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Colliding heavy nuclei take multiple identities on the path to fusion

The properties of superheavy elements probe extremes of physics and chemistry. They are synthesised at accelerator laboratories using nuclear fusion, where two atomic nuclei collide, stick together (capture), then with low probability evolve to a compact superheavy nucleus. The fundamental microscop...

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Detalles Bibliográficos
Autores principales: Cook, Kaitlin J., Rafferty, Dominic C., Hinde, David J., Simpson, Edward C., Dasgupta, Mahananda, Corradi, Lorenzo, Evers, Maurits, Fioretto, Enrico, Jeung, Dongyun, Lobanov, Nikolai, Luong, Duc Huy, Mijatović, Tea, Montagnoli, Giovanna, Stefanini, Alberto M., Szilner, Suzana
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10693600/
https://www.ncbi.nlm.nih.gov/pubmed/38042951
http://dx.doi.org/10.1038/s41467-023-43817-8
Descripción
Sumario:The properties of superheavy elements probe extremes of physics and chemistry. They are synthesised at accelerator laboratories using nuclear fusion, where two atomic nuclei collide, stick together (capture), then with low probability evolve to a compact superheavy nucleus. The fundamental microscopic mechanisms controlling fusion are not fully understood, limiting predictive capability. Even capture, considered to be the simplest stage of fusion, is not matched by models. Here we show that collisions of (40)Ca with (208)Pb, experience an ‘explosion’ of mass and charge transfers between the nuclei before capture, with unexpectedly high probability and complexity. Ninety different partitions of the protons and neutrons between the projectile-like and target-like nuclei are observed. Since each is expected to have a different probability of fusion, the early stages of collisions may be crucial in superheavy element synthesis. Our interpretation challenges the current view of fusion, explains both the successes and failures of current capture models, and provides a framework for improved models.