Promoting Recycling Efficiency Through the Use of Sub-Terahertz Waves for Proper Wood Identification

Past studies have reported that carbon dioxide emissions during combustion vary depending on the tree species used as fuel. It has also been reported that the moisture content of wood affects combustion efficiency. From this perspective, identifying the tree species and moisture content is crucial for utilizing waste wood as a resource. Therefore, this study verified the effectiveness of non-destructive diagnosis using terahertz waves. Samples with adjusted moisture content were prepared for eight types of wood. Each wood sample was irradiated with multiple broadband terahertz electromagnetic waves, and their transmission characteristics were compared. Experimental results revealed a strong negative correlation (Pearson Correlation coefficient: −0.98~−0.71 square meter/gram) between the sample’s specific gravity and transmittance when irradiated with 65 GHz and 90 GHz sub-terahertz waves. This trend was particularly pronounced during 90 GHz sub-terahertz irradiation. Furthermore, it was found that the trend in transmittance variation differed depending on the wood’s moisture content. These results indicate that terahertz waves are effective as a wood identification method capable of distinguishing between coniferous and broadleaf trees. Furthermore, they are considered effective for predicting wood moisture content. This research is expected to contribute to promoting wood recycling and the sustainable use of wood resources.