RESUMEN
An innovative approach for the rapid identification of wood species is presented. By combining X-ray fluorescence spectrometry with convolutional neural network machine learning, 48 different wood specimens were clearly differentiated and identified with a 99% accuracy. Wood species identification is imperative to assess illegally logged and transported lumber. Alternative options for identification can be time consuming and require some level of sampling. This non-invasive technique offers a viable, cost-effective alternative to rapidly and accurately identify timber in efforts to support environmental protection laws and regulations.
RESUMEN
Recently, ultrafast lasers exhibiting high peak powers and extremely short pulse durations have created a new paradigm in materials processing. The precision and minimal thermal damage provided by ultrafast lasers in the machining of metals and dielectrics also suggests a novel application in obtaining precise cross-sections of fragile, combustible paint layers in artwork and cultural heritage property. Cross-sections of paint and other decorative layers on artwork provide critical information into its history and authenticity. However, the current methodology which uses a scalpel to obtain a cross-section can cause further damage, including crumbling, delamination, and paint compression. Here, we demonstrate the ability to make controlled cross-sections of paint layers with a femtosecond pulsed laser, with minimal damage to the surrounding artwork. The femtosecond laser cutting overcomes challenges such as fragile paint disintegrating under scalpel pressure, or oxidation by the continuous-wave (CW) laser. Variations in laser power and translational speed of the laser while cutting exhibit different benefits for cross-section sampling. The use of femtosecond lasers in studying artwork also presents new possibilities in analyzing, sampling, and cleaning of artwork with minimal destructive effects.