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, NH₃, and NO₂. 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.

NO₂ is a toxic gas that can damage the lungs with prolonged exposure and contribute to health conditions, such as asthma in children. Detecting NO₂ is therefore crucial for maintaining a healthy environment. Carbon nanotubes (CNTs) are promising materials for NO₂ gas sensors due to their excellent electronic properties and high adsorption energy for NO₂ molecules. However, conventional CNT-based sensors face challenges, including low responses at room temperature (RT) and slow recovery times. This study introduces a memristor-based NO₂ gas sensor comprising CNT/ZnO/ITO decorated with an N-[3-(trimethoxysilyl)propyl] ethylene diamine (en-APTAS) membrane to enhance room-temperature-sensing performance. The amine groups in the en-APTAS membrane increase adsorption sites and boost charge transfer interactions between NO₂ and the CNT surface. This modification improves the sensor’s response by 60% at 20 ppm compared to the undecorated counterpart. However, the high adsorption energy of NO₂ slows the recovery process. To overcome this, a pulse-recovery method was implemented, applying a −2.5 V pulse with a 1 ms width, enabling the sensor to return to its baseline within 1 ms. These findings highlight the effectiveness of en-APTAS decoration and pulse-recovery techniques in improving the sensitivity, response, and recovery of CNT-based gas sensors.

Resistive random-access memory (RRAM) has been widely explored with different resistive switching (RS) materials including oxides and nitrides, aiming for high-density and low-cost nonvolatile memory (NVM) solutions. This review focuses on the advancements in nitride-based RS materials for RRAM devices. The unique properties of nitride materials, including their reduced initial forming voltage and low dielectric strength, make them a promising choice for RRAM development. Nitride-based RRAM also addresses critical challenges, such as optimizing RS materials, reducing operating currents, enhancing thermal stability, and improving device reliability. These attributes position nitride-based RRAM as a strong contender for next-generation NVM technologies. Additionally, the potential of nitride materials in neuromorphic applications is explored, highlighting their ability to replicate synaptic behavior and enable energy-efficient computing architectures. Recent progress, challenges, and future directions in nitride-based materials are reviewed to provide valuable insights into their optimization and scalability. This study serves as a foundation for advancing electronic materials research, offering a pathway for the development of high-performance memory and neuromorphic systems involving nitride-based materials.