|
|
|
|
|
|
Quantitative Study of Borax Filler in Heat-Treated Rubies |
XIANG Zi-han1, YIN Zuo-wei2, 3, ZHANG Zhi-qing1*, WANG Wen-wei2, 3* |
1. Hubei Land Resources Vocational College, Wuhan 430090, China
2. Gemmological Institute, China University of Geosciences (Wuhan), Wuhan 430074, China
3. Jewelry School, West Yunnan University of Applied Sciences, Tengchong 679100,China
|
|
|
Abstract The heat treatment of ruby belongs to the optimization treatment, and borax is commonly used as the flux. In the market, heat-treated rubies are sold naturally and have a high value, but the amount of borax in the crack will affect the price. Therefore, in this paper, the borax in this kind of ruby was quantitatively studied, and NanoVoxel-4000X X-ray computed tomography was used to scan and analyze the sample, and 2D images of borax filling in fractures were obtained. The grayscale values of light and shade were used to characterize the different densities of the material in the crack, and different colors characterized the 3D images of the borax filling distribution. By comparing the color scale of thickness, it can be seen that the overall content of borax is small. At the same time, the histogram of the borax thickness distribution of samples shows that the thickness of borax in all fractures is less than 130 μm, and the thickness-to-volume ratio of all borax is less than 10%, which also indicates that the filling amount is small. Finally, Avizo 9.0 software was used to segment the ruby sample and borax content to obtain the actual volume of the two. Then, the percentage of borax content in the total volume of the ruby could be calculated. The proportion of borax in the sample is 10.69%, which is not only an important index in the quantitative detection of borax in heat-treated rubies but also provides data support for the study of the quantitative classification standard of borax in heat-treated rubies.
|
Received: 2022-08-28
Accepted: 2024-03-08
|
|
Corresponding Authors:
ZHANG Zhi-qing, WANG Wen-wei
E-mail: 2461808441@qq.com;405382651@qq.com
|
|
[1] Corundum With Residue From the Heating Process Present in Healed Fractures and/or Cavities. Information Sheet #1Standardised Gemmological Report Wording. LMHC (Laboratory Manual Harmonisation Committee), 2004.
[2] QI Li-jian,Zeng C G,YUAN Xin-qiang(亓利剑,Zeng C G, 袁心强). Journal of Gems & Gemmology(宝石和宝石学杂志),2005, 7(2): 1.
[3] WEI Ran, LI Yan, XIE Yu, et al(魏 然,李 妍,谢 予,等). Journal of Gems & Gemmology(宝石和宝石学杂志),2009, 11(3): 22.
[4] XIANG Zi-han, YIN Zuo-wei, ZHENG Xiao-hua(向子涵,尹作为, 郑晓华). Spectroscopy & Spectral Analysis(光谱学与光谱分析),2019, 39(4): 1274.
[5] WANG Xin-min, LU Wen-ting, LIU Yan (王新民,卢雯婷,刘 燕). 2013 China Jewelry Academic Exchange Conference(2013年中国珠宝首饰学术交流会),2013.
[6] Mcclure S F, Smith C P, Wang W Y, et al. Gem & Gemology, 2006, 42(1): 22.
[7] Monarumit N, Boonmee C, Ingavanija S, et al. Internal Features of Glass Filled Ruby Samples Probed by EPMA[C]. International Symposium on Material Science and Engineering, Kuala Lumpur, 2017.
[8] WU Jie, LIU Cheng-dong, ZHANG Shou-peng, et al(吴 洁,刘成东,张守鹏,等). Jiangxi Science(江西科学), 2012, 30(5): 634.
[9] Zhou G, Pei S, Li L, et al. Advanced Materials, 2014, 26(4): 664.
[10] Li R, Chen K, Li G, et al. Journal of Molecular Structure,2016, 1120: 34.
|
[1] |
YAN Xia1, HU Cong-cong1, YANG Zhi-yuan2, ZHAO Hang2*, SHI Xiao-feng2, MA Jun2*. SERS Detection of Carbendazim Based on Convex Polyhedrons Shaped Au@4-ATP@Au Nanoparticle[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(07): 1843-1851. |
[2] |
LÜ Shu-bin1, 2, YANG Wan-qi1, 2, LI Fu-sheng1, 2*. Quantitative Analysis of Lead and Cadmium Heavy Metal Elements in Soil Based on Principal Component Analysis and Broad Learning System[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(07): 1852-1857. |
[3] |
WANG An1, CUI Jia-cheng2, SONG Wei-ran2, HOU Zong-yu2, 3*, CHEN Xiang4, CHEN Fei4. Quantitative Analysis of Coal Properties Using Laser-Induced Breakdown Spectroscopy and Semi-Supervised Learning[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(07): 1940-1945. |
[4] |
YU Shui1, HUAN Ke-wei1*, LIU Xiao-xi2, WANG Lei1. Quantitative Analysis Modeling of Near Infrared Spectroscopy With
Parallel Convolution Neural Network[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(06): 1627-1635. |
[5] |
DAI Yu-jia1, GAO Xun2*, LIU Zi-yuan1*. Accuracy Improvement of Mn Element in Aluminum Alloy by the
Combination of LASSO-LSSVM and Laser-Induced Breakdown
Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(04): 977-982. |
[6] |
FU Xiao-fen1, SONG You-gui1, 2*, ZHANG Ming-yu3, FENG Zhong-qi4, ZHANG Da-cheng4, LIU Hui-fang1. Application of Laser-Induced Breakdown Spectroscopy in Quantitative
Analysis of Sediment Elements[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(03): 641-648. |
[7] |
LIU Hao-dong1, 2, JIANG Xi-quan1, 2, NIU Hao1, 2, LIU Yu-bo1, LI Hui2, LIU Yuan2, Wei Zhang2, LI Lu-yan1, CHEN Ting1,ZHAO Yan-jie1*,NI Jia-sheng2*. Quantitative Analysis of Ethanol Based on Laser Raman Spectroscopy Normalization Method[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3820-3825. |
[8] |
LIN Hong-jian1, ZHAI Juan1*, LAI Wan-chang1, ZENG Chen-hao1, 2, ZHAO Zi-qi1, SHI Jie1, ZHOU Jin-ge1. Determination of Mn, Co, Ni in Ternary Cathode Materials With
Homologous Correction EDXRF Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3436-3444. |
[9] |
HUANG Li, MA Rui-jun*, CHEN Yu*, CAI Xiang, YAN Zhen-feng, TANG Hao, LI Yan-fen. Experimental Study on Rapid Detection of Various Organophosphorus Pesticides in Water by UV-Vis Spectroscopy and Parallel Factor Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3452-3460. |
[10] |
LI Zhong-bing1, 2, JIANG Chuan-dong2, LIANG Hai-bo3, DUAN Hong-ming2, PANG Wei2. Rough and Fine Selection Strategy Binary Gray Wolf Optimization
Algorithm for Infrared Spectral Feature Selection[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3067-3074. |
[11] |
LIU Shu1, JIN Yue1, 2, SU Piao1, 2, MIN Hong1, AN Ya-rui2, WU Xiao-hong1*. Determination of Calcium, Magnesium, Aluminium and Silicon Content in Iron Ore Using Laser-Induced Breakdown Spectroscopy Assisted by Variable Importance-Back Propagation Artificial Neural Networks[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3132-3142. |
[12] |
KONG De-ming1, LIU Ya-ru1, DU Ya-xin2, CUI Yao-yao2. Oil Film Thickness Detection Based on IRF-IVSO Wavelength Optimization Combined With LIF Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2811-2817. |
[13] |
ZHAO Yu-wen1, ZHANG Ze-shuai1, ZHU Xiao-ying1, WANG Hai-xia1, 2*, LI Zheng1, 2, LU Hong-wei3, XI Meng3. Application Strategies of Surface-Enhanced Raman Spectroscopy in Simultaneous Detection of Multiple Pathogens[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2012-2018. |
[14] |
CHENG Xiao-xiang1, WU Na2, LIU Wei2*, WANG Ke-qing2, LI Chen-yuan1, CHEN Kun-long1, LI Yan-xiang1*. Research on Quantitative Model of Corrosion Products of Iron Artefacts Based on Raman Spectroscopic Imaging[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2166-2173. |
[15] |
CHEN Rui1, WANG Xue1, 2*, WANG Zi-wen1, QU Hao1, MA Tie-min1, CHEN Zheng-guang1, GAO Rui3. Wavelength Selection Method of Near-Infrared Spectrum Based on
Random Forest Feature Importance and Interval Partial
Least Square Method[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(04): 1043-1050. |
|
|
|
|