Silicon–air batteries

A new “metal”–air battery based on silicon–oxygen couple is described. Silicon–air battery employing EMI·2.3HF·F room temperature ionic liquid (RTIL) as an electrolyte and highly-doped silicon wafers as anodes (fuels) has an undetectable self-discharge rate and high tolerance to the environment (extreme moisture/dry conditions). Such a battery yields an effectively infinite shelf life with an average working voltage of 1–1.2 V. Silicon–air battery can support relatively high current densities (up to 0.3 mA/cm²) drawn from flat polished silicon wafers anodes. Such batteries may find immediate applications, as they can provide an internal, built-in autonomous and self sustained energy source.     

Convex feasibility modeling and projection methods for sparse signal recovery

A computationally-efficient method for recovering sparse signals from a series of noisy observations, known as the problem of compressed sensing (CS), is presented. The theory of CS usually leads to a constrained convex minimization problem. In this work, an alternative outlook is proposed. Instead of solving the CS problem as an optimization problem, it is suggested to transform the optimization problem into a convex feasibility problem (CFP), and solve it using feasibility-seeking sequential and simultaneous subgradient projection methods, which are iterative, fast, robust and convergent schemes for solving CFPs. As opposed to some of the commonly-used CS algorithms, such as Bayesian CS and Gradient Projections for sparse reconstruction, which become inefficient as the problem dimension and sparseness degree increase, the proposed methods exhibit robustness with respect to these parameters. Moreover, it is shown that the CFP-based projection methods are superior to some of the state-of-the-art methods in recovering the signal’s support. Numerical experiments show that the CFP-based projection methods are viable for solving large-scale CS problems with compressible signals.     

Enhanced Electrochemical Performance of Supercapacitors via Atomic Layer Deposition of ZnO on the Activated Carbon Electrode Material

Fabricating electrical double-layer capacitors (EDLCs) with high energy density for various applications has been of great interest in recent years. However, activated carbon (AC) electrodes are restricted to a lower operating voltage because they suffer from instability above a threshold potential window. Thus, they are limited in their energy storage. The deposition of inorganic compounds’ atomic layer deposition (ALD) aiming to enhance cycling performance of supercapacitors and battery electrodes can be applied to the AC electrode materials. Here, we report on the investigation of zinc oxide (ZnO) coating strategy in terms of different pulse times of precursors, ALD cycles, and deposition temperatures to ensure high electrical conductivity and capacitance retention without blocking the micropores of the AC electrode. Crystalline ZnO phase with its optimal forming condition is obtained preferably using a longer precursor pulse time. Supercapacitors comprising AC electrodes coated with 20 cycles of ALD ZnO at 70 °C and operated in TEABF₄/acetonitrile organic electrolyte show a specific capacitance of 23.13 F g⁻¹ at 5 mA cm⁻² and enhanced capacitance retention at 3.2 V, which well exceeds the normal working voltage of a commercial EDLC product (2.7 V). This work delivers an additional feasible approach of using ZnO ALD modification of AC materials, enhancing and promoting stable EDLC cells under high working voltages.     

The Distributedk-Server Problem—A Competitive Distributed Translator fork-Server Algorithms

We consider thek-server problem23in a distributed setting. Given a network ofnprocessors andkidentical mobile servers, requests for service appear at the processors and a server must reach the request point. In addition to modeling problems in computer networks wherekidentical mobile resources are shared by the processors of the network, this models a realistic situation where the transfer of information is costly and there is no central control that governs the behavior of servers that move around to satisfy requests for service. We give a general translator to transform any deterministic global-control competitivek-server algorithm into a distributed competitive one. As consequences we get poly(k)-competitive distributed algorithms for the line, trees, and the ring. In contrast to the global-control case where there arek-server algorithms with competitive ratio that depends solely onk, we have a lower bound of Ω(max{k, (1/D) ·(log n/log log n)}) on the competitive ratio of any distributedk-server algorithm, where 1/Dis the ratio between the cost to transmit a message and the cost to move a server over the same distance. We also give a distributed version of the Harmonic randomizedk-server algorithm.     

On the effectiveness of projection methods for convex feasibility problems with linear inequality constraints

The effectiveness of projection methods for solving systems of linear inequalities is investigated. It is shown that they often have a computational advantage over alternatives that have been proposed for solving the same problem and that this makes them successful in many real-world applications. This is supported by experimental evidence provided in this paper on problems of various sizes (up to tens of thousands of unknowns satisfying up to hundreds of thousands of constraints) and by a discussion of the demonstrated efficacy of projection methods in numerous scientific publications and commercial patents (dealing with problems that can have over a billion unknowns and a similar number of constraints).     

A blind policy for equalizing cumulative idleness

We consider a system with a single queue and multiple server pools of heterogeneous exponential servers. The system operates under a policy that always routes a job to the pool with longest cumulative idleness among pools with available servers, in an attempt to achieve fairness toward servers. It is easy to find examples of a system with a fixed number of servers, for which fairness is not achieved by this policy in any reasonable sense. Our main result shows that in the many-server regime of Halfin and Whitt, the policy does attain equalization of cumulative idleness, and that the equalization time, defined within any given precision level, remains bounded in the limit. An important feature of this policy is that it acts ‘blindly’, in that it requires no information on the service or arrival rates.     

Finding a best approximation pair of points for two polyhedra

Given two disjoint convex polyhedra, we look for a best approximation pair relative to them, i.e., a pair of points, one in each polyhedron, attaining the minimum distance between the sets. Cheney and Goldstein showed that alternating projections onto the two sets, starting from an arbitrary point, generate a sequence whose two interlaced subsequences converge to a best approximation pair. We propose a process based on projections onto the half-spaces defining the two polyhedra, which are more negotiable than projections on the polyhedra themselves. A central component in the proposed process is the Halpern–Lions–Wittmann–Bauschke algorithm for approaching the projection of a given point onto a convex set.