Quantitative Analysis of Minium and Vermilion Mixtures Using Low-Frequency Vibrational Spectroscopy.
Elyse M KleistTimothy M KorterPublished in: Analytical chemistry (2019)
Low-frequency vibrational spectroscopy offers a compelling solution for the nondestructive and noninvasive study of pigments in historical artifacts by revealing the characteristic sub-200 cm-1 spectral features of component materials. The techniques of terahertz time-domain spectroscopy (THz-TDS) and low-frequency Raman spectroscopy (LFRS) are complementary approaches to accessing this spectral region and are valuable tools for artifact identification, conservation, and restoration. In this investigation of historical pigments, pure and mixed samples of minium (Pb3O4) and vermilion (HgS) were studied using a combination of THz-TDS and LFRS experiments to determine the limits of detection (LOD) and quantitation (LOQ) for each compound with both methods. The measurements were also supported using solid-state density functional theory simulations of the pigment structures and vibrations, enabling spectral peaks to be assigned to specific atomic motions in these solids. The THz-TDS LOD was found to be similar for both minium and vermilion at 6% by mass on average. In comparison, LFRS was found to be more sensitive to both pigments, particularly to the presence of vermilion with an LFRS LOD of 0.2%. These results demonstrate that low-frequency vibrational spectroscopy can be used for successful quantitative analysis of pigment mixtures and provide reliable new data for use in heritage science.
Keyphrases
- solid state
- density functional theory
- raman spectroscopy
- molecular dynamics
- high resolution
- optical coherence tomography
- dual energy
- molecular dynamics simulations
- ionic liquid
- single molecule
- public health
- ms ms
- mass spectrometry
- magnetic resonance imaging
- heavy metals
- risk assessment
- big data
- artificial intelligence
- real time pcr
- machine learning
- liquid chromatography
- high performance liquid chromatography
- electron microscopy
- deep learning