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Argonne: Where AI research meets education and training
Last September, in the Chicago suburb of Lemont, Ill., Argonne National Laboratory hosted its first AI STEM Education Summit. More than 180 educators from high schools, community colleges, and universities; STEM administrators; and experts in various disciplines convened at “One Ecosystem, Many Pathways–Building an AI-Ready STEM Workforce” to discuss how artificial intelligence is reshaping STEM-related industries, including the implications for the nuclear engineering classroom and workforce.
Dieter M. Gruen, Patricia A. Finn, Dennis L. Page
Nuclear Technology | Volume 29 | Number 3 | June 1976 | Pages 309-317
Technical Paper | Fusion Reactor Material / Material | doi.org/10.13182/NT76-A31595
Articles are hosted by Taylor and Francis Online.
Impurity control in magnetically-confined thermonuclear plasmas depends in part on control of sputtered products arising from plasma particle-first wall interactions. Although sputtering of unitary targets (metals) is reasonably well understood, sputtering of binary targets (oxides) lacks a sound theoretical base. It was demonstrated that molecular species can dominate the total sputtered product from ion-bombarded aluminum oxide surfaces. The nature of the bombarding ion (Ar+ versus H+), the nature of the target surface, as well as the ion flux and fluence, determine the fraction of sputtered species appearing as aluminum atoms or Al2O and AlO molecules. The results show that the materials sensitive parameters entering collision cascade theory are the surface binding energies of the sputtered species. The surface binding energies in turn are functions of the surface composition prevailing at the time of a particular sputtering event, and are identified with the partial molar enthalpies of vaporization of the sputtered species. This approach provides the rationalization of the complex distribution of sputtered products encountered in studies of secondary ion emission from binary targets.