Novel characterization techniques for cultural heritage using a TEM orientation imaging in combination with 3D precession diffraction tomography: a case study of green and white ancient Roman glass tesserae

We present new transmission electron microscopy (TEM) based electron diffraction characterization techniques (orientation imaging combined with 3D precession electron diffraction tomography-ADT) applied on cultural heritage materials. We have determined precisely unit cell parameters, crystal symmetry, atomic structure, and orientation/phase mapping of various pigment/opacifier crystallites at nm scale which are present in green and white Roman glass tesserae. Such TEM techniques can be an alternative to Synchrotron based techniques, and allow to distinguish accurately at nm scale between different crystal structures even in cases of same/very close chemical composition, where is also possible to visualize between different crystal orientations and amorphous/crystalline phases. This study additionally demonstrates that although opacifiers in green and white tesserae are found to have average Pb2Sb2O7 cubic and CaSb2O6 trigonal structures, their pyrochlore related framework can host many other elements like Cu, Ca, Fe through ionic exchanges at high firing temperatures which in turn may also contribute to the tesserae colour appearance

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio