Direct Colorimetric Temperature Measurement Ahead of Flame Zone with Polydiacetylenes
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37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
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Biobased Flame Retardants Towards Sustainable Building Materials with Low Embodied Carbon
Incorporation of fire retardants in different types of products is one of the essential steps in the material production process to minimize fire risk and meet fire safety requirements. A variety of commonly used flame retardants based on halogen, mineral, and other compounds has gained popularity due to their efficient flame-retardant behaviors. However, especially in the case of halides, there are numerous toxicity related issues and environmental pollution effects that have urged the building sector to deviate from their use and focus on the development of non-toxic alternatives. Therefore, to enhance the safety of flame retardants, the synthesis of flame retardants from waste feedstocks using phosphorous chemistry with a dual flame-retardant mechanism has been established. A range of waste feedstocks that include cardanol, vanillin, and gallic acids has been converted into a series of flame retardants using a one-step approach by incorporating phosphorous moieties into their structures. The established pathways allow to develop a range of flame-retardant materials by converting waste feedstocks into phosphorous-based materials with inherent flame retardancy. The introduction of abundant aromatic structures from biobased feedstocks enables high charring capabilities in materials in which these biobased flame retardants have been incorporated, increasing their char yields which significantly enhances the flame suppressing properties of the designed materials. Studies show that inclusion of phosphoric moieties into structures allows the displacement of flame enhancing radicals, by releasing non-flammable and non-toxic gases, therefore inhibiting the spread of fire in the gas phase. The resulting biobased flame retardants have been incorporated in wood substrates, foam insulation, and hemp fibers where the results show that only 1-5% of loading of biobased flame retardant suppressed the flame completely. This study represents a novel approach for the development of flame retardants with high performance while utilizing waste feedstocks as a source for their design.
Demchuk, Zoriana [ORNL] (ORCID:0000000326292235)↗
A Safe Response to Renewable Energy Hazards
The International Association of Fire Fighters (IAFF) and Underwriters Laboratories, LLC (UL) in conjunction with UL Solutions initiated a joint project in 2022 under an agreement with the United States Department of Energy-Office of Energy Efficiency and Renewable Energy (DOE-EERE). This project focused on two separate and important initiatives related to energy efficiency in residential buildings. Initiative 1: Fire Performance on Energy Efficient Exterior Walls. Initiative 2: Firefighting Tactics in Residential Properties with Building Energy Storage Systems (BESS). The project’s first initiative addresses concerns surrounding new technologies with enhanced, energy-efficient exterior walls installed on residential properties. The concerns of fire rapidly traveling vertically up the exterior of these walls were examined. This addressed a growing concern from the first responder community that many times, the fires on the exterior of residential buildings have already evolved into an attic fire by the time of arrival – making it problematic to address the fire scenario. The test plan for Initiative 1 incorporated a modified version of an American Society for Testing and Materials (ASTM) test method, ASTM E2707, Standard Test Method for Determining Fire Penetration of Exterior Wall Assemblies Using a Direct Flame Impingement Exposure, as the foundation of the research. The test method involved a wall structure intended to represent retrofit construction to evaluate how fire would spread vertically or laterally. The second aspect of the UL-IAFF Project focuses on the fire service response to Residential Battery Energy Storage System (RBESS) incidents. These simulation tests were constructed in the large-scale fire test facility at UL Solutions’ Northbrook, IL campus. A baseline test was conducted that involved a test structure with no batteries—shelving units populated with standardized commodities, representing a typical garage with cellulosic and plastic contents. Three additional tests have been conducted to generate data with the contribution of energy storage system (ESS) batteries to compare fire and explosion hazards against the baseline test. Through this work, fire service tactical considerations can be explored. From the data, the team can determine 1) the visual indicators of a residential fire that has involved an RBESS (or, potentially, other large batteries) and 2) the impact of fire service-initiated ventilation of the structure on the fire conditions and explosion risks.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
Toward an AI-Powered Software Pipeline for Real-Time Tracking and Analysis of Wildfire and Smoke
Real-time tracking of wildfires and smoke is crucial for effective response, minimizing damage, protecting lives, and efficiently managing resources during fire emergencies. We develop a web-based AI-powered pipeline that detects wildfires in aerial video and estimates deployment-relevant behavior metrics, including cumulative burned area, burned-area growth rate, fire spread direction, and smoke dispersion. The system combines a YOLO-based detector with YCbCr-based fire segmentation, HSV-based smoke segmentation, Farneback optical flow, and centroid-based spatiotemporal tracking. Using ground sampling distance (GSD), pixel-level fire masks are converted to physical burned-area measurements by correlating fire pixel counts with camera altitude and tilt angle. We benchmark YOLO variants and non-YOLO baselines (GoogLeNet, CNN, DBN, Autoencoder, U-Net, and AlexNet) on the IEEE FLAME dataset and a newly created aerial frame dataset, Wildfire-DB. Cross-dataset evaluation uses a strict threshold-transfer protocol: decision thresholds are selected on FLAME validation and transferred unchanged to Wildfire-DB to quantify generalization under domain shift. YOLOv6 achieves the strongest cross-dataset frame-level fire detection on Wildfire-DB (ROC-AUC 0.8200, PR-AUC 0.8044, and transferred-threshold F1 0.7596). For tracking-oriented deployment requiring oriented localization, YOLO11-OBB provides the most reliable cross-dataset behavior among OBB-capable models while remaining computationally feasible. To analyze the feasibility of UAV deployment, we further measure inference efficiency using synchronized GPU and CPU power logs on a fixed workload of 1569 frames. YOLO-family models process the video in 5.73–12.47 seconds with net energy of 1247.28–1775.39 J, substantially lower latency and energy than heavier classification and reconstruction baselines. Overall, model optimality depends on operational objectives: YOLOv6 is best for cross-dataset detection robustness, whereas YOL...
Color segmentation↗