexity.

What This Could Mean

By combining MEMS design, pulsed thermal modulation, and machine learning, the researchers have created a compact gas sensor that delivers high performance without the need for arrays or extensive signal conditioning. The single-element setup simplifies manufacturing and operation while opening the door to more power-efficient, portable gas-sensing devices.

This approach could be particularly useful in applications where size, cost, and energy use matter, such as wearable devices, smart home monitors, or distributed environmental sensing networks.

Journal Reference

Luo W., Dai F., et al. (2025). Pulse-driven MEMS gas sensor combined with machine learning for selective gas identification. Microsystems & Nanoengineering 11, 72. DOI: 10.1038/s41378-025-00934-2, https://www.nature.com/articles/s41378-025-00934-2

Dr. Noopur Jain

Written by

Dr. Noopur Jain

Dr. Noopur Jain is an accomplished Scientific Writer based in the city of New Delhi, India. With a Ph.D. in Materials Science, she brings a depth of knowledge and experience in electron microscopy, catalysis, and soft materials. Her scientific publishing record is a testament to her dedication and expertise in the field. Additionally, she has hands-on experience in the field of chemical formulations, microscopy technique development and statistical analysis.    

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