Wood Science and Technology最新文献

Microbial degradation of lignocellulose is a complex process mainly carried out by filamentous fungi and bacteria. Among prokaryotes, the genus Streptomyces stands out, with laccases playing a key role in its lignocellulolytic enzyme system. However, bacterial laccases have a lower redox potential than fungal laccases, suggesting that their action on lignin is indirect, via high redox potential intermediates. Prominent examples of intermediates are hydroxyl radicals. In Basidiomycota fungi, the production of these radicals occurs through quinone redox cycling, involving a Fenton reaction. This study demonstrates, for the first time, extracellular hydroxyl radical production via quinone redox cycling in the bacterium Streptomyces cyaneus CECT 3335, with laccase playing an essential role. The process begins with the extracellular oxidation of quinones to semiquinones, catalyzed by laccase. In the presence of Fe³⁺, semiquinones produce hydroxyl radicals via a Fenton reaction. The cycle is restored through quinone reduction by mycelium-associated reductase activity. H₂O₂ production, Fe³⁺ reduction, and hydroxyl radical generation were confirmed in S. cyaneus. The key role of laccase was verified using a mutant strain lacking laccase activity, in which hydroxyl radical production was absent. The oxidative potential of this mechanism in S. cyaneus was evidenced by the degradation of non-phenolic lignin-related compounds homoveratric acid and veratraldehyde and by the ability to depolymerize kraft lignin. This novel finding of quinone redox cycling in bacteria has important implications for Streptomyces’ role in lignin degradation, as well as potential biotechnological applications, including lignin biotransformation and bioremediation of organic pollutants.

In regions where microbial contamination of groundwater and surface water remains a significant public health concern, leading to around 505,000 annual deaths, there is an urgent need for accessible, cost-effective, and simple household water treatment solutions. This study investigated the feasibility of wood as a filtration system, with a focus on its ability to remove nanoparticles. The research underscores the remarkable potential of wood filters, particularly in radial and tangential directions, exhibiting superior particle removal capabilities (> 99%) due to extended residence time and intricate microstructures. The study reveals that wood type selection in this study, i.e., yellow poplar (Liriodendron tulipifera), European beech (Fagus sylvatica), Douglas fir (Pseudotsuga menziesii), and silver fir (Abies alba), plays a crucial role in filtration efficiency, with beech emerging as a high-performing option alongside silver fir. Importantly, the optimal range of size exclusion was identified (160–490 nm), aiding in designing wood filters for specific particle size reduction goals. Wood filters also show great potential for removing a broad range of microorganisms, i.e., bacteria and protozoa, as well as nanoplastics and microplastics, which could have profound implications for water treatment and environmental remediation. Furthermore, the study highlights the adsorption/diffusion process through the amorphous domains of the wood biopolymers, i.e., cellulose, hemicelluloses and lignin, enhanced by electrostatic interactions in the filtration efficiency for small organic molecules, providing valuable insights into filtration mechanisms.

Species identification of carbonized wood holds significance for various scientific disciplines, including botany, palaeontology, and archaeology. Identification also contributes to the preservation of endangered wood species and forests, and supports climate research. With regard to the identification of wood and wood products, all international research institutions adhere to the IAWA list of microscopic features for hardwood and softwood identification, established by the IAWA Committee in 1989.

Our comparative anatomical studies of 30 different species reveal significant dimensional losses of quantitative features during the charring process. Specifically, the findings indicate a shift in size classes, with varying percentages of loss in anatomical features from solid wood to charcoal for most of the taxa analyzed. Consequently, the size classes defined in databases for solid wood differentiation cannot be directly applied to charcoal identification. Furthermore, the present study employs statistical evaluations to illustrate the application of conventional size classes for the parameters: tangential diameter of vessel elements, intervessel pit diameter, ray height, and width. The implications of these findings for charcoal research are discussed in detail.

The Young’s modulus and loss tangent of a Sitka spruce wood sample in the longitudinal direction were determined using free flexural vibration (FRFV) and forced flexural vibration (FOFV) tests. During the tests, the attached weight and sample length were varied, and their effects on the Young’s modulus and loss tangent of the sample were examined. The Young’s modulus could be accurately and easily obtained from both the FRFV and FOFV tests using a modified Euler-Bernoulli’s equation, with the effect of the attached weight mitigated. No significant difference was observed between the two values of the Young’s modulus obtained from the two tests. The loss tangent slightly increased as the attached weight increased when the ratio of attached weight/sample weight was below 10%; however, it significantly increased with the increase in the attached weight when the ratio of the attached weight/sample weight exceeded 10%. The values of the loss tangent obtained from the FOFV tests for different samples were often higher than the corresponding values obtained from the FRFV tests, whereas the loss tangent values of different samples obtained from the FOFV tests were lower than the corresponding values obtained from the FRFV tests.

Monomer impregnation is a great strategy to modify various wood properties. By choosing the right impregnant, it may lead to a higher flame retardancy of treated wood, contributing to its use in specific sectors such as building interior finishes. Yellow birch (Betula alleghaniensis Britt.) and sugar maple (Acer saccharum Marsh.) were surface-impregnated with an acrylate and a phosphorus acrylate monomer under vacuum and exposed to an electron beam for polymerisation. A surface chemical retention of 100 g.m^− 2 was obtained for sugar maple, while the impregnation of yellow birch samples reached one around 200 g.m^− 2. X-ray densitometry confirmed an asymmetric density profile due to the monomer penetration concentrated in the first millimetres of the samples. Microtomography and Raman spectroscopy highlighted the penetration path of the monomers in the wood, mainly through the vessels. The lumens of the cells close to the surface were also filled with polymers. The phosphorus monomer surface impregnation positively impacted the thermal and fire properties of the modified wood. A 25% decrease in the peak of heat release rate was observed, and the residual mass was multiplied by two compared to the reference. The phosphorus monomer contributed to the char formation.

Wood pyrolysis is a complex process, and understanding its mechanism is challenging due to the interaction of multiple components. In this study, the pyrolysis kinetic properties of experimentally extracted wood components and wood pseudo components simulated via Fraser–Suzuki function deconvolution methods were analyzed. Additionally, the differences between the independent parallel reaction model (IPRM) and the deconvolution method were compared to investigate the pyrolysis characteristics of wood. The activation energy (E) and pre-exponential factor (A) were calculated using the Flynn-Wall-Ozawa (FWO) method. The results indicated that the average E for chemically extracted cellulose from Chinese fir was 165.4 kJ/mol and 157.1 kJ/mol for birch cellulose. The corresponding values for their pseudo-cellulose were 109.9 kJ/mol and 153.8 kJ/mol, respectively. Within the range of conversion rates less than 0.8, the pseudo components required a higher temperature to achieve the same conversion rate as the experimentally extracted components. The IPRM method accurately predicted the pyrolysis properties by combining holocellulose and lignin. However, its accuracy was low when combining cellulose, hemicellulose, and lignin, which was attributed to the interaction between in-situ components influencing wood pyrolysis.

This paper proposes an unsupervised wood species identification approach utilizing multiobjective optimization clustering and feature fusion. To address the inherent limitations of single-band spectra in capturing comprehensively wood characteristics, this approach integrates preprocessed low-dimensional terahertz (THz) and hyperspectral data. Additionally, to address the challenge of selecting the optimal k-value in clustering, an unsupervised wood clustering algorithm was developed, employing multiobjective optimization and evolutionary algorithms. This proposed algorithm incorporated a prototype coding method for initialization, density peak clustering for pattern identification, and an improved firefly optimization algorithm to introduce cross-variation and maintain population diversity. To further refine the clustering process, a selection operator based on grid division and fast non-dominated sorting was designed, optimizing the clustering performance. The model was evaluated on a dataset containing hyperspectral and THz spectra from 400 samples, representing ten wood species—five coniferous and five broadleaf species. Experimental results indicated that fusing the spectral data resulted in a 3.5% increase in clustering purity compared to individual datasets. Moreover, the proposed algorithm outperformed established clustering methods such as DBSCAN, OPTICS, and density peak clustering, achieving a maximum clustering purity of 91.25% across both internal and external clustering metrics. These findings demonstrate the effectiveness of the multi-spectral fusion approach and the proposed algorithm in enhancing wood species identification accuracy, offering a promising avenue for improving non-destructive evaluation methods in forestry and material sciences.

The interplay between the anatomical structure, chemical compositions, and color of teak wood is not yet fully understood. An analysis of the anatomical structure and chemical compositions from sapwood to heartwood of cultivated teak (Tectona grandis L.f.) was conducted to elucidate the radial variation in wood color. Extractives were generated within the ray parenchyma cells located in the transition (T) zone, and subsequently infiltrated vessel lumina through the pits in the heartwood, accompanied by the disintegration of the nucleus and the disappearance of starch grains. As heartwood developed, the conjugated carbonyl groups in lignin increased, while the S/G ratio experienced a marked rise within the inner heartwood. The extractive content reached a maximum value in the outer heartwood. Signal intensities for squalene and 2-methyl-9,10-anthracenedione in heartwood were approximately 2.8 and 1.4 times greater than those found in sapwood, respectively. Lapachol only appeared in heartwood. A decrease in the L^* value of heartwood was noted in contrast to sapwood, and the color shifted towards red and yellow from sapwood to heartwood. The color difference quantified between sapwood and heartwood was 12.90. The anatomical structure provides the formation and storage spaces, and transportation channels for the extractives. The changes in lignin structure and extractive components are directly linked to the development of wood coloration. Overall, the study contributes to a deep understanding of the relationships between the microstructure, chemical compositions and color formation of teak wood, and will provide insights for the high-value utilization of cultivated precious wood.

Bamboo exposed to variations in humidity is prone to cracking, which can reduce its usability. As a natural material, bamboo’s hygroscopicity causes dimensional changes, influenced by the gradient distribution of fibres throughout the wall thickness. This study evaluated the dimensional changes resulting from variations in moisture content. Hygroscopic coefficients were extracted and applied in a finite element model to assess the circumferential stresses generated during sorption and desorption processes. Conditioning tests showed that open ring samples tend to close during sorption and open during desorption, due to the predominant swelling and shrinking behaviour of the fibre cells. The developed finite element model successfully replicated the aperture behaviour and dimensional changes in the thickness of open ring bamboo samples. The optimized parameters were subsequently used to predict the stresses under varying humidity conditions in closed-ring samples The circumferential stresses ranged from 9.8 MPa to -12.5 MPa from the inner to the outer layer in the saturated condition, and from − 7.1 MPa to 11.4 MPa in the dried condition. The values achieved reflect stresses that can lead to cracks and the failure of bamboo, thereby demonstrating the model’s ability to predict the hygroscopic behaviour of the material.

This study proposes a novel method for wood species identification, that employs importance-based feature selection integrated with a multiple spectral fusion technique. Specifically, the fusion integrates near-infrared spectroscopy (NIR), hyperspectral imaging spectral information, and terahertz (THz) spectroscopy. The experimental samples comprised four conifers and one broadleaf wood. Preprocessing of the spectral data was conducted using a combination of Savitzky-Golay smoothing (SG), Standard Normal Variate (SNV) correction, and normalization techniques. A hybrid feature selection method, combining random forest (RF) and gradient boosting decision tree (GBDT) algorithms, was then employed to extract the most important spectral features. To enhance clustering stability and mitigate the risk of overfitting, data augmentation was performed using a variational auto-encoder (VAE) augmented with self-attention (SA) mechanisms. Subsequently, the fused multiple spectral data, containing the most significant features from both individual and combined spectra, were subjected to K-means clustering. The clustering performance was assessed using metrics such as accuracy (ACC), normalized mutual information (NMI), and adjusted rand index (ARI). The results revealed that the fusion of NIR features with the top 50 features with the highest importance of the top 60 THz features yielded the most optimal results. The clustering evaluation metrics demonstrated an ACC of 0.945, an NMI of 0.957, and an ARI of 0.959. The hybrid feature selection approach facilitates a deeper understanding of the critical features influencing the performance of wood species identification models, thereby enabling more effective feature selection during the development of machine learning models.