Advances in composite and polymer flame retardants – a review on mechanism, properties and applications
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Challapalli Sruthi
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BVS Praveen
,
Raj Kumar Verma
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Achuvelli Venkata Raghavendra Rao
und
Gowrishetty Srinivas
Abstract
Flame retardants are critical additives that enhance the fire safety of polymers and composites by reducing flammability, delaying ignition, and inhibiting flame spread. They function through diverse mechanisms, including radical quenching in the gas phase, char formation in the condensed phase, endothermic heat absorption, gas dilution, and intumescence. Based on chemical composition and mode of action, flame retardants are broadly classified into organic and inorganic types, each with distinct thermal, environmental, and compatibility profiles. Synergistic systems, combining multiple additives such as phosphorus–nitrogen, boron–metal hydroxide, and nanomaterial-enhanced formulations, offer improved fire resistance at lower loadings, preserving mechanical and processing properties. Thermal and mechanical impacts are key considerations, as high additive concentrations may compromise structural performance, necessitating optimized formulations. Substrate type, processing requirements, target performance, and evolving environmental and regulatory constraints guide application-specific selection. In the construction, transportation, electronics, and textile sectors, flame retardants enable compliance with stringent safety standards while meeting durability, weight, and functional requirements. Emerging trends focus on halogen-free, bio-based, and nanotechnology-enabled systems that balance fire performance with sustainability and reduced toxicity. This review examines flame-retardant classifications, mechanisms, performance influences, and sector-specific applications, highlighting advances in sustainable materials and multifunctional systems.
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Research ethics: Not applicable.
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Informed consent: Not applicable.
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Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
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Use of Large Language Models, AI and Machine Learning Tools: None declared.
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Conflict of interest: The authors state no conflict of interest.
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Research funding: None declared.
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Data availability: Not applicable.
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