Applications of bio-enhanced cementitious composites for sustainable building materials: emission-reduction and...

This article uses knowledge mining to examine microbial strengthening mechanism in cementitious composites. Knowledge graphs and large language models (LLMs) support literature collection and curation to link established findings with recent advances in the development of bio-cement. The review covers pathways of microbially induced calcite precipitation (MICP), microbial species, reinforcement/delivery strategies, metabolism under cementitious environments, and functional properties (i.e., self-healing, and carbon sequestration). The review article discusses the microbial physiology within cementitious matrices that consist of high alkalinity, restricted mass transport, and biomineralization. The article examines engineering limitations, including multistage and relatively slow production, dependence on moisture and nutrients, treatment byproducts, nonuniform precipitation, and uncertain long-term viability. Priorities include standardized field testing, protective delivery systems, omics-based analysis of microbial responses in cement, and machine learning ready databases. Coupling transcriptomics, metabolomics, metabolic flux analysis, and genome-scale models with AI/ML or digital twins may support strain selection and prediction of mineralization and self-healing. However, these approaches require interoperable datasets and field validation. The paper elaborates that bio-enhanced cementitious composites can support emission reduction and durable carbon sequestration. Whereas net negative performance must be demonstrated through complete life cycle analysis that accounts for greenhouse gases and microbial survivability in cementitious environments.