A Comprehensive Review of Advanced Sensor Technologies for Fire Detection with a Focus on Gasistor-Based Sensors
Early fire detection plays a crucial role in minimizing harm to human life, buildings, and the environment. Traditional fire detection systems struggle with detection in dynamic or complex situations due to slow response and false alarms. Conventional systems are based on smoke, heat, and gas sensor...
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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-06-01
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| Series: | Chemosensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-9040/13/7/230 |
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| Summary: | Early fire detection plays a crucial role in minimizing harm to human life, buildings, and the environment. Traditional fire detection systems struggle with detection in dynamic or complex situations due to slow response and false alarms. Conventional systems are based on smoke, heat, and gas sensors, which often trigger alarms when a fire is in full swing. In order to overcome this, a promising approach is the development of memristor-based gas sensors, known as gasistors, which offer a lightweight design, fast response/recovery, and efficient miniaturization. Recent studies on gasistor-based sensors have demonstrated ultrafast response times as low as 1–2 s, with detection limits reaching sub-ppm levels for gases such as CO, NH<sub>3</sub>, and NO<sub>2</sub>. Enhanced designs incorporating memristive switching and 2D materials have achieved a sensitivity exceeding 90% and stable operation across a wide temperature range (room temperature to 250 °C). This review highlights key factors in early fire detection, focusing on advanced sensors and their integration with IoT for faster, and more reliable alerts. Here, we introduce gasistor technology, which shows high sensitivity to fire-related gases and operates through conduction filament (CF) mechanisms, enabling its low power consumption, compact size, and rapid recovery. When integrated with machine learning and artificial intelligence, this technology offers a promising direction for future advancements in next-generation early fire detection systems. |
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| ISSN: | 2227-9040 |