BRITE Pivot: Identifying Premature Deterioration in Cementitious Materials Using Volatilomics

Oregon State University, Corvallis OR

Investigators

Abstract

For millennia, the medical field has utilized the sense of smell for qualitative assessment of health, but recent research shows we can tap into volatile organic compounds, which create the odors that we perceive, for quantitative detection and analysis. In this Boosting Research Ideas for Transformative and Equitable Advances in Engineering (BRITE) Pivot project, volatile organic compounds produced by the microbes in aged concrete, both undamaged and undergoing deterioration, will be analyzed. Cementitious materials are the most used building materials in the entire world; twice as much as all other materials combined. These materials can undergo premature deterioration and reacting to that as quickly as possible can mean a successful remediation plan that ensures the intended lifespan. This is a key part of meeting increasingly demanding sustainability goals. Identifying volatile organic compound biomarkers of this material’s health will lay the groundwork for the development of sensors that can provide early warnings of deterioration, enabling more effective remediation/repair strategies. Microbes may also be sensitive to environmental stress (e.g., climate change), and a sensor based on microbial volatile organic compounds could be used not only monitor infrastructure health, but to also provide data to detect environmental stressors relevant to other fields. The overall goal of this project is for the PI to pivot to the field of volatilomics for disease detection and bring back new quantitative tools for diagnosing deterioration in the abiotic environment of concrete. Volatile organic compounds metabolites, an oft unused resource of chemical information that are produced by concrete-associated microbial communities, will be used to detect and characterize concrete deterioration. Scanning electron microscopy with energy disperse x-ray analysis, expansion measurements, and visual observations will be used to confirm deterioration mechanisms identified using volatilomics. Volatiles will be analyzed using two-dimensional gas chromatography–time-of-flight mass spectrometry. To determine if the volatile organic compound metabolites can distinguish between different deterioration mechanisms, the data gathered will be analyzed using machine learning (Random Forest). Descriptive statistics will be used to summarize and compare sample classes. Volatile organic compounds data will be submitted to Metabolomics Workbench for public use. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.