In order to decrease the global dependence on fossil fuels, high energy density, rechargeable batteries with high charge capacity are required for mobile applications and efficient utilization of intermittent sources of renewable energy. Metal-air batteries are promising due to their high theoretical energy density. In particular, the iron-air battery, with a maximum specific energy output of 764 W h kg−1Fe, represents a low cost possibility. This paper considers an iron-air battery with nanocomposite electrodes, which achieves an energy density of 453 W h kg−1Fe and a maximum charge capacity of 814 mA h g−1Fe when cycled at a current density of 10 mA cm−2, with a cell voltage of 0.76 V. The cell was manufactured by 3D printing, allowing rapid modifications and improvements to be implemented before an optimized prototype can be manufactured using traditional computer numerical control machining.
An approach for estimating bias between 2 analytical methods at different clinical ranges is introduced in this article. The approach models replicated data obtained from the reference and the test method in terms of repeatability and trueness bias. The latter can be partitioned into constant and proportional bias. The approach is based on maximum likelihood estimation and can accommodate normal as well as Poisson and binomial distributions that apply to hematology applications and/or other laboratory methods that count particles per unit of volume and/or time. A full spectrum of statistical inference in the form of confidence intervals for each estimate as well as any statistical hypothesis testing is provided. At the same time these estimates can be practically interpreted and related to any clinical important range or decision point. We recommend this approach as an alternative to the National Committee for Clinical Laboratory Standards (NCCLS) EP9-A2 approach in cases where the application of the NCCLS standard is not appropriate.
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