首页 > 分享 > 木材微观结构分析方法与特征参数

木材微观结构分析方法与特征参数

摘要:

木材是一种多尺度且具有高度各向异性的天然高分子材料,其由纤维素、半纤维素、木质素等组分组成复杂三维网络结构。这些结构是影响木材微宏观物理力学性能的关键因素,木材的微观结构和主要特征参数对认识木材性质以及木材改性和加工利用具有重要意义。本文综合分析了木材微观结构分析的方法,并归纳总结了其主要特征参数。在分析方法方面,显微观察、X射线照相及衍射分析、计算机断层扫描等技术被广泛应用,结合数据采集和二维、三维成像方法,可以重建木材微观结构模型,测量微观结构特征参数,重建的结构模型可以揭示出更为清晰的微观形貌特征。木材微观特征参数主要包含木材的细胞结构特征参数、孔隙率、纤维素微纤丝等参数。目前微观结构的观测研究多局限在微米尺度,对于纳米尺度和更小分子尺度结构的分析主要以理论模拟为主,可以进行直接观察表征的结构分析方法有待进一步研发。

Abstract:

Wood is a multi-scale and highly anisotropic natural polymer material, which consists of cellulose, hemicellulose, lignin and other components of a complex three-dimensional network structure. These structures are the key factors affecting micro-macroscopic physical and mechanical properties of wood, and the microstructure and main characteristic parameters of wood are of great significance to the understanding of properties of wood as well as wood modification and processing and utilization. This paper comprehensively analyzes the methods of wood microstructure analysis and summarizes its main characteristic parameters. In terms of analytical methods, microscopic observation, X-ray radiography and diffraction analysis, computed tomography and other technologies were widely used, combined with data acquisition and two-dimensional and three-dimensional imaging methods, can reconstruct wood microstructural models, measure microstructural parameters, and reconstructed structural models revealed clearer micro-morphological features. The microstructural parameters of wood mainly include cellular structure parameters, porosity, cellulose microfilaments and other parameters of wood. At present, the observation and study of microstructure is mostly limited to the micron scale, and the analysis of nanoscale and smaller molecular scale structure is mainly based on theoretical simulation, and the structural analysis methods that can be directly observed and characterized need to be further studied in depth.

图  1   木材多层级结构示意图(a)木材; (b)针、阔叶材结构示意图; (c)木材细胞; (d)典型木材细胞壁的壁层构造示意图[1]

Figure  1.   Schematic diagram of multi-layer structure of wood (a) Schematic diagram of wood; (b) Schematic diagram of needle and broadleaved wood structure; (c) Schematic diagram of wood cells; (d) Schematic diagram of wall layer structure of typical wood cell walls[1]

图  2   木材组织的虚拟切片、生成体积渲染解剖图[43]

Figure  2.   Virtual slices of wood tissue, generating volume-rendered anatomical images[43]

表  1   二维平面结构特征分析方法

Table  1   Analysis method for two-dimensional planar structural features

显微观察设备 测试参数类型 测试样品树种 样品制备方法 测试样品尺寸 分辨率/
放大倍率 引用 配备紫外光源的光学显微镜 木材微观结构、木质复合材料胶黏剂分布 花旗松
(Pseudotsuga menziesii) 切下100 mm × 10 mm × 2.8 mm的木块,在20℃和65%相对湿度下进行预处理,使用热压机制成双层板后制成样品。在质量分数0.05%甲苯胺蓝溶液中染色 5 min,并在质量分数30%乙醇中清洗20 min进行脱水,最后用甘油密封在盖玻片上 5 mm × 3 mm × 20 μm [2] 扫描电子显微镜 管胞的形状、数量和分布 欧洲山毛榉(Fagus sylvatica)、胡桃木(Juglans regia)等 用刀片将木材样品切成棱柱状,放入乙醇中脱水,使用液态 CO2 进行干燥,镀金后进行测试 0.5 cm × 0.5 cm × 0.1 cm 1 500 × [12] 压缩前后木材的微观结构 花旗松
(Pseudotsuga menziesii) 在65%相对湿度和20 ℃下预处理木块,将样品分为3组:一组作为对照,另两组分别在 180和 210 ℃饱和蒸汽下处理 2 h,在 103 ℃下烘干5 h后进行测试 10 mm × 10 mm ×
3.5 mm(晚材)
10 mm × 10 mm ×
6.5 mm(早材)
10 mm × 10 mm ×
10 mm(早材和晚材) [3] 细胞壁纹孔 香椿
(Toona sinensis) 用边材制样,在103 ℃下烘干,样品分成两组,一组用亚麻籽油进行真空浸渍,另一组作为对照,处理后将样品风干7天至亚麻籽油固化 20 mm × 20 mm × 20 mm 5 μm [6] 原子力显微镜 木材老化前后的显微特征 挪威云杉
(Picea abies) 从风干的木材中径向切下10个45 mm × 6.5 mm × 10 mm的样本,其中5个样本在紫外线气候箱中进行加速老化 5 mm × 5 mm × 5 mm [8] 不同切割木材的粗糙度 异味豆
(Dinizia excelsa) 将材料沿纹理方向切成35 cm厚的圆片,再沿着正交方向切割成边长为2 cm的立方体试样,用1 200目的砂纸抛光并在超声波清洗器中去除杂质,并在45 ℃下烘干 2 cm × 2 cm × 2 cm 10 μm [7] 紫外可见光谱仪,傅里叶变换红外光谱仪 木材烧焦表面碳水化合物的降解 云杉
(Picea asperata) 将190 mm × 24 mm × 4 000 mm的锯材板进行预干燥,后用两种不同的炭化方法进行表面处理 4 cm−1 [9] 傅里叶变换红外光谱仪 木材细胞壁成分的化学结构 落叶松
(Larix gmelinii) 从木材的表面和内部采集样本,并使用超微切片机制备样本 15 μm(厚度,横切面) 8 cm−1 [11]

表  2   木材纤维素晶体结构及微纤丝角参数分析方法

Table  2   Analysis method for crystal structure and microfibril angle parameters of wood cellulose

设备 测量参数类型 测试样品树种 样品制备方法 测试样品尺寸 引用 X射线衍射仪 纤维素结晶结构,包括纤维素结晶尺寸、纤维素微纤丝中纤维素分子的排列以及结晶纤维素的比例 将木结构建筑中花拱的样本磨成木粉进行测试 粉末 [11] 纤维素晶体的晶格间距 杉木(Cryptomeria japonica)、桧木(Chamaecyparis obtusa) 将木材制成横截面和四等分锯切的样本,在密封的干燥室中用蒸馏水调节约一个月至达到纤维饱和点,将样本分成3组进行不同的干湿处理周期 5 mm × 15 mm × 15 mm
(横切样品)
5 mm × 18 mm × 15 mm
(四等分锯切样品) [13] 纤维素微纤丝角 云杉
(Picea asperata) 从树干上采集 10 mm厚包括髓心的径向切片,后从每片树干的髓心至树皮处锯下包含晚材的纵向切片,每条再分成4片获得约 1 mm厚的弦向切片 10 mm (R) × 10 mm
(T) × 15 mm (L) [14] 冷冻透射电子显微镜 纤维素微纤丝角 云杉
(Picea asperata) 使用低温超微切片机切割制样,将切片收集到碳涂层铜栅上,并在 1%柠檬酸缓冲液中用 1% KMnO4染色25 min 50 nm(厚度,梯形超薄
切片) [15] 广角 X 射线衍射仪 纤维素微纤丝角分布、结晶度和纤维素晶粒宽度 杨木
(Populus spp.) 用手术刀从每根茎干的髓心和树皮中部切下相应测量尺寸的薄片样本 0.5 mm (R) × 0.5 mm
(T) × 5 mm (L) [16] ITRAX-X射线显微密度计 生长环宽度、早材和晚材宽度及比例、环内平均木材密度、最小和最大木材密度、早材和晚材密度 测量可以在有完整钻孔的原木或矩形样本上进行 [17] SilviScan扫
描仪 木材密度、纤维素微纤丝角 云杉
(Picea asperata) 从木材髓部切得20 mm的径向切片,在丙酮中浸泡 12 h后在70 ℃下萃取8 h。在20 ℃和40%相对湿度的环境中调节含水量至8%,后将样品锯成测量尺寸,表面用砂纸进行抛光 截面尺寸2 mm (T) ×
7 mm (R) [18]

表  3   三维空间结构特征分析方法

Table  3   Analysis method for three-dimensional spatial structural features

设备 测试参数类型 测试样品树种 样品制备方法 测试样品大小 最佳分
辨率 扫描时间 引用 X 射线显微计算机断层扫描 压缩过程中木材试样内部结构的性能 花旗松(Pseudotsuga menziesii) 从树干切下 100 mm × 10 mm × 2.8 mm 的木块,在20 ℃和65%相对湿度下放置4周,热压成双层板,从板材上切割样本进行测试 4.0 mm × 4.0 mm ×
5.0 mm 12 μm 25 min [2] 管胞尺寸、纤维方向 云杉
(Picea asperata) 切割6个150 mm × 150 mm × 500 mm木材样本,在窑炉中缓慢干燥至含水率约为 18%后,储存在20 ℃和65%相对湿度的恒温室中,样品每次切割和扫描后立即用塑料薄膜包裹 S1:75 mm × 75 mm ×
107 mm
S2:32 mm × 32 mm ×
75 mm
S3a,S3b:9.5 mm ×
9.5 mm × 75 mm
S4a,S4b:3.5 mm ×
3.5 mm × 75 mm 52 μm
24 μm
7 μm
2.6 μm [30] 导管孔的数量、圆度、面积、周长 紫檀(Pterocarpus indicus)、阔叶黄檀(Dalbergia latifolia) 2 mm × 2 mm × 15 mm 1.95 μm [36] 导管直径、导管面积、导管密度和孔隙率 山毛榉(Fagus longipetiolata)、橡树(Quercus palustris) 将树茎切成约 30 mm长的段,软化后储存在甘油和乙醇的混合物中,制样进行分析 5 mm × 5 mm × 25 mm 7 μm [37] 木质部三维剖面连接情况 金合欢
(Acacia aneura) 用单面刀片从树枝上切下约 10 cm的末端小枝 10 ~ 15 mm分枝 3.4 μm [31] 真菌驱动的无机颗粒在定植木材中的分布 挪威云杉(Picea abies)、欧洲赤松(Pinus sylvestris) 将木质试验桩(5 mm × 10 mm × 100 mm)放置在土壤中进行生物降解,放置不同的时长后移除并在 50℃下烘干,以阻止真菌生长 2 mm × 2 mm × 15 mm 1 μm [32] 木质部含水率和横截面木材密度 云杉(Picea abies)、橡树(Quercus Robur) 制取5 cm厚的树茎圆盘,迅速放入干冰中冷冻,然后将样本放入103 ℃的烘箱中烘干,脱水后再次进行扫描 5 cm(厚度) 0.122 5 mm 2 s(扫描每 1 mm厚度) [20] 木材中水分的时间演变和空间
分布 挪威云杉
(Picea abies) 从树干切得约有73个生长年轮的木盘,沿木材的主要生长方向切割,制备两组样本用于测试 A组:30.80 mm(L) × 8.70 mm(R) × 3.07 mm(T)
B组:29.91 mm(R) × 8.90 mm(T) × 2.09 mm(L) 18 μm 10 s(每张图像) [21] 木质部形成过程中细胞壁密度的变化
黑松(Pinus nigra)、山毛榉(Fagus sylvatica)、夏栎(Quercus robur) 从树干中采集试样,样品在乙醇中脱水后,安装在圆柱形碳棒上进行扫描,在25 ℃和40%相对湿度下在扫描室中放置12 h 2mm直径圆柱形微芯片 2.5 μm 700 ms(每张图像) [22] 木材干燥过程中的微观收缩 山毛榉
(Fagus sylvatica) 2 mm × 2 mm × 2 mm 1.42 μm < 5 min [24] 木塑的微观结构、木塑加固的几何形状 使用切割器和立式钻孔机从木塑样品中提取圆柱形心材后,将样品密封在 PEEK 管中进行测试 5.8 mm(直径) ×
9.68 mm(长度) 3.1 μm 16 h(2 880次射线投影次数) [25] 纤维素纯化过程中的三维结构、管胞纤维、细胞壁厚和纤维细胞 杉木(Cunninghamia lanceolata) 经过两步木质纤维素提纯的木材样本及其对照样本在-40℃的冷冻干燥机中干燥 48 h后,立即密封进行测试 1 mm × 1 mm × 10 mm 0.3 μm 2 s(每个点的扫描时间) [34] 同步辐射 X 射线显微断层显微镜 木材的微观结构以及两种白腐真菌培养后密度分布的变化 挪威云杉(Picea abies)、铁杉 (Acer pseudoplatanus) 制取尺寸为 100 mm(径向) × 2.5 mm(切向) × 200 mm(纵向)的样本,用环氧乙烷灭菌后,在不同条件下用真菌进行处理,后切下约 1 mm3的样本用于测试 1 mm × 1 mm × 1 mm 0.7 μm 130 ms(每张图片) [33] 细胞壁厚度、管胞大小、具缘纹孔的大小和孔
隙率 挪威云杉
(Picea abies) 在心材中切边长为8 mm的立方体样本,煮沸数小时后,在早晚材之间的过渡区切下约 200 μm 厚的弦向切片,后通过垂直夹持法获得另一个 200 μm厚的径向切片 8 mm × 200 μm × 200 μm 0.7 μm 67 min
(950张轴切片图像) [29] 阔叶材导管孔隙参数的显微图像 挪威云杉
(Picea abies) 沿木材纤维方向切割得牙签状薄片样品,使用氰基丙烯酸酯黏合剂将样品安装到样品架上,在制样和测试过程中始终保持样品在相对湿度(33%)可控的恒温室中 500 μm × 500 μm × 8 mm 0.7 μm 4 min
(1 024 张射线投影) [23] 工业 CT 扫描仪 树环测量 5 μm [26] 医用 CT 扫描仪 髓、节和局部纤维取向 云杉(Picea abies) 原木长度范围3.4 ~ 5.6 m,干燥至含水率12% 50 mm × 100 mm ×
(3.4 ~ 5.6)m 0.68 mm 10 ms(每张图像) [27]

表  4   数据收集分析及成像方法

Table  4   Data collection analysis and imaging methods

测试方法 数据收集、分析及成像方法 测量参数 引用 同步加速器显微断层成像 重建垂直于旋转轴的单切片图像;使用滤波后投影,并将所有负值设为零 管胞大小/形状、管壁厚度、孔隙率、纹孔 [29] 显微计算机断层扫描 开发了μCTanalysis 软件,利用灰度图像阈值对二维图像进行更快、更简单的图像分割 导管内径、导管横截面积、导管密度和孔隙率 [37] 使用 datos|reconstruction® 软件将投影图像叠加转换成容积模型,使用 Avizo 的区域生长工具对感兴趣的区域进行分割 真菌引起的早期木质部细胞变形和晚期木质部细胞厚壁中的孔洞 [32] 利用各种形态学处理方法获得血管孔隙的二值图像,并提取其特征进行分类 导管孔隙的数量、圆度、面积、周长和其他特征参数 [36] 使用 Drishti 对NaI浸渍前后的木塑样品断层图中的相位进行可视化,并根据体素强度创建了二维图像和三维动画 木质颗粒的体积、表面积和长宽比 [25] 使用 Dragonfly 软件进行可视化和定量分析,从图像中分割出细胞裂缝和孔腔 杉木和杨木纤维素提纯过程中三维结构的变化,细胞壁厚度和腔体体积比 [34] LBP变形形成均匀LBP、旋转不变LBP和旋转不变均匀LBP,并与 GLCM 特征融合,形成3种融合特征 木材三切面显微图像 [41] 同步辐射相衬X射线断层显微镜 使用灰度阈值法将断层扫描图像分割成二进制的木材和空气体素数据集 测定木材膨胀/收缩应变 [23] 相位对比 X 射线成像 利用分析或迭代算法计算不同 X 射线吸收路径的衰减系数,以重建图像 吸收对比度不够的样品内部结构 [40] 配备LCD相机的显
微镜 细胞分割,图像降噪,将彩色图像转换为灰度图像,分水岭算法 细胞厚度和尺寸 [39] 医用CT扫描仪 利用空间对齐CT图像的位移数据计算径向和切向收缩,将 CT 图像导入 CAD 软件,直接测量收缩率 径向和切向收缩 [42]

表  5   木材细胞壁厚度、细胞直径、导管/管胞参数、孔隙率

Table  5   Wood cell wall thickness, cell diameter, vessel/tracheid parameters and porosity

树种 测试技术 细胞壁厚度/μm 细胞直径/μm 导管/管胞参数 孔隙率% 引用 花旗松
(Pseudotsuga menziesii) 配备紫外光源的光学显微镜 2.4 ± 0.6
(早材双层厚度)
8.5 ± 2.1
(晚材双层厚度) 38.9 ± 11(早材弦向)
7.8 ± 4.1(晚材弦向) [2] 原型 X 射线超显
微镜 72 ± 2(早材)
21 ± 2(晚材) [35] 杂交杨木(P. tremula L. × P. tremuloides Michx. ) X射线断层扫描、X射线衍射 2.9 ± 0.2(双层厚度) 10.5 ± 0.5 [16] 云杉
(Picea asperata) X射线显微计算机断层扫描 3.16 ± 0.70
(无纹孔径向)
1.56 ± 0.40
(有纹孔径向)
2.99 ± 0.58(弦向) 35.3 ± 5.9(弦向)
35.0 ± 6.4(径向) 59.8 ± 2.5 [29] 挪威云杉
(Picea abies) X射线显微计算机断层扫描 20 ~ 50
(无瑕疵木材径向管腔)
15 ~ 35
(无瑕疵木材弦向管腔)
10 ~ 30
(过渡区管腔)
5 ~ 25
(分枝木径向管腔)
5 ~ 20
(分枝木弦向管腔) 1.2 mm
(无瑕疵木材管胞)
0.5 mm
(分枝木管胞) [30] 50(早材管腔) 76(早材)
27(晚材) [33] 榉木
(Fagus sylvatica) X射线显微计算机断层扫描 33 ± 9 (0.89 ± 0.47) × 103 μm2
(导管表面积),
(108 ± 15) mm2
(导管数密度) 9.7 ± 1.7 [37] 夏栎
(Quercus robur) X射线显微计算机断层扫描 34 ± 23 (56 ± 5) mm2
(导管数密度)
(1.24 ± 1.57) × 103 μm2
(导管表面积) 7.2 ± 0.7 火炬松
(Pinus taeda) X射线显微计算机断层扫描 39 ± 1(早材)
64 ± 1(晚材) [35] 金合欢(Acacia aneura,Acacia ayersiana) X射线显微计算机断层扫描 238 mm2
(导管表面积)
822 mm2
(A. ayersiana导管数密度)
637/mm2
(A. aneura导管数密度) [31] 梧桐(Acer pseudoplatanus) X射线显微计算机断层扫描 10 ~ 25(纵向管腔) 51(早材) [33] 白杨(Populus sp.) X射线显微计算机断层扫描 2.52 [34] 杉木(Cunninghamia lanceolata) X射线显微计算机断层扫描 2.55 欧洲白蜡
(Fraxinus excelsior) X射线显微计算机断层扫描 210(早材)
63(晚材) 9 967 086 μm3
(早材导管管腔)
850 927 μm3
(晚材导管管腔) [44] [1]

Chen C, Kuang Y, Zhu S, et al. Structure-property-function relationships of natural and engineered wood[J]. Nature Reviews Materials, 2020, 5(9): 642−666. doi: 10.1038/s41578-020-0195-z

[2]

Li W, Zhang Z, Mei C, et al. Understanding the mechanical strength and dynamic structural changes of wood-based products using X-ray computed tomography[J]. Wood Material Science & Engineering, 2022, 18(2): 454−463.

[3]

Wang J, Yang K, Li W, et al. The impact of earlywood and latewood on the compressive stress of thermally modified Douglas fir[J]. Forests, 2023, 14(7): 1376. doi: 10.3390/f14071376

[4] 方旋, 张景朋, 熊怡心, et al. 自然老化杉木梁的分区可处理性[J]. 北京林业大学学报, 2024, 46(11): 124−132. doi: 10.12171/j.1000-1522.20240176

Fang X, Zhang J P, Xiong Y X, et al. Sub-regional treatability of a naturally aging Chinese fir beam[J]. Journal of Beijing Forestry University, 2024, 46(11): 124−132. doi: 10.12171/j.1000-1522.20240176

[5] 王瑜瑶, 马尔妮. 不同预处理方法对木材细胞壁孔隙结构影响[J]. 北京林业大学学报, 2023, 45(11): 140−151. doi: 10.12171/j.1000-1522.20230158

Wang Y Y, Ma E N. Effects of different pretreatment methods on the pore structure of wood cell wall[J]. Journal of Beijing Forestry University, 2023, 45(11): 140−151. doi: 10.12171/j.1000-1522.20230158

[6]

Liu M, Wang J, Xu G, et al. Efficacy of linseed oil-treated wood to improve hydrophobicity, dimensional stability, and thermostability[J]. Wood Research, 2021, 66(5): 777−788. doi: 10.37763/wr.1336-4561/66.5.777788

[7]

Conceição W S D, Matos R S, Melo I D C, et al. Measurement of wood surface roughness in Dinizia excelsa Ducke using an atomic force microscope[J]. Acta Scientiarum Technology, 2022, 44: e56509. doi: 10.4025/actascitechnol.v44i1.56509

[8]

Mao J, Abushammala H, Kasal B. Monitoring the surface aging of wood through its pits using atomic force microscopy with functionalized tips[J]. Colloids and Surfaces A: Physicochemical and Engineering Aspects, 2021, 609: 125871. doi: 10.1016/j.colsurfa.2020.125871

[9]

Ebner D H, Tortora M, Bedolla D E, et al. Comparative investigation of chemical and structural properties of charred fir wood samples by Raman and FTIR spectroscopy as well as X-ray-micro-CT technology[J]. Holzforschung, 2023, 77(9): 734−742. doi: 10.1515/hf-2023-0024

[10]

Kanbayashi T, Ishikawa A, Matsunaga M, et al. Application of confocal raman microscopy for the analysis of the distribution of wood preservative coatings[J]. Coatings, 2019, 9(10): 621. doi: 10.3390/coatings9100621

[11]

Guo J, Zhou H, Stevanic J S, et al. Effects of ageing on the cell wall and its hygroscopicity of wood in ancient timber construction[J]. Wood Science and Technology, 2017, 52(1): 131−147.

[12]

Mallik A, Tarrío-Saavedra J, Francisco-Fernández M, et al. Classification of wood micrographs by image segmentation[J]. Chemometrics and Intelligent Laboratory Systems, 2011, 107(2): 351−362. doi: 10.1016/j.chemolab.2011.05.005

[13]

Toba K, Yamamoto H, Yoshida M. Mechanical interaction between cellulose microfibrils and matrix substances in wood cell walls induced by repeated wet-and-dry treatment[J]. Cellulose, 2012, 19(4): 1405−1412. doi: 10.1007/s10570-012-9700-x

[14]

Yin Y, Bian M, Song K, et al. Influence of microfibril angle on within-tree variations in the mechanical properties of Chinese fir (Cunninghamia lanceolata)[J]. IAWA Journal, 2011, 32(4): 431−442. doi: 10.1163/22941932-90000069

[15]

Reza M, Ruokolainen J, Vuorinen T. Out-of-plane orientation of cellulose elementary fibrils on spruce tracheid wall based on imaging with high-resolution transmission electron microscopy[J]. Planta, 2014, 240(3): 565−573. doi: 10.1007/s00425-014-2107-1

[16]

Svedström K, Lucenius J, van den Bulcke J, et al. Hierarchical structure of juvenile hybrid aspen xylem revealed using X-ray scattering and microtomography[J]. Trees, 2012, 26(6): 1793−1804. doi: 10.1007/s00468-012-0748-x

[17]

Kilpeläinen A, Peltola H, Ryyppö A, et al. Wood properties of Scots pines (Pinus sylvestris) grown at elevated temperature and carbon dioxide concentration[J]. Tree Physiology, 2003, 23(13): 889−897. doi: 10.1093/treephys/23.13.889

[18]

Evans R, White R M. Art, science and informatics: visualisation of large, complex data sets in high-speed measurement of the microstructure of wood[J]. 2002.

[19]

Bergsten U, Lindeberg J, Rindby A, et al. Batch measurements of wood density on intact or prepared drill cores using x-ray microdensitometry[J]. Wood Science and Technology, 2001, 35(5): 435−452. doi: 10.1007/s002260100106

[20]

Fromm J R H, SautteR I, Matthies D, et al. Xylem water content and wood density in spruce and oak trees detected by high-resolution computed tomography[J]. Plant Physiology, 2001, 127(2): 416−425. doi: 10.1104/pp.010194

[21]

Martin B, Colin J, Lu P, et al. Monitoring imbibition dynamics at tissue level in Norway spruce using X-ray imaging[J]. Holzforschung, 2021, 75(12): 1081−1096. doi: 10.1515/hf-2020-0269

[22]

Lehnebach R, Campioli M, Gričar J, et al. High-resolution X-Ray computed tomography: a new workflow for the analysis of xylogenesis and intra-seasonal wood biomass production[J]. Frontiers in Plant Science, 2021, 12: 698640. doi: 10.3389/fpls.2021.698640

[23]

Derome D, Griffa M, Koebel M, et al. Hysteretic swelling of wood at cellular scale probed by phase-contrast X-ray tomography[J]. Journal of Structural Biology, 2011, 173(1): 180−190. doi: 10.1016/j.jsb.2010.08.011

[24]

Taylor A, Plank B, Standfest G, et al. Beech wood shrinkage observed at the micro-scale by a time series of X-ray computed tomographs (μXCT)[J]. Holzforschong, 2013, 67(2): 201−205. doi: 10.1515/hf-2012-0100

[25]

Feng D, Turner M, Evans P D. Sodium iodide as a contrast agent for X-ray micro-CT of a wood plastic composite[J]. Applied Sciences, 2021, 12(1): 208. doi: 10.3390/app12010208

[26]

Bill J, Daly A, Johnsen Ø, et al. DendroCT – dendrochronology without damage[J]. Dendrochronologia, 2012, 30(3): 223−230. doi: 10.1016/j.dendro.2011.11.002

[27]

Huber J A J, Broman O, Ekevad M, et al. A method for generating finite element models of wood boards from X-ray computed tomography scans[J]. Computers & Structures, 2022, 260: 106702.

[28] 刘晨君, 杨淑敏, 薛紫荞, 等. 计算机断层扫描马尾松缺陷及其图像解译[J]. 北京林业大学学报, 2024, 46(10): 144−152.

Liu C J, Yang S M, Xue Z Q, et al. Computerized tomography of defects in Pinus massoniana and its image interpretation[J]. Journal of Beijing Forestry University, 2024, 46(10): 144−152.

[29]

Trtik P, Dual J, Keunecke D, et al. 3D imaging of microstructure of spruce wood[J]. Journal of Structural Biology, 2007, 159(1): 46−55. doi: 10.1016/j.jsb.2007.02.003

[30]

Hu M, Olsson A, Hall S, et al. Fibre directions at a branch-stem junction in Norway spruce: a microscale investigation using X-ray computed tomography[J]. Wood Science and Technology, 2022, 56(1): 147−169. doi: 10.1007/s00226-021-01353-y

[31]

Page G F, Liu J, Grierson P F. Three-dimensional xylem networks and phyllode properties of co-occurring Acacia[J]. Plant Cell Environ, 2011, 34(12): 2149−2158. doi: 10.1111/j.1365-3040.2011.02411.x

[32]

Koddenberg T, Zauner M, Militz H. Three-dimensional exploration of soft-rot decayed conifer and angiosperm wood by X-Ray micro-computed tomography[J]. Micron, 2020, 134: 102875. doi: 10.1016/j.micron.2020.102875

[33]

Sedighi G M, Boone M N, Mader K, et al. Synchrotron X-ray micro-tomography imaging and analysis of wood degraded by Physisporinus vitreus and Xylaria longipes[J]. Journal of Structural Biology, 2014, 187(2): 149−157. doi: 10.1016/j.jsb.2014.06.003

[34]

Shi J, Liu X, Xia C, et al. Visualization of wood cell structure during cellulose purification via high resolution X-ray CT and spectroscopy[J]. Industrial Crops and Products, 2022, 189: 115869. doi: 10.1016/j.indcrop.2022.115869

[35]

Mayo S C, Chen F, Evans R. Micron-scale 3D imaging of wood and plant microstructure using high-resolution X-ray phase-contrast microtomography[J]. Journal of Structural Biology, 2010, 171(2): 182−188. doi: 10.1016/j.jsb.2010.04.001

[36]

Yang X, Zhao Z, Wang Z, et al. Microstructure identification based on vessel pores feature extraction of high-value hardwood species[J]. BioResources, 2021, 16(3): 5329. doi: 10.15376/biores.16.3.5329-5340

[37]

Steppe K, Cnudde V, Girard C, et al. Use of X-ray computed microtomography for non-invasive determination of wood anatomical characteristics[J]. Journal of Structural Biology, 2004, 148(1): 11−21. doi: 10.1016/j.jsb.2004.05.001

[38]

Florisson S, Gamstedt E K. An overview of lab-based micro computed tomography aided finite element modelling of wood and its current bottlenecks[J]. Holzforschung, 2023, 77(11−12): 793−815. doi: 10.1515/hf-2023-0061

[39]

Brunel G, Borianne P, Subsol G, et al. Automatic identification and characterization of radial files in light microscopy images of wood[J]. Annals of Botany, 2014, 114(4): 829−840. doi: 10.1093/aob/mcu119

[40]

Endrizzi M. X-ray phase-contrast imaging[J]. Nuclear instruments and methods in physics research section a: accelerators, spectrometers, detectors and associated equipment, 2018, 878: 88−98.

[41]

Yang X, Jiang H, Ma L, et al. Micro image classification of 19 high-value hardwood species based on texture feature fusion[J]. BioResources, 2023, 18(2): 3373−3386. doi: 10.15376/biores.18.2.3373-3386

[42]

Hansson L, Couceiro J, Fjellner B-A. Estimation of shrinkage coefficients in radial and tangential directions from CT images[J]. Wood Material Science & Engineering, 2016, 12(4): 251−256.

[43]

Brodersen C R, Lee E F, Choat B, et al. Automated analysis of three-dimensional xylem networks using high-resolution computed tomography[J]. New Phytologist, 2011, 191(4): 1168−1179. doi: 10.1111/j.1469-8137.2011.03754.x

[44]

Koddenberg T, Militz H. Morphological imaging and quantification of axial xylem tissue in Fraxinus excelsior L. through X-ray micro-computed tomography[J]. Micron, 2018, 111: 28−35. doi: 10.1016/j.micron.2018.05.004

[45]

He X, Qi D. Density and moisture content forecasting based on X-ray computed tomography[J]. European Journal of Wood and Wood Products, 2013, 71(5): 647−652. doi: 10.1007/s00107-013-0722-3

相关知识

长春季节性冻土地区土体微观结构与水分迁移的关系
银杏木材结构和用途
玫瑰花花瓣微观结构与水滴黏附性质的关系
【木材鉴定】真假金丝楠树种木材识别研究
木材识别与鉴定技术(国家林业和草原局普通高等教育十三五规划教材)
木材的纹理和颜色变化
木材的纹理和纹样鉴别
13、技术——木材如何识别木材识别的基础知识1
木材识别方法和技巧
实木家具木材纹理特征与设计

网址: 木材微观结构分析方法与特征参数 https://m.huajiangbk.com/newsview1676233.html

所属分类:花卉
上一篇: 巴花到底是什么木头,不是国标红木
下一篇: 2025年中国木材行业现状分析及