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Supersaturated solid solutions of Al and corrosion-resistant alloying elements (M: V, Mo, Cr, Ti, Nb), produced by non-equilibrium processing techniques, have been reported to exhibit high corrosion resistance and strength. The corrosion mechanism for such improved corrosion performance has not been well understood. We present a fundamental understanding of the role of V in corrosion of an Al-V alloy, which will provide a theoretical background for developing corrosion-resistant Al alloys. High-energy ball milling of the elemental powder of Al and V produced an in situ consolidated Al-V alloy, which exhibited high solid solubility of V. The corrosion resistance of Al-V alloy was significantly higher than that of pure Al, which was attributed to the (1) enrichment of V at the passive film/substrate interface, (2) incorporation of V into the passive film, and (3) deposition of V on the iron-containing cathodic particles and therefore, retardation of cathodic reaction.

 

The high cost and growing environmental concerns surrounding lithium-ion batteries have motivated research into extending the life of electric vehicle(EV) batteries by repurposing them for second life grid applications. The incorporation of repurposed electric vehicle batteries (REVBs) has the potential to decrease the overall cost of new battery energy storage systems (BESS) and extend the useful life of the materials. This paper focuses on maximizing daily profit that can be made from REVBs by stacking two grid services such as frequency regulation and energy arbitrage while minimizing battery capital cost by using second life EV batteries. A model for battery management with stacked frequency regulation and energy arbitrage is developed and tested using PJM market data. A mixed integer linear programming (MILP) is used to solve the optimization problem. It is found that REVBs can generate higher net profits than a new BESS. 

Abstract The control of the in-plane domain evolution in ferroelectric thin films is not only critical to understanding ferroelectric phenomena but also to enabling functional device fabrication. However, in-plane polarized ferroelectric thin films typically exhibit complicated multi-domain states, not desirable for optoelectronic device performance. Here we report a strategy combining interfacial symmetry engineering and anisotropic strain to design single-domain, in-plane polarized ferroelectric BaTiO 3 thin films. Theoretical calculations predict the key role of the BaTiO 3 /PrScO 3 $${({{{{{\boldsymbol{110}}}}}})}_{{{{{{\bf{O}}}}}}}$$ ( 110 ) O substrate interfacial environment, where anisotropic strain, monoclinic distortions, and interfacial electrostatic potential stabilize a single-variant spontaneous polarization. A combination of scanning transmission electron microscopy, piezoresponse force microscopy, ferroelectric hysteresis loop measurements, and second harmonic generation measurements directly reveals the stabilization of the in-plane quasi-single-domain polarization state. This work offers design principles for engineering in-plane domains of ferroelectric oxide thin films, which is a prerequisite for high performance optoelectronic devices. 

We consider distributed optimization under communication constraints for training deep learning models. We propose a new algorithm, whose parameter updates rely on two forces: a regular gradient step, and a corrective direction dictated by the currently best-performing worker (leader). Our method differs from the parameter-averaging scheme EASGD in a number of ways:(i) our objective formulation does not change the location of stationary points compared to the original optimization problem;(ii) we avoid convergence decelerations caused by pulling local workers descending to different local minima to each other (ie to the average of their parameters);(iii) our update by design breaks the curse of symmetry (the phenomenon of being trapped in poorly generalizing sub-optimal solutions in symmetric non-convex landscapes); and (iv) our approach is more communication efficient since it broadcasts only parameters of the leader rather than all workers. We provide theoretical analysis of the batch version of the proposed algorithm, which we call Leader Gradient Descent (LGD), and its stochastic variant (LSGD). Finally, we implement an asynchronous version of our algorithm and extend it to the multi-leader setting, where we form groups of workers, each represented by its own local leader (the best performer in a group), and update each worker with a corrective direction comprised of two attractive forces: one to the local, and one to the global leader (the best performer among all workers). The multi-leader setting is well-aligned with current hardware architecture, where local workers forming a group lie within a single computational node and …