A generalized Gaussian process model for computer experiments with binary time series

Non-Gaussian observations such as binary responses are common in some computer experiments. Motivated by the analysis of a class of cell adhesion experiments, we introduce a generalized Gaussian process model for binary responses, which shares some common features with standard GP models. In addition, the proposed model incorporates a flexible mean function that can capture different types of time series structures. Asymptotic properties of the estimators are derived, and an optimal predictor as well as its predictive distribution are constructed. Their performance is examined via two simulation studies. The methodology is applied to study computer simulations for cell adhesion experiments. The fitted model reveals important biological information in repeated cell bindings, which is not directly observable in lab experiments.Comment: 49 pages, 4 figure

Traceable GISAXS measurements for pitch determination of a 25 nm self-assembled polymer grating

The feature sizes of only a few nanometers in modern nanotechnology and next-generation microelectronics continually increase the demand for suitable nanometrology tools. Grazing incidence small-angle X-ray scattering (GISAXS) is a versatile technique to measure lateral and vertical sizes in the nm-range, but the traceability of the obtained parameters, which is a prerequisite for any metrological measurement, has not been demonstrated so far. In this work, the first traceable GISAXS measurements, demonstrated with a self-assembled block copolymer grating structure with a nominal pitch of 25 nm, are reported. The different uncertainty contributions to the obtained pitch value of 24.83(9) nm are discussed individually. The main uncertainty contribution results from the sample-detector distance and the pixel size measurement, whereas the intrinsic asymmetry of the scattering features is of minor relevance for the investigated grating structure. The uncertainty analysis provides a basis for the evaluation of the uncertainty of GISAXS data in a more general context, for example in numerical data modeling.Comment: 9 pages, 6 figures; submitted to Journal of Applied Crystallograph

Preparation of Biochar from Different Biomasses and Their Application in the Li-S Battery

通过可再生生物质制备的生物炭具有成本低、环保和资源可再生的优势。本研究以分布广泛的稻谷壳、芒草、杉木和柚子皮等生物质为原料,制备了4种不同类型生; 物炭,然后研究了其作为锂-硫电池硫/碳正极的载体的性能。研究表明由稻谷壳制备的硫/生物炭正极材料表现出最高的比容量和最优的循环稳定性。为了进一步; 改善其电性能,以SiO_2溶胶为模板制备了具有高孔隙率的稻谷壳生物炭,其多孔结构可有效抑制多硫化物的溶解。由此得到的硫/生物炭(硫含量为60%; (w,质量分数))材料中的硫以无定型态均匀地分散在碳载体中。该材料表现出更优异的电化学性能:在0.2C(1C= 1675; mA·g~(-1))倍率下,首周放电容量为1534.1 mAh·g~(-1), 循环100周后仍可保持在783.7; mAh·g~(-1);倍率性能测试中,在2.0C倍率下,材料的可逆容量为485.3 mAh·g~(-1)。Biochar derived from reproducible massive biomasses presents the advantages of low cost and renewable resources. In this work aiming to solve the existing problems of the lithium-sulfur battery, sulfur@biochar (S@biochar) composite cathode materials with high capacity and good cycle performance were developed. Specifically, four kinds of biochar prepared from rice husk, miscanthus, fir, and pomelo peel were used as host matrices for the Li-S battery. Among them, the S@biochar derived from rice husk delivered the highest specific capacity and the best cycle stability according to electrochemical tests. To further optimize its performance, we prepared a highly porous rice husk derived biochar (HPRH-biochar) using silica gel as the template. The S@HPRH-biochar composite (60% (w, mass fraction) S) enables the homogeneous dispersion of amorphous sulfur in the carbon matrix and its porous structure could effectively suppress the dissolution of the polysulfide. As a result, its electrochemical performance improved, achieving a high initial charge capacity of 1534.1 mAh.g(-1) and maintaining a high capacity of 738.7 mAh.g(-1) after 100 cycles at 0.2C (1C corresponds to a current density of 1675 mA.g(-1)). It also gives a capacity of 485.3 mAh.g(-1) at 2.0C in the rate capacity test.National Natural Science Foundation of China [21373008]; Fundamental; Research Funds for the Central Universities, China [20720160124