Wood Science and Technology最新文献

’Tracheid effect’, or laser light scattering in wood is an important phenomenon in the study of wood, but not well researched for Malaysian timber species. Sixty (60) common commercial timber species of Malaysia, as defined by the Forest Research Institute Malaysia (FRIM), were tested for ’tracheid effect’ response using 650 nm (red) laser. 5 samples each of 11 heavy hardwood, 15 medium hardwood, 32 light hardwood, and 2 softwood species were tested. Medium density fibreboard (MDF) was used as a reference. The grain angle performance metric, which is the root mean squared error (RMSE) between the observed and actual angles of ellipse generated by the dot laser and imaged by the camera, was determined and tabulated. To predict the grain angle performance for unknown species using colour and density features, two machine learning (ML) classification approaches were tested, namely k-Nearest Neighbour (k-NN), and shallow feed-forward artificial neural network (ANN), as well as one function-fitting ANN. For predicting RMSE (<5^circ), the function-fitting ANN performed the best at 82.7%, while k-NN scored the highest overall performance of 89.3% when predicting RMSE (<10^circ). The density of wood did not directly correlate with the grain angle performance, but its inclusion as a feature together with the colour features improved the accuracy of the ML predictions. The colour features related to brightness were dominant features that affected performance. In summary, this study confirmed that wood colour as well as density plays an important role in the ability to determine grain angle by means of the tracheid effect using 650 nm lasers.

In recent years, there has been a marked increase in demand for timber. Conventional timber drying methods, characterized by long drying times and significant pollution, have struggled to meet this demand effectively. In contrast, microwave drying, as an emerging technology, offers the benefits of speed, high efficiency, environmental friendliness, safety, and sterilization capabilities. However, this method also faces challenges such as uneven drying and low efficiency during the process. To address these issues, the article initially explores the theoretical relationship between the dielectric permittivity, moisture content, energy utilization rate, and microwave penetration depth. It then delves into the principles of microwave heating and outlines the main factors influencing timber drying, specifically moisture content and microwave frequency. Subsequently, a three-port circular resonant cavity was designed using finite element analysis software using a homogeneous wood material as a simulation object in order to simulate the effect of microwave application on the uniformity of the internal temperature distribution during the drying process. The simulation analyses and fits the relationship between moisture content, dielectric permittivity, energy utilization rate, temperature coefficient of variation, and microwave penetration depth, culminating in a model of high predictive accuracy. Simulated results confirm that the methods and parameter settings proposed in this paper are highly effective, offering potential solutions to the challenges of uneven temperature distribution and low energy utilization rates in microwave timber drying.

Cellulose and lignin offer advantages of low cost and environmental friendliness. In this study, a multi-responsive shape memory smart composite material was proposed based on carboxymethyl cellulose and lignin. Lignin imparts photothermal responsiveness to the composite, while cellulose provides water responsiveness. A bio-inspired structure mimicking the water transport mechanism of plant leaves was developed to improve the water responsive functionalities of composite material (shape recovery within 30 s). A self-driven device that mimics the blooming of a flower was successfully fabricated using this composite material. The shape memory smart composite material exhibits a high degree of design flexibility. Based on the mechanisms of water response, a simple structure programming method was proposed, enabling the design of programmable structures with smart and controllable features. This study provides a new approach to the design of multifunctional smart materials, enhancing the application potential of shape memory materials under multiple environmental factors.

The way of trees adaptation to environments is a vital concern. Presented research focused on wood tissue diversity in terms of the juvenile wood proportion (wood located near the pith and of structure and properties different from outer wood zone, called as mature wood), an important characteristic for wood properties, to assess the evolutionary and functional impact of genetic variations. In this paper, the material from experimental provenance plot in Poland (Rogów) was presented. The tested trees were grown at the same time, in the same soil for the same period of time, but the parental stands of tested trees were from the different Polish regions. Based on the results it was concluded that origin of parental trees has an influence on the amount of juvenile wood expressed by the number of annual growth rings, as well as the volume occupied in the trunk of the trees. The wood formation, particularly the amount of juvenile wood, is influenced by the climatic conditions of parental trees’ habitat (epigenetic indicators). The amount of precipitation is predominantly important in this respect. The new mathematical model for estimating the number of annual increments corresponding to the juvenile wood zone was proposed. The obtained results highpoint the necessity of taking epigenetic indicators into account in future breeding strategies composed with genetic markers for both wood production and quality in the context of climate change that requires adaptation.

Reports of illegal logging are increasing globally, driving a need for tools that can effectively identify wood products at the species level. This identification is crucial for regulatory purposes, certifying legal lumber, preventing environmental crimes, and protecting ecosystems and society. Current wood identification methods are primarily based on anatomical observation of wood tissues, chemical profiling using direct analysis in real-time time-of-flight mass spectrometry (DART-TOFMS), and DNA-based analyses. While these approaches have their advantages, they also present challenges, particularly when species-level identification is required for enforcement. As an alternative, metabolite profiling using separation techniques coupled with mass spectrometry shows potential as a robust species-level wood identification method. Here, we present a method for classifying wood at the species level through chemical profiling of the wood metabolome using comprehensive two-dimensional gas chromatography coupled to time-of-flight mass spectrometry (GC×GC-TOFMS) combined with chemometric analysis. In this study, different tissues, including sapwood, heartwood, and branches from seven wood species collected from genetically characterized trees representing three distinct genera (Quercus, Acer, Picea) were investigated. Principal component analysis (PCA) was used to visualize differences between species and tissue types, while partial least squares-discriminant analysis (PLS-DA) and feature selection were used to construct classification models for species-level wood identification. The classification models were built using data from wood cores, branches, or a mixture of wood cores and branch samples. Each classification model was tested with an external validation set, and the performance of the classification model was evaluated based on the prediction of the external validation data. Our results show that classification modelling using wood metabolomic data is promising, especially with the same tissue type, presenting accuracies of 100%, 100%, and 93.2% in the prediction of wood core samples at the species level for Quercus, Acer, and Picea, respectively.

Contaminated waste wood is a highly variable material, which makes it challenging to use in recycling applications. One of the potential recycling applications could be wood-cement composites, assuming the composition of the wood composite does not hinder the hydration of cement. In this study, four wood composite types (plywood (PLY), alkaline copper quaternary-treated plywood (ACQ PLY), oriented strand board (OSB) and particleboard (PB)) were ground and sieved into fraction size. Length, width, and thickness of particles from the 2–4 mm and 4–10 mm fractions were then characterized, and the slenderness ratio and specific surface area calculated. Using particles from the four wood composite types and both fractions, hydration tests were conducted, and the compatibility factor computed. The results demonstrated there was little effect of size fractions on the slenderness ratio and specific surface area. However, the wood composite type did impact slenderness ratio and specific surface area, with OSB particles being characterized by the largest slenderness ratio and specific surface area. As expected from their slenderness ratio, but contradictory to their high specific surface area values, OSB particles were characterized by a high compatibility factor. But so were the PLY and ACQ PLY particles, which was unexpected based on their slenderness ratio. Of all hydration curves, only PB particles did not have a second temperature peak and preliminary cement-bonded boards made with PB particles were very brittle. The compatibility factor alone should not be used to assess compatibility of wood composite with cement.

Addressing the challenges of low classification accuracy and protracted identification times posed by lightweight convolutional neural networks (CNNs) for wood micrograph classification, this study introduces ARNet, a novel model tailored for wood CT image analysis.ARNet significantly enhances the overall image recognition performance by boosting its dynamic feature extraction capabilities and refining its proficiency in processing salient features.The methodology employs residual dynamic convolution that dynamically aggregates convolutional kernels in response to the input image, optimizing adaptability.This optimized field of view across disparate feature layers facilitates the extraction of critical information such as wood texture, pore distribution, and cellular arrangement, thereby enhancing analytical depth.Additionally, ARNet incorporates multi-scale dilated attention mechanisms to capture nuanced feature maps across multiple scales, thereby broadening the scope of feature analysis.This approach not only achieves a profound understanding and efficient processing of the input data but also accentuates critical features, significantly enhancing the distinguishability between diverse image categories.The combination of CNNs and Transformers not only extracts rich local and global information but also captures unique features of images on a point-to-point basis, thereby improving classification accuracy. Experiments were conducted on the Mini-ImageNet, CIFAR100, and CIFAR10 public datasets. The results show that ARNet achieved top-1 accuracies of 65.21%, 78.32%, and 93.39% on Mini-ImageNet, CIFAR100, and CIFAR10, respectively, outperforming other models such as RMT, TCFormer, and SSViT. Additionally, we applied ARNet at the Shandong base of the national wood industry engineering research center to identify transverse section micrographs of 20 precious wood types, achieving an accuracy of 99.50% on the test set. After loading the parameters into the re-parameterized model, the validation set accuracy was 99.20%, with a detection time of 0.024s per image. This demonstrates that by combining residual dynamic convolution with multi-scale dilated attention, the accuracy and real-time performance of wood micrograph classification can be effectively improved.

The strength degradation resulting from duration-of-load (DOL) effect and bacterial decay poses significant challenges to historical timber piles. Many historical European cities still heavily rely on the infrastructure supported by their original timber foundations. A reliable modelling approach on the structural performance of timber piles is needed to avoid the economic loss caused by closing down infrastructure. In this work, we consider a simplified bacterial decay model and develop a numerical framework to integrate the decay model into a standard DOL model. Two approaches are proposed and compared: one considering the homogenised effect of bacterial decay over the entire cross section, and the other taking into account the localised failure accelerated by bacterial decay and applying stiffness reduction to allow stress redistribution. Although the homogenised failure criterion is found to potentially underestimate the effect of bacterial decay, both approaches are able to capture the designated decay pattern. Ultimately, there is a potential for future extension to more intricate loading conditions and decay patterns.

Biological deposition is commonly observed on wooden shipwrecks. This study employs analytical techniques, including optical microscopy (OM), scanning electron microscope equipped with an energy dispersive spectrometer (SEM-EDS), X-ray diffraction (XRD) and Fourier infrared transform spectroscopy (FTIR), to investigate the microscopic morphology and structural composition of barnacles and their adhesion to the wooden surface of the Yangtze Estuary II shipwreck. Results indicate that microorganisms like diatoms and Trichoderma spp. were present at the interface between the barnacles and the wooden surface. These microorganisms played a crucial role in the formation of SiO₂, CaHPO₄•2(H₂O), FeO(OH) and CaCO₃. Specifically, the bio-mineralized cell walls of diatoms and their cellular contents, including polyphosphates, provided the necessary Si and P for SiO₂ and CaHPO₄•2(H₂O), respectively. Furthermore, during their metabolic processes, diatoms and Trichoderma spp. supply dissolved Fe ions, which contribute to the formation of FeO(OH) on the wooden surface. This study elucidates four types of bio-mineralized products resulting from microbial activity on the salvaged wooden shipwreck.