How does ultrasound contribute to the migration of extractives inside Ailanthus altissima wood?

Extractives have an impact on the processing and commercial value of wood. Ultrasound is an environmentally friendly technology commonly employed to reduce the extractive content and thus enhance the permeability of wood. This study aimed to understand the migration mechanism of extractives inside wood during ultrasonic treatment, which may help to obtain the desired wood properties. The extractive distribution of Ailanthus altissima was observed by using stereo microscopy, optical microscopy, and scanning electron microscopy, the extractive content was determined, and the relationship between the concentration of water-soluble extractives and absorbance was measured using a UV/Vis spectrophotometer, and the migration model of extractives was studied using layered extraction by innovatively combining the weight and the absorbance methods. The results revealed that the extractives were predominantly distributed in the vessels and diminished after ultrasonic treatment. The extractive content gradually decreased over time (0 ∼ 5 h), with a rapid decline observed within the first 2 h. The concentration of the water-soluble extractives exhibited a proportional relationship with the absorbance. Through the comparison of the layered-extractive concentration, accumulating evidence suggested that the migration of the extractives was a dynamic process, which included the extractives migrating towards easy-extracted area, moving along the direction of ultrasound propagation inside the wood, and leaching out of wood during ultrasonic treatment.

Quality Control of Thermally Modified Western Hemlock Wood Using Near-Infrared Spectroscopy and Explainable Machine Learning.

The quality control of thermally modified wood and identifying heat treatment intensity using nondestructive testing methods are critical tasks. This study used near-infrared (NIR) spectroscopy and machine learning modeling to classify thermally modified wood. NIR spectra were collected from the surfaces of untreated and thermally treated (at 170 °C, 212 °C, and 230 °C) western hemlock samples. An explainable machine learning approach was practiced using a TreeNet gradient boosting machine. No dimensionality reduction was performed to better explain the feature ranking results obtained from the model and provide insight into the critical wavelengths contributing to the performance of classification models. NIR spectra in the ranges of 1100-2500 nm, 1400-2500 nm, and 1700-2500 nm were fed into the TreeNet model, which resulted in classification accuracy values (test data) of 94.35%, 89.29%, and 84.52%, respectively. Feature ranking analysis revealed that when using the range of 1100-2500 nm, the changes in wood color resulted in the highest variation in NIR reflectance amongst treatments. As a result, associated features were given higher importance by TreeNet. Limiting the wavelength range increased the significance of features related to water or wood chemistry; however, these predictive models were not as accurate as the one benefiting from the impact of wood color change on the NIR spectra. The developed framework could be applied to different applications in which NIR spectra are used for wood characterization and quality control to provide improved insights into selected NIR wavelengths when developing a machine learning model.

The Role of Drying Schedule and Conditioning in Moisture Uniformity in Wood: A Machine Learning Approach.

Monitoring the moisture content (MC) of wood and avoiding large MC variation is a crucial task as a large moisture spread after drying significantly devalues the product, especially in species with high green MC spread. Therefore, this research aims to optimize kiln-drying and provides a predictive approach to estimate and classify target timber moisture, using a gradient-boosting machine learning model. Inputs include three wood attributes (initial moisture, initial weight, and basic density) and three drying parameters (schedule, conditioning, and post-storage). Results show that initial weight has the highest correlation with the final moisture and possesses the highest relative importance in both predictive and classifier models. This model demonstrated a drop in training accuracy after removing schedule, conditioning, and post-storage from inputs, emphasizing that the drying parameters are significant in the robustness of the model. However, the regression-based model failed to satisfactorily predict the moisture after kiln-drying. In contrast, the classifying model is capable of classifying dried wood into acceptable, over-, and under-dried groups, which could apply to timber pre- and post-sorting. Overall, the gradient-boosting model successfully classified the moisture in kiln-dried western hemlock timber.

The Acoustic Properties of Water Submerged Lodgepole Pine (Pinus contorta) and Spruce (Picea spp.) Wood and Their Suitability for Use as Musical Instruments.

Wood is a common material used for the manufacture of many products, and submerged wood, in particular, has been used in niche markets and musical instruments. In order to examine if submerged wood in British Columbia, Canada, would be appropriate for use as musical instruments, a study was performed in 2007 on submerged wood from Ootsa Lake, British Columbia, Canada. The results of that study showed the wood was not suitable for musical instruments. In this paper, the wood samples were allowed to age untouched in a laboratory setting and were then retested under the hypothesis that physical acoustic characteristics would improve. It was shown, however, that acoustic properties became less adequate after being left to dry over time. This article describes the density, speed of sound, acoustic constant and characteristic impedance properties for submerged wood and a comparison is made for different applications for musical instruments.