Ionothermal Synthesis of High-Voltage Alluaudite Na2+2xFe2-x(SO4)(3) Sodium Insertion Compound: Structural, Electronic, and Magnetic Insights

Exploring future cathode materials for sodium-ion batteries, alluaudite class of Na2Fe2II(SO4)(3) has been recently unveiled as a 3.8 V positive insertion candidate (Barpanda et al. Nat. Commun. 2014, 5, 4358). It forms an Fe-based polyanionic compound delivering the highest Fe-redox potential along with excellent rate kinetics and reversibility. However, like all known SO4-based insertion materials, its synthesis is cumbersome that warrants careful processing avoiding any aqueous exposure. Here, an alternate low temperature ionothermal synthesis has been described to produce the alluaudite Na2+2xFe2-xII(SO4)(3). It marks the first demonstration of solvothermal synthesis of alluaudite Na2+2xM2-xII(SO4)(3) (M = 3d metals) family of cathodes. Unlike classical solid-state route, this solvothermal route favors sustainable synthesis of homogeneous nanostructured alluaudite products at only 300 degrees C, the lowest temperature value until date. The current work reports the synthetic aspects of pristine and modified ionothermal synthesis of Na2+2xFe2-xII(SO4)(3) having tunable size (300 nm similar to 5 mu m) and morphology. It shows antiferromagnetic ordering below 12 K. A reversible capacity in excess of 80 mAh/g was obtained with good rate kinetics and cycling stability over 50 cycles. Using a synergistic approach combining experimental and ab initio DFT analysis, the structural, magnetic, electronic, and electrochemical properties and the structural limitation to extract full capacity have been described.

FAST AND ACCURATE BILATERAL FILTERING USING GAUSS-POLYNOMIAL DECOMPOSITION

The bilateral filter is a versatile non-linear filter that has found diverse applications in image processing, computer vision, computer graphics, and computational photography. A common form of the filter is the Gaussian bilateral filter in which both the spatial and range kernels are Gaussian. A direct implementation of this filter requires O(sigma(2)) operations per pixel, where sigma is the standard deviation of the spatial Gaussian. In this paper, we propose an accurate approximation algorithm that can cut down the computational complexity to O(1) per pixel for any arbitrary sigma (constant-time implementation). This is based on the observation that the range kernel operates via the translations of a fixed Gaussian over the range space, and that these translated Gaussians can be accurately approximated using the so-called Gauss-polynomials. The overall algorithm emerging from this approximation involves a series of spatial Gaussian filtering, which can be efficiently implemented (in parallel) using separability and recursion. We present some preliminary results to demonstrate that the proposed algorithm compares favorably with some of the existing fast algorithms in terms of speed and accuracy.