By Stephen Beech via SWNS
Pigments used to paint murals on the Berlin Wall have been identified with the help of artificial intelligence in a bid to preserve them.
The vibrant street art daubed on the Cold War landmark that divided Germany are historic expression of people's opinions.
But there was often secrecy around the processes for creating the paintings, which scientists say makes them hard to preserve.
Now, scientists have uncovered information about this historic site from paint chips by combining a handheld detector and artificial intelligence (AI) data analysis.
Study co-author Dr. Francesco Armetta, of the University of Palermo in Italy, said: "The research highlights the powerful impact of the synergy between chemistry and deep learning in quantifying matter, exemplified in this case by pigments that make street art so captivating."
To restore or conserve art, he explained that it's important to collect information on the materials and application techniques.
But the painters of the Berlin Wall didn't document their methods.
In previous studies of other historic artifacts, scientists brought fragments or even whole objects into the lab and, without destroying the samples, identified pigments on them using a technique known as Raman spectroscopy.
Although handheld Raman devices are available for on-site investigations, they lack the precision of full-sized lab equipment.
Dr. Armetta and his colleagues wanted to develop an AI algorithm that could analyse the output of portable Raman devices to more accurately identify pigments and dyes.
In an initial test of the new approach, they analyzed 15 paint chips from the Berlin Wall.
The research team first magnified the chips and observed that they all had two or three layers of paint with visible brush strokes.
The third layer in contact with the masonry appeared white, which they suggest is from a base coat used to prepare the wall for painting.
The researchers then used a handheld Raman spectrometer to analyze the chips and compared them to spectra collected from a commercial pigment spectra library.
They identified the primary pigments in the samples as: azopigments (yellow- and red-colored chips), phthalocyanins (blue and green chips), lead chromate (green chips) and titanium white (white chips).
Their findings, published in the Journal of the American Chemical Society, were confirmed using other non-destructive techniques, including X-ray fluorescence and optical fibre reflectance spectroscopy.
The research team then mixed pigments from a commercial acrylic paint brand, used in Germany since the 1800s, with different ratios of titanium white, trying to match colors and the range of tints typical for painters.
Dr. Armetta says a knowledge of the ratios could help art conservators prepare the right materials for restoration.
Using the mixtures' handheld Raman spectral data, they trained a machine learning algorithm to determine the percentage of pigment.
Dr. Armetta said: "The approach indicated that the Berlin Wall paint chips contained titanium white and up to 75% of pigment, depending on the piece analyzed and according to the color tone."
The research team says the results indicate that their AI model could provide high-quality information for art conservation, forensics and materials science in locations where it's difficult to take lab equipment on-site.
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