Nickel-rich layered materials LiNi 1-x-y Mn x Co y O 2 are promising candidates for high energy density lithium-ion battery cathodes. Unfortunately, they suffer from capacity fading upon cycling, especially with high voltage charging. It is critical to have mechanistic understanding of such fade. Herein, synchrotron-based techniques (including scattering, spectroscopy, and microcopy) and finite element analysis were utilized to understand the LiNi 0.6 Mn 0.2 Co 0.2 O 2 material from structural, chemical, morphological, and mechanical points of view. The lattice structural changes are shown to be relatively reversible during cycling, even when 4.9V charging was applied. However, local disorder and strain were induced by high voltage charging. Nano-resolution 3D transmission X-ray microscopy data analyzed by machine learning methodology reveals that high-voltage charging induced significant oxidation state inhomogeneities in the cycled particles. Regions at the surface have rock-salt type structure with lower oxidation state and build up the impedance while regions with higher oxidization state are scattered in the bulk and are likely deactivated during cycling. In addition, the development of micro-cracks is highly dependent on the pristine state morphology and cycling conditions. Hollow particles seem to be more robust against stress-induced cracks than the solid ones, suggesting that morphology engineering can be effective in mitigating the crack problem in these materials. The engineering support from D. Van Campen, V. Borzenets and D. Day for the TXM experiment at beamline 6-2C of SSRL is gratefully acknowledged. The work done at Brookhaven National Laboratory was supported by the Assistant Secretary for Energy Efficiency and Renewable Energy, Vehicle Technology Office of the U.S. Department of Energy through the Advanced Battery Materials Research (BMR) Program, including Battery500 Consortium under contract DE-SC0012704. This research used beamlines 7-BM and 28-ID-2 of the National Synchrotron Light Source II, a U.S.

BackgroundMost analyses of microarray data are based on point estimates of expression levels and ignore the uncertainty of such estimates. By determining uncertainties from Affymetrix GeneChip data and propagating these uncertainties to downstream analyses it has been shown that we can improve results of differential expression detection, principal component analysis and clustering. Previously, implementations of these uncertainty propagation methods have only been available as separate packages, written in different languages. Previous implementations have also suffered from being very costly to compute, and in the case of differential expression detection, have been limited in the experimental designs to which they can be applied.Resultspuma is a Bioconductor package incorporating a suite of analysis methods for use on Affymetrix GeneChip data. puma extends the differential expression detection methods of previous work from the 2-class case to the multi-factorial case. puma can be used to automatically create design and contrast matrices for typical experimental designs, which can be used both within the package itself but also in other Bioconductor packages. The implementation of differential expression detection methods has been parallelised leading to significant decreases in processing time on a range of computer architectures. puma incorporates the first R implementation of an uncertainty propagation version of principal component analysis, and an implementation of a clustering method based on uncertainty propagation. All of these techniques are brought together in a single, easy-to-use package with clear, task-based documentation.ConclusionFor the first time, the puma package makes a suite of uncertainty propagation methods available to a general audience. These methods can be used to improve results from more traditional analyses of microarray data. puma also offers improvements in terms of scope and speed of execution over previously available methods. puma is recommended for anyone working with the Affymetrix GeneChip platform for gene expression analysis and can also be applied more generally.

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Background: Clustering is an important analysis performed on microarray gene expression data since it groups genes which have similar expression patterns and enables the exploration of unknown gene functions. Microarray experiments are associated with many sources of experimental and biological variation and the resulting gene expression data are therefore very noisy. Many heuristic and model-based clustering approaches have been developed to cluster this noisy data. However, few of them include consideration of probe-level measurement error which provides rich information about technical variability.