The invention of smart fire extinguishing agents, which satisfy the growing need for efficient, eco-friendly, and adaptable fire suppression systems, is a significant advancement in fire safety. Traditional fire extinguisher chemicals, such as water, foam, and chemical powders, are effective, but they often have disadvantages, including toxicity, negative environmental effects, and inefficiency in difficult situations. Conversely, smart extinguishing agents maximize fire suppression through intelligent, tailored responses by utilizing advancements in material science, nanotechnology, and autonomous control systems. These agents employ intelligent components like phase-change materials, nanofluids, and stimuli-responsive polymers to adapt dynamically to fire conditions. For example, agents based on nanofluids enhance thermal conductivity, enabling more efficient suppression and faster heat absorption. Similarly, stimuli-responsive polymers modify their chemical characteristics in response to heat or flame, improving extinguishing effectiveness, whereas phase-change materials store and release energy to counteract abrupt temperature increases. The performance of these agents is further improved with the addition of intelligent systems, such as sensors and machine learning algorithms. Fire conditions are continuously monitored by embedded sensors, which enable accurate deployment according to variables, including fire kind, intensity, and spread dynamics. Proactive and effective response tactics are made possible by machine learning models that use real-time data analysis to forecast fire behavior. These devices solve significant flaws in traditional fire suppression methods by reducing collateral harm and resource waste. Sustainability is a key consideration in the development of smart fire extinguishing agents. To lessen the environmental impact of fire suppression operations, researchers are investigating environmentally friendly formulations, such as halogen-free chemicals, green aerosols, and biodegradable foams. Additionally, these agents are being developed for a variety of uses, ranging from home and commercial fires to more specific situations like electrical fires, wildfires, and fires in high-risk or confined spaces. There are still issues with long-term dependability, scalability, and cost-effectiveness. Ongoing multidisciplinary research, however, is quickly getting past these obstacles. Material scientists, engineers, and legislators are working together to develop smart fire safety solutions that have the potential to revolutionize communities, ecosystems, and industries. The technical advancements that supported the creation of smart fire extinguishing chemicals, their working principles, and their possible influence on contemporary fire safety are highlighted in this study. These agents provide a promising route toward more responsible and efficient fire suppression solutions by addressing environmental issues and improving flexibility, opening the door to safer and more sustainable surroundings.

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1. Bisht N, Jaiswal S, Vishwakarma J, Gupta SK, Yeo RJ, Sankaranarayanan SKRS, et al. MXene enhanced shape memory polymer composites: The rise of MXenes as fillers for stimuli-responsive materials. Chem Eng J. 2024;498:155154. doi: https://doi.org/10.1016/j.cej.2024.155154.
2. Liu C, Li G, Lu S. Brief introduction on advances in fire suppression. Fire. 2024;7(9):309. doi: https://doi.org/10.3390/fire7090309.
3. Rabajczyk A, Zielecka M, Gniazdowska J. Application of nanotechnology in extinguishing agents. Materials. 2022;15(24):8876. doi: https://doi.org/10.3390/ma15248876.
4. Khan AA, Khan MA, Leung K, Huang X, Luo M, Usmani A. A review of critical fire event library for buildings and safety framework for smart firefighting. Int J Disaster Risk Reduct. 2022;83:103412. doi: https://doi.org/10.1016/j.ijdrr.2022.103412.
5. Sun L, Li W, Duan S, Lv X, Shi Q, Qi G, et al. Temperature-sensitive intelligent gel to prevent coal spontaneous combustion: Large-scale simulation fire extinguishing experimental study. Fuel. 2025;384:133812. doi: https://doi.org/10.1016/j.fuel.2024.133812.
6. Yu M, Yang N, Liu Z, Li H, Wang L, Wu M, et al. Experimental preparation and mechanism analysis of a neotype composite gel for coal spontaneous combustion prevention and coal-fire extinguishment. Fuel. 2023;339:127448. doi: https://doi.org/10.1016/j.fuel.2023.127448.
7. Li S, Zhou G, Wang Y, Jing B, Qu Y. Synthesis and characteristics of fire extinguishing gel with high water absorption for coal mines. Process Saf Environ Prot. 2019;125:207–18. doi: https://doi.org/10.1016/j.psep.2019.03.022.
8. Digiesi S, Laurieri N, Lucchese A, Piccininno G. T-fire system: A novel integrated fire monitoring and extinguishing system for trucks. Procedia Comput Sci. 2024;232:2468–77. doi: https://doi.org/10.1016/j.procs.2024.02.066.
9. Zhang L, Huang Y, Dong H, Xu R, Jiang S. Flame-retardant shape memory polyurethane/MXene paper and the application for early fire alarm sensor. Compos B Eng. 2021;223:109149. doi: https://doi.org/10.1016/j.compositesb.2021.109149.
10. Wang M, Zhou Z, Liang Z, Du S, Cai G, Wan X, et al. The preparation and fire extinguishing mechanism research of a novel high-efficiency KHCO₃@HM dry powder. Mater Today Commun. 2024;38:107817. doi: https://doi.org/10.1016/j.mtcomm.2023.107817.
11. Wang K, Wang Y, Yao A, Hu H, Fan W, Lin S, et al. A natural clay-based Janus micro-nanosystem with ultra-temperature resistant piezoresistive sensing and self-switching fire warning for smart fire safety. Chem Eng J. 2025;505:159483. doi: https://doi.org/10.1016/j.cej.2025.159483.
12. Zhu Y, Zhao B, Lei L, Cheng Z, Yu T, Li Y. Facile construction of a flexible smart core-sheath flax yarns with temperature-responsive resistance for ultra-fast fire-alarm response. Chem Eng J. 2023;471:144718. doi: https://doi.org/10.1016/j.cej.2023.144718.
13. Chen X, Fu J, Zhou J, Li Y. Distributed reinforcement learning algorithm for multi-wave fire fighting scheduling problem. IFAC-Pap Online. 2022;55(3):245–50. doi: https://doi.org/10.1016/j.ifacol.2022.05.043.
14. Zavala CE, Baca JE, Baldwin LC, McClain KR, Harvey BG. Fire-resistant propargyl ether networks derived from bio-based hydroxycinnamic acids. Mater Adv. 2024;5(22):8787–97. doi: https://doi.org/10.1039/d4ma00610k.
15. Lv L-Y, Cao C-F, Qu Y-X, Zhang G-D, Zhao L, Cao K, et al. Smart fire-warning materials and sensors: design principle, performances, and applications. Mater Sci Eng R Rep. 2022;150:100690. doi: https://doi.org/10.1016/j.mser.2022.100690.
16. Yang L, Zhou Y, Xu J, Ma X, Yuan J, Yuan B. Multi-crosslinked gelatin-based composite hydrogel featuring high thermoelectric performance and excellent flame retardancy for intelligent fire-warning system. Int J Biol Macromol. 2024;282(Pt 2):136881. doi: https://doi.org/10.1016/j.ijbiomac.2024.136881.
17. Abdel-Salam AH, Morsy A. Novel high-efficiency nano metal oxide based on phosphorus as smart flame retardants with multiple reactive for sustainable cotton-polyester fabrics. Int J Biol Macromol. 2025;294:139502. doi: https://doi.org/10.1016/j.ijbiomac.2025.139502.
18. Liu J, Zhang J, Cui X, Gu W, Liu Q, Li H, et al. Eco-friendly wearable textiles: Asymmetric structures for EMI shielding, thermal management, and fire safety. Chem Eng J. 2024;491:152097. doi: https://doi.org/10.1016/j.cej.2024.152097.
19. Liu G, Liu Z, Qu L, Ren L, Wang L, Yan M. Dual-agent intelligent fire detection method for large commercial spaces based on numerical databases and artificial intelligence. Process Saf Environ Prot. 2024;191(Pt B):2485–99. doi: https://doi.org/10.1016/j.psep.2024.10.010.
20. Challenger M, Eslampanah R, Karaduman B, Denil J, Vangheluwe H. Development of an IoT and WSN based CPS using MPM approach: A smart fire detection case study. In: Multi-Paradigm Modelling Approaches for Cyber-Physical Systems. 2021. p. 245–70. doi: https://doi.org/10.1016/B978-0-12-819105-7.00015–5.
21. Ji W, Li G-Q, Zhu S, Li J, Qi H, Wang Y. Machine learning-driven real-time identification of large-space building fires and forecast of temperature development. Expert Syst Appl. 2024;255(Pt C):124758. doi: https://doi.org/10.1016/j.eswa.2024.124758.
22. Durukan O, Akyuz E, Destanoğlu O, Arslanoğlu Y, Sezer SI. Quantitive HAZOP and D-S evidence theory-fault tree analysis approach to predict fire and explosion risk in inert gas system on-board tanker ship. Ocean Eng. 2024;308:118274. doi: https://doi.org/10.1016/j.oceaneng.2024.118274.
23. Solukloei HRJ, Nematifard S, Hesami A, Mohammadi H, Kamalinia M. A fuzzy-HAZOP/ant colony system methodology to identify combined fire, explosion, and toxic release risk in the process industries. Expert Syst Appl. 2022;192:116418. doi: https://doi.org/10.1016/j.eswa.2021.116418.
24. Zhang X, Wu X, Huang X. Smart real-time forecast of transient tunnel fires by a dual-agent deep learning model. Tunn Undergr Space Technol. 2022;129:104631. doi: https://doi.org/10.1016/j.tust.2022.104631.

The invention of smart fire extinguishing agents, which satisfy the growing need for efficient, eco-friendly, and adaptable fire suppression systems, is a significant advancement in fire safety. Traditional fire extinguisher chemicals, such as water, foam, and chemical powders, are effective, but they often have disadvantages, including toxicity, negative environmental effects, and inefficiency in difficult situations. Conversely, smart extinguishing agents maximize fire suppression through intelligent, tailored responses by utilizing advancements in material science, nanotechnology, and autonomous control systems. These agents employ intelligent components like phase-change materials, nanofluids, and stimuli-responsive polymers to adapt dynamically to fire conditions. For example, agents based on nanofluids enhance thermal conductivity, enabling more efficient suppression and faster heat absorption. Similarly, stimuli-responsive polymers modify their chemical characteristics in response to heat or flame, improving extinguishing effectiveness, whereas phase-change materials store and release energy to counteract abrupt temperature increases. The performance of these agents is further improved with the addition of intelligent systems, such as sensors and machine learning algorithms. Fire conditions are continuously monitored by embedded sensors, which enable accurate deployment according to variables, including fire kind, intensity, and spread dynamics. Proactive and effective response tactics are made possible by machine learning models that use real-time data analysis to forecast fire behavior. These devices solve significant flaws in traditional fire suppression methods by reducing collateral harm and resource waste. Sustainability is a key consideration in the development of smart fire extinguishing agents. To lessen the environmental impact of fire suppression operations, researchers are investigating environmentally friendly formulations, such as halogen-free chemicals, green aerosols, and biodegradable foams. Additionally, these agents are being developed for a variety of uses, ranging from home and commercial fires to more specific situations like electrical fires, wildfires, and fires in high-risk or confined spaces. There are still issues with long-term dependability, scalability, and cost-effectiveness. Ongoing multidisciplinary research, however, is quickly getting past these obstacles. Material scientists, engineers, and legislators are working together to develop smart fire safety solutions that have the potential to revolutionize communities, ecosystems, and industries. The technical advancements that supported the creation of smart fire extinguishing chemicals, their working principles, and their possible influence on contemporary fire safety are highlighted in this study. These agents provide a promising route toward more responsible and efficient fire suppression solutions by addressing environmental issues and improving flexibility, opening the door to safer and more sustainable surroundings.

🔒 This is a subscription article

Full text is available to subscribers and institutional members. Please choose an option below to access it.

1. Bisht N, Jaiswal S, Vishwakarma J, Gupta SK, Yeo RJ, Sankaranarayanan SKRS, et al. MXene enhanced shape memory polymer composites: The rise of MXenes as fillers for stimuli-responsive materials. Chem Eng J. 2024;498:155154. doi: https://doi.org/10.1016/j.cej.2024.155154.
2. Liu C, Li G, Lu S. Brief introduction on advances in fire suppression. Fire. 2024;7(9):309. doi: https://doi.org/10.3390/fire7090309.
3. Rabajczyk A, Zielecka M, Gniazdowska J. Application of nanotechnology in extinguishing agents. Materials. 2022;15(24):8876. doi: https://doi.org/10.3390/ma15248876.
4. Khan AA, Khan MA, Leung K, Huang X, Luo M, Usmani A. A review of critical fire event library for buildings and safety framework for smart firefighting. Int J Disaster Risk Reduct. 2022;83:103412. doi: https://doi.org/10.1016/j.ijdrr.2022.103412.
5. Sun L, Li W, Duan S, Lv X, Shi Q, Qi G, et al. Temperature-sensitive intelligent gel to prevent coal spontaneous combustion: Large-scale simulation fire extinguishing experimental study. Fuel. 2025;384:133812. doi: https://doi.org/10.1016/j.fuel.2024.133812.
6. Yu M, Yang N, Liu Z, Li H, Wang L, Wu M, et al. Experimental preparation and mechanism analysis of a neotype composite gel for coal spontaneous combustion prevention and coal-fire extinguishment. Fuel. 2023;339:127448. doi: https://doi.org/10.1016/j.fuel.2023.127448.
7. Li S, Zhou G, Wang Y, Jing B, Qu Y. Synthesis and characteristics of fire extinguishing gel with high water absorption for coal mines. Process Saf Environ Prot. 2019;125:207–18. doi: https://doi.org/10.1016/j.psep.2019.03.022.
8. Digiesi S, Laurieri N, Lucchese A, Piccininno G. T-fire system: A novel integrated fire monitoring and extinguishing system for trucks. Procedia Comput Sci. 2024;232:2468–77. doi: https://doi.org/10.1016/j.procs.2024.02.066.
9. Zhang L, Huang Y, Dong H, Xu R, Jiang S. Flame-retardant shape memory polyurethane/MXene paper and the application for early fire alarm sensor. Compos B Eng. 2021;223:109149. doi: https://doi.org/10.1016/j.compositesb.2021.109149.
10. Wang M, Zhou Z, Liang Z, Du S, Cai G, Wan X, et al. The preparation and fire extinguishing mechanism research of a novel high-efficiency KHCO₃@HM dry powder. Mater Today Commun. 2024;38:107817. doi: https://doi.org/10.1016/j.mtcomm.2023.107817.
11. Wang K, Wang Y, Yao A, Hu H, Fan W, Lin S, et al. A natural clay-based Janus micro-nanosystem with ultra-temperature resistant piezoresistive sensing and self-switching fire warning for smart fire safety. Chem Eng J. 2025;505:159483. doi: https://doi.org/10.1016/j.cej.2025.159483.
12. Zhu Y, Zhao B, Lei L, Cheng Z, Yu T, Li Y. Facile construction of a flexible smart core-sheath flax yarns with temperature-responsive resistance for ultra-fast fire-alarm response. Chem Eng J. 2023;471:144718. doi: https://doi.org/10.1016/j.cej.2023.144718.
13. Chen X, Fu J, Zhou J, Li Y. Distributed reinforcement learning algorithm for multi-wave fire fighting scheduling problem. IFAC-Pap Online. 2022;55(3):245–50. doi: https://doi.org/10.1016/j.ifacol.2022.05.043.
14. Zavala CE, Baca JE, Baldwin LC, McClain KR, Harvey BG. Fire-resistant propargyl ether networks derived from bio-based hydroxycinnamic acids. Mater Adv. 2024;5(22):8787–97. doi: https://doi.org/10.1039/d4ma00610k.
15. Lv L-Y, Cao C-F, Qu Y-X, Zhang G-D, Zhao L, Cao K, et al. Smart fire-warning materials and sensors: design principle, performances, and applications. Mater Sci Eng R Rep. 2022;150:100690. doi: https://doi.org/10.1016/j.mser.2022.100690.
16. Yang L, Zhou Y, Xu J, Ma X, Yuan J, Yuan B. Multi-crosslinked gelatin-based composite hydrogel featuring high thermoelectric performance and excellent flame retardancy for intelligent fire-warning system. Int J Biol Macromol. 2024;282(Pt 2):136881. doi: https://doi.org/10.1016/j.ijbiomac.2024.136881.
17. Abdel-Salam AH, Morsy A. Novel high-efficiency nano metal oxide based on phosphorus as smart flame retardants with multiple reactive for sustainable cotton-polyester fabrics. Int J Biol Macromol. 2025;294:139502. doi: https://doi.org/10.1016/j.ijbiomac.2025.139502.
18. Liu J, Zhang J, Cui X, Gu W, Liu Q, Li H, et al. Eco-friendly wearable textiles: Asymmetric structures for EMI shielding, thermal management, and fire safety. Chem Eng J. 2024;491:152097. doi: https://doi.org/10.1016/j.cej.2024.152097.
19. Liu G, Liu Z, Qu L, Ren L, Wang L, Yan M. Dual-agent intelligent fire detection method for large commercial spaces based on numerical databases and artificial intelligence. Process Saf Environ Prot. 2024;191(Pt B):2485–99. doi: https://doi.org/10.1016/j.psep.2024.10.010.
20. Challenger M, Eslampanah R, Karaduman B, Denil J, Vangheluwe H. Development of an IoT and WSN based CPS using MPM approach: A smart fire detection case study. In: Multi-Paradigm Modelling Approaches for Cyber-Physical Systems. 2021. p. 245–70. doi: https://doi.org/10.1016/B978-0-12-819105-7.00015–5.
21. Ji W, Li G-Q, Zhu S, Li J, Qi H, Wang Y. Machine learning-driven real-time identification of large-space building fires and forecast of temperature development. Expert Syst Appl. 2024;255(Pt C):124758. doi: https://doi.org/10.1016/j.eswa.2024.124758.
22. Durukan O, Akyuz E, Destanoğlu O, Arslanoğlu Y, Sezer SI. Quantitive HAZOP and D-S evidence theory-fault tree analysis approach to predict fire and explosion risk in inert gas system on-board tanker ship. Ocean Eng. 2024;308:118274. doi: https://doi.org/10.1016/j.oceaneng.2024.118274.
23. Solukloei HRJ, Nematifard S, Hesami A, Mohammadi H, Kamalinia M. A fuzzy-HAZOP/ant colony system methodology to identify combined fire, explosion, and toxic release risk in the process industries. Expert Syst Appl. 2022;192:116418. doi: https://doi.org/10.1016/j.eswa.2021.116418.
24. Zhang X, Wu X, Huang X. Smart real-time forecast of transient tunnel fires by a dual-agent deep learning model. Tunn Undergr Space Technol. 2022;129:104631. doi: https://doi.org/10.1016/j.tust.2022.104631.