Volume 79, Issue 6

The objective of this study was to use colorimetry and visible spectroscopy to discriminate wood species obtained from the Amazon floodplain, in addition to determining the basic density and relating it to the wood colorimetric parameters. The studied material was from a community forest management area in the state of Amazonas, Brazil. For colorimetric and visible spectrum analyses, data were obtained randomly in the transverse anatomical surface. The spectral behavior of the wood samples in the visible region was evaluated. In the statistical analyses, comparisons of colorimetric parameters and visible spectra of wood from floodplain tree species were carried out. Furthermore, the basic density of the wood was determined. The best differentiation of species was achieved mainly by the hue angle. The species Pseudobombax munguba (Mart.) Dugand presented the highest median for the coordinate of the red-green axis, and the medians of the species P. munguba and Hevea spruceana (Benth.) Müll.Arg. obtained higher values of the blue-yellow axis coordinate and saturation. All species evaluated were classified as light and having low-density wood. Thus, it can be concluded that the colorimetric technique has potential to distinguish Amazon floodplain forest species.

Haloxylon ammodendron is a small psammophyte tree that grows under challenging environmental conditions in the Mongolian Gobi Desert. It plays a key role in sustaining the structure and function of the Gobi ecosystem, and it is a commodity with high ecological value on the regional scale. In this study, thin cross-sections of the wooden trunk were used to determine the type of tree ring boundaries and axial parenchyma characteristics by Raman imaging. The wood samples were analysed for their content, composition, and radial distribution of extractives, which provided insight into the ultrastructure of the wood trunk as well as the transport and storage of various metabolites. The ecologic success of the H. ammodendron is in part due to the deployment of lignin both as a protective component and key structural material. The tree species appear to have a potential as a precursor for various applications, such as the development of lignocellulosic sorbent materials, activated carbons, etc. due to the high lignin content, while the structure permits few utilizations, including composite pulps, particle boards and panels etc.

This study employed time domain nuclear magnetic resonance by using inversion recovery and Carr–Purcell–Meiboom–Gill pulse sequences to identify the relaxation properties of bound water in elm and pine wood under various relative humidity levels. Results showed that the sizes of micropores and mesopores in elm and pine increased with water accumulation. Furthermore, the sizes of micropores and mesopores in sapwood were larger than those in heartwood of both elm and pine wood. Hydroxyl bound water (OH bound water) molecules were tightly bound to the primary sorption sites of the polymers. More freely bound water occupied the void spaces between microfibrils within the cell wall. Elm wood exhibited higher T 1 /T 2 ratios for bound water than pine, suggesting that elm had less mobility than pine. Moreover, OH bound water exhibited higher T 1 /T 2 ratios than more freely bound water in pine and elm, indicating greater molecular mobility constraints and stronger interactions with the wood polymer. Notably, OH bound water mobility increased significantly with water accumulation.

Assessing the total crack formation on wood surfaces is important to further our understanding of the wood degradation and aging process and to examine both untreated wood as well as different modification systems. Manually assessing cracks can be both time consuming and difficult to perform accurately without bias and is therefore usually performed according to a rating scheme based on visual assessment. To improve accuracy and generate more possible output parameters, a model to detect and classify surface cracks on naturally- and artificially exposed wood surfaces was developed using region-based image segmentation. The model uses an iterative thresholding approach aimed to provide better results on complex, multi-coloured wood surfaces. The model was able to detect all cracks with widths greater than 0.6 mm in both exposure classes and calculated the total length of the cracks close to those that were measured manually (R 2  = 0.990–0.995). In its current state, the model is best used to calculate the total ratio of the surface that is cracked which allows for analysis of the crack development over time.

Moisture content (MC) plays a crucial role in determining the mechanical and physical properties of wood, including strength, stiffness, and dimensional stability. Stratified MC measurement is particularly valuable for understanding the moisture distribution across different layers of wood, which is essential for optimizing wood processing and applications. However, existing stratified MC measurement methods are often time-consuming, costly, and complex, limiting their widespread use in industrial settings. This study proposes a novel, non-destructive method for stratified MC measurement based on the electrical conductivity of wood. By utilizing the relationship between electrical conductivity and moisture distribution, this method provides a rapid, accurate, and cost-effective alternative. Experimental comparisons with the slice methods and the single-sided nuclear magnetic resonance (NMR) validate the accuracy of the proposed method, with average errors controlled within ±1 %, and the measurement time limited to 10 s. The newly developed instrument is portable, efficient, and easy to operate, making it highly suitable for real-time applications in academic research and industrial settings. By addressing the limitations of existing methods, this technology significantly enhances measurement efficiency. Moreover, it improves resource utilization and reduces energy consumption, thereby promoting sustainable practices in the wood industry.