Corrosion mechanisms for lead-barium glass from the Warring States period

Zhang, Kexin; Wang, Jun; Yu, Wendi; Zhao, Jing; Yue, Xuezheng; Luo, Hongjie

doi:10.1186/s40494-023-00930-7

Cited by 7 publications

(6 citation statements)

References 26 publications

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“…This study confirmed this as significant correlation was only observed between glass type and surface weathering, revealing that the weathering of glass artifacts was chemical weathering. Therefore, suitable protective measures should be employed in the protection and restoration processes to minimize potential environmental threats to ancient glass artifacts [35,36].…”

Section: Discussionmentioning

confidence: 99%

Prediction of Glass Chemical Composition and Type Identification Based on Machine Learning Algorithms

Chen,

Xu,

Zhang

et al. 2024

Applied Sciences

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Ancient glass artifacts were susceptible to weathering from the environment, causing changes in their chemical composition, which pose significant obstacles to the identification of glass products. Analyzing the chemical composition of ancient glass has been beneficial for evaluating their weathering status and proposing measures to reduce glass weathering. The objective of this study was to explore the optimal machine learning algorithm for glass type classification based on chemical composition. A set of glass artifact data including color, emblazonry, weathering, and chemical composition was employed and various methods including logistic regression and machine learning techniques were used. The results indicated that a significant correlation (p < 0.05) could only observed between surface weathering and the glass types (high-potassium and lead–barium). Based on the random forest and logistic regression models, the primary chemical components that signify glass types and weathering status were determined using PbO, K2O, BaO, SiO2, Al2O3, and P2O5. The random forest model presented a superior ability to identify glass types and weathering status, with a global accuracy of 96.3%. This study demonstrates the great potential of machine learning for glass chemical component estimation and glass type and weathering status identification, providing technical guidance for the appraisal of ancient glass artifacts.

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Section: Discussionmentioning

confidence: 99%

Prediction of Glass Chemical Composition and Type Identification Based on Machine Learning Algorithms

Chen,

Xu,

Zhang

et al. 2024

Applied Sciences

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show abstract

“…This study confirmed the significant correlation was only observed between glass type and surface weathering, revealing that the weathering of glass artifacts was chemical weathering. Therefore, suitable protective measures should be employed in the protection and restoration to minimize potential environmental threats to ancient glass artifacts [24][25].…”

Section: Discussionmentioning

confidence: 99%

Prediction of glass chemical composition and type identification based on machine learning algorithms

Chen

Zhang

et al. 2023

Preprint

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Glass was a significant symbol of early trade exchanges on the Silk Road. However, the glass artifacts were susceptible to environmental influences when buried underground, leading to weathering and changes in their chemical composition. Analyzing the chemical composition of ancient glass was conducive to evaluate weathering status and propose the measures to reduce glass weathering. The objective of this study was to explore the optimal machine learning algorithm for glass type classification based on chemical composition. A set of glass artifact data including color, emblazonry, weathering, and chemical composition was employed and various methods including logistic regression and machine learnings were used. Results indicated a significant correlation (p < 0.05) was only observed between surface weathering and the glass types (high-potassium and lead-barium). Based on the random forest and logistic regression, the primary chemical components that signifying glass types and weathering status were determined by PbO, K2O, BaO, SiO2, Al2O3, and P2O5. The random forest model presented superior capability for identifying glass types and weathering status, with a global accuracy of 96.3%. This study demonstrates great potential of machine learning for glass chemical component estimation and glass type and weathering status identification providing technical guidance for the appraisal of ancient glass artifacts.

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“…In recent years, the scientific and technological research of glass eye beads mainly focuses on the discussion of the formation reason of the glass matrix [13,14] and the analysis and identification of weathering products [14][15][16][17]. The color formation mechanism is mostly based on the results of elemental analysis of the sample using XRF or LA-ICP-AES technology.…”

Section: Research Aimmentioning

confidence: 99%

“…The presence of a large number of fungal microorganisms in the vicinity of the corpse can produce significant damage to the glass surface [23][24][25][26]. Moreover, the metabolites of the microorganisms accelerate the destruction of the glass network structure and the formation of corrosion pits [14]. After the decay of the corpse the phosphate in it enters the sample corrosion cracks, combines with the Ba ions lost from glass weathering, and then further reacts with the Cl ions brought about by the water-salt transport in the soil environment, ultimately generating alforsite [Ba 5 (PO 4 ) 3 Cl], a special weathering product.…”

Section: Weathering Mechanismmentioning

confidence: 99%

Scientific analysis of two compound eye beads unearthed in Hejia Village, Zhouling

Li,

Sun,

Zhang

et al. 2024

Herit Sci

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The glass compound eye bead is the exquisite embodiment of the glassmaking technology of ancient craftsmen, and is an example of the cultural exchange between China and the West during the Warring States Period. This study takes two dots and mesh beads with seriously weathered surfaces excavated from Hejia village, Zhouling as the research object. Micromorphology, X-ray fluorescence spectroscopy (XRF), microscopic laser Raman spectroscopy and X-ray photoelectron spectroscopy (XPS) were respectively used to determine the chemical elements, valence states and compositions of the glass matrix and its weathering products. In this study, the valence state analysis of group-d elements is used to reveal the electron transition mode and explain the colour formation reason of blue-black glass matrix. In terms of weathering products, in addition to the identification of common cerusite [PbCO3] and barite [BaSO4], the study also found the blue weathering product alforsite [Ba5(PO4)3Cl] for the first time. The study also makes reasonable assumptions about the reasons for their appearance. The X-ray photoelectron spectroscopy (XPS) used in this study has positive significance for the study of the formation mechanism of glass colour. The scientific and technological analysis data of these two glass beads provide basic data for the related research of the lead-barium glass system produced in China, and also provide a certain scientific basis for the related protection research.

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Corrosion mechanisms for lead-barium glass from the Warring States period

Cited by 7 publications

References 26 publications

Prediction of Glass Chemical Composition and Type Identification Based on Machine Learning Algorithms

Prediction of Glass Chemical Composition and Type Identification Based on Machine Learning Algorithms

Prediction of glass chemical composition and type identification based on machine learning algorithms

Scientific analysis of two compound eye beads unearthed in Hejia Village, Zhouling

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