Einfluss der Verarbeitungstechnologie und Werkstoffzusammensetzung auf die Struktur-Eigenschafts-Beziehungen von thermoplastischen Nanoverbundwerkstoffen

Die Einarbeitung von nanoskaligen Füllstoffen zur Steigerung von polymeren Eigenschaftsprofilen ist sehr viel versprechend und stößt daher heutzutage sowohl in der Forschung als auch in der Industrie auf großes Interesse. Bedingt durch ausgeprägte Oberflächen und hohe Anziehungskräfte, liegen Nanopartikel allerdings nicht singulär sondern als Partikelanhäufungen, so genannten Agglomeraten oder Aggregaten, vor. Zur Erzielung der gewünschten Materialverbesserungen gilt es, diese aufzuspalten und homogen in der polymeren Matrix zu verteilen. Bei thermoplastischen Kunststoffen ist die gleichläufige Doppelschneckenextrusion eines der gängigsten Verfahren zur Einarbeitung von Additiven und Füllstoffen. Aus diesem Grund war es Ziel dieser Arbeit, mittels dieses Verfahrens verbesserte Verbundwerkstoffe mit Polyamid 66- und Polyetheretherketon-Matrix, durch Einarbeitung von nanoskaligem Titandioxid (15 und 300 nm), zu generieren. In einem ersten Schritt wurden die verfahrenstechnischen Parameter, wie Drehzahl und Durchsatz, sowie die Prozessführung und damit deren Einfluss auf die Materialeigenschaften beleuchtet. Der spezifische Energieeintrag ist ausschlaggebend zur Deagglomeration der Nanopartikel. Dieser zeigte leichte Abhängigkeiten von der Drehzahl und dem Durchsatz und verursachte bei der Einarbeitung der Partikel keine wesentlichen Unterschiede in der Aufspaltung der Partikel sowie gar keine in den resultierenden mechanischen Eigenschaften. Die Prozessführung wurde unterteilt in Mehrfach- und Einfachextrusion. Die Herstellung eines hochgefüllten Masterbatches, dessen mehrfaches Extrudieren und anschließendes Verdünnen, führte zu einer sehr guten Deagglomeration und stark verbesserten Materialeigenschaften. Mittels Simulation des Extrusionsprozesses konnte festgestellt werden, dass das Vorhandensein von ungeschmolzenem Granulat in der Verfahrenszone zu einer Schmelze/Nanopartikel/ Feststoffreibung führt, die die Ursache für eine sehr gute Aufspaltung der Partikel zu sein scheint. Durch Modifikation des Extrusionsprozesses erreichte die Einfachextrusion annähernd den Grad an Deagglomeration bei Mehrfachextrusion, wobei die Materialien bei letzterem Verfahren die besten Eigenschaftsprofile aufwiesen. In einem zweiten Schritt wurde ein Vergleich der Einflüsse von unterschiedlichen Partikelgrößen und –gehalten auf die polymeren Matrizes vollzogen. Die 15 nm Partikel zeigten signifikant bessere mechanische Ergebnisse auf als die 300 nm Partikel, und die Wirkungsweise des 15 nm Partikels auf Polyetheretherketon war stärker als auf Polyamid 66. Es konnten Steigerungen in Steifigkeit, Festigkeit und Zähigkeit erzielt werden. Rasterelektronenmikroskopische Aufnahmen bestätigten diese Ergebnisse. Eine Berechnung der Plan-Selbstkosten von einem Kilogramm PEEK-Nanoverbundwerkstoff im Vergleich zu einem Kilogramm unverstärktem PEEK verdeutlichte, dass ein Material kreiert wurde, welches deutlich verbesserte Eigenschaften bei gleichem Preis aufweist. Zusammenfassend konnte in dieser Arbeit ein tieferes Verständnis des Extrusionsvorganges zur Herstellung von kostengünstigen und verbesserten Thermoplasten durch das Einbringen von Nanopartikeln gewonnen werden

Identification of Diterpenoids From Icacina Oliviformis

Icacina oliviformis (Poir.) J. Raynal (Icacinaceae) is an indigenous plant commonly found in west and central Africa. It has been traditionally used as a source of starch especially during the famine periods. Medicinally the plant is considered a panacea; there is long history of the leaves or roots being used by local herbalists for the treatment of a variety of common diseases such as fever and malaria. Despite the extensive use of Icacina oliviformis by the local tribes, most of early studies on the plant were focused on its nutritional properties, and biological evaluations were all conducted with extracts rather than pure compounds isolated from the plant. Only limited research has been done on its chemical constituents, leading to the identification of several diterpenes and sterol glycosides. In this first systematic phytochemical study of diterpenes in Icacina oliviformis. A total of eleven secondary metabolites were isolated and identified, including three new structures, viz., icacinlactone M, icacinlactone H 2-O-β-D-glucopyranoside, and icacinlactone N 3-O-β-D-glucopyranoside. Among the known structures, icacinlactone A, icacinlactone B, icacinlatone H, 12-hydroxyicacinlatone A, 14α-methoxyhumirianthol, annonalide and acrenol (isolated as an acetone adduct), were reported from I. oliviformis for the first time, whereas icacinol has been found in this plant in other studies. This is also the first report of pimarane glycosides in genus Icacina. Icacinol, 14α-methoxyhumirianthol and annonalide displayed moderate cytotoxic activity in a panel of human cancer cell lines. These phytochemical and biological findings have expanded our knowledge of this under-studied medicinal plant species

Estimating relationships and relatedness from dense genome-wide data

Relationship and relatedness estimation from genetic markers is relevant to many areas, including genealogical research, genetic counselling, forensics, linkage analysis and association analyses in genetic epidemiology. Traditionally unlinked genetic markers (microsatellites) are used. But the problems which can be solved by such markers are limited. Linked genetic markers are not only available in much larger numbers, but also provide extra information which is not available from unlinked markers. It is desirable to exploit the increasing availability of dense genome-wide single nucleotide polymorphisms (SNP) data for estimating relationships and relatedness. While Method of Moments (MoM) methods and other non-pedigree approaches only give a degree of pairwise relatedness, a pedigree likelihood approach can distinguish exact relationships. The pedigree likelihood approach also has advantages in that extra individuals can be considered jointly and extra data such as Y-chromosomal and mitochondrial SNPs can be incorporated with autosomal SNPs easily. In this thesis I firstly confirm that the increase in information obtained from large sets of linked markers substantially increases the number of problems that can be solved with pedigree approach. Furthermore, when two distant relatives do share genome segments through identity by descent (IBD), we usually have greater power to distinguish more distant relatives from unrelated pairs than was previously believed. Data on extra individuals always improve discriminatory power, but the position of the extra individuals in the pedigree dictates the extent of this increase of power. Linkage Disequilibrium (LD) is an issue for pedigree likelihood approach and it needs to be dealt with. MoM methods are easy to use and are generally robust to the effect of LD, but they are only accurate for relatives up to second cousins. I propose using pedigree likelihood approach to estimate pairwise relatedness and find we can greatly improve the accuracy in detecting distant relatives

Generation of Chemical Concentration Gradients in Mobile Droplet Arrays via Fragmentation of Long Immiscible Diluting Plugs

We report a one-step passive microfluidic technique to generate arrays of moving droplets containing variation of chemical concentration between individual drops. We find that a concentration gradient can be established in a long diluting plug by on-chip dilution and extraction of samples via orthogonal coalescence of the plug with a static array of sample drops. The diluting plug containing the gradient is subsequently fragmented by a droplet generator. We show that the technique is flexible, as the dilution range can be tuned by a variety of control parameters including the carrier fluid flow rate, volume of diluting plugs, and stationary drops. We also find that the concentration gradients have a fine resolution and are reproducible to within 2% relative standard deviation. As one demonstrative application, we show the suitability of the technique for generating a dose-response curve for an enzyme inhibition assay. Because of the ability to inject multiple plugs, our technique has the potential for unlimited as well as sequential dilution of a series of substrates. Thus, our method could be valuable as a high-throughput and high-resolution screening tool for assays that require interrogation of the response of one or more target species to numerous distinct chemical concentrations

Generation of Chemical Concentration Gradients in Mobile Droplet Arrays via Fragmentation of Long Immiscible Diluting Plugs

We report a one-step passive microfluidic technique to generate arrays of moving droplets containing variation of chemical concentration between individual drops. We find that a concentration gradient can be established in a long diluting plug by on-chip dilution and extraction of samples via orthogonal coalescence of the plug with a static array of sample drops. The diluting plug containing the gradient is subsequently fragmented by a droplet generator. We show that the technique is flexible, as the dilution range can be tuned by a variety of control parameters including the carrier fluid flow rate, volume of diluting plugs, and stationary drops. We also find that the concentration gradients have a fine resolution and are reproducible to within 2% relative standard deviation. As one demonstrative application, we show the suitability of the technique for generating a dose-response curve for an enzyme inhibition assay. Because of the ability to inject multiple plugs, our technique has the potential for unlimited as well as sequential dilution of a series of substrates. Thus, our method could be valuable as a high-throughput and high-resolution screening tool for assays that require interrogation of the response of one or more target species to numerous distinct chemical concentrations

Generation of Chemical Concentration Gradients in Mobile Droplet Arrays via Fragmentation of Long Immiscible Diluting Plugs

We report a one-step passive microfluidic technique to generate arrays of moving droplets containing variation of chemical concentration between individual drops. We find that a concentration gradient can be established in a long diluting plug by on-chip dilution and extraction of samples via orthogonal coalescence of the plug with a static array of sample drops. The diluting plug containing the gradient is subsequently fragmented by a droplet generator. We show that the technique is flexible, as the dilution range can be tuned by a variety of control parameters including the carrier fluid flow rate, volume of diluting plugs, and stationary drops. We also find that the concentration gradients have a fine resolution and are reproducible to within 2% relative standard deviation. As one demonstrative application, we show the suitability of the technique for generating a dose-response curve for an enzyme inhibition assay. Because of the ability to inject multiple plugs, our technique has the potential for unlimited as well as sequential dilution of a series of substrates. Thus, our method could be valuable as a high-throughput and high-resolution screening tool for assays that require interrogation of the response of one or more target species to numerous distinct chemical concentrations

The AI values change according to the standard deviation of fiber orientations in computer-generated collagen networks.

<p>The accuracy of orientation detection was calculated by determining the average value of the maximum CDF difference of 15 computer-generated images for each standard deviation condition. The error of the algorithm was quantified by the average maximum absolute difference between the theoretical and estimated CDF values for 15 images. The error range is obtained by the standard deviation of maximum CDF difference within the 15 images. Computer generated images of increasing fiber fraction were used until errors > 10% were detected when AI < 0.94. The algorithm more accurately calculates ‘fiber’ orientation when the ‘fibers’ are less aligned. The average error of the algorithm (4.32%) in the range of AI ~ [0,0.94] leads to an average error in AI ~ 0.029. The larger errors when AI ~ [0.94,1] may be due to fiber overlap of the highly aligned fibers.</p><p>* The standard deviations of the estimated AI are at most 0.02 within 15 samples.</p><p>The AI values change according to the standard deviation of fiber orientations in computer-generated collagen networks.</p

Computer-generated fiber images validate the orientation detection algorithm.

<p>The computer-generated random fiber network (a) mimicked the actual acellular collagen gel fiber network at 2 mg/ml (b). The scale bars are 20 <i>μm</i>. Histograms of the theoretical (c) and the algorithm determined (d) orientation values of the computer-generated fiber network (a) were compared using KS statistics.</p

The spheroid embedded collagen gels at 2 mg/ml had an apparent peak in pixel-wise orientation distributions.

<p>Images of acellular (a) and spheroid embedded (b) 2 mg/ml collagen gels were analyzed using the orientation detection algorithm. The scale bars are 20 <i>μm</i>. The orientation histogram of the acellular collagen gel (c) appeared to have a random distribution (AI = 0.031) while the orientation histogram of the spheroid embedded collagen gel (d) appeared to have an apparent mode orientation (AI = 0.416).</p

Alignment analysis of collagen gels at 3 mg/ml and comparison with 2 mg/ml.

<p>Images of acellular (a) and spheroid embedded (b) 3 mg/ml collagen gels were analyzed using the orientation detection algorithm. The scale bars are 20 <i>μm</i>. AI values were determined from the histograms of acellular collagen gels (c), AI = 0.086, and spheroid embedded collagen gels (d), AI = 0.357. (e) Among all the experimental collagen gel images, the AIs of acellular collagen gels are 0.096±0.027 at 2 mg/ml, 0.115±0.031 at 3 mg/ml, and 0.127±0.025 at 4 mg/ml. The AIs of 2mg/ml and 3mg/ml spheroid embedded collagen gel are 0.386±0.027 and 0.346±0.079. The error bars are the standard deviation calculated among all images under the same experimental conditions. They are all significantly different with each other by the Welch two sample t-test.</p