Electrolysis of a molten semiconductor

Metals cannot be extracted by electrolysis of transition-metal sulfides because as liquids they are semiconductors, which exhibit high levels of electronic conduction and metal dissolution. Herein by introduction of a distinct secondary electrolyte, we reveal a high-throughput electro-desulfurization process that directly converts semiconducting molten stibnite (Sb[subscript 2]S[subscript 3]) into pure (99.9%) liquid antimony and sulfur vapour. At the bottom of the cell liquid antimony pools beneath cathodically polarized molten stibnite. At the top of the cell sulfur issues from a carbon anode immersed in an immiscible secondary molten salt electrolyte disposed above molten stibnite, thereby blocking electronic shorting across the cell. As opposed to conventional extraction practices, direct sulfide electrolysis completely avoids generation of problematic fugitive emissions (CO[subscript 2], CO and SO[subscript 2]), significantly reduces energy consumption, increases productivity in a single-step process (lower capital and operating costs) and is broadly applicable to a host of electronically conductive transition-metal chalcogenides.United States. Advanced Research Projects Agency-Energy (Award DE-AR0000047)TOTAL (Firm

Extraction of valuable lanthanum, zirconium and lithium elements from solid electrolyte LI7LA3ZR2O12 (LLZO)

Valuable elements in lithium-ion batteries is a very significant secondary resource. Based on the recovery and recycling of the spent all-solid-state lithium-ion batteries in the future, we explore extracting valuable lanthanum, zirconium, and lithium elements from the solid electrolyte tetragonal phase LLZO by the leaching with the dilute sulfuric, and then by precipitation methods of double salts and carbonate salt, and recrystallization, respectively

WSN Data Transmission Algorithm Based on Spatial Data Aggregation

In this paper, considering the high energy consumption, loss of network lifetime and data leak in transmission during the data aggregation of the wireless sensor network, we propose an improved spatial data aggregation algorithm. Through comparison with traditional data aggregation algorithms, we verify the feasibility and rationality of the proposed algorithm and obtain the following conclusions: the proposed algorithm carries out node sensing and data aggregation within a certain area based on multiple dynamic routes. The calculation process does not require encryption and decryption, and is not affected by network topology, so it can better address the data aggregation problems in the dynamic change of network structure. Compared with other traditional data aggregation algorithms, the proposed algorithm has the advantages of low traffic, low energy consumption in data transmission, low probability of data leakage and high transmission accuracy. In data aggregation, 3 slices is the optimal quantity

Corrosion and protection of metallic materials in molten carbonates for concentrating solar power and molten carbonate electrolysis applications

Molten carbonates have recently attracted increasing interest for use as effective functional media in the fields of sustainable energy processes such as the Concentrating Solar Power (CSP) plants and the molten carbonate electrolysis (MCE) process. The compatibility between metallic materials and molten carbonate media is one of the important considerations and significant technical challenges for the practical molten carbonate application. Herein, we summarized the behaviors and mechanisms of molten carbonate-induced corrosion of metallic materials. The effects of operating temperature, gas atmosphere, electrochemical polarization, alloy elements, gas-liquid interface, and dynamic conditions on the corrosion behaviors and mechanisms of metals in molten carbonates were systematically reviewed. In addition, the corrosion mitigation approaches including regulation of melt basicity and surface treatments of metals are discussed. This review will serve as the foundation for further research addressing the challenges of molten carbonate-induced corrosion and enabling the effective applications of molten carbonates in a sustainable and low-carbon world

Wind Load and Structural Parameters Estimation from Incomplete Measurements

The extended minimum variance unbiased estimation approach can be used for joint state/parameter/input estimation based on the measured structural responses. However, it is necessary to measure the structural displacement and acceleration responses at each story for the simultaneous identification of structural parameters and unknown wind load. A novel method of identifying structural state, parameters, and unknown wind load from incomplete measurements is proposed. The estimation is performed in a modal extended minimum variance unbiased manner, based on incomplete measurements of wind-induced structural displacement and acceleration responses. The feasibility and accuracy of the proposed method are numerically validated by identifying the wind load and structural parameters on a ten-story shear building structure with incomplete measurements. The effects of crucial factors, including sampling duration and the number of measurements, are discussed. Furthermore, the practical application of the developed inverse method is evaluated based on wind tunnel testing results of a 234 m tall building structure. The results indicate that the structural state, parameters, and unknown wind load can be identified accurately using the proposed approach

Approximate solution for interactive dynamic influence diagrams based on belief-behavior graphs

Conference Name:2011 3rd International Workshop on Intelligent Systems and Applications, ISA 2011. Conference Address: Wuhan, China. Time:May 28, 2011 - May 29, 2011.Hubei University of Technology; Research Association of Modern Education and Computer Science; Huazhong University of Science and Technology; Wuhan University; YOCSEF; IEEE Wuhan Section CIS ChapterInteractive Dynamic Influence Diagrams(I-DIDs) constitute a graphic model for multi-agent decision making under uncertainty, but solving them is provably intractable. Algorithms for solving I-DIDs face the challenge of an exponentially growing space of candidate models ascribed to other agents, over time. Pruning behaviorally equivalent models is one way toward minimizing the model set, but composing behavioral equivalence classes is a complex process as we need to compare all solutions of possible models of other agents in the merge operation. In this paper, we seek a more efficient way to construct behavioral equivalence classes using belief-behavior Graph(BBG). We present a method of solving I-DIDs approximately that reduces the candidate model space by clustering models that are likely to be -behavioral equivalence and selecting a representative one from each cluster. We discuss the complexity of the approximation technique and demonstrate its empirical performance. ? 2011 IEEE