The Development of an Improved Zinc/silver-oxide Battery
Separator system development for primary zinc- silver oxide battery
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Separator system development for primary zinc- silver oxide battery
Primary zinc, silver oxide battery design
Active compound formation on battery nickel oxide electrode during charge, overcharge, and discharge
Molecular systems capable of directing the flow of excitons are key to the development and optimization of optoelectronic materials. The transport of excitons across multiple molecules is governed by an intermolecular electronic coupling network. In this article, we consider the effects of complex-valued intermolecular electronic coupling on exciton transport. Here, we present a molecular motif capable of generating complex-valued coupling under excitation with circularly polarized light. We use theoretical modeling and simulation to illustrate how complex coupling can be leveraged to drive the rotational flux of excitons in cyclic molecular networks and direct the exciton population in branched molecular networks.
X-ray diffraction patterns of nickel-cadmium battery electrodes and stabilization of nickel oxides and hydroxides
Identification and characterization of battery active compound structures formed on nickel oxide electrode during charging and discharging
Neptunium oxalate (Np(C 2 O 4 ) 2 ·6H 2 O) is a historically relevant solid phase used in nuclear processing as a precursor for neptunium dioxide (NpO 2 ). Although Np oxalate has been synthesized and used for NpO 2 production for decades, the thermal decomposition mechanism of this phase remains poorly understood and has not been evaluated in over 30 years. Conflicting reports in historical literature suggest either a direct conversion from anhydrous oxalate to NpO 2 or a decomposition that includes the formation of Np carbonate or oxidized Np intermediate phases. In this work, we reexamine the decomposition pathway of Np(C 2 O 4 ) 2 ·6H 2 O using thermal analysis coupled with evolved gas analysis and temperature-dependent Raman spectroscopy to elucidate decomposition mechanisms and intermediate phases using modern analytical techniques. Thermal analysis revealed a three-stage decomposition process, including dehydration below 200 °C, oxalate breakdown between 170 and 370 °C, and NpO 2 formation by 500 °C. However, an unidentified plateau in the thermal data was observed during measurements. Raman spectroscopy confirmed the stages of decomposition, and in the analysis of potential intermediate phases, no carbonate phases or Np 2 O 5 were identified. Raman data suggest that residual oxalate or nonstoichiometric oxide are present during decomposition before pure NpO 2 is formed. These findings clarify aspects of the Np oxalate decomposition mechanism and address longstanding discrepancies in the literature, with a specific focus on Np-specific materials chemistry.
In this work, we evaluated the ability of single-particle inductively coupled plasma time-of-flight mass spectrometry (SP-ICP-TOF-MS) to characterize particulates generated from 300 series stainless steels and oxide dispersion–strengthened (ODS) steels. Particulates were produced using two different rotary tools (cutting disk and grinding bit) to assess the influence of particle generation on effluent particulates. Across all materials and preparation techniques, SP-ICP-TOF-MS detected a substantial population of particles containing the expected constituent elements, with average compositions closely matching the nominal alloy values. A secondary population of Y-bearing particles was evident in ODS steels; their formation is strongly favored in particulates generated using the grinding bit. Specifically, in PM2000, Y-bearing particles were predominantly colocated with Al, whereas in 14YWT, Y-bearing particles were less frequently Fe-free but showed stronger colocation with Ti. These trends are consistent with the known microstructural differences between Y–Al–O oxide particles in PM2000 and finer Y–Ti–O nanoclusters in 14YWT. Together, these results demonstrate that SP-ICP-TOF-MS can resolve distinct particulate populations in complex, multicomponent steels at the femtogram mass scale, enabling particle-resolved insight into microstructure-driven phase separation and preparation-dependent particle generation.
Experiments have shown that plasma environments can induce discharges in solar arrays. These plasmas simulate the environments found in low earth orbits where current plans call for operation of very large power systems. The discharges could be large enough to couple into the power system and possibly disrupt operations. Here, the general concepts of the discharge mechanism and the techniques of coupling are discussed. Data from both ground and flight experiments are reviewed to obtain an expected basis for the interactions. These concepts were applied to the Space Station solar array and distribution system as an example of the large space power system. The effect of discharges was found to be a function of the discharge site. For most sites in the array discharges would not seriously impact performance. One location at the negative end of the array was identified as a position where discharges could couple to charge stored in system capacitors. This latter case could impact performance.
Hydrogels are characterized by substantial volume changes in response to external stimuli, making them promising candidates for developing smart materials with enhanced adaptability and responsiveness. By integrating chemical reactions, hydrogels acquire dynamic and tunable responsiveness to external stimuli through chemo‐mechanical coupling, expanding their potential in emerging applications. However, capturing their transient behavior remains challenging due to the complex interplay of chemical reactions, solvent transport, and polymer network deformation. Classical theories capture equilibrium swelling but fail to describe time‐dependent phenomena. To address this, a time‐dependent continuum model is developed that explicitly couples these processes. Volume phase transition in hydrogels with homogeneous chemical reactions is investigated, then the effects of reaction kinetics on these transitions are analyzed. The coupling of these mechanisms is further explored through a study of transient mechanical instabilities. To illustrate the impact of distinct reaction and diffusion timescales, a photo‐active gripper is studied for robotic applications. Finally, a photo‐active microswimmer that exhibits non‐reciprocal motion is proposed to highlight the solvent diffusion for locomotion at the micro‐ and nano‐ scales. The work establishes that transient dynamics of chemo‐mechanical hydrogels generate functions not accessible by steady state models and provides a predictive platform for designing adaptive materials in emerging applications.
A novel Bayesian approach significantly accelerates data collection for metal oxide reduction/re-oxidation thermodynamic fitting.
Nuclear thermal propulsion (NTP), with its increased efficiency compared to traditional rocket engines, is a potentially enabling technology for space missions of higher complexity, such as delivering heavy machinery or crew to the moon or other planets. Thus, exploring modeling approaches that can effectively couple the thermal, fluid, and neutronic behaviors of NTP reactors is essential to mitigate cost and schedule challenges with hardware development. This paper details one such approach which utilizes Cardinal to unify reactor physics analysis performed using OpenMC, heat transfer analysis performed using the MOOSE framework, and thermal hydraulic analysis performed using the MOOSE Thermal Hydraulic Module. This approach is demonstrated with a proof of concept that focuses on a legacy reactor geometry from the historic NERVA program. This preliminary model found an overprediction in, when compared to the reference data, axial power peaking and the temperatures of both solids and fluids by roughly 20%. It is clear, however, that the model conserved a number of broad trends, and a few model refinements should reduce the observed error. This approach has definite merit and should enable the production of informative results for more modern NTP systems. This work is a first step in creating a transient reactor model that can be updated across a sequence of time steps based on continually fluctuating, time-dependent boundary conditions.
This chapter covers the epitaxy of complex oxides on common inorganic semiconductors. The chapter opens with an introduction, motivating the subject and highlighting key general challenges (Section 6.1). We then proceed to describe the general steps of the growth procedure in Section 6.2, which concludes with an overall discussion about trade-offs and expectation management, with an emphasis on the oxide–semiconductor interface. From there, the text describes oxide growth on the common semiconductors, starting with silicon (Section 6.3), where surface reactions and oxidation are the most challenging aspects. Oxide epitaxy on germanium is described in Section 6.4, which is a less challenging oxide growth scheme on semiconductors. Section 6.5 describes oxide epitaxy on gallium arsenide, one of the more challenging schemes, where interface stability and surface preparation pose key challenges. Finally, in Section 6.6, oxide epitaxy on gallium nitride is described, where the lattice mismatch takes the stage as the key challenge. Altogether, this chapter aims to provide the reader with the practical knowledge, tactics and strategies for oxide epitaxy on semiconductors.
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Accurate modeling of particle transport within fusion blankets is essential for predicting performance metrics such as heat deposition and the tritium breeding ratio (TBR). However, high-fidelity coupling of thermal fluids from computational fluid dynamics (CFD) to neutronics simulations often incurs significant computational costs due to the complexity of surface intersection calculations in Monte Carlo codes. This paper presents an accelerated multiphysics coupling method for neutronics that utilizes hierarchical agglomerative clustering to map complex material property distributions to a neutronics model. Implemented within the fusion reactor design and assessment (FREDA) framework, the method leverages existing Python packages to automate the creation of clustered geometries for OpenMC. The approach is demonstrated on a sector model of an ARC-class tokamak with an immersion molten salt blanket, and an simple geometry with varying isotopic concentrations. Results show that the clustering method significantly reduces computational burden without compromising fidelity, providing a foundation for agile iteration of neutronics simulations involving multiple coupled material properties.
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