Non-volatile Fermi level tuning for the control of spin-charge conversion at room temperature
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Ferrocene was introduced as a solid additive in organic solar cells (OSCs). The use of ferrocene provides PM6:Y6 based device with improved performance and stability, demonstrating its great potential in the fabrication of efficient and stable OSCs.
Analog hardware accelerators, which perform computation within a dense memory array, have the potential to overcome the major bottlenecks faced by digital hardware for data-heavy workloads such as deep learning. Exploiting the intrinsic computational advantages of memory arrays, however, has proven to be challenging principally due to the overhead imposed by the peripheral circuitry and due to the non-ideal properties of memory devices that play the role of the synapse. We review the existing implementations of these accelerators for deep supervised learning, organizing our discussion around the different levels of the accelerator design hierarchy, with an emphasis on circuits and architecture. We explore and consolidate the various approaches that have been proposed to address the critical challenges faced by analog accelerators, for both neural network inference and training, and highlight the key design trade-offs underlying these techniques.
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In the K-Area Complex (KAC), blend cans are used to down blend plutonium oxide for future disposal at the Waste Isolation Pilot Plant (WIPP). The blend can is a thin walled screw lid container that is used to down blend a measured amount of plutonium oxide with adulterant to reduce the attractiveness of the plutonium. The oxide and adulterant are mixed within the blend can and the blend can is packaged into a thick walled outer container. The blend cans are procured from an offsite vendor that uses a lubricant in the can manufacturing process. A thin coating of the lubricant adheres to the outer surface of the blend can and both inner and outer surfaces of the blend can cap.
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