Synthesis,characterization, and mechanical properties of an injection moldable polyimide based on 1,4-diaminobutane

A partially aliphatic polyimide was synthesized in a two-step process in N-methylpyrrolidone (NMP) from 3,3′,4,4′-benzophenonetetracarboxylic dianhydride (BTDA) and 1,4-diaminobutane (DAB). Weight average molecular weights ranging from 35,000 to over 160,000 were obtained by adding different amounts of the chain stopper phthalic anhydride, or by using a slight excess of BTDA. Rheological measurements, supported by a size exclusion chromatography (SEC) analysis before and after injection molding, point to branching or even partial crosslinking during molding of poly(BTDA-DAB) if reactive amine endgroups are present. By reacting these amine endgroups with benzoyl chloride, branching is limited and a more Newtonian flow is observed. Both non-endcapped and endcapped reactor powders are ∼60% crystalline (Tg = 170 to 175°C, Tm = 290 to 300°C), but according to wide and small angle X-ray scattering (WAXS and SAXS) studies on test bars, both the more linear polyimides and their branched counterparts are fully amorphous after injection molding. Nevertheless, poly(BTDA-DAB) shows interesting mechanical properties like a bending modulus of ∼3.9 GPa, a tensile strength of ∼130 MPa, a tensile elongation at break of ∼13%, and a notched Izod impact of ∼4 kJ/m².     

Visualization of single-wall carbon nanotube (SWNT) networks in conductive polystyrene nanocomposites by charge contrast imaging

The morphology of conductive nanocomposites consisting of low concentration of single-wall carbon nanotubes (SWNT) and polystyrene (PS) has been studied using atomic force microscopy (AFM), transmission electron microscopy (TEM) and, in particular, scanning electron microscopy (SEM). Application of charge contrast imaging in SEM allows visualization of the overall SWNT dispersion within the polymer matrix as well as the identification of individual or bundled SWNTs at high resolution. The contrast mechanism involved will be discussed. In conductive nanocomposites the SWNTs are homogeneously dispersed within the polymer matrix and form a network. Beside fairly straight SWNTs, strongly bended SWNTs have been observed. However, for samples with SWNT concentrations below the percolation threshold, the common overall charging behavior of an insulating material is observed preventing the detailed morphological investigation of the sample.     

A sparse nearest mean classifier for high dimensional multi-class problems

The analysis of small datasets in high dimensional spaces is inherently difficult. For two-class classification problems there are a few methods that are able to face the so-called curse of dimensionality. However, for multi-class sparsely sampled datasets there are hardly any specific methods. In this paper, we propose four multi-class classifier alternatives that effectively deal with this type of data. Moreover, these methods implicitly select a feature subset optimized for class separation. Accordingly, they are especially interesting for domains where an explanation of the problem in terms of the original features is desired.In the experiments, we applied the proposed methods to an MDMA powders dataset, where the problem was to recognize the production process. It turns out that the proposed multi-class classifiers perform well, while the few utilized features correspond to known MDMA synthesis ingredients. In addition, to show the general applicability of the methods, we applied them to several other sparse datasets, ranging from bioinformatics to chemometrics datasets having as few as tens of samples in tens to even thousands of dimensions and three to four classes. The proposed methods had the best average performance, while very few dimensions were effectively utilized.     

LESS: a model-based classifier for sparse subspaces

In this paper, we specifically focus on high-dimensional data sets for which the number of dimensions is an order of magnitude higher than the number of objects. From a classifier design standpoint, such small sample size problems have some interesting challenges. The first challenge is to find, from all hyperplanes that separate the classes, a separating hyperplane which generalizes well for future data. A second important task is to determine which features are required to distinguish the classes. To attack these problems, we propose the LESS (Lowest Error in a Sparse Subspace) classifier that efficiently finds linear discriminants in a sparse subspace. In contrast with most classifiers for high-dimensional data sets, the LESS classifier incorporates a (simple) data model. Further, by means of a regularization parameter, the classifier establishes a suitable trade-off between subspace sparseness and classification accuracy. In the experiments, we show how LESS performs on several high-dimensional data sets and compare its performance to related state-of-the-art classifiers like, among others, linear ridge regression with the LASSO and the Support Vector Machine. It turns out that LESS performs competitively while using fewer dimensions.     

Experiment and modelling of parameters influencing natural wind drying of willow chunks

The objective of this study was to investigate the parameters that govern the drying process of willow chunks. Indicative chunk drying trials were conducted to assess the potential of natural wind drying. Supportive model simulations were conducted to gain insight into the influence of different process parameters (particle size, pile depth) on drying. Natural wind drying experiments showed that willow chunks could be dried from 50% (wet basis) to around 10% within 5 months. Internal heating in the pile did not occur and dry matter losses were reasonably low (3.5–5%). The drying time of willow chunks depended on drying air conditions, particle size and pile dimensions (depth). The particle size of chunks should be large enough to create a low airflow resistance in the pile, but small enough to avoid that internal diffusion of moisture limits the drying process.     

Reference-material-based collaborative test of flame atomic absorption spectroscopic determination of calcium and magnesium in foods and biological materials

Summary A flame atomic absorption spectroscopic (FAAS) method is described for the determination of calcium and magnesium in a wide variety of foods and biological substrates. Results for reference materials (n=9) are presented that demonstrate the validity of the procedure. Samples are digested with nitric acid at 150° C in a pressure decomposition vessel, diluted and adjusted to pH 2 with ammonia. Lanthanum chloride solution is added to suppress phosphate interferences and the ionization of calcium and magnesium in the AAS flame. Additional dilutions are made as appropriate, whereupon the atomic absorption of calcium and magnesium is measured in an oxidizing air-acetylene flame. The wavelength settings for calcium is 422.7 nm and for magnesium 285.2 nm. The method was tested in a collaborative trial involving a milk powder practice sample and four test samples, all of which were reference materials. Participants were requested to carry out duplicate determinations exclusively. Results were obtained from 12 laboratories. However, the results of 3 laboratories had to be rejected for various reasons. The remaining set of data was statistically evaluated according to ISO 5725; the method of analysis proved to be precise and accurate. Coefficients of variation values for calcium ranged from 1.19% to 4.44% within laboratories (CV_r) and from 5.30% to 15.9% between laboratories (CV_R). For magnesium, the corresponding values were CV_r, 1.07% to 3.52% and CV_R, 3.07% to 5.99%. The method is recommended for the determination of calcium and magnesium at the levels considered in foods and biological substrates.

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Referenzmaterialien aus einem Ringversuch zur flammenatomabsorptions-spektroskopischen Bestimmung von Calcium und Magnesium in Lebensmitteln und biologischen Materialien

Zusammenfassung Es wird eine FAAS-Methode zur Bestimmung von Calcium und Magnesium in verschiedenen Lebensmitteln und biologischen Materialien beschrieben. An Hand von Ergebnissen für Referenzmaterialien (n=9) wird die Aussagekraft der Arbeitsweise erläutert. Die Proben werden mit Salpetersäure bei 150°C in einem Druckgefäß aufgeschlossen, verdünnt und auf pH 2 mit Ammoniaklösung gebracht. Lanthanchloridlösung wird zur Unterdrückung von Phosphatinterferenzen und Ionisation von Calcium und Magnesium in der AAS-Flamme zugesetzt. Je nach Bedarf wird weiter verdünnt und sodann die Atomabsorption von Calcium und Magnesium in einer oxidierenden Luft/Acetylenfiamme (Wellenlänge für Calcium 422,7 nm und für Magnesium 285,2 nm) gemessen. Die Methode wurde in einem Ringversuch erprobt, wobei ein Milchpulver als Übungsmaterial und vier Testproben benutzt wurden; alle Proben waren Referenzmaterialien. Alle Teilnehmer wurden aufgefordert, ausschließlich Doppelbestimmungen durchzuführen. Von zwölf Laboratorien wurden Ergebnisse erhalten, von dreien wurden die Ergebnisse aus verschiedenen Gründen nicht ausgewertet. Die übrigen Daten wurden nach ISO 5725 statistisch ausgewertet und zeigen, daß die Analysenmethode genaue und richtige Werte liefert. Die Variationskoeffizienten für Calcium betrugen 1,19–4,44% innerhalb der Laboratorien (CV_r; Wiederholbarkeit) und 5,30–15,9% zwischen den Laboratorien (CV_R; Vergleichbarkeit). Für Magnesium sind die entsprechenden Zahlen: CV_r 1,07–3,52% und CV_R 3,07–5,99%. Die Methode wird für die Calcium- und Magnesiumbestimmung in Lebensmitteln und biologischen Materialien in den angegebenen Konzentrationsbereichen empfohlen.

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This study was sponsored by the Commission on Food Chemistry (VI.1) of IUPAC (International Union of Pure and Applied Chemistry)