Binder-Free, Thin-Film Ceramic-Coated Separators for Improved Safety of Lithium-Ion Batteries.

Separators play a crucial role in ensuring the safety of lithium-ion batteries (LIBs). Commercial polyolefin-based separators such as polyethylene (PE) still possess serious safety risks under abuse conditions because of their poor thermal stability. In this work, a novel type of binder-free, thin ceramic-coated separators with superior safety characteristics is demonstrated. A thin layer of alumina (Al2O3) is coated on commercial PE separators using the electron-beam physical vapor deposition (EB-PVD) technique. Scanning electron microscopy (SEM), contact angle, impedance spectroscopy, and adhesion test techniques were employed to evaluate structure-property correlations. When compared to commercial slurry-coated separators, the EB-PVD-coated separators display (i) higher thermal stability, (ii) stronger ceramic-polymer adhesion, and (iii) competitive electrochemical performance of full LIB cells. Thermal stability, in terms of improved shutdown and breakdown characteristics of the separator, was studied using the in situ impedance technique up to 190 °C. In addition, the improved adhesion of the ceramic layer deposited on the PE separator was studied following the tape adhesion strength test. We prove that the thin (binder-free) ceramic layer coated by EB-PVD is far more effective in improving separator safety than those made using the conventional thick slurry coating.

Benchmark Dose Analysis via Nonparametric Regression Modeling.

Estimation of benchmark doses (BMDs) in quantitative risk assessment traditionally is based upon parametric dose-response modeling. It is a well-known concern, however, that if the chosen parametric model is uncertain and/or misspecified, inaccurate and possibly unsafe low-dose inferences can result. We describe a nonparametric approach for estimating BMDs with quantal-response data based on an isotonic regression method, and also study use of corresponding, nonparametric, bootstrap-based confidence limits for the BMD. We explore the confidence limits' small-sample properties via a simulation study, and illustrate the calculations with an example from cancer risk assessment. It is seen that this nonparametric approach can provide a useful alternative for BMD estimation when faced with the problem of parametric model uncertainty.

RECENT PROGRESS IN THE NONPARAMETRIC ESTIMATION OF MONOTONE CURVES -WITH APPLICATIONS TO BIOASSAY AND ENVIRONMENTAL RISK ASSESSMENT.

Three recent nonparametric methodologies for estimating a monotone regression function F and its inverse F-1 are (1) the inverse kernel method DNP (Dette et al. (2005), Dette and Scheder (2010)), (2) the monotone spline (Kong and Eubank (2006)) and (3) the data adaptive method NAM (Bhattacharya and Lin (2010), (2011)), with roots in isotonic regression (Ayer et al. (1955), Bhattacharya and Kong (2007)). All three have asymptotically optimal error rates. In this article their finite sample performances are compared using extensive simulation from diverse models of interest, and by analysis of real data. Let there be m distinct values of the independent variable x among N observations y. The results show that if m is relatively small compared to N then generally the NAM performs best, while the DNP outperforms the other methods when m is O(N) unless there is a substantial clustering of the values of the independent variable x.

Nonparametric estimation of benchmark doses in environmental risk assessment.

An important statistical objective in environmental risk analysis is estimation of minimum exposure levels, called benchmark doses (BMDs), that induce a pre-specified benchmark response in a dose-response experiment. In such settings, representations of the risk are traditionally based on a parametric dose-response model. It is a well-known concern, however, that if the chosen parametric form is misspecified, inaccurate and possibly unsafe low-dose inferences can result. We apply a nonparametric approach for calculating benchmark doses, based on an isotonic regression method for dose-response estimation with quantal-response data (Bhattacharya and Kong, 2007). We determine the large-sample properties of the estimator, develop bootstrap-based confidence limits on the BMDs, and explore the confidence limits' small-sample properties via a short simulation study. An example from cancer risk assessment illustrates the calculations.

NONPARAMETRIC BENCHMARK ANALYSIS IN RISK ASSESSMENT: A COMPARATIVE STUDY BY SIMULATION AND DATA ANALYSIS.

We consider the finite sample performance of a new nonparametric method for bioassay and benchmark analysis in risk assessment, which averages isotonic MLEs based on disjoint subgroups of dosages, and whose asymptotic behavior is essentially optimal (Bhattacharya and Lin (2010)). It is compared with three other methods, including the leading kernel-based method, called DNP, due to Dette et al. (2005) and Dette and Scheder (2010). In simulation studies, the present method, termed NAM, outperforms the DNP in the majority of cases considered, although both methods generally do well. In small samples, NAM and DNP both outperform the MLE.

An adaptive nonparametric method in benchmark analysis for bioassay and environmental studies.

We present a novel nonparametric method for bioassay and benchmark analysis in risk assessment, which averages isotonic MLEs based on disjoint subgroups of dosages. The asymptotic theory for the methodology is derived, showing that the MISEs (mean integrated squared error) of the estimates of both the dose-response curve F and its inverse F(-1) achieve the optimal rate O(N(-4/5)). Also, we compute the asymptotic distribution of the estimate ζ~p of the effective dosage ζ(p) = F(-1) (p) which is shown to have an optimally small asymptotic variance.