Melamine/epichlorohydrin prepolymers: syntheses and characterization

The basic catalysis of melamine with epichlorohydrin gives prepolymers that can be used in the preparation of energetic materials. In this work, sodium hydroxide and triethylamine were used as catalysts and ethyleneglycol as initiator. Different reaction conditions were tested and the characterization of the products was carried out by IR and NMR spectroscopy and MS spectrometry, hydroxyl groups content, vapour pressure osmometry and elemental and thermal analysis. Epichlorohydrin reacts with the amine groups of melamine and forms lateral chains with hydroxyl and epoxide end groups, which can be used for curing purposes. The two catalysts lead to similar products, confirmed both by the structure and number of the lateral chains. The melamine/epichlorohydrin ratio was found important for the structure of the final compounds. Chlorine atoms leave the molecules during reaction due to basic catalysis. In the light of the use of the prepolymers in energetic materials, the presence of the 1,3,5-s-triazine ring and the lateral chains with end groups curable by e.g. isocyanates was accomplished with success. However, the loss of chlorine atoms limits to a certain extent their possible substitution by energetic groups.http://www.sciencedirect.com/science/article/B6TXW-4F9247B-5/1/7bee9df489c5d6ca2f506eeeb1d5708

The Bayesian methodology of Sir Harold Jeffreys as a practical alternative to the p value hypothesis test

Contains fulltext : 226717.pdf (publisher's version ) (Closed access)Despite an ongoing stream of lamentations, many empirical disciplines still treat the p value as the sole arbiter to separate the scientific wheat from the chaff. The continued reign of the p value is arguably due in part to a perceived lack of workable alternatives. In order to be workable, any alternative methodology must be (1) relevant: it has to address the practitioners' research question, which - for better or for worse- most often concerns the test of a hypothesis, and less often concerns the estimation of a parameter; (2) available: it must have a concrete implementation for practitioners' statistical workhorses such as the t test, regression, and ANOVA; and (3) easy to use: methods that demand practitioners switch to the theoreticians' programming tools will face an uphill struggle for adoption. The above desiderata are fulfilled by Harold Jeffreys's Bayes factor methodology as implemented in the open-source software JASP. We explain Jeffreys's methodology and showcase its practical relevance with two examples.9 p

A tutorial on conducting and interpreting a Bayesian ANOVA in JASP

Analysis of variance (ANOVA) is the standard procedure for statistical inference in factorial designs. Typically, ANOVAs are executed using frequentist statistics, where p-values determine statistical significance in an all-or-none fashion. In recent years, the Bayesian approach to statistics is increasingly viewed as a legitimate alternative to the p-value. However, the broad adoption of Bayesian statistics - and Bayesian ANOVA in particular - is frustrated by the fact that Bayesian concepts are rarely taught in applied statistics courses. Consequently, practitioners may be unsure how to conduct a Bayesian ANOVA and interpret the results. Here we provide a guide for executing and interpreting a Bayesian ANOVA with JASP, an open-source statistical software program with a graphical user interface. We explain the key concepts of the Bayesian ANOVA using two empirical examples

The JASP guidelines for conducting and reporting a Bayesian analysis

Despite the increasing popularity of Bayesian inference in empirical research, few practical guidelines provide detailed recommendations for how to apply Bayesian procedures and interpret the results. Here we offer specific guidelines for four different stages of Bayesian statistical reasoning in a research setting: planning the analysis, executing the analysis, interpreting the results, and reporting the results. The guidelines for each stage are illustrated with a running example. Although the guidelines are geared towards analyses performed with the open-source statistical software JASP, most guidelines extend to Bayesian inference in general