Research Articles

The integrated strategy using coupling photocatalysis and adsorption to treat liquid waste has attracted increased attention. In this work, a carbon enhanced photocatalyst (BiOBr@PSBM) with dopamine bridge was constructed using porous straw biochar (SBM) as loading carrier for efficient, multitasking water purification engineering, enhancing adjustable adsorption and visible-light degradation. A cellulose-targeted etching strategy was used to construct porous biochar carriers. Benefiting from the synergy of dopamine and carbon enhancement, the electron migration ability of composites enabled the heightened visible-light catalysis activities, and the efficient visible-light degradation (96.5%) for various dye pollutants was achieved. The optimized porous structure, amino-rich surface, and pH control adjustable surface charge properties endowed composites with multitasking ability. They achieved excellent and rapid capture Cr(VI) through static-dynamic adsorption (157.7 mg·g^-1, 40 min). The theoretical calculation with DFT framework was used to study the proposed adsorption-degradation mechanism and the degradation pathway of organic polluted molecules. Significantly, multiple recycling and environmental experiments indicated that photocatalysts boasted stable structure and regeneration, supporting their cost-effective and efficient remediation of wastewater containing various polluted species. This work provided a feasible strategy for developing advanced water purification materials by the utilization of low-value solid-waste.

Identifying the transition age between juvenile and mature wood is key for designing more efficient silvicultural strategies and optimizing timber exploitation.  The objective of this study was to identify the juvenile wood transition age and analyze certain growth characteristics of Cuban Pinus caribaea M. var. caribaea B&G by comparing plantation and native populations. Radial variations in growth ring width, latewood proportion, and ultrasonic longitudinal speed were examined to identify the delimitation age from juvenile to mature wood. Visual assessment and statistical analyses, including segmented regression and k-means clustering, were applied. The findings indicated that juvenile wood is formed within the first 5 to 9 years, while mature wood develops after 21 to 26 years. Plantation trees exhibited higher variability and a wider juvenile wood zone (60 mm from the pith) than native trees (43 mm). The mean growth ring in the mature wood was 3.14 mm in native and 3.67 mm in plantation. The latewood proportion stabilized above 50% beyond the transition age, confirming the shift to mature wood, trees from native population developing 22% more latewood than trees from plantation. The ultrasonic speed pattern was similar between populations, validating its use as an indirect indicator of wood maturation.

Rotary friction welding of wood typically uses dowels made from the same material as the base wood or involves specific modifications to the dowels, but these methods have practical limitations and are complex. This study focused on commonly used dowel materials (softwood: Scots pine, hardwood: birch), with moisture content adjusted to 7 to 10%, and examined the welding performance and micro-mechanisms. Through orthogonal experiments, the influence of process parameters on the welding strength of both wood types was systematically investigated. The microstructures of the welded areas were analyzed using a depth-of-field microscope and scanning electron microscope (SEM) to explore the friction mechanisms. The results indicated that both Scots pine and birch dowels can be effectively welded using rotary friction. The optimal parameters were identified as follows: Scots pine dowels—hole diameter ratio of 8/12, rotational speed of 3000 r/min, feed rate of 25 mm/s; birch dowels—hole diameter ratio of 8/12, rotational speed of 2500 r/min, feed rate of 20 mm/s. Depth-of-field microscopy revealed larger weld areas and well-preserved surface structures. SEM images showed that during welding, the materials between the dowels and base wood melted, flowed, and re-solidified into a tightly bonded structure, ensuring a durable connection.

Most mushrooms are sensitive to ethylene, and exposure leads to degradation in mushroom quality, particularly in appearance and organoleptic properties. This study investigated the feasibility of using heated wood for ethylene removal. The hypothesis was that ethylene accumulation can be limited by using heated wood-based ethylene scavengers in mushroom packaging. The applicability and benefits of heated wood-based ethylene scavengers in mushroom home delivery are discussed. The heated wood-based ethylene remover used in the courier maintained the color of Lentinula edodes during transportation. These results were supported by quantitative analysis, in which the ethylene concentration in packaging headspace was significantly reduced (p < 0.05) by the wood-based scavenger, and rate of weight change also showed significant improvement (p < 0.05) compared to the control. Overall, this heated wood-based ethylene scavenger has potential in terms of mushroom packaging and food shelf-life extension.

Nutritional values and biological activity of Persicaria longiseta inflorescence extract (PLE) were assessed relative to the antioxidant, anticancer, and antibacterial impact of ethanolic extracts from the plant’s inflorescence. Using high-performance liquid chromatography (HPLC), the PLE ethanolic extract revealed that naringenin, chlorogenic acid, quercetin, rosmarinic acid, rutin, and gallic acid were all present in high concentrations (9780, 8250, 5190, 4320, 2520, and 2430 µg/g, respectively). Nutritional values assessment showed carbohydrates (166 mg/g), protein (53.2 mg/g), phenols (36.2 mg/g), and flavonoids (13.2 mg/g). When applied to pathogen species and cancer cells, the ethanolic extract of PLE showed inhibitory effects. Additionally, the minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) were evaluated for the tested microorganisms. The P. longiseta extract exhibited significant inhibition of hemolysis, particularly in the presence of B. subtilis (77.7, 90.8, and 95.9%) compared to K. pneumonia (54.1 ± 1.16, 75.8, and 85.3%) at varying doses (25, 50, 75% MIC, respectively). These results represent the beginning of the use of this extract in advanced medical aspects as an antimicrobial, antioxidant, antihemolytic, and antitumor agent, with some confirmatory and accurate experiments in vivo.

Leguminous wood occupies an important position in the market of cultural and high-end wood. Accurate identification and classification of its species is crucial for the development of the industry. However, existing studies are still deficient in classification methods under small sample conditions. This paper uses hyperspectral image data and combines models such as support vector machine (SVM), random forest (RF), logistic regression (LR), and one-dimensional convolutional neural network (1-CNN). The synthetic minority oversampling technique (SMOTE) data enhancement technology was introduced to classify and recognize 18 common legume woods. After data processing, the classification accuracy of the traditional models was improved by about 5% on average, with the SVM model reaching 98.86%; the accuracy of the 1-CNN model was increased to 97.67% after adding the first-order derivative transform and Savitzky-Golay filtering, it reached 98.89% after further adding the SMOTE.

This study examined the feasibility and benefits of children completing woodworking projects at home in Chinese families, using online video tutorials and parental guidance. A survey assessed family interest and gathered background information, selecting 36 Chinese families with children aged 7 to 12 for an experiment on making traditional Luban locks. The projects were divided into two levels: a basic, video-assisted three-post lock completed by children with parental help; and an advanced, six-post lock, requiring families to find resources independently. Results indicate that the majority of families showed strong interest in parent-child woodworking (76.7%) and successfully completed the basic project (94.4%). However, only a small minority managed to complete the advanced project (8.3%). The study indicates that while children aged 7 to 12 are in a concrete operational stage of cognitive development, success in these projects isn’t solely age-dependent. Parent-child woodworking in Chinese families appears feasible and beneficial for cognitive growth when projects are age-appropriate. Findings suggest designing tasks within a child’s zone of proximal development with corresponding resources, offering insights for family-based learning approaches.

Hybrid epoxy composites reinforced with Luffa acutangula fiber (LAF) and Sal wood sawdust (SWD) were examined for their tribological and acoustic properties. A consistent 20 wt% LAF was employed throughout all composites, with the SWD content adjusted to 0%, 5%, 15%, and 25%.  The engineered composites underwent assessment for wear loss, coefficient of friction (CoF), sound absorption coefficient, and noise reduction coefficient. The results demonstrated a notable reduction in wear loss with the addition of SWD up to 15 wt%, with the 20FL/15SWD sample exhibiting the lowest wear at 0.32%. In a similar manner, the CoF decreased to 0.26 for the identical composition, indicating an ideal equilibrium between filler dispersion and fiber-matrix interaction. The enhancement of sound absorption and noise reduction coefficients was observed with increased SWD content, reaching peaks of 0.23 and 0.13, respectively for the 20FL/15SWD composite. The enhancements observed can be linked to the superior void-filling capacity and interfacial bonding facilitated by the SWD particles. Nonetheless, a high concentration of SWD (25 wt%) led to a minor decrease in performance attributed to particle agglomeration. The findings indicate that the 20FL/15SWD composite demonstrates enhanced tribo-acoustic performance, positioning it as a strong contender for applications requiring noise insulation and wear resistance.

Improper disposal of agricultural residues is becoming a looming environmental issue as a major contributor to pollution and depletion of natural resources. The current study aimed to evaluate the proximate compositions, phytochemical profile along with antioxidant properties of the residues of hemp (Cannabis sativa) and parthenium (Parthenium hysterophorus) as sustainable resources. The standard protocol of AOAC was followed for proximate analysis. Phytochemical profiling was done to identify key bioactive compounds through qualitative assays. Their antioxidant activity was assayed by DPPH (2,2-diphenyl-1-picrylhydrazyl) free radical scavenging. Hemp recorded 50.2% more fibers than parthenium at 27.5% making it a better candidate for bio-material development. Future in vivo studies are recommended to elucidate the metabolic effects of these plants and their potential health benefits. Phytochemical analysis revealed the presence alkaloids, flavonoids, tannins, steroids, coumarins, and sterols, thus showing their bioactivity and possible health benefits. The antioxidant activity was significantly increased in hemp residues (0.1 ± 0.13 mg Trolox/g) compared to parthenium (0.057 ± 0.21 mg Trolox/g), whereas the activity from aerial parts was found lower. This demonstrates the wider application potentials of these residues in the industrial and pharmaceutical sectors as eco-friendly substitutes for conventional materials.

Four Vision Transformer (ViT)-based models were optimized to classify microscopic wood images. The models were DeiT, Google ViT, BeiT, and Microsoft Swin Transformer. Training was performed on a set enriched with data augmentation techniques. The generalization ability of the model was strengthened by increasing the number of images for each class with data augmentation. The dataset used in the study consisted of 112 different species belonging to 30 families, 37 of which were coniferous and 75 were angiosperms. The samples had been softened, cut into thin sections, colored with the triple staining method, and imaged with fixed magnification. The Google ViT model was the most successful, with 99.40% accuracy. The DeiT model, which stood out with its data efficiency, ranked second with 98.51% accuracy, while the BEiT and Microsoft Swin Transformer models reached 96.43% and 98.21% accuracy, respectively. The Microsoft Swin Transformer model required the least training time. Data augmentation techniques improved the performance of all models by 3% to 5%, thus increasing the resistance of the models to overfitting and providing more robust predictions. It was found that ViT-based models gave superior performance in microscopic wood image classification tasks and that data augmentation significantly improved model performance.