Characteristics of fast-growing wood impregnated with nanoparticles

Falcataria moluccana or sengon is one of the fast-growing wood species widely grown in Indonesia.However,its wood is low quality with low density,and poor strength,durability,and dimensional stability.This study determined the effects of impregnation with monoethylene glycol(MEG)and nano-SiO2 on the characteristics of sengon wood,including its dimensional stability and density.Impregnation with MEG and nano-SiO2 had a significant effects on dimensional stability in terms of the weight percent gain,anti-swelling efficiency,water uptake,bulking effect,and density.The impregnated wood was examined by scanning electron microscopy coupled with energy-dispersive X-ray spectroscopy and X-ray diffraction.The results show that MEG and nano-SiO2 were distributed homogeneously into cell walls of wood treated with 0.5%MEGSiO2.

Stain capacity of three fungi on two fast-growing wood

We investigated the stain of fast-growing wood(Cunninghamia lanceolate,CL;Paulownia,PT)inoculated with three fungi(Arthrinium phaeospermum,AP;Vibrio anguillarum,VA;Aspergillacea,AS)to explore the new wood dyeing ways and the better combination of wood and fungi for dyeing.Only AP could dye on CL and PT.Especially for CL,its percentage of internal spalting,percentage of external spalting and dyeing depth were the highest(48%,15%and 5.06 mm,respectively).Surprisingly,the bigger weight loss occurs on PT.The results showed that the dyeing eff ect of AP dyeing CL was the best,and the wood color change was obviously(Orange to dark red).AP could produce more pigments than the other two fungi(VA;AS),CL was more suitable for fungus staining than PT,indicating that AP could off ered a new potential market and a chance for areas to earning higher income for CL.This research paves the way for improving color change was obviously(Orange to dark red).AP could produce more pigments than the other two fungi(VA;AS),CL was more suitable for fungus staining than PT,indicating that AP could off er a new potential market and a chance for areas to earn higher income for CL.

Non-Targeted Metabolomics Reveals the Metabolic Alterations in Response to Artificial Selective Breeding in the Fast-Growing Strains of Pacific Oyster

Pacific oyster(Crassostrea gigas)is one of the most important mollusks cultured all around the world.Selective breeding programs of Pacific oysters in China is initiated since 2006 and developed the genetically improved strain with fast-growing trait.However,little is known about the metabolic signatures of the fast-growing trait.In the present study,the non-targeted metabolomics was performed to analyze the metabolic signatures of adductor muscle tissue in one-year old Pacific oysters from fast-growing strain and the wild population.A total of 7767 and 10174 valid peaks were extracted and quantified in ESI^(+)and ESI^(−)modes,resulting in 399 and 381 annotated metabolites,respectively.PCA and OPLS-DA revealed that considerable separation among samples from fastgrowing strain and wild population,suggesting the differences in metabolic signatures.Meanwhile,81 significantly different metabolites(SDMs)were identified in the comparisons between fast-growing strain and wild population,based on the strict thresholds.It was found that there were highly correlation and conserved coordination among these SDMs.KEGG enrichment analysis indicated that the SDMs were tightly related to pantothenate and CoA biosynthesis,steroid hormone biosynthesis,riboflavin metabolism,and arginine and proline metabolism.Of them,the CoA biosynthesis and metabolism,affected by pantetheine and pantothenic acid,might be important for the growth of Pacific oysters under artificial selective breeding.The study provides the comprehensive views of metabolic signatures in response to artificially selective breeding,and is helpful to better understand the molecular mechanism of fastgrowing traits in Pacific oysters.

基于YOLOv7的木材缺陷检测模型Wood-Net的研究

为改善利用人工方式识别木材缺陷存在的效率低、人工成本高的问题,同时实现在木材加工过程中使用新兴方式对不同的缺陷进行快速准确检测以提高木材利用率,针对现有的目标检测网络在木材缺陷检测方面存在诸如检测精度低、报错率高以及识别种类少等局限,设计了用于木材缺陷检测的深度学习网络Wood-Net。Wood-Net将注意力机制ECA(efficient channel attention module)引入YOLOv7的主干网络,以便更好地区分木材缺陷之间的细微差别;将ECA与Res2Net结合后形成ECA-Res2Net模块,ECA-Res2Net模块克服了单纯的Res2Net跨通道交流能力不足的问题,增强了网络对更细粒度特征的提取能力;将ECA-Res2Net模块与SPPCSPC(spatial pyramid pooling and channel spatial pyramid convolution)并联形成ResSPPCSPC模块,增加了描述图像本身特征数量的能力,由此构成新方法Wood-Net。本研究将准确度、召回值、mAP@0.5以及mAP@0.5∶mAP@0.95 4个数值作为系统性能的评价指标。利用自建数据集训练Wood-Net,得到试验数据。试验结果表明:Wood-Net模型比基准模型YOLOv7在木材优选上精确率提高了4.52%,mAP@0.5∶mAP@0.95提高了6.62%;比基准模型YOLOv5s在木材优选上精确率提高了6.79%,mAP@0.5∶mAP@0.95提高了5.67%。ECA注意力机制能够有效提升E-ELAN的通道间信息交互能力;Res2Net模块具有很强的细粒度特征提取能力,在网络中引入Res2Net模块后,网络各项性能指标收敛速度快,在Res2Net中加入ECA后能够使单纯的Res2Net考虑多通道特征之间的关系,完成信息融合,提高检测性能。

UiO-66-NH_(2)/wood复合材料的制备及其甲醛吸附性能研究

以巴沙木为载体,通过溶剂热法在木材管道内原位生成UiO-66-NH_(2)/wood复合材料;以甲醛为降解目标,对复合材料的甲醛吸附性能进行研究。并借助XRD、FT-IR、SEM和热重分析表征手段探讨了材料结构对吸附动力学的影响。结果表明,以木材为载体制备的UiO-66NH_(2)/wood复合材料的比表面积和孔径尺寸为木材的2倍,且其在200℃以下热稳定性较UiO-66-NH_(2)材料有明显提高,UiO-66-NH_(2)/wood复合材料对甲醛有良好的吸附性能,在100 min内对甲醛最高吸附效率为95.64%,其对甲醛的吸附过程符合准二级动力学模型,主要表现为物理吸附,经颗粒内扩散模型分析,其甲醛的吸附过程以内部扩散为主。

Vacuum press drying studies on two fast-growing Indian wood species

Two fast-growing Indian species, Melia composita Benth. and Eucalyptus tereticornis Sm., which have different sets of physical properties, were dried together in a vacuum press dryer(VPD) under two drying conditions,i.e., above boiling point(ABP) and below boiling point(BBP). The ABP and BBP conditions were maintained by keeping the temperature constant at 75 ℃ and maintaining two pressure levels: 300 mm of Hg(ABP) and 450 mm of Hg(BBP). In order to understand pressure conditions at the core during vacuum drying, a cylindrical brass pipe was inserted in both wood cores and attached with pressure gauges placed outside of the VPD. The results indicate that the Melia wood core attained equilibrium pressure immediately with the pressure of VPD, while Eucalyptus attained it very slowly, reaching equilibrium at later stages of drying when cracks and checks advanced to the core.The drying rate was higher for Melia than Eucalyptus under both drying conditions. The drying rate of Melia(ABP) was higher than Melia(BBP), however, the drying rate for Eucalyptus(ABP) was not significantly different from the BBP drying rate.

Evolutionary history shapes variation of wood density of tree species across the world

The effect of evolutionary history on wood density variation may play an important role in shaping variation in wood density,but this has largely not been tested.Using a comprehensive global dataset including 27,297 measurements of wood density from 2621 tree species worldwide,we test the hypothesis that the legacy of evolutionary history plays an important role in driving the variation of wood density among tree species.We assessed phylogenetic signal in different taxonomic(e.g.,angiosperms and gymnosperms)and ecological(e.g.,tropical,temperate,and boreal)groups of tree species,explored the biogeographical and phylogenetic patterns of wood density,and quantified the relative importance of current environmental factors(e.g.,climatic and soil variables)and evolutionary history(i.e.,phylogenetic relatedness among species and lineages)in driving global wood density variation.We found that wood density displayed a significant phylogenetic signal.Wood density differed among different biomes and climatic zones,with higher mean values of wood density in relatively drier regions(highest in subtropical desert).Our study revealed that at a global scale,for angiosperms and gymnosperms combined,phylogeny and species(representing the variance explained by taxonomy and not direct explained by long-term evolution process)explained 84.3%and 7.7%of total wood density variation,respectively,whereas current environment explained 2.7%of total wood density variation when phylogeny and species were taken into account.When angiosperms and gymnosperms were considered separately,the three proportions of explained variation are,respectively,84.2%,7.5%and 6.7%for angiosperms,and 45.7%,21.3%and 18.6%for gymnosperms.Our study shows that evolutionary history outpaced current environmental factors in shaping global variation in wood density.

Vibration properties of Paulownia wood for Ruan sound quality using machine learning methods

As an important material for manufacturing resonant components of musical instruments,Paulownia has an important influence on the sound quality of Ruan.In this paper,a model for evaluating the sound quality of Ruan based on the vibration characteristics of wood is developed using machine learning methods.Generally,the selection of materials for Ruan manufacturing relies primarily on manually weighing,observing,striking,and listening by the instrument technician.Deficiencies in scientific theory have hindered the quality of the finished Ruan.In this study,nine Ruans were manufactured,and a prediction model of Ruan sound quality was proposed based on the raw material information of Ruans.Out of a total of 180 data sets,145 and 45 sets were chosen for training and validation,respec-tively.In this paper,typical correlation analysis was used to determine the correlation between two single indicators in two adjacent pairwise combinations of the measured objects in each stage of the production process in Ruan.The vibra-tion characteristics of the wood were tested,and a model for predicting the evaluation of Ruan’s acoustic qualities was developed by measuring the vibration characteristics of the resonating plate material.The acoustic quality of the Ruan sound board wood was evaluated and predicted using machine learning model generalized regression neural net-work.The results show that the prediction of Ruan sound quality can be achieved using Matlab simulation based on the vibration characteristics of the soundboard wood.When the model-predicted values were compared with the tradi-tional predicted results,it was found that the generalized regression neural network had good performance,achieving an accuracy of 93.8%which was highly consistent with the experimental results.It was concluded that the model can accurately predict the acoustic quality of the Ruan based on the vibration performance of the soundboards.

Wood灯检查在白癜风治疗期间不同复色模式观察研究中的应用

目的:通过观察Wood灯下白癜风患者治疗过程中皮损的复色模式,探究不同复色模式的相关影响因素,分析评估不同复色模式间的疗效差异。方法:收集选取2021年8月-2022年8月笔者医院就诊的90例(170块),采集患者基本信息及病情相关指标。治疗后,观察Wood灯下皮损复色模式,将复色模式与收集指标进行统计分析,筛选出差异有统计学意义的指标后,采用Logistic回归分析阳性指标与复色模式的相关性。并将Wood灯下观察的皮损面积作为疗效判定标准,分析对比不同复色模式间的疗效差异。结果:One-way ANOVA分析、χ^(2)检验显示白癜风疾病分期、皮损部位、治疗方式与皮损复色模式有关(P<0.05)。Logistic回归分析显示:治疗方式与复色模式存在相关性(P<0.05)。不同复色模式达到有效复色面积的时间比较,差异有统计学意义(H=8.04,P<0.05)。结论:通过观察Wood灯下白癜风患者治疗过程中皮损的复色模式发现,治疗方式可能是白癜风皮损复色模式的决定因素;不同复色模式之间的复色速度存在差异,可作为临床医师调整治疗方案的参考依据。

Biobased Furfurylated Poplar Wood for Flame-Retardant Modification with Boric Acid and Ammonium Dihydrogen Phosphate

Furfurylated wood exhibits excellent dimensional stability and corrosion resistance,making it a promising material for constructing buildings,but it is highly flammable.Herein,flame-retardant furfurylated poplar wood was produced via a two-step process utilizing boric acid(BA)and ammonium dihydrogen phosphate(ADP)as flame-retardant components,and biomass-derived furfuryl alcohol(FA)as a modifier.The acidity of BA and ADP allowed them to catalyze the polymerization of FA,which formed a cross-linked network that immobilized BA and ADP inside the wood.The addition of BA/ADP substantially delayed the time to ignition from 10 to 385 s and reduced the total heat release and total smoke release by 58.75%and 77.31%,respectively.Analysis of the pyrolysis process showed that the decomposition products of BA and ADP protected the underlying furfurylated wood and diluted combustible gases.This method significantly improved the fire retardancy and smokeless properties of furfurylated wood,providing promising prospects for its application as an engineering material.

Enhancing wood efficiency through comprehensive wood flow analysis:Methodology and strategic insights

Wood,an essential natural resource in human civilization,remains widely used despite advances in technology and material substitution.The surge in greenhouse gas emissions and environmental concerns accentuates the need for optimizing wood utilization.Material flow analysis is a powerful tool for tracking material flows and stocks,aiding resource management and environmental decision-making.However,the full extent of its methodological dimensions,particularly within the context of the wood supply chain,remains relatively unexplored.In this study,we delve into the existing literature on wood flow analysis,discussing its primary objectives,materials involved,temporal and spatial scales,data sources,units,and conversion factors.Additionally,data uncertainty,data reconciliation and crucial assumptions in material flow analysis are highlighted in this paper.Key findings reveal the significance of wood cascading and substitution effects by replacing non-wood materials,where they can reduce greenhouse gas emissions more than the natural carbon sink of forests and wood products.The immediate impact of short-term wood cascading might not be as robust as the substitution effect,with energy substitution showcasing better results than material substitution.However,it's crucial to note that these conclusions could experience significant reversal from a long-term and global perspective.Strategies for improving wood efficiency involve maximizing material use,advancing construction technologies,extending product lifespans,promoting cascade use,and optimizing energy recovery processes.The study underscores the need for standardized approaches in wood flow analysis and emphasizes the potential of wood efficiency strategies in addressing environmental challenges.

Wood By-Products as UV Protection:A Consequence Review

In recent decades,the ozone layer has suffered considerable damage,increasing the entry of ultraviolet(UV)light into the atmosphere and reaching the earth’s surface,negatively affecting life.Accordingly,researchers aimed to solve this problem by synthesizing advanced UV-shielding materials.On the other hand,developing an easy and green strategy to prepare functional materials with outstanding properties based on naturally abundant and environmentally friendly raw materials is highly desirable for sustainable development.Because biomass-derived materials are sustainable and biodegradable,they present a promising substitute for petroleum-based polymers.The three main structural constituents of the plant biomass-based materials that are naturally available are cellulose,hemicellulose,and lignin.This review details current developments using wood-based products such as cellulose,hemicellulose,and lignin in UV-shielding applications.It will start with assembling the structure and chemistry of cellulose,hemicellulose,and lignin,followed by their contributions to preparing UV-shielding materials.Finally,it will briefly discuss the different processing methods for the design of UV-shielding materials.The wood by-products offer additional opportunities to use the whole tree harvest.

Configuring single-layer MXene nanosheet onto natural wood fiber via C-Ti-C covalent bonds for high-stability Li-S batteries

Lithium-sulfur batteries(LSBs)are considered promising candidates for next-generation battery technologies owing to their outstanding theoretical energy density and cost-effectiveness.However,the low conductivity and polysulfide shuttling effect of S cathodes severely hamper the practical performance of LSBs.Herein,in situ-generated single layer MXene nanosheet/hierarchical porous carbonized wood fiber(MX/PCWF)composites are prepared via a nonhazardous eutectic activation strategy coupled with pyrolysis-induced gas diffusion.The unique architecture,wherein single layer MXene nanosheets are constructed on carbonized wood fiber walls,ensures rapid polysulfide conversion and continuous electron transfer for redox reactions.The C-Ti-C bonds formed between MXene and PCWF can considerably expedite the conversion of polysulfides,effectively suppressing the shuttle effect.An impressive capacity of 1301.1 m A h g^(-1)at 0.5 C accompanied by remarkable stability is attained with the MX/PCWF host,as evidenced by the capacity maintenance of 722.6 m A h g^(-1)after 500 cycles.Notably,the MX/PCWF/S cathode can still deliver a high capacity of 886.8 m A h g^(-1)at a high S loading of 5.6 mg cm^(-2).The construction of two-dimensional MXenes on natural wood fiber walls offers a competitive edge over S-based cathode materials and demonstrates a novel strategy for developing high-performance batteries.

Leakage Proof,Flame-Retardant,and Electromagnetic Shield Wood Morphology Genetic Composite Phase Change Materials for Solar Thermal Energy Harvesting

Phase change materials(PCMs)offer a promising solution to address the challenges posed by intermittency and fluctuations in solar thermal utilization.However,for organic solid-liquid PCMs,issues such as leakage,low thermal conductivity,lack of efficient solar-thermal media,and flamma-bility have constrained their broad applications.Herein,we present an innova-tive class of versatile composite phase change materials(CPCMs)developed through a facile and environmentally friendly synthesis approach,leveraging the inherent anisotropy and unidirectional porosity of wood aerogel(nanowood)to support polyethylene glycol(PEG).The wood modification process involves the incorporation of phytic acid(PA)and MXene hybrid structure through an evaporation-induced assembly method,which could impart non-leaking PEG filling while concurrently facilitating thermal conduction,light absorption,and flame-retardant.Consequently,the as-prepared wood-based CPCMs showcase enhanced thermal conductivity(0.82 W m^(-1)K^(-1),about 4.6 times than PEG)as well as high latent heat of 135.5 kJ kg^(-1)(91.5%encapsula-tion)with thermal durability and stability throughout at least 200 heating and cooling cycles,featuring dramatic solar-thermal conversion efficiency up to 98.58%.In addition,with the synergistic effect of phytic acid and MXene,the flame-retardant performance of the CPCMs has been significantly enhanced,showing a self-extinguishing behavior.Moreover,the excellent electromagnetic shielding of 44.45 dB was endowed to the CPCMs,relieving contemporary health hazards associated with electromagnetic waves.Overall,we capitalize on the exquisite wood cell structure with unidirectional transport inherent in the development of multifunctional CPCMs,showcasing the operational principle through a proof-of-concept prototype system.

Relating estimates of wood properties of birch to stem form, age and species

Birch has long suffered from a lack of active forest management,leading many researchers to use mate-rial without a detailed management history.Data collected from three birch(Betula pendula Roth,B.pubescens Ehrh.)sites in southern Sweden were analyzed using regression analysis to detect any trends or differences in wood proper-ties that could be explained by stand history,tree age and stem form.All sites were genetics trials established in the same way.Estimates of acoustic velocity(AV)from non-destructive testing(NDT)and predicted AV had a higher correlation if data was pooled across sites and other stem form factors were considered.A subsample of stems had radial profiles of X-ray wood density and ring width by year created,and wood density was related to ring number from the pith and ring width.It seemed likely that wood density was negatively related to ring width for both birch species.Linear models had slight improvements if site and species were included,but only the youngest site with trees at age 15 had both birch species.This paper indicated that NDT values need to be considered separately,and any predictive models will likely be improved if they are specific to the site and birch species measured.

BACNN: Multi-scale feature fusion-based bilinear attention convolutional neural network for wood NIR classification

Effective development and utilization of wood resources is critical.Wood modification research has become an integral dimension of wood science research,however,the similarities between modified wood and original wood render it challenging for accurate identification and classification using conventional image classification techniques.So,the development of efficient and accurate wood classification techniques is inevitable.This paper presents a one-dimensional,convolutional neural network(i.e.,BACNN)that combines near-infrared spectroscopy and deep learning techniques to classify poplar,tung,and balsa woods,and PVA,nano-silica-sol and PVA-nano silica sol modified woods of poplar.The results show that BACNN achieves an accuracy of 99.3%on the test set,higher than the 52.9%of the BP neural network and 98.7%of Support Vector Machine compared with traditional machine learning methods and deep learning based methods;it is also higher than the 97.6%of LeNet,98.7%of AlexNet and 99.1%of VGGNet-11.Therefore,the classification method proposed offers potential applications in wood classification,especially with homogeneous modified wood,and it also provides a basis for subsequent wood properties studies.

Evaluation of Mechanical Properties and Surface Quality of Wood from Bosnia and Herzegovina Exposed to Outdoor Conditions

This study investigated the mechanical properties of beech(Fagus sylvatica L.)and fir(Abies alba)wood from Bosnia and Herzegovina under outdoor exposure.Samples were exposed for 3-month exposure to assess bending strength,color changes,and surface quality.Results showed outdoor exposure negatively affected mechanical properties,particularly in samples with extended finger joints,causing significant surface cracks in uncoated samples.Beech wood exhibited notable color changes under exposure,with approximately 50%darkening without coating compared to 25%under covered conditions.Coated samples displayed minimal color changes,affirming the efficacy of surface treatment.Fir wood exhibited a roughness of 8.264μm,while beechwood average roughness increased from 6.767 to 13.916μm after exposure,with micro-pore development affecting water performance.Microscopic analysis identified prevalent fungal colonies,including Penicillium,Aureobasidium,Sclerophoma,and Chaetomium,underscoring their role in organic matter decomposition.This study highlights the importance of wood exposure and treatment selection for various applications.

Overview of the Synthesis, Characterization, and Application of Tannin-Glyoxal Adhesive for Wood-Based Composites

More than a century after its initial synthesis,urea-formaldehyde(UF)resins still have dominant applications as adhesives,paints,and coatings.However,formaldehyde in this industry produces formaldehyde emissions that are dangerous to health.Scientists have spent the last decade replacing formaldehyde and phenol with environmentally friendly substances such as glyoxal and tannin to create bio-based adhesives.This review covers recent advances in synthesizing glyoxal tannin-based resins,especially those made from sustainable raw material substitutes and changes made to synthetic processes to improve mechanical properties.The efficacy of using tannin-glyoxal adhesives in producing wood-based composites has been proven.The glyoxylate reaction forms cross-linked bridges between the aromatic sites of the tannin and glyoxal molecular structures.Glyoxal tannin adhesive with a greater percentage of glyoxal than tannin will produce an adhesive with better characteristics.The gel time reduces as the hardener concentration rises from 7.5%to 15%when glyoxal is used in adhesives.However,excessive amounts of glyoxal will result in a decrease in viscosity values.Glyoxal exhibits faster delivery degradation when it reaches a maximum temperature of approximately 130°C,although it initiates the curing process slightly slower at 110°C.Adding glyoxal to tannin-based adhesives can improve the mechanical properties of composite boards.The wet shear strength of the resulting plywood is increased by 105.4%with the addition of 5-weight percent tannin-based resin with glyoxal as a cross-linker in Soy Protein Adhesive.With glyoxal as a hardener,the panels produced showed good internal bond strengths(>0.35 MPa)and met the international standard specifications for interior-grade panels.

Estimating wood quality attributes from dense airborne LiDAR point clouds

Mapping individual tree quality parameters from high-density LiDAR point clouds is an important step towards improved forest inventories.We present a novel machine learning-based workflow that uses individual tree point clouds from drone laser scanning to predict wood quality indicators in standing trees.Unlike object reconstruction methods,our approach is based on simple metrics computed on vertical slices that summarize information on point distances,angles,and geometric attributes of the space between and around the points.Our models use these slice metrics as predictors and achieve high accuracy for predicting the diameter of the largest branch per log (DLBs) and stem diameter at different heights (DS) from survey-grade drone laser scans.We show that our models are also robust and accurate when tested on suboptimal versions of the data generated by reductions in the number of points or emulations of suboptimal single-tree segmentation scenarios.Our approach provides a simple,clear,and scalable solution that can be adapted to different situations both for research and more operational mapping.

Preparation of Environmentally Friendly Urea-Hexanediamine-Glyoxal(HUG)Resin Wood Adhesive

Using non-toxic,low-volatile glyoxal to completely replace formaldehyde for preparing urea-glyoxal(UG)resin adhesive is a hot research topic that could be of great interest for the wood industry.However,urea-glyoxal(UG)resins prepared by just using glyoxal instead of formaldehyde usually yields a lower degree of polymerization.This results in a poorer bonding performance and water resistance of UG resins.A good solution is to pre-react urea to preform polyurea molecules presenting already a certain degree of polymerization,and then to condense these with glyoxal to obtain a novel UG resin.Therefore,in this present work,the urea was reacted with hexamethylene diamine to form a polyurea named HU,and then this was used to react it with different amounts of glyoxal to synthesize hexamethylenediamine-urea-glyoxal(HUG)polycondensation resins,and to use this for bonding plywood.The results show that the glyoxal can well react with HU polyuria via addition and schiff base reaction,and also the HUG resin exhibits excellent bonding strength and water resistance.The shear strength of the plywood bonded with this HUG at 160°C hot press temperature as high as 1.93 MPa,2.16 MPa and 1.61 MPa,respectively,which meets the requirement of the China national standard GB/T 9846-2015(≥0.7 MPa),and can be a good choice as a wood adhesive for industrial application.