STTR Phase I: Rapid Characterization of Wood-based Materials

Award ID: 2233237

The broader impact/commercial potential of this Small Business Technology Transfer (STTR) Phase I project will enable the wood industry to certify materials along its entire supply chain, achieving reproducibility and reducing delays in the movement of goods, and supporting the economic competitiveness of American wood companies. The technology being developed by this project entails rapid and accurate wood species identification, which will bring transparency to the wood industry, helping to enable proper forest management that follows social, economic, and government standards. The technology will also promote quality control of products and characterization of residues leftover from wood processing, which in turn would facilitate their recovery and reutilization. By allowing manufacturers to achieve reproducibility in composite material manufacturing, the technology will encourage the recycling of wood waste and byproducts, supporting efforts to increase the sustainability and reduce the environmental impact of the wood industry. Lastly, this technology will empower organizations combating illegal logging by providing a new and powerful forensic tool, thus addressing deforestation and forest depletion with significant economic, societal, and ecological benefits. This Small Business Technology Transfer (STTR) Phase I project is applying state-of-the-art chemical analysis methods to develop a novel wood identification technology capable of determining with accuracy the species, age, and geographical origin of uniform or composite products. With the increase in demand for transparency in the wood industry and more stringent regulations comes a demand for new technologies that support compliance. Despite being a critical step in this process, current methods for species identification are time consuming, require highly specialized training, and provide little information on age and origin. By combining a highly sensitive type of spectroscopy with advanced statistical approaches (i.e., machine learning), this project is developing a method that can reliably identify chemical fingerprints that inform about wood species, age, and origin when compared against a database. This Phase I project will develop and demonstrate this approach when applied to U.S. woods, including the creation of a wood species database, with these key objectives: 1) analyze domestic woods to develop statistical methods to identify the species; 2) test the methods ability identify species within composite materials; 3) test the methods ability to identify the geographical origin of samples; and 4) test the methods ability to determine wood age. 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.

NSF Program Director: Rajesh Mehta