A New Crystalline LiPON Electrolyte: Synthesis, Properties, and Electronic Structure

The new crystalline compound, Li2PO2N, was synthesized using high temperature solid state methods starting with a stoichiometric mixture of Li2O, P2O5, and P3N5. Its crystal structure was determined ab initio from powder X-ray diffraction. The compound crystallizes in the orthorhombic space group Cmc2(1) (# 36) with lattice constants a = 9.0692(4) angstrom, b = 53999(2) angstrom, and c = 4.6856(2) angstrom. The crystal structure of SD-Li2PO2N consists of parallel arrangements of anionic chains formed of corner sharing (PO2N2) tetrahedra. The chains are held together by Li+ cations. The structure of the synthesized material is similar to that predicted by Du and Holzwarth on the basis of first principles calculations (Phys. Rev. B 81,184106 (2010)). The compound is chemically and structurally stable in air up to 600 degrees C and in vacuum up to 1050 degrees C. The Arrhenius activation energy of SD-Li2PO2N in pressed pellet form was determined from electrochemical impedance spectroscopy measurements to be 0.6 eV, comparable to that of the glassy electrolyte LiPON developed at Oak Ridge National Laboratory. The minimum activation energies for Li ion vacancy and interstitial migrations are computed to be 0.4 eV and 0.8 eV, respectively. First principles calculations estimate the band gap of SD-Li2PO2N to be larger than 6 eV. (C) 2013 Elsevier B.V. All rights reserved.

Advantages and Applications of Transforming Empirical Model Input Space with Dimensional Models

Model-based calibration of steady-state engine operation is commonly performed with highly parameterized empirical models that are accurate but not very robust, particularly when predicting highly nonlinear responses such as diesel smoke emissions. To address this problem, and to boost the accuracy of more robust non-parametric methods to the same level, GT-Power was used to transform the empirical model input space into multiple input spaces that simplified the input-output relationship and improved the accuracy and robustness of smoke predictions made by three commonly used empirical modeling methods: Multivariate Regression, Neural Networks and the k-Nearest Neighbor method. The availability of multiple input spaces allowed the development of two committee techniques: a 'Simple Committee' technique that used averaged predictions from a set of 10 pre-selected input spaces chosen by the training data and the "Minimum Variance Committee" technique where the input spaces for each prediction were chosen on the basis of disagreement between the three modeling methods. This latter technique equalized the performance of the three modeling methods. The successively increasing improvements resulting from the use of a single best transformed input space (Best Combination Technique), Simple Committee Technique and Minimum Variance Committee Technique were verified with hypothesis testing. The transformed input spaces were also shown to improve outlier detection and to improve k-Nearest Neighbor performance when predicting dynamic emissions with steady-state training data. An unexpected finding was that the benefits of input space transformation were unaffected by changes in the hardware or the calibration of the underlying GT-Power model.