Researchers from the Netherlands and Germany combined forces to reexamine Rembrandt’s iconic The Night Watch, painted in 1642, using X-ray imaging to reveal a detail long hidden from view. The discoveries were published in Science Advances, highlighting how modern techniques illuminate old masterwork mysteries.
The team turned to X-ray fluorescence to create a map of the painting’s lower paint layers, tracing the distribution of elements that make up the ground beneath the visible pigments. This analysis uncovered unusually high levels of lead beneath a clay soil that contained a notable amount of quartz. The result surprised scholars because lead is not a typical component of such clay mixtures.
Zooming in further, researchers observed that the lead lay down in broad semicircular strokes. In one region, a faint trace of a sieve suggested a deliberate step in the impregnation process used during the surface’s preparation, a clue about the workshop methods of the time and the practical choices involved in preparing the ground for paint.
The investigators noted that lead impregnation had not been documented in Rembrandt’s known practices or by his contemporaries, marking a substantive deviation from established methods of the era and inviting fresh questions about experimental techniques in 17th-century painting.
One interpretation proposed by the study is that the choice of lead-based material may have been a cost-saving and weight-reducing alternative to the traditional soil. The conventional soil habitually required two layers to create a stable base for the painting, so replacing or reducing layers with lead-bearing mixtures could have offered practical advantages for artists facing constraints in materials and workmanship.
Another practical consideration at the time involved the painting’s envisioned display on a damp exterior wall. In the 17th century, artists and craftspeople recognized that lead could enhance moisture resistance in paints, a property that likely influenced Rembrandt’s experiments with impregnation techniques when preparing a surface that would endure environmental exposure.
In a separate line of inquiry, earlier research employing neural networks explored links among diverse Russian artworks, including the works associated with Kandinsky, to understand co-authorship patterns and stylistic connections within the artist community. This broader, data-driven approach showcases how computational methods can illuminate creative networks across time and geography.