Publications by authors named "D Donadio"

An atomic-level understanding of radiation-induced damage in simple polymers like polyethylene is essential for determining how these chemical changes can alter the physical and mechanical properties of important technological materials such as plastics. Ensembles of quantum simulations of radiation damage in a polyethylene analog are performed using the Density Functional Tight Binding method to help bind its radiolysis and subsequent degradation as a function of radiation dose. Chemical degradation products are categorized with a graph theory approach, and occurrence rates of unsaturated carbon bond formation, crosslinking, cycle formation, chain scission reactions, and out-gassing products are computed.

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Article Synopsis
  • AI in dental diagnostics is evolving, particularly in cephalometric analysis, where new open-source software helps extract important measurements from limited field of view images, reducing manual input.
  • The software uses predictive algorithms to estimate missing cephalometric landmarks, testing its accuracy against actual measurements and showing promising results, although some variability remains.
  • This advancement in AI could make dental diagnostics more efficient and reduce the necessity for additional X-rays, highlighting the need for further development and integration of AI in healthcare practices.
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Nanoengineered metal@zeolite materials have recently emerged as a promising class of catalysts for several industrially relevant reactions. These materials, which consist of small transition metal nanoclusters confined within three-dimensional zeolite pores, are interesting because they show higher stability and better sintering resistance under reaction conditions. While several such hybrid catalysts have been reported experimentally, key questions such as the impact of the zeolite frameworks on the properties of the metal clusters are not well understood.

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Crystals with complicated geometry are often observed with mixed chemical occupancy among Wyckoff sites, presenting a unique challenge for accurate atomic modeling. Similar systems possessing exact occupancy on all the sites can exhibit superstructural ordering, dramatically inflating the unit cell size. In this work, a crystal graph convolutional neural network (CGCNN) is used to predict optimal atomic decorations on fixed crystalline geometries.

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Three polyanionic tellurides, BaCuTe ( = K, Rb, Cs), were synthesized in salt flux. The isostructural tellurides crystallize in a new structure type, in the cubic 3 space group with a Wyckoff sequence of and large unit cell volumes of over 5500 Å. The structures feature a framework of [CuTe] tetrahedra and [CuTe] trigonal pyramids with disorder in the Cu sites.

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