Comprehensive study of a versatile polyol synthesis approach for cathode materials for Li-ion batteries

This work reports a comprehensive study of a novel polyol method that can successfully synthesize layered LiNi0.4Mn0.4Co0.2O2,spinel LiNi0.5Mn1.5O4,and olivine LiCoPO4 cathode materials.When properly designed,polyol method offers many advantages such as low cost,ease of use,and proven scalability for industrial applications.Most importantly,the unique properties of polyol solvent allow for greater morphology control as shown by all the resulting materials exhibiting monodispersed nanoparticles morphology.This morphology contributes to improved lithium ion transport due to short diffusion lengths.Polyol-synthesized LiNi0.4Mn0.4Co0.2O2 delivers a reversible capacity of 101 and 82 mAh.g-1 using high current rate of 5C and 10C,respectively.It also displays surprisingly high surface structure stability after chargedischarge processes.Each step of the reaction was investigated to understand the underlying polyol synthesis mechanism.A combination of in situ and ex situ studies reveal the structural and chemical transformation of Ni-Co alloy nanocrystals overwrapped by a Mn-and Li-embedded organic matrix to a sedes of intermediate phases,and then eventually to the desired layered oxide phase with a homogeneous distribution of Ni,Co,and Mn.We envisage that this type of analysis will promote the development of optimized synthesis protocols by establishing links between experimental factors and important structural and chemical properties of the desired product.The insights can open a new direction of research to synthesize high-performance intercalation compounds by allowing unprecedented control of intermediate phases using experimental parameters.

Automated generation and ensemble-learned matching of X-ray absorption spectra

X-ray absorption spectroscopy(XAS)is a widely used materials characterization technique to determine oxidation states,coordination environment,and other local atomic structure information.Analysis of XAS relies on comparison of measured spectra to reliable reference spectra.However,existing databases of XAS spectra are highly limited both in terms of the number of reference spectra available as well as the breadth of chemistry coverage.In this work,we report the development of XASdb,a large database of computed reference XAS,and an Ensemble-Learned Spectra IdEntification(ELSIE)algorithm for the matching of spectra.XASdb currently hosts more than 800,000 K-edge X-ray absorption near-edge spectra(XANES)for over 40,000 materials from the open-science Materials Project database.We discuss a high-throughput automation framework for FEFF calculations,built on robust,rigorously benchmarked parameters.FEFF is a computer program uses a real-space Green’s function approach to calculate X-ray absorption spectra.We will demonstrate that the ELSIE algorithm,which combines 33 weak“learners”comprising a set of preprocessing steps and a similarity metric,can achieve up to 84.2% accuracy in identifying the correct oxidation state and coordination environment of a test set of 19 K-edge XANES spectra encompassing a diverse range of chemistries and crystal structures.The XASdb with the ELSIE algorithm has been integrated into a web application in the Materials Project,providing an important new public resource for the analysis of XAS to all materials researchers.Finally,the ELSIE algorithm itself has been made available as part of veidt,an open source machine-learning library for materials science.

Author Correction:Automated generation and ensemblelearned matching of X-ray absorption spectra

Correction to:npj Computational Materials https://doi.org/10.1038/s41524-018-0067-x,published online 20 March 2018 The following text has been added to the Acknowledgements section:“L.F.J.P.acknowledges support from the National Science Foundation(DMREF-1627583).”