Cellulose aerogel is a promising thermal insulation material with terrific thermal insulation and environmental friendliness. However, the intrinsic flammability of polysaccharide molecules and dependence on freeze-drying have limited its application in flame-retardant and thermal management systems. Here, we develop a flame-retardant biomass aerogel based on a dual-network matrix of bacterial cellulose and sodium alginate. This innovative material enables high-efficiency and low-cost preparation via ambient pressure drying technology (only ~3.5% volume shrinkage), while achieving flame retardancy by introducing an inorganic nanosheet microstructure within a polymer matrix. The resulting dual-network flame-retardant cellulose aerogel demonstrates thermal performance superior to that of most polymer foams and conventional cellulose aerogels, featuring an ultra-low thermal conductivity of ~0.04 W m⁻¹ K⁻¹ and a high limiting oxygen index (LOI) of ~69%. This research provides a novel strategy for simultaneous flame-retardant modification and energy-efficient manufacturing of biomass-derived aerogels.

Emergency response in urban gas pipeline networks is highly sensitive to stochastic traffic conditions, which introduce substantial uncertainty in crew travel times to leakage sites. Existing facility location models typically rely on predefined candidate sites and deterministic travel assumptions, limiting their ability to capture full-cycle dynamic recovery processes under random leakage events and traffic congestion. This study develops a candidate-free location optimization framework for repair station siting under stochastic road resistance conditions, aiming to characterize spatial variability in emergency response capability. The framework integrates a candidate-free facility location model with a hybrid greedy–Monte Carlo solution strategy to optimize station layouts across network-wide stochastic scenarios. Coverage reliability, response time, and construction cost are jointly considered to support robust siting decisions. A case study based on the real road and gas pipeline networks of City H demonstrates the effectiveness of the proposed approach. Across 20,000 stochastic road resistance scenarios, the optimized layout achieves an average service coverage rate of 97.77% within the specified response time threshold, while maintaining stable performance under variability. Although increasing the number of stations enhances response capability, the improvement exhibits clear diminishing marginal returns. These findings provide quantitative guidance for determining cost-effective station scale and prioritizing core hub locations under uncertainty. The proposed framework offers a structured decision-support tool for resilience-oriented planning, prioritization of critical segments, and evaluation of emergency response and maintenance strategies in urban gas pipeline systems.