|
|
|
|
|
|
Study on the Production Technology of Baihuimian From Suyang Site |
SUN Tian-qiang1, WEI Guo-feng1*, CHENG Bao-zeng2, REN Guang2 |
1. History College of Anhui University, Hefei 230039, China
2. Luoyang City Cultural Relics and Archaeology Research Institute, Luoyang 471028, China |
|
|
Abstract The Suyang site is located in Yiyang County, Luoyang City, Henan Province is one of the important sites in the prehistoric period in the Central Plains. The ground and walls of the unearthed houses are painted with “Baihuimian”(Lime layer). The raw materials and production methods are important for the study of prehistoric building materials significance. In this work, an X-ray diffraction analyzer (XRD) and X-ray fluorescence spectrometer (XRF) was used to analyze the Baihuimian collected from the Suyang site, and several natural calcareous raw materials (loess-doll, limestone and oysters) were collected from the surrounding areas of the site to explore the raw materials of Baihuimian from the Suyang site. In addition, Fourier Transform Infrared Spectroscopy (FTIR) and Scanning Electron Microscope (SEM) were used to analyze and detect the Baihuimian from the Suyang site, natural loess-doll and simulated Baihuimian. The ν2/ν4 value of calcite in the infrared spectrum can be used to reflect the disorder degree of its crystals, draw the characteristic trend line of ν2/ν4, and finally combine the observation of the microscopic appearance to explore whether the ancestors of Suyang used artificially burned raw materials into lime when making Baihuimian. The results show that the main mineral phases of the Baihuimian samples from the Suyang site are quartz, calcite and a small amount of feldspar. The chemical composition has a higher calcium and silicon content, and its chemical composition and substance are similar to those of natural loess-doll, but different from limestone and oysters; according to the result displayed by the infrared spectrum, the average ν2/ν4 value of the Baihuimian from Suyang site is about 3.6, which is much lower than the ν2/ν4 value of artificially fired lime, and its ν2/ν4 characteristic trend line is also similar to that of natural loess-doll; combined with the results of scanning electron microscopy, it can be preliminarily believed that the ancestors of Suyang used natural loess-doll as raw material when making Baihuimian, and the loess-doll was not artificially fired. It was directly crushed and mixed with water in proportion to form a slurry, and smeared on the wall, the ground and the pit wall. This paper provides a relatively easy research method for distinguishing the nature loess-doll and the carbonized products of artificially fired loess-doll and helps to understand further the raw materials and production processes of prehistoric Baihuimian. It also provides a reference for archaeologists to study the craftsmanship of prehistoric residents to make building materials and the awareness of prehistoric residents to improve their living environment.
|
Received: 2020-12-30
Accepted: 2021-03-26
|
|
Corresponding Authors:
WEI Guo-feng
E-mail: weigf@mail.ustc.edu.cn
|
|
[1] HU Ji-gao(胡继高). Cultural Relics(文物参考资料),1955,(7):120.
[2] YU Jun(余 军). Social Sciences in Ningxia(宁夏社会科学),1998,(6):3.
[3] QIU Shi-hua(仇士华). Archaeology and Cultural Relics(考古与文物),1980,3:126.
[4] GUO Wen-xiu,WU Mei,LU Ji-tang,et al(郭文秀,吴 梅,卢积堂,等). Acta Geological Sinica of Henan(河南地球科学通报). Geological Society of Henan Press(河南省地质学会出版),2011. 3.
[5] ZHAO Lin-yi,LI Li,LI Zui-xiong,et al(赵林毅,李 黎,李最雄,等). Journal of Inorganic Materials(无机材料学报),2011,26(12):1327.
[6] CHEN Xing-can,LI Yong-qiang,LIU Li(陈星灿,李永强,刘 莉). Acta Archaeologica Sinica(考古学报),2010,(3):393.
[7] Michael B Toffolo,Lior Regev,Yannick Lefrais. Minerals,2019,9: 121. |
[1] |
CHENG Jia-wei1, 2,LIU Xin-xing1, 2*,ZHANG Juan1, 2. Application of Infrared Spectroscopy in Exploration of Mineral Deposits: A Review[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 15-21. |
[2] |
LI Jie, ZHOU Qu*, JIA Lu-fen, CUI Xiao-sen. Comparative Study on Detection Methods of Furfural in Transformer Oil Based on IR and Raman Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 125-133. |
[3] |
YANG Cheng-en1, 2, LI Meng3, LU Qiu-yu2, WANG Jin-ling4, LI Yu-ting2*, SU Ling1*. Fast Prediction of Flavone and Polysaccharide Contents in
Aronia Melanocarpa by FTIR and ELM[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 62-68. |
[4] |
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. |
[5] |
LIU Jia, ZHENG Ya-long, WANG Cheng-bo, YIN Zuo-wei*, PAN Shao-kui. Spectra Characterization of Diaspore-Sapphire From Hotan, Xinjiang[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 176-180. |
[6] |
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. |
[7] |
GUO Ya-fei1, CAO Qiang1, YE Lei-lei1, ZHANG Cheng-yuan1, KOU Ren-bo1, WANG Jun-mei1, GUO Mei1, 2*. Double Index Sequence Analysis of FTIR and Anti-Inflammatory Spectrum Effect Relationship of Rheum Tanguticum[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 188-196. |
[8] |
SUN Wei-ji1, LIU Lang1, 2*, HOU Dong-zhuang3, QIU Hua-fu1, 2, TU Bing-bing4, XIN Jie1. Experimental Study on Physicochemical Properties and Hydration Activity of Modified Magnesium Slag[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3877-3884. |
[9] |
LI Xiao-dian1, TANG Nian1, ZHANG Man-jun1, SUN Dong-wei1, HE Shu-kai2, WANG Xian-zhong2, 3, ZENG Xiao-zhe2*, WANG Xing-hui2, LIU Xi-ya2. Infrared Spectral Characteristics and Mixing Ratio Detection Method of a New Environmentally Friendly Insulating Gas C5-PFK[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3794-3801. |
[10] |
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. |
[11] |
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. |
[12] |
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. |
[13] |
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. |
[14] |
DANG Rui, GAO Zi-ang, ZHANG Tong, WANG Jia-xing. Lighting Damage Model of Silk Cultural Relics in Museum Collections Based on Infrared Spectrum[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3930-3936. |
[15] |
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. |
|
|
|
|