Wood Science and Technology最新文献

Traditional construction is slow, labor-intensive and wasteful. Additive manufacturing (AM) enables faster, automated and efficient buildings with less waste and more design flexibility. This study looked at the possible applications of a biobased novolac (pyrolysis oil / phenol-formaldehyde) resin and hexamethylenetetramine (HMTA) hardener with either 40 mesh and 100 mesh wood fiber (30%), and extrusion of wood composites. Wood pyrolysis oil was partially substituted (50%) for phenol in the novolac resin preparation to increase its biobased content. The materials were characterized by a combination of thermal analysis, rheology, mechanical, and water absorption properties. The flow characteristics of the uncured resin and composites were determined by dynamic rheometry. The curing behavior of the resin and composites was determined by differential scanning calorimetry (DSC). The presence of wood decreased the curing enthalpies and increased the curing peak temperature. The wood resin composite blends displayed good pseudoplastic behavior. Composites made with smaller wood particles showed more thermal stability and lower glass transition temperature (T_g) because of the increased interaction between the fiber and the resin matrix. Extrusion experiments on the wood resin composites successfully produced a continuous rod. The extruded wood resin composite rods were cured (150℃ for 5 min) and showed good flexural properties. The successful extrusion of biobased novolac with wood demonstrates its potential for AM, making it a promising sustainable alternative for construction applications.

Automating wood identification through computer vision offers improved objectivity, time-efficiency, and accuracy over traditional methods. Conventional wood anatomical assessments rely on intact mature tissue, avoiding damage (cracks, fungi deterioration, insect damage) and other anomalies (pith, bark, traumatic canals). The impact of using images from anomalous surfaces on automated identification remains underexplored in current research. This study evaluates the performance of convolutional neural networks (CNNs) for classifying the presence of anomalies on images, and studies the impact of anomalies on genus identification by in- or excluding images of anomalous surfaces in the training data and assessing recall on the test data. The Xception network architecture was used to train the two types of classification models, on macroscopic cross-sectional images of 26 Congolese wood genera. The first model was trained for binary classification on the presence or absence of anomalies on > 250.000 images of ~ 1000 Congolese tree species, demonstrating accuracy, precision, recall and f1-score of ~ 93% on 25.000 test images. This shows that CNNs can learn patterns to detect the presence of anomalies. The second model was trained and evaluated on a subset of those Congolese tree species, consisting of 26 timber genera with abundant different types of anomalies (cracks, fungi deterioration, insect damage, pith, bark, traumatic canals). Three different wood identification models were trained and evaluated on the images featuring a model trained only on all images (regardless of anomalies), a second model trained only on perfect (anomaly-free) images, and a third model trained only on images with anomalies. The three models were evaluated on different specimens and demonstrated macro-averaged recall scores of 88.4, 90.5%, and 79.1% for the respective models, showing that a model trained on images from intact end-grain wood/anomaly-free images performed best. Class (genus) specific recall scores demonstrated for the three models that model performance varies between genera. The class (genus) specific recall scores of Millettia, Tessmannia, Celtis, Afzelia, Beilschmiedia, and Vitex are highest for the model trained on all images (with and without anomalies). Conversely, the recall scores of Cynometra and Microcos were lower for this model. GradCAM analysis was performed to visualize which regions on images were more activated for classification (wood identification), and revealed that the model focuses more on anomaly-free regions for wood identification, underscoring the importance of clear wood anatomy in training CNNs for wood identification.

The increasing emphasis on sustainable biorefinery processes has brought black liquor, a complex byproduct of pulp mills, to the fore as a pivotal source for extracting valuable organic compounds, particularly lignin. The chemical composition of black liquor exhibits significant variations depending on the lignocellulosic source, the pulping method, and the conditions during processing. By examining pH adjustments and analytical methods such as HPLC and GC-MS, this study provides insights into the chemical behavior of black liquor and proposes strategies for its efficient characterization and utilization. The presence of different acids (H₂SO₄, HCl, and H₃PO₄) and pH adjustments (2, 5.5, 7, 9) in black liquor significantly influence its physical properties, such as density and viscosity, as well as its chemical composition. Softwood black liquor showed greater viscosity changes due to pH levels, while hardwood black liquor was primarily affected by the type of acid used. Syringyl-type compounds dominate in hardwood black liquor, whereas softwood black liquor is richer in guaiacyl-type compounds such as vanillin and catechol. HPLC analyses revealed higher phenolic yields at higher pH levels (7–9), with vanillin and protocatechuic acid being most abundant in softwood samples and syringaldehyde and syringic acid in hardwood samples. The optimal pH for extracting lignin-derived phenolics is 5.5–7, while pH 2 is preferred for extracting organic acids, highlighting the critical role of pH in maximizing extraction efficiency.

Addressing the issue that thick bamboo slices (≥ 1 mm) exhibit high bending stiffness and cause premature cutting-tip splitting, resulting in poor surface quality. This study investigated the effects of saturated steam treatment on the surface quality and mechanical properties of the bamboo during slicing process, and analyzed physicochemical mechanisms through XRD, FTIR, and SEM test. The results show that chemical functional groups, microstructure, and relative crystallinity of bamboo were changed, glass transition temperature and bending stiffness was reduced, and Mode I fracture toughness was enhanced, so that saturated steam treatment mitigates the degree of premature splitting during slicing and significantly improves surface quality. Under optimal softening conditions (160 °C/10 min), saturated steam treatment reduced sliced surface roughness by 37.98%, and lowered glass transition temperature by 19.67%, flexural strength and elastic modulus decreased by 59.66% and 42.88%, respectively, while Mode I critical displacement increased by 113.08% and ({G_{IC}})increased by 179.08%. Studying bamboo surface roughness, chemical, and mechanical properties are expected to lay a foundation for bamboo slicing, mechanics research, and slicing cracking.

Douglas-fir (Pseudotsuga menziesii) is the principal species for lumber production in the Pacific northwest and has been the subject of ongoing tree improvement and silvicultural research aimed at enhancing growth. With industry shifting to lower planting densities, it is critical that we improve our knowledge of the wood quality implications of these practices. Hyperspectral imaging (HSI) presents a promising tool for studying within-tree variation of wood properties and it was utilized to provide data for the development of maps illustrating within-tree variation of physicomechanical, tracheid and chemical properties of 25-year-old Douglas-fir grown at different spacings (narrow, conventional and wide). Property maps showed that the different spacings had similar patterns with a more abrupt change in wood properties radially in narrowly spaced trees compared to those widely spaced, regardless of the property assessed. Radial variation patterns showed increases from pith to bark in density, stiffness, tracheid length and width, and glucose/cellulose content, while microfibril angle, lignin, and xylose decreased. Although planting density may not determine absolute wood quality traits, it can influence their developmental patterns across the stem.

Coconuts are one of nature’s toughest lignocellulosic materials, possessing a fracture toughness on par with dentin and a compressive strength ten times that of bamboo. The coconut’s hierarchical structure has been characterized before, except prior studies left out one key aspect, the smallest length scales, approaching the molecular level. Here we exfoliate the hard shell of Cocos nucifera, revealing the true cellular organization and the dimensions of the crystalline cellulose nanofibrils found in the cell walls. After chemical pretreatments, we found entanglement between elongated sclereid cells that was not visible in the untreated coconut shell. This may contribute to the mechanical performance of the endocarp; it also utilizes elongated, high-aspect ratio structural elements at the cellular level, in addition to the nanofibrillar level previously known. Compared to other wood-like materials, the cellulose nanofibrils were shorter and represented a smaller weight fraction. This reduced length and the lower filler-to-matrix ratio could be the optimal lignocellulosic nanostructure for tough biomaterials. These newly discovered unique features explain how the endocarp of Cocos nucifera mechanically outperforms materials consisting of the same molecular components.

The water sorption and hysteresis behavior of four high extractive content woods —western red cedar, Chinese juniper, tubi, and messmate—were examined at 30, 45, 60, 75, 90, and 99.5 °C by comparing the water sorption isotherms before and after extraction. A significant dry weight loss during water sorption tests was observed for cedar and juniper, complicating their water sorption and hysteresis behavior. Gas adsorption analysis revealed that the cumulative pore volume of dried cell walls in all four species significantly increased after extraction, indicating a comparable extent of likely bulking of extractives within the lignin structure. Across the temperature range of 30 to 99.5 °C, cedar and juniper showed significantly higher equilibrium moisture content values after extraction throughout the entire hygroscopic range. In contrast, only minor differences were observed for tubi and messmate after extraction. This discrepancy may be attributed to variations in the hygroscopicity of extractives deposited in the cell walls. When applying the bulking effect theory, it is important to consider the hygroscopicity of bulked extractives. Tubi shows a distinctly higher magnitude of sorption hysteresis even at 99.5 °C, and no significant differences in the magnitude of hysteresis were observed for either tubi or messmate after extraction.

The mechanical properties of wood are significantly enhanced and its application scope is broadened through compression treatment. However, balancing deformation fixation with the performance of compressed wood is challenging. This study significantly enhanced the dimensional stability of compressed wood while preserving its mechanical properties by grafting N-Methylolacrylamide (NMA) onto wood cell walls and applying thermally assisted surface compression. The NMA-modified compressed wood exhibited improved hydrophobic qualities and a considerably low set recovery of only 0.29%, which is attributable to the cross-linked network formed by the condensation of NMA with wood components. Furthermore, the NMA-modified compressed wood exhibited outstanding mechanical properties, such as improved hardness, bending strength, and impact toughness. Its bending strength (263.5 MPa) was approximately 2.6 times greater than that of natural wood (100.3 MPa). NMA-modified compressed wood, which is characterized by high-dimensional stability and mechanical properties, exhibits significant potential as an engineering material.

Photonic crystals (PCs) with periodic nanostructures enable high-quality structural coloration, making them highly suitable for applications in printing and dyeing. However, PC-based structural coloration on wood surfaces remains a challenge. In this work, PCs were successfully constructed on wood (Birch, Betula spp.) surfaces via gravitational deposition, displaying bright, vibrant, and angular-dependent structural colors with stimulus-responsivity to water. Monodisperse polystyrene (PS) microspheres were used as building units. The colors were tunable through precisely modulating the diameter of the PS colloidal microspheres. The stacking configuration of the PS spheres within the interfacial transition region transitions from disordered to ordered. Drying-induced deformation of the micro-nano structures on the wood surface can lead to interfacial defects, such as disordered packing and cracks. Thanks to the water-responsive property of PCs, the coatings show promising potential for application in anti-counterfeiting labels. This work provides methodological and mechanistic insights for structural coloration on wood substrates, while offering many attractive opportunities for biomass materials.

This contribution aims to increase the understanding of the complex mechanical behavior of wood through a framework for simulating mixed-mode failure. Based on physical properties assessment, appropriate constitutive laws, and experimental validation, a generally applicable numerical strength prediction tool for wood from different species and with various natural imperfections is introduced. The 3D orthotropic elastic plastic non-local CDM model considers the local fiber orientation and is implemented as material subroutines in the commercial software Abaqus. Herein, orthotropic Hill-plasticity with exponential hardening represents the plastic behavior in compression. Separated stress-based gradient-enhanced transient non-local damage represents the brittle material behavior in tension and shear. The methodology is validated with experimental data on tensile veneer tests, shear- and compression tests. Moreover, the methodology is applied to four-point bending tests of boards with heterogeneities. The numerical results demonstrate that the proposed model is able to reproduce different crack patterns observed in the four-point bending tests. Detailed investigations of the impact on the strength of the boards can be performed with this method to optimize species-independent strength prediction and engineered wood products. Further combination with other material laws e.g. moisture is possible.