Wood Science and Technology最新文献

Bamboo is one of the most rapidly growing plants with a highly sophisticated root and rhizome system in its culm base, where conducting tissue plays a key role in water and nutrient absorption and transportation. However, our knowledge of the three-dimensional structure of the conducting tissue is incomplete due to the opacity of the bamboo. In this paper, the spatial relationships of the conducting tissues among the main stem, root and rhizome of the culm base are explored. The culm base of a Chimonobambusa tumidissinoda was used for the analysis and high-resolution X-ray microtomography (μCT) was employed. A deep learning algorithm was used to segment the conducting tissue from the culm base. 3D model reconstruction and semi-quantitative characterization of the conducting tissue were realized. It was found that the anatomical characteristics among the main stem, root and rhizome are different, but the conducting tissues in these structures are interconnected in different ways. The transverse conducting tissue mainly originated from the rhizome rather than the root, and its thickness gradually decreased from the bottom of culm base to its top, contrary to the structure of the axial conducting tissue. The results indicate that μCT combined with deep learning segmentation effectively visualizes complex conducting tissue structures, volume filtering enhances detailed observation of network structures within conducting tissues, which provides new insights into the bamboo’s culm base structure and evidence of the sophisticated and interconnected fluid motion pathways among the different tissues of the culm base.

Steaming of green timber, a common industrial process for various hardwood species, significantly influences wood properties, including coloration and drying characteristics. However, the environmental implications of substantial volumes of condensate generated during wood steaming underscore the urgency for its sustainable management. This study explores the chemical composition of the condensate obtained during the 90-hour indirect steaming of walnut timber (WTSC), aiming to identify potential applications for this wastewater while addressing environmental risks. Chemical characterization of WTSC included qualitative LC-MS/MS analysis, determination of the total phenolic content (TPC), total flavonoid content (TFC) and the content of selected phenolics. WTSC exhibited high TPC (188 mg gallic acid equivalents per L) and TFC (9.74 mg quercetin equivalents per L) values. Additionally, WTSC showed significant antioxidant activity (IC₅₀ (DPPH) = 61.4 µg/mL and 103 µg ascorbic acid equivalents per mL in FRAP assay). Specific phenolic compounds detected in the WTSC distinguish it from other wood industry effluents and are a consequence of the unique characteristics of walnut wood and conditions during steaming process. A variety of acids (p-hydroxybenzoic, protocatechuic, syringic, gallic, cinnamic, cinnamic, p-coumaric, o-coumaric, vanillic) and flavonoids (apigenin, genistein, naringenin, luteolin, kaempferol, chrysoeriol, isorhamnetin, apigenin 7-O-glucoside, vitexin, kaempferol 3-O-glucoside, catechin, epicatechin, and quercitrin) were identified and quantified. The condensate exhibited higher TPC value and antioxidant activity than other wood industry effluents, positioning it as a promising natural antioxidant with potential applications in pharmaceutical and food industries. However, our short-term goal is to explore the potential use of WTSC as received – without isolating individual compounds – in studies focused on plant protection, textile dyeing, and wood-based panel production.

The article investigates the charring and the char front temperature of beech, the most widespread hardwood species in Central Europe. The current Eurocode standard EN 1995-1-2 specifies the char front temperature to be 300 (^{circ })C, albeit this determination primarily applies to softwood species. Consequently, this article aims to examine whether this assumption applies to beech. Through advanced experimental analysis and numerical modelling, it was determined that the char front temperature for beech exceeds 300 (^{circ })C. This finding represents crucial information for the correct validation of fire-resistant design for structural elements made of beech. Moreover, it lays the groundwork for improving simplified methods of fire design, particularly for a more accurate determination of the charring depth.

Predicting the influence of structural parameters on wood elasticity is useful for engineering application, however due to the complex imbrication of several scales it is important to know which features need to be taken into account. The aim of this work is to investigate the influence on wood stiffness of waviness and interconnection of cellulosic fibrils, an observed feature usually overlooked in micromechanical models. For that, a multi-scale model estimating the macroscopic behavior of wood is developed. This model integrates three different scales of wood structure: that of the cell wall, that of the cellular tissue and that of the growth ring. It relies on both numerical and analytical homogenization procedures to determine their effective behavior by defining at each scale a periodic representative volume element. Using this multi-scale model, it is shown that the influence of the oscillations and interconnections of the fibrils is significant for certain moduli at the macroscopic level (ring scale), such as the macroscopic shear moduli, while it can be neglected for others. Furthermore, although the effect of fibril crosslinks is quite strong for certain components of elastic behavior at the cell wall level, it loses its importance at the macroscopic level, especially for low-density wood. This trend can be explained by the anti-symmetric tilt of fibrils in adjacent cell walls. On the other hand, for denser woods where the interactions between adjacent cell walls are less dominant, or in the case of softened wood, the effect of fibril oscillations remains important.

In this study, the rheological Burger model combining Maxwell and Voigt–Kelvin model units as well as modified mechanical models were employed to analyze the shear creep mechanism of wood. Off-axis compression tests were conducted on Japanese Hinoki cypress specimens (Chamaecyparis obtusa), and a mechanical analysis of the shear creep mechanism was performed. First, the measured creep compliance curves [J_TL(t)] were fitted using this Burger model, which is a typical model used to explain the creep behavior of wood. Furthermore, three modified Burger models with non-Newtonian dashpots were proposed to explain the measured data more accurately: model 1—only the dashpot in the permanent strain unit is non-Newtonian; model 2—both dashpots are non-Newtonian; and model 3—only the dashpot in the delayed elastic strain unit is non-Newtonian. The mean value of the coefficient of determination was highest for model 1. The number of specimens that could be fitted with a tolerance error of 0.1% was 43 out of 50 with the Burger model, 45 with model 1, 25 with model 2, and 45 with model 3. The Burger model exhibited large discrepancies between the theoretical and measured values, model 2 could not be used to explain several specimens, and model 3 exhibited a delayed elastic strain behavior that was inconsistent with the definition. Therefore, we conclude that model 1 is the most appropriate for studying the shear creep behavior of wood.

A new technique based on machine learning algorithms was introduced to detect internal wood defects. This technique relies on analyzing segmented propagation rays of stress waves and successfully generates the tomographic images of the defects by using the stress wave velocity. Utilizing a dual-stage methodology, the initial phase involves ray segmentation for the precise delineation of stress wave propagation, while the subsequent stage integrates advanced classification and clustering algorithms to facilitate the generation of tomographic images. This approach effectively tackles the inherent challenges associated with accurate segmentation and classification of stress wave velocity rays. The effectiveness of the proposed method was evaluated using both synthetic and experimental data. The results showed that the proposed method, when compared with some state-of-the-art methods, has a superior ability to accurately detect defective regions in the wood. The success of the proposed method is evaluated with four different evaluation metrics. It determined that over 90% success is achieved for all metrics. In comparison with related studies, it determined that the results are improved by 7–22% compared to the literature.

Due to the time-consuming and labor-intensive characteristic of wood collection, especially the high cost associated with collecting precious wood, utilizing prior knowledge becomes more effective when facing limitations such as few-shot samples, multi-category samples, and unbalanced samples during recognition training. Prior knowledge is a technique that helps algorithms to adapt new data quickly, generalize better to new situations, and understand the results of learning models more effectively. In this study, the DMV (Dual-input MobileViT) model, which incorporates prior knowledge into the MobileViT model, is proposed to improve the recognition accuracy of few-shot samples of wood. The incorporation of texture features as prior knowledge in the deep learning model is motivated by their high discriminative capability in distinguishing various types of wood, supported by mature techniques and algorithms in digital image processing. This integration ultimately enhances the efficiency and accuracy of the recognition system. The effectiveness of incorporating texture features as structural prior knowledge into the model is demonstrated by a final training accuracy of 97.8% and a testing accuracy of 92%. To enhance robustness, the texture loss is weighted with the original loss function, creating a new loss function applied to the model. Extensive experiments have shown promising results, demonstrating the advantages of the proposed approach.

To evaluate the formation and changes in graphitic structures in transition-metal loaded charcoal, charcoal samples synthesized from Japanese cedar wood impregnated with 3d-transition metal (Cr, Mn, Fe, Co, Ni, Cu, Zn) ions were analyzed using microscopic Raman spectroscopy and powder X-ray diffractometry. The metal-loaded charcoal samples were carbonized at 650, 700, 750, 800, and 850 °C in downstream N₂ gas. The Raman Gˊ-band, which shows the structural ordering of carbon atoms, was observed in the Raman spectra of Fe-, Co-, and Ni-loaded charcoal. The Gˊ-bands occurred at ≤ 2670 cm^− 1 and shifted to 2700–2690 cm^− 1 with increasing carbonization temperature. The Gˊ-band observed in the higher wavenumbers (2700–2690 cm^− 1) range corresponded to an X-ray diffraction (XRD) peak at ∼ 26.3 ° assigned to the (002) plane of graphite-like structures. The high-wavenumber Gˊ-band also corresponded to the XRD detection of the carbide of the three metals. However, the XRD peak was not found for Co- and Ni-loaded charcoal samples exhibiting Gˊ-bands at ≤ 2670 cm^− 1.

Global climate change is accompanied by a change in tree composition in many regions. In Europe, the distribution areas of many species are expanding towards the north so that, among others, black locust (Robinia pseudoacacia), which is native to the USA and has long been established in south-eastern Europe, is also becoming increasingly important in central and northern Europe. Many other tree species are known to have different properties between their original and new locations, including the biological durability of the wood. Hence, the resistance of black locust wood against decay fungi was studied concerning origin-specific differences. Wood was sampled from seven different origins in Europe and original habitats in the United States. Fungal incubation experiments were conducted, wood extractives were analysed, and different anatomical characteristics were quantified such as ring width, vessel size distribution and the presence of tyloses. In addition to differences in durability between juvenile and mature wood, origin-specific differences within the mature heartwood were attributed to extractive contents and the percentages of earlywood vessels containing tyloses. Based on parameters that contributed at least 20% to mass loss, susceptibility to fungal decay was modelled with multiple regressions.

A significant challenge in applying nuclear magnetic resonance (NMR) in examining the wood-water system is accurately and effectively conditioning wood samples before such tests. The common approaches, such as the saturated salt solution method, have drawbacks of long equilibrium time and significant moisture content deviations. The water-addition-equilibrium method proposed here is an alternative conditioning approach that adds liquid water directly to oven-dried samples following sealing and equilibrating at 45 ℃ for 72 h until obtaining the even water distribution in samples. The equilibrium time in the latter method was determined by analyzing evolutions of the spin-spin relaxation time ((:{T}_{2})) spectra with five equilibrium time durations, i.e., 0, 24, 48, 72 and 96 h. Compared with the salt solution method, it is much easier and faster to achieve target moisture content using the water-addition method. When the actual target moisture of the samples is similar, no apparent differences are observed between the (:{T}_{2}) spectra obtained using the two methods. For this study, the water-addition method was applied to poplar samples with the moisture content target of 32% and below. The proposed method may be applied to other wood species and is expected to contribute to NMR examinations where the accurate and continuous control of sample moisture is required.