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Astatine sorption by ion exchange resins from nitric acid media.

 

Astatine-211 has been produced at Texas A&M University on the K150 cyclotron, with a yield of 890 ± 80 MBq through the 209 Bi(α,2n) 211 At reaction via an 8 h bombardment with a beam current of 4–8 μA and an α-particle beam energy of 28.8 MeV. The target was then dissolved in HNO 3 and the extraction of 211 At was investigated into a variety of organic solvents in 1–3 M HNO 3 . Extraction of 211 At with distribution ratios as high as 11.3 ± 0.6, 12.3 ± 0.8, 42.2 ± 2.2, 69 ± 4, and 95 ± 6 were observed for diisopropyl ether, 1-decanol, 1-octanol, 3-octanone, and methyl isobutyl ketone, respectively, while the distribution ratios for 207 Bi were ≤0.05 in all cases. The extraction of 211 At into both methyl isobutyl ketone and 3-octanone showed a strong, linear dependence on the HNO 3 initial aqueous concentration and better extraction than other solvents. DFT calculations show stronger binding between the carbonyl oxygen of the ketone and the At metal center. 

Elastic scaling is a central promise of NFV but has been hard to realize in practice. The difficulty arises because most Network Functions (NFs) are stateful and this state need to be shared across NF instances. Implementing state sharing while meeting the throughput and latency requirements placed on NFs is challenging and, to date, no solution exists that meets NFV’s performance goals for the full spectrum of NFs. S6 is a new framework that supports elastic scaling of NFs without compromising performance. Its design builds on the insight that a distributed shared state abstraction is well-suited to the NFV context. We organize state as a distributed shared object (DSO) space and extend the DSO concept with techniques designed to meet the need for elasticity and high-performance in NFV workloads. S6 simplifies development: NF writers program with no awareness of how state is distributed and shared. Instead, S6 transparently migrates state and handles accesses to shared state. In our evaluation, compared to recent solutions for dynamic scaling of NFs, S6 improves performance by 100x during scaling events [25], and by 2-5x under normal operation