JOURNAL ARTICLE

Mössbauer study of the black‐glazed Jian bowl in the Song dynasty.

  • Published In: International Journal of Applied Ceramic Technology, 2023, v. 20, n. 6. P. 3795 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Gong, Chenying; Lu, Zeyi; Shen, Jiyu; Zhang, Qinghang; Cao, Huanhuan; Liu, Guoqing; Wu, Zhongjin; Zhou, Kexuan; Xia, Yanfang; Liu, Min 3 of 3

Abstract

Jian ware, also known as "Tenmoku," is one of the famous black‐glazed porcelains in China. It was highly coveted in the Song dynasty (960–1279 AD) and was also a tribute to the royal family. The black‐glazed Jian wares are mainly made from iron‐rich clay. In this study, black‐glazed Jian bowl sherds excavated from the Song strata of Jian kiln sites were adopted as test samples. The iron phase and firing techniques of the black‐glazed Jian bowl from the Song dynasty were analyzed and discussed through Mössbauer spectroscopy on the both of body and glaze, together with X‐ray diffraction and scanning electron microscopy. According to the different iron content and the unique iron oxide phase reflected in the Mössbauer spectra, we analyzed the firing atmosphere, temperature, and other conditions of the ancient Jian bowl, as well as the difference of iron phase between the body and the glaze layer due to the collapse of the silicate framework. It provides new ideas for deciphering the firing technology and improving the synthesis of ancient black‐glazed Jian wares. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:International Journal of Applied Ceramic Technology. 2023/11, Vol. 20, Issue 6, p3795
  • Document Type:Article
  • Subject Area:Music
  • Publication Date:2023
  • ISSN:1546-542X
  • DOI:10.1111/ijac.14439
  • Accession Number:172755548
  • Copyright Statement:Copyright of International Journal of Applied Ceramic Technology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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