光谱学与光谱分析 |
|
|
|
|
|
Characterization of Wood Surface Treated with Electroless Copper Plating by Near Infrared Spectroscopy Technology |
QIN Jing1, 3, ZHANG Mao-mao2, ZHAO Guang-jie1, YANG Zhong2* |
1. College of Material Science and Technology, Beijing Forestry University, Beijing 100083, China 2. Research Institute of Wood Industry,Chinese Academy of Forestry, Beijing 100091, China 3. College of Forestry, Beihua University, Jilin 132002, China |
|
|
Abstract Wood electromagnetic shielding material, which was made by treating wood with electroless plating, not only keep the superior characteristics of wood, but also improve the conductivity, thermal conductivity and electromagnetic shielding properties of wood. The emergence of this material opens the way to the value-added exploitation of wood and widens the processing and application field for the electromagnetic shielding material. In order to explore the feasibility of using NIR technology to investigate the properties of wood electromagnetic shielding material, this study analysis the samples before and after copper plated process by the NIR spectroscopy coupled with principal component analysis (PCA). The results showed that (1) there exist significant differences between samples before and after copper plated process both on the spectral shape and absorption, and the great differences can also be seen in the samples with different treat time, especially for the samples with 5 min treat time; (2) after PCA analysis, six clusters from the samples before and after copper plated process were separately distributed in the score plot, and the properties of untreated wood and sensitized wood were similar, and the properties of samples for 25 and 40 min treat time were also similar in order that these samples were close to each other, all of which might suggest that the NIR spectroscopy reflected major feature information about material treatment; (3) After comparing the PCA performance between NIR and visible spectral region, it could be found that the classification performance of samples before and after copper plated process based on the NIR region were better than that based on the visible region, and the information of color on the surface of samples were preferably reflected in the visible region, which could indicate that there are more information about samples’ surface characters using the visible spectroscopy coupled with NIR spectroscopy and it is feasible to use visible-NIR technology to investigate the surface characteristics of natural polymers treated with electroless copper plating.
|
Received: 2014-02-28
Accepted: 2014-06-08
|
|
Corresponding Authors:
YANG Zhong
E-mail: zyang@caf.ac.cn
|
|
[1] Xue L L, liang Q, Lu Y X. J. Mater Sci: Mater Electron, 2013, 24: 2211. [2] Sun L L, Li J, Wang L. J. Wood Science and Technology, 2012, 46: 1061. [3] Wang L J, Li J, Liu Y X. Journal of Forestry Research, 2006, 17(1): 66. [4] Zhou G, Zhao G J. Chinese Forestry Science and Technology, 2004, 4 (3): 8. [5] Charbonnier M, Romand M, Harry E, et al. Journal of Applied Electrochemistry, 2001, 31: 57. [6] Chohachiro N, Yaom K Kei U, et al. Journal of Porous Materials, 1998, 12: 725. [7] Hein P R G, Campos A C M, Mende R F. European Journal of Wood and Wood Products, 2011, 69(3): 431. [8] Taylor A M, Labbé N, Noehmer A. Holzforschung, 2013, 65(2): 185. [9] Tsuchikawa S, Schwanninger M. Applied Spectroscopy Reviews, 2013, 48(7): 560. [10] Sun B L, Liu J L, Liu S J, et al. Holzforschung, 2011, 65(5): 689. [11] Hageman J A, Westerhuis J A, Smilde A K. J. Near Infrared Spectrosc., 2005, 13: 53. [12] Schwanninger M, Rodrigues J C, Fackler K. J. Near Infrared Spectrosc., 2011, 19: 287. |
[1] |
GAO Feng1, 2, XING Ya-ge3, 4, LUO Hua-ping1, 2, ZHANG Yuan-hua3, 4, GUO Ling3, 4*. Nondestructive Identification of Apricot Varieties Based on Visible/Near Infrared Spectroscopy and Chemometrics Methods[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 44-51. |
[2] |
BAO Hao1, 2,ZHANG Yan1, 2*. Research on Spectral Feature Band Selection Model Based on Improved Harris Hawk Optimization Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 148-157. |
[3] |
HU Cai-ping1, HE Cheng-yu2, KONG Li-wei3, ZHU You-you3*, WU Bin4, ZHOU Hao-xiang3, SUN Jun2. Identification of Tea Based on Near-Infrared Spectra and Fuzzy Linear Discriminant QR Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3802-3805. |
[4] |
LIU Xin-peng1, SUN Xiang-hong2, QIN Yu-hua1*, ZHANG Min1, GONG Hui-li3. Research on t-SNE Similarity Measurement Method Based on Wasserstein Divergence[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3806-3812. |
[5] |
BAI Xue-bing1, 2, SONG Chang-ze1, ZHANG Qian-wei1, DAI Bin-xiu1, JIN Guo-jie1, 2, LIU Wen-zheng1, TAO Yong-sheng1, 2*. Rapid and Nndestructive Dagnosis Mthod for Posphate Dficiency in “Cabernet Sauvignon” Gape Laves by Vis/NIR Sectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3719-3725. |
[6] |
WANG Qi-biao1, HE Yu-kai1, LUO Yu-shi1, WANG Shu-jun1, XIE Bo2, DENG Chao2*, LIU Yong3, TUO Xian-guo3. Study on Analysis Method of Distiller's Grains Acidity Based on
Convolutional Neural Network and Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3726-3731. |
[7] |
LUO Li, WANG Jing-yi, XU Zhao-jun, NA Bin*. Geographic Origin Discrimination of Wood Using NIR Spectroscopy
Combined With Machine Learning Techniques[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3372-3379. |
[8] |
ZHANG Shu-fang1, LEI Lei2, LEI Shun-xin2, TAN Xue-cai1, LIU Shao-gang1, YAN Jun1*. Traceability of Geographical Origin of Jasmine Based on Near
Infrared Diffuse Reflectance Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3389-3395. |
[9] |
YANG Qun1, 2, LING Qi-han1, WEI Yong1, NING Qiang1, 2, KONG Fa-ming1, ZHOU Yi-fan1, 2, ZHANG Hai-lin1, WANG Jie1, 2*. Non-Destructive Monitoring Model of Functional Nitrogen Content in
Citrus Leaves Based on Visible-Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3396-3403. |
[10] |
HUANG Meng-qiang1, KUANG Wen-jian2, 3*, LIU Xiang1, HE Liang4. Quantitative Analysis of Cotton/Polyester/Wool Blended Fiber Content by Near-Infrared Spectroscopy Based on 1D-CNN[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3565-3570. |
[11] |
HUANG Zhao-di1, CHEN Zai-liang2, WANG Chen3, TIAN Peng2, ZHANG Hai-liang2, XIE Chao-yong2*, LIU Xue-mei4*. Comparing Different Multivariate Calibration Methods Analyses for Measurement of Soil Properties Using Visible and Short Wave-Near
Infrared Spectroscopy Combined With Machine Learning Algorithms[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3535-3540. |
[12] |
KANG Ming-yue1, 3, WANG Cheng1, SUN Hong-yan3, LI Zuo-lin2, LUO Bin1*. Research on Internal Quality Detection Method of Cherry Tomatoes Based on Improved WOA-LSSVM[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3541-3550. |
[13] |
HUANG Hua1, LIU Ya2, KUERBANGULI·Dulikun1, ZENG Fan-lin1, MAYIRAN·Maimaiti1, AWAGULI·Maimaiti1, MAIDINUERHAN·Aizezi1, GUO Jun-xian3*. Ensemble Learning Model Incorporating Fractional Differential and
PIMP-RF Algorithm to Predict Soluble Solids Content of Apples
During Maturing Period[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3059-3066. |
[14] |
CHEN Jia-wei1, 2, ZHOU De-qiang1, 2*, CUI Chen-hao3, REN Zhi-jun1, ZUO Wen-juan1. Prediction Model of Farinograph Characteristics of Wheat Flour Based on Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3089-3097. |
[15] |
GUO Ge1, 3, 4, ZHANG Meng-ling3, 4, GONG Zhi-jie3, 4, ZHANG Shi-zhuang3, 4, WANG Xiao-yu2, 5, 6*, ZHOU Zhong-hua1*, YANG Yu2, 5, 6, XIE Guang-hui3, 4. Construction of Biomass Ash Content Model Based on Near-Infrared
Spectroscopy and Complex Sample Set Partitioning[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3143-3149. |
|
|
|
|