Dyes from the Ashes: Discovering and Characterizing Natural Dyes from Mineralized Textiles.
Alessandro CiccolaIlaria SerafiniFrancesca RipantiFlaminia VincentiFrancesca ColettiArmandodoriano BiancoClaudia FasolatoCamilla MontesanoMarco GalliRoberta CuriniPaolo PostorinoPublished in: Molecules (Basel, Switzerland) (2020)
Vesuvius eruption that destroyed Pompeii in AD 79 represents one of the most important events in history. The cataclysm left behind an abundance of archeological evidence representing a fundamental source of the knowledge we have about ancient Roman material culture and technology. A great number of textiles have been preserved, rarely maintaining traces of their original color, since they are mainly in the mineralized and carbonized state. However, one outstanding textile sample displays a brilliant purple color and traces of gold strips. Since the purple was one of the most exclusive dyes in antiquity, its presence in an important commercial site like Pompeii induces us to deepen the knowledge of such artifacts and provide further information on their history. For this reason, the characterization of the purple color was the main scope of this research, and to deepen the knowledge of such artifacts, the SERS (Surface Enhanced Raman Scattering) in solution approach was applied. Then, these data were enriched by HPLC-HRMS analyses, which confirmed SERS-based hypotheses and also allowed to hypothesize the species of the origin mollusk. In this context, a step-by-step integrated approach resulted fundamental to maximize the information content and to provide new data on textile manufacturing and trade in antiquity.
Keyphrases
- healthcare
- gold nanoparticles
- electronic health record
- wastewater treatment
- sensitive detection
- ms ms
- big data
- aqueous solution
- health information
- raman spectroscopy
- bone regeneration
- mass spectrometry
- image quality
- magnetic resonance imaging
- simultaneous determination
- municipal solid waste
- data analysis
- risk assessment
- social media
- silver nanoparticles
- machine learning
- quantum dots
- deep learning
- artificial intelligence
- tandem mass spectrometry