Effect of precursor particle size and morphology on lithiation of Ni0.6Mn0.2Co0.2(OH)2

In this paper, Ni_0.6Mn_0.2Co_0.2(OH)₂ precursors with several different morphologies and particle sizes are mixed with Li₂CO₃ and heat treated for 5, 7.5 and 10 h. The effects of the precursor properties on the degree of lithiation, electrochemical properties and volumetric capacities of lithiated product are compared. Based on the characterization results, a small (3 μm), narrow span precursor can be lithiated in a short period of time (5 h) and has good initial discharge capacity (185 mA h g^??1) and capacity retention (93% for 55 cycles). In contrast, a large wide-span precursor requires over 10 h for full lithiation. A highly porous precursor can be lithiated faster than traditional large wide-span materials, and has low cation mixing and good crystallinity. However, the volumetric energy density of porous material is low after lithiation compared to the other tested materials. Capacity retention after washing correlated with crystallographic properties of the sample.

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Efficient Multisplitting Revisited: Optima-Preserving Elimination of Partition Candidates

We consider multisplitting of numerical value ranges, a task that is encountered as a discretization step preceding induction and also embedded into learning algorithms. We are interested in finding the partition that optimizes the value of a given attribute evaluation function. For most commonly used evaluation functions this task takes quadratic time in the number of potential cut points in the numerical range. Hence, it is a potential bottleneck in data mining algorithms.We present two techniques that speed up the optimal multisplitting task. The first one aims at discarding cut point candidates in a quick linear-time preprocessing scan before embarking on the actual search. We generalize the definition of boundary points by Fayyad and Irani to allow us to merge adjacent example blocks that have the same relative class distribution. We prove for several commonly used evaluation functions that this processing removes only suboptimal cut points. Hence, the algorithm does not lose optimality.Our second technique tackles the quadratic-time dynamic programming algorithm, which is the best schema for optimizing many well-known evaluation functions. We present a technique that dynamically—i.e., during the search—prunes partitions of prefixes of the sorted data from the search space of the algorithm. The method works for all convex and cumulative evaluation functions.Together the use of these two techniques speeds up the multisplitting process considerably. Compared to the baseline dynamic programming algorithm the speed-up is around 50 percent on the average and up to 90 percent in some cases. We conclude that optimal multisplitting is fully feasible on all benchmark data sets we have encountered.