CsAc and HPbX 3 were adopted in CsPbX 3 perovskite preparation, which led to high-quality CsPbX 3 perovskite films with large film thickness (>500 nm). Taking advantage of this new precursor system, efficient CsPbIBr 2 inorganic perovskite solar cells with record power conversion efficiency (PCE) of 8.54% were achieved. By introducing a judicious amount of PEAI into the new precursor pair, inorganic quasi-2D perovskites emerged and delivered a reproducible PCE of 12.4% for a-CsPbI 3 with greatly improved stability.
The capture of radioactive I 2 vapor from nuclear waste under industrial operating conditions remains a challenging task, as the practical industrial conditions of high temperature (≥150 °C) and low I 2 concentration (∼150 ppmv) are unfavorable for I 2 adsorption. We report a novel guanidinium-based covalent organic framework (COF), termed TGDM, which can efficiently capture I 2 under industrial operating conditions. At 150 °C and 150 ppmv I 2 , TGDM exhibits an I 2 uptake of ∼30 wt %, which is significantly higher than that of the industrial silver-based adsorbents such as Ag@MOR (17 wt %) currently used in the nuclear fuel reprocessing industry. Characterization and theoretical calculations indicate that among the multiple types of adsorption sites in TGDM, only ionic sites can bond to I 2 through strong Coulomb interactions under harsh conditions. The abundant ionic groups of TGDM account for its superior I 2 capture performance compared to various benchmark adsorbents. In addition, TGDM exhibits exceptionally high chemical and thermal stabilities that fully meet the requirements of practical radioactive I 2 capture (high-temperature, humid, and acidic environment) and differentiate it from other ionic COFs. Furthermore, TGDM has excellent recyclability and low cost, which are unavailable for the current industrial silver-based adsorbents. These advantages make TGDM a promising candidate for capturing I 2 vapor during nuclear fuel reprocessing. This strategy of incorporating chemically stable ionic guanidine moieties in COF would stimulate the development of new adsorbents for I 2 capture and related applications.
Data center networks encode locality and topology information into their server and switch addresses for performance and routing purposes. For this reason, the traditional address configuration protocols such as DHCP require huge amount of manual input, leaving them error-prone.In this paper, we present DAC, a generic and automatic Data center Address Configuration system. With an automatically generated blueprint which defines the connections of servers and switches labeled by logical IDs, e.g., IP addresses, DAC first learns the physical topology labeled by device IDs, e.g., MAC addresses. Then at the core of DAC is its device-to-logical ID mapping and malfunction detection. DAC makes an innovation in abstracting the device-to-logical ID mapping to the graph isomorphism problem, and solves it with low time-complexity by leveraging the attributes of data center network topologies. Its malfunction detection scheme detects errors such as device and link failures and miswirings, including the most difficult case where miswirings do not cause any node degree change.We have evaluated DAC via simulation, implementation and experiments. Our simulation results show that DAC can accurately find all the hardest-to-detect malfunctions and can autoconfigure a large data center with 3.8 million devices in 46 seconds. In our implementation, we successfully autoconfigure a small 64-server BCube network within 300 milliseconds and show that DAC is a viable solution for data center autoconfiguration.
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