Properties of Ammonium Nitrate based fertilisers

The text is divided into three parts; Properties, Application and Safety of Ammonium Nitrate (AN) based fertilisers. In Properties, the structures and phase transitions of ammonium and potassium nitrate are reviewed. The consequences of phase transitions affect the proper use of fertilisers. Therefore the products must be stabilised against the volume changes and consequent loss of bulk density and hardness, formation of dust and finally caking of fertilisers. The effect of different stabilisers is discussed. Magnesium nitrate, ammonium sulphate and potassium nitrate are presented as a good compromise. In the Application part, the solid solutions in the systems (K+,NH4+)NO3- and (NH4+,K+)(Cl-,NO3-) are presented based on studies made with DSC and XRD. As there are clear limits for solute content in the solvent lattice, a number of disproportionation transitions exist in these process phases, e.g., N3 (solid solution isomorphous to NH4NO3-III) disproportionates to phases K3 (solid solution isomorphous to KNO3-III) and K2 (solid solution isomorphous to KNO3-II). In the crystallisation experiments, the formation of K3 depends upon temperature and the ratio K/(K+NH4). The formation of phases K3, N3, and K2 was modelled as a function of temperature and the mole ratios. In introducing chlorides, two distinct maxima for K3 were found. Confirmed with commercial potash samples, the variables affecting the reaction of potassium chloride with AN are the particle size, time, temperature, moisture content and amount of organic coating. The phase diagrams obtained by crystallisation studies were compared with a number of commercial fertilisers and, with regard to phase composition, the temperature and moisture content are critical when the formation and stability of solid solutions are considered. The temperature where the AN-based fertiliser is solidified affects the amount of compounds crystallised at that point. In addition, the temperature where the final moisture is evaporated affects the amount and type of solid solution formed at this temperature. The amount of remaining moisture affects the stability of the K3 phase. The K3 phase is dissolved by the moisture and recrystallised into the quantities of K3, which is stable at the temperature where the sample is kept. The remaining moisture should not be free; it should be bound as water in the final product. The temperatures during storage also affect the quantity of K3 phase. As presented in the figures, K3 phase is not stable at temperatu¬res below 30 °C. If the temperature is about 40 °C, the K3 phase can be formed due to the remaining moisture. In the Safety part, self-sustaining decomposition (SSD), oxidising and energetic properties of fertilisers are discussed. Based on the consequence analysis of SSD, early detection of decomposition in warehouses and proper temperature control in the manufacturing process is important. SSD and oxidising properties were found in compositions where K3 exists. It is assumed that potassium nitrate forms a solid matrix in which AN can decompose. The oxidising properties can be affected by the form of the product. Granular products are inherently less oxidising. Finally energetic properties are reviewed. The composition of the fertiliser has an importance based on theoretical calculations supported by experimental studies. Materials such as carbonates and sulphates act as diluents. An excess of ammonium ions acts as a fuel although this is debatable. Based on the experimental work, the physical properties have a major importance over the composition. A high bulk density is of key importance for detonation resistance.

Tailoring polyolefin properties by metallocene catalysis

This doctoral thesis deals with the syntheses of olefin homo- and copolymers using different kind of metallocene catalyst. Ethene, propene, 1-hexene, 1-hexadecene, vinylcyclohexane and phenylnorbornene were homo- or copolymerized with the catalysts. The unbridged benzyl substituted zirconium dichloride catalysts (1-4), ansa- bridged acenaphtyl substituted zirconium dichloride catalysts, ( 5, 6), rac- and meso-ethylene-bis(1-indenyl)zirconium dichlorides, (rac- and meso-8), rac-ethylene-bis(1-indenyl)hafnium dichloride, ( 12), bis(9-fluorenyl)hafnium dichloride (14 ) enantiomerically pure (R)- phenylethyl[(9-fluorenyl-1-indenyl)]ZrCl2, (11), 14 and asymmetric dimethylsilyl[(3-benzylindenyl-(2-methylbenzen[e]indenyl)] zirconium dichloride, (13), were prepared in our laboratory. Dimethylsilyl-bis(1-indenyl)zirconium dichloride, (9), isopropylidene(9-fluorenyl-cyclopentadienyl)zirconium dichloride, (10), and were obtained commercially. The solid-state structures of the catalysts rac- and meso-1 were determined by X-ray crystallography. Computational methods were used for the structure optimization of the catalyst rac- and meso-1 in order to compare the theoretical calculations with the experimental results. Polymerization experiments were conducted in a highly purified autoclave system using low pressures (< 5 bar) of gaseous monomers. The experiments were designed to attain the optimal catalytic activity and a uniform copolymer composition. The prepared homo- and copolymers were characterized by the gel permeation chromatography, GPC, differential scanning calorimetry, DSC, nuclear magnetic resonance, NMR, and Fourier transform infrared spectrometry, FTIR . Molar mass (Mw, Mn), molar mass distribution (Mw/Mn), tacticity, comonomer content, melting temperature, glass transition temperature, and end group structures and content were determined. A special attention was paid on the correlation of the polymer properties with the catalyst structures and polymerization conditions. An intramolecular phenyl coordination was found in phenyl substituted benzyl zirconocenes 1-3 explaining the decreased activity of the catalysts. Novel copolymers poly(propene-co-phenylnorbornene) and poly(propene co-vinylcyclohexane), were synthesized and high molar mass poly(ethene-co-1-hexene) and poly(ethene-co-1-hexadecene) copolymers with elastic properties were prepared. Activation of a hafnocene catalyst was studied with UV-Vis spectrometry and activation process for the synthesis of ultra high molar mass poly(1-hexene) was found out.

On probability-based inference under data missing by design

Whether a statistician wants to complement a probability model for observed data with a prior distribution and carry out fully probabilistic inference, or base the inference only on the likelihood function, may be a fundamental question in theory, but in practice it may well be of less importance if the likelihood contains much more information than the prior. Maximum likelihood inference can be justified as a Gaussian approximation at the posterior mode, using flat priors. However, in situations where parametric assumptions in standard statistical models would be too rigid, more flexible model formulation, combined with fully probabilistic inference, can be achieved using hierarchical Bayesian parametrization. This work includes five articles, all of which apply probability modeling under various problems involving incomplete observation. Three of the papers apply maximum likelihood estimation and two of them hierarchical Bayesian modeling. Because maximum likelihood may be presented as a special case of Bayesian inference, but not the other way round, in the introductory part of this work we present a framework for probability-based inference using only Bayesian concepts. We also re-derive some results presented in the original articles using the toolbox equipped herein, to show that they are also justifiable under this more general framework. Here the assumption of exchangeability and de Finetti's representation theorem are applied repeatedly for justifying the use of standard parametric probability models with conditionally independent likelihood contributions. It is argued that this same reasoning can be applied also under sampling from a finite population. The main emphasis here is in probability-based inference under incomplete observation due to study design. This is illustrated using a generic two-phase cohort sampling design as an example. The alternative approaches presented for analysis of such a design are full likelihood, which utilizes all observed information, and conditional likelihood, which is restricted to a completely observed set, conditioning on the rule that generated that set. Conditional likelihood inference is also applied for a joint analysis of prevalence and incidence data, a situation subject to both left censoring and left truncation. Other topics covered are model uncertainty and causal inference using posterior predictive distributions. We formulate a non-parametric monotonic regression model for one or more covariates and a Bayesian estimation procedure, and apply the model in the context of optimal sequential treatment regimes, demonstrating that inference based on posterior predictive distributions is feasible also in this case.