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Despite significant progress having been made in the study of flame retardancy of cellulosic building materials, the quest to find the ideal flame retardant material still continues. This review paper explores various options to substitute halogenated flame retardants by halogen-free alternatives in green building materials. Conventional flame retardants do not chemically react with cellulosic building materials such as bamboo. Owing to this, they remain an extraneous part of the substrate and could easily leach out due to various factors leading to low efficiency of the flame-retardant action. Durability of flame retardancy can be ensured by applying an additive that would chemically react with at least one of the ingredients of the green building material so that the flame retardant would remain an inherent part of the substrate. This paper also describes the reaction mechanisms of various treatment methodologies for ensuring the efficacious actions of non-halogenated flame retardants in green building materials such as wood and bamboo.

Abstract Topology optimization (TO) has rapidly evolved from an academic exercise into an exciting discipline with numerous industrial applications. Various TO algorithms have been established, and several commercial TO software packages are now available. However, a major challenge in TO is the post-processing of the optimized models for downstream applications. Typically, optimal topologies generated by TO are faceted (triangulated) models, extracted from an underlying finite element mesh. These triangulated models are dense, poor quality, and lack feature/parametric control. This poses serious challenges to downstream applications such as prototyping/testing, design validation, and design exploration. One strategy to address this issue is to directly impose downstream requirements as constraints in the TO algorithm. However, this not only restricts the design space, it may even lead to TO failure. Separation of post-processing from TO is more robust and flexible. The objective of this paper is to provide a critical review of various post-processing methods and categorize them based both on targeted applications and underlying strategies. The paper concludes with unresolved challenges and future work.