STTR Phase I: Rapid Characterization of Wood-based Materials

这项小企业技术转移（STTR）第一阶段项目的广泛影响/商业潜力将使木材行业能够对整个供应链中的材料进行认证，实现可复制性，减少货物运输的延误，并支持美国木材公司的经济竞争力。该项目正在开发的技术需要快速和准确地识别木材品种，这将为木材工业带来透明度，有助于按照社会、经济和政府标准进行适当的森林管理。这项技术还将促进产品的质量控制和木材加工后残留物的特性，从而促进它们的回收和再利用。通过允许制造商实现复合材料制造的可重复性，该技术将鼓励木材废料和副产品的回收利用，支持提高可持续性和减少木材工业对环境的影响的努力。最后，这项技术将为打击非法采伐的组织提供一种新的、强大的法医工具，从而解决森林砍伐和森林枯竭问题，带来巨大的经济、社会和生态效益。这个小型企业技术转让（STTR）第一阶段项目应用最先进的化学分析方法来开发一种新的木材识别技术，能够准确地确定均匀或复合产品的种类、年龄和地理来源。随着木材行业对透明度需求的增加和更严格的法规，对支持合规性的新技术的需求也随之增加。尽管是这一过程中的关键一步，但目前的物种鉴定方法耗时，需要高度专业化的培训，并且提供的关于年龄和起源的信息很少。通过将高度敏感的光谱学与先进的统计方法（即机器学习）相结合，该项目正在开发一种方法，可以与数据库进行比较，可靠地识别化学指纹，从而了解木材的种类、年龄和来源。这个第一阶段的项目将开发和演示这种方法，并将其应用于美国的木材，包括建立一个木材物种数据库，其主要目标是：1)分析国内木材，开发统计方法来识别物种；2)测试该方法识别复合材料内部物种的能力；3)测试方法识别样品地理来源的能力；4)测试该方法确定木材年龄的能力。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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 method’s ability identify species within composite materials; 3) test the method’s ability to identify the geographical origin of samples; and 4) test the method’s 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.