Characterisation and processing of amorphous binary mixtures with low glass transition temperature

The number of drug substances in formulation development in the pharmaceutical industry is increasing. Some of these are amorphous drugs and have glass transition below ambient temperature, and thus they are usually difficult to formulate and handle. One reason for this is the reduced viscosity, related to the stickiness of the drug, that makes them complicated to handle in unit operations. Thus, the aim in this thesis was to develop a new processing method for a sticky amorphous model material. Furthermore, model materials were characterised before and after formulation, using several characterisation methods, to understand more precisely the prerequisites for physical stability of amorphous state against crystallisation. The model materials used were monoclinic paracetamol and citric acid anhydrate. Amorphous materials were prepared by melt quenching or by ethanol evaporation methods. The melt blends were found to have slightly higher viscosity than the ethanol evaporated materials. However, melt produced materials crystallised more easily upon consecutive shearing than ethanol evaporated materials. The only material that did not crystallise during shearing was a 50/50 (w/w, %) blend regardless of the preparation method and it was physically stable at least two years in dry conditions. Shearing at varying temperatures was established to measure the physical stability of amorphous materials in processing and storage conditions. The actual physical stability of the blends was better than the pure amorphous materials at ambient temperature. Molecular mobility was not related to the physical stability of the amorphous blends, observed as crystallisation. Molecular mobility of the 50/50 blend derived from a spectral linewidth as a function of temperature using solid state NMR correlated better with the molecular mobility derived from a rheometer than that of differential scanning calorimetry data. Based on the results obtained, the effect of molecular interactions, thermodynamic driving force and miscibility of the blends are discussed as the key factors to stabilise the blends. The stickiness was found to be affected glass transition and viscosity. Ultrasound extrusion and cutting were successfully tested to increase the processability of sticky material. Furthermore, it was found to be possible to process the physically stable 50/50 blend in a supercooled liquid state instead of a glassy state. The method was not found to accelerate the crystallisation. This may open up new possibilities to process amorphous materials that are otherwise impossible to manufacture into solid dosage forms.

Polymer Protected Gold Nanoparticles

Polymer protected gold nanoparticles have successfully been synthesized by both "grafting-from" and "grafting-to" techniques. The synthesis methods of the gold particles were systematically studied. Two chemically different homopolymers were used to protect gold particles: thermo-responsive poly(N-isopropylacrylamide), PNIPAM, and polystyrene, PS. Both polymers were synthesized by using a controlled/living radical polymerization process, reversible addition-fragmentation chain transfer (RAFT) polymerization, to obtain monodisperse polymers of various molar masses and carrying dithiobenzoate end groups. Hence, particles protected either with PNIPAM, PNIPAM-AuNPs, or with a mixture of two polymers, PNIPAM/PS-AuNPs (i.e., amphiphilic gold nanoparticles), were prepared. The particles contain monodisperse polymer shells, though the cores are somewhat polydisperse. Aqueous PNIPAM-AuNPs prepared using a "grafting-from" technique, show thermo-responsive properties derived from the tethered PNIPAM chains. For PNIPAM-AuNPs prepared using a "grafting-to" technique, two-phase transitions of PNIPAM were observed in the microcalorimetric studies of the aqueous solutions. The first transition with a sharp and narrow endothermic peak occurs at lower temperature, and the second one with a broader peak at higher temperature. In the first transition PNIPAM segments show much higher cooperativity than in the second one. The observations are tentatively rationalized by assuming that the PNIPAM brush can be subdivided into two zones, an inner and an outer one. In the inner zone, the PNIPAM segments are close to the gold surface, densely packed, less hydrated, and undergo the first transition. In the outer zone, on the other hand, the PNIPAM segments are looser and more hydrated, adopt a restricted random coil conformation, and show a phase transition, which is dependent on both particle concentration and the chemical nature of the end groups of the PNIPAM chains. Monolayers of the amphiphilic gold nanoparticles at the air-water interface show several characteristic regions upon compression in a Langmuir trough at room temperature. These can be attributed to the polymer conformational transitions from a pancake to a brush. Also, the compression isotherms show temperature dependence due to the thermo-responsive properties of the tethered PNIPAM chains. The films were successfully deposited on substrates by Langmuir-Blodgett technique. The sessile drop contact angle measurements conducted on both sides of the monolayer deposited at room temperature reveal two slightly different contact angles, that may indicate phase separation between the tethered PNIPAM and PS chains on the gold core. The optical properties of amphiphilic gold nanoparticles were studied both in situ at the air-water interface and on the deposited films. The in situ SPR band of the monolayer shows a blue shift with compression, while a red shift with the deposition cycle occurs in the deposited films. The blue shift is compression-induced and closely related to the conformational change of the tethered PNIPAM chains, which may cause a decrease in the polarity of the local environment of the gold cores. The red shift in the deposited films is due to a weak interparticle coupling between adjacent particles. Temperature effects on the SPR band in both cases were also investigated. In the in situ case, at a constant surface pressure, an increase in temperature leads to a red shift in the SPR, likely due to the shrinking of the tethered PNIPAM chains, as well as to a slight decrease of the distance between the adjacent particles resulting in an increase in the interparticle coupling. However, in the case of the deposited films, the SPR band red-shifts with the deposition cycles more at a high temperature than at a low temperature. This is because the compressibility of the polymer coated gold nanoparticles at a high temperature leads to a smaller interparticle distance, resulting in an increase of the interparticle coupling in the deposited multilayers.

Efficient search for statistically significant dependency rules in binary data

Analyzing statistical dependencies is a fundamental problem in all empirical science. Dependencies help us understand causes and effects, create new scientific theories, and invent cures to problems. Nowadays, large amounts of data is available, but efficient computational tools for analyzing the data are missing. In this research, we develop efficient algorithms for a commonly occurring search problem - searching for the statistically most significant dependency rules in binary data. We consider dependency rules of the form X->A or X->not A, where X is a set of positive-valued attributes and A is a single attribute. Such rules describe which factors either increase or decrease the probability of the consequent A. A classical example are genetic and environmental factors, which can either cause or prevent a disease. The emphasis in this research is that the discovered dependencies should be genuine - i.e. they should also hold in future data. This is an important distinction from the traditional association rules, which - in spite of their name and a similar appearance to dependency rules - do not necessarily represent statistical dependencies at all or represent only spurious connections, which occur by chance. Therefore, the principal objective is to search for the rules with statistical significance measures. Another important objective is to search for only non-redundant rules, which express the real causes of dependence, without any occasional extra factors. The extra factors do not add any new information on the dependence, but can only blur it and make it less accurate in future data. The problem is computationally very demanding, because the number of all possible rules increases exponentially with the number of attributes. In addition, neither the statistical dependency nor the statistical significance are monotonic properties, which means that the traditional pruning techniques do not work. As a solution, we first derive the mathematical basis for pruning the search space with any well-behaving statistical significance measures. The mathematical theory is complemented by a new algorithmic invention, which enables an efficient search without any heuristic restrictions. The resulting algorithm can be used to search for both positive and negative dependencies with any commonly used statistical measures, like Fisher's exact test, the chi-squared measure, mutual information, and z scores. According to our experiments, the algorithm is well-scalable, especially with Fisher's exact test. It can easily handle even the densest data sets with 10000-20000 attributes. Still, the results are globally optimal, which is a remarkable improvement over the existing solutions. In practice, this means that the user does not have to worry whether the dependencies hold in future data or if the data still contains better, but undiscovered dependencies.

Studies on diamond-like carbon and novel diamond-like carbon polymer hybrid coatings deposited with filtered pulsed arc discharge method

The main obstacle for the application of high quality diamond-like carbon (DLC) coatings has been the lack of adhesion to the substrate as the coating thickness is increased. The aim of this study was to improve the filtered pulsed arc discharge (FPAD) method. With this method it is possible to achieve high DLC coating thicknesses necessary for practical applications. The energy of the carbon ions was measured with an optoelectronic time-of-flight method. An in situ cathode polishing system used for stabilizing the process yield and the carbon ion energies is presented. Simultaneously the quality of the coatings can be controlled. To optimise the quality of the deposition process a simple, fast and inexpensive method using silicon wafers as test substrates was developed. This method was used for evaluating the suitability of a simplified arc-discharge set-up for the deposition of the adhesion layer of DLC coatings. A whole new group of materials discovered by our research group, the diamond-like carbon polymer hybrid (DLC-p-h) coatings, is also presented. The parent polymers used in these novel coatings were polydimethylsiloxane (PDMS) and polytetrafluoroethylene (PTFE). The energy of the plasma ions was found to increase when the anode-cathode distance and the arc voltage were increased. A constant deposition rate for continuous coating runs was obtained with an in situ cathode polishing system. The novel DLC-p-h coatings were found to be water and oil repellent and harder than any polymers. The lowest sliding angle ever measured from a solid surface, 0.15 ± 0.03°, was measured on a DLC-PDMS-h coating. In the FPAD system carbon ions can be accelerated to high energies (≈ 1 keV) necessary for the optimal adhesion (the substrate is broken in the adhesion and quality test) of ultra thick (up to 200 µm) DLC coatings by increasing the anode-cathode distance and using high voltages (up to 4 kV). An excellent adhesion can also be obtained with the simplified arc-discharge device. To maintain high process yield (5µm/h over a surface area of 150 cm2) and to stabilize the carbon ion energies and the high quality (sp3 fraction up to 85%) of the resulting coating, an in situ cathode polishing system must be used. DLC-PDMS-h coating is the superior candidate coating material for anti-soiling applications where also hardness is required.

Studies on Binary Time Series Models with Applications to Empirical Macroeconomics and Finance

This thesis studies binary time series models and their applications in empirical macroeconomics and finance. In addition to previously suggested models, new dynamic extensions are proposed to the static probit model commonly used in the previous literature. In particular, we are interested in probit models with an autoregressive model structure. In Chapter 2, the main objective is to compare the predictive performance of the static and dynamic probit models in forecasting the U.S. and German business cycle recession periods. Financial variables, such as interest rates and stock market returns, are used as predictive variables. The empirical results suggest that the recession periods are predictable and dynamic probit models, especially models with the autoregressive structure, outperform the static model. Chapter 3 proposes a Lagrange Multiplier (LM) test for the usefulness of the autoregressive structure of the probit model. The finite sample properties of the LM test are considered with simulation experiments. Results indicate that the two alternative LM test statistics have reasonable size and power in large samples. In small samples, a parametric bootstrap method is suggested to obtain approximately correct size. In Chapter 4, the predictive power of dynamic probit models in predicting the direction of stock market returns are examined. The novel idea is to use recession forecast (see Chapter 2) as a predictor of the stock return sign. The evidence suggests that the signs of the U.S. excess stock returns over the risk-free return are predictable both in and out of sample. The new "error correction" probit model yields the best forecasts and it also outperforms other predictive models, such as ARMAX models, in terms of statistical and economic goodness-of-fit measures. Chapter 5 generalizes the analysis of univariate models considered in Chapters 2 4 to the case of a bivariate model. A new bivariate autoregressive probit model is applied to predict the current state of the U.S. business cycle and growth rate cycle periods. Evidence of predictability of both cycle indicators is obtained and the bivariate model is found to outperform the univariate models in terms of predictive power.

Patterns in permuted binary matrices

Reorganizing a dataset so that its hidden structure can be observed is useful in any data analysis task. For example, detecting a regularity in a dataset helps us to interpret the data, compress the data, and explain the processes behind the data. We study datasets that come in the form of binary matrices (tables with 0s and 1s). Our goal is to develop automatic methods that bring out certain patterns by permuting the rows and columns. We concentrate on the following patterns in binary matrices: consecutive-ones (C1P), simultaneous consecutive-ones (SC1P), nestedness, k-nestedness, and bandedness. These patterns reflect specific types of interplay and variation between the rows and columns, such as continuity and hierarchies. Furthermore, their combinatorial properties are interlinked, which helps us to develop the theory of binary matrices and efficient algorithms. Indeed, we can detect all these patterns in a binary matrix efficiently, that is, in polynomial time in the size of the matrix. Since real-world datasets often contain noise and errors, we rarely witness perfect patterns. Therefore we also need to assess how far an input matrix is from a pattern: we count the number of flips (from 0s to 1s or vice versa) needed to bring out the perfect pattern in the matrix. Unfortunately, for most patterns it is an NP-complete problem to find the minimum distance to a matrix that has the perfect pattern, which means that the existence of a polynomial-time algorithm is unlikely. To find patterns in datasets with noise, we need methods that are noise-tolerant and work in practical time with large datasets. The theory of binary matrices gives rise to robust heuristics that have good performance with synthetic data and discover easily interpretable structures in real-world datasets: dialectical variation in the spoken Finnish language, division of European locations by the hierarchies found in mammal occurrences, and co-occuring groups in network data. In addition to determining the distance from a dataset to a pattern, we need to determine whether the pattern is significant or a mere occurrence of a random chance. To this end, we use significance testing: we deem a dataset significant if it appears exceptional when compared to datasets generated from a certain null hypothesis. After detecting a significant pattern in a dataset, it is up to domain experts to interpret the results in the terms of the application.

Interfacial Phenomena in Pharmaceutical Low Moisture Content Powder Processing

Powders are essential materials in the pharmaceutical industry, being involved in majority of all drug manufacturing. Powder flow and particle size are central particle properties addressed by means of particle engineering. The aim of the thesis was to gain knowledge on powder processing with restricted liquid addition, with a primary focus on particle coating and early granule growth. Furthermore, characterisation of this kind of processes was performed. A thin coating layer of hydroxypropyl methylcellulose was applied on individual particles of ibuprofen in a fluidised bed top-spray process. The polymeric coating improved the flow properties of the powder. The improvement was strongly related to relative humidity, which can be seen as an indicator of a change in surface hydrophilicity caused by the coating. The ibuprofen used in the present study had a d50 of 40 μm and thus belongs to the Geldart group C powders, which can be considered as challenging materials in top-spray coating processes. Ibuprofen was similarly coated using a novel ultrasound-assisted coating method. The results were in line with those obtained from powders coated in the fluidised bed process mentioned above. It was found that the ultrasound-assisted method was capable of coating single particles with a simple and robust setup. Granule growth in a fluidised bed process was inhibited by feeding the liquid in pulses. The results showed that the length of the pulsing cycles is of importance, and can be used to adjust granule growth. Moreover, pulsed liquid feed was found to be of greater significance to granule growth in high inlet air relative humidity. Liquid feed pulsing can thus be used as a tool in particle size targeting in fluidised bed processes and in compensating for changes in relative humidity of the inlet air. The nozzle function of a two-fluid external mixing pneumatic nozzle, typical for small scale pharmaceutical fluidised bed processes, was studied in situ in an ongoing fluidised bed process with particle tracking velocimetry. It was found that the liquid droplets undergo coalescence as they proceed away from the nozzle head. The coalescence was expected to increase droplet speed, which was confirmed in the study. The spray turbulence was studied, and the results showed turbulence caused by the event of atomisation and by the oppositely directed fluidising air. It was concluded that particle tracking velocimetry is a suitable tool for in situ spray characterisation. The light transmission through dense particulate systems was found to carry information on particle size and packing density as expected based on the theory of light scattering by solids. It was possible to differentiate binary blends consisting of components with differences in optical properties. Light transmission showed potential as a rapid, simple and inexpensive tool in characterisation of particulate systems giving information on changes in particle systems, which could be utilised in basic process diagnostics.