Jörg Kraft

Friedrich-Schiller-University Jena, Jena, Thuringia, Germany

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Publications (13)20.17 Total impact

  • Macromolecular Theory and Simulations - MACROMOL THEORY SIMUL. 01/2008; 17(1):1-1.
  • Macromolecular Theory and Simulations 12/2007; 17(1):32 - 38. · 1.61 Impact Factor
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    D.S. Brauer, C. Rüssel, J. Kraft
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    ABSTRACT: Phosphate glasses in the system P2O5-CaO-MgO-Na2O-TiO2 for use as degradable implant materials were produced. In order to classify their solubility behavior, dissolution experiments were performed in deionized water for 60 min at 98 degrees C. Resulting solutions were analyzed using ICP-OES. In addition, pH measurements were carried out in physiological NaCl solution. With increasing phosphorus oxide content, the glasses showed a higher solubility and gave lower pH values in aqueous solution. This was caused by changes in the glass structure, as long phosphate chains are more susceptible to hydration than smaller phosphate groups. These changes in glass structure were followed by P-31 MAS-NMR experiments. Increasing sodium oxide concentrations in exchange for calcium or magnesium oxide also increased the glass solubility by disrupting ionic cross links between chains. By contrast, addition of titania made the glasses more stable towards dissolution by cross linking smaller phosphate groups. The aim of this study was to find a relationship between glass composition and solubility behavior. As classical linear methods of data analysis were unsuitable due to the complexity of the relationship, preliminary artificial neural networks analyses were performed and were found to be an interesting tool for modeling the solubility behavior of phosphate glasses. (c) 2006 Elsevier B.V. All rights reserved
    Journal of Non-Crystalline Solids 01/2007; 353:263-270. · 1.72 Impact Factor
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    ABSTRACT: Phosphate glasses in the system P2O5–CaO–MgO–Na2O–TiO2 for use as degradable implant materials were produced. In order to classify their solubility behavior, dissolution experiments were performed in deionized water for 60min at 98°C. Resulting solutions were analyzed using ICP-OES. In addition, pH measurements were carried out in physiological NaCl solution. With increasing phosphorus oxide content, the glasses showed a higher solubility and gave lower pH values in aqueous solution. This was caused by changes in the glass structure, as long phosphate chains are more susceptible to hydration than smaller phosphate groups. These changes in glass structure were followed by 31P MAS-NMR experiments. Increasing sodium oxide concentrations in exchange for calcium or magnesium oxide also increased the glass solubility by disrupting ionic cross links between chains. By contrast, addition of titania made the glasses more stable towards dissolution by cross linking smaller phosphate groups. The aim of this study was to find a relationship between glass composition and solubility behavior. As classical linear methods of data analysis were unsuitable due to the complexity of the relationship, preliminary artificial neural networks analyses were performed and were found to be an interesting tool for modeling the solubility behavior of phosphate glasses.
    Journal of Non-crystalline Solids - J NON-CRYST SOLIDS. 01/2007; 353(3):263-270.
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    ABSTRACT: This study deals with the application of several multivariate statistical methods (cluster analysis, principal components analysis, multiple regression on absolute principal components scores) for assessment of soil pollution by heavy metals. The sampling was performed in a heavily polluted region and the chemometric analysis revealed four latent factors, which describe 84.5 % of the total variance of the system, responsible for the data structure. These factors, whose identity was proved also by cluster analysis, were conditionally named “ore specific”, “metal industrial”, “cement industrial”, and “steel production” factors. Further, the contribution of each identified factor to the total pollution of the soil by each metal pollutant in consideration was determined.
    Central European Journal of Chemistry 02/2005; 3(1):1-9. · 1.17 Impact Factor
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    ABSTRACT: Environmental pollution data are often ranked in rule-based classification systems. These environmental data are separated in predetermined classes of a classification system for a better and smarter characterization of the state of pollution. Often the measured values are transformed, e.g. in pseudocolor maps, and can then be presented in maps. For some environmental compartments different classification systems for evaluating environmental loadings are used. Because of the dissimilarity of the various classification systems direct visual comparison is difficult. However, by means of information theory an objective comparison of these various classification systems based on their information content enables a decision to be made about which system is the most informative for objective assessment of the state of pollution. By means of the new measure "multiple medium information content" (multiple entropy) objective and simultaneous comparison of all channels (in an environmental classification system: pollutants) of each classification system is now possible. Furthermore the development of the state of pollution over the whole investigation period can be detected by means of information theory. On the basis of the conditions of the established rule-based systems the use of information theory enables definition of new ranges of classes in order to reach the optimum of information during conversion into the environmental classification system.
    Analytical and Bioanalytical Chemistry 11/2004; 380(3):475-83. · 3.66 Impact Factor
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    ABSTRACT: Goal, Scope and BackgroundThe distribution of elements cannot only scatter widely in investigation areas, but also to a small scale in investigation fields. Chemometric methods are useful tools to describe the spatial distribution of the elements and are suitable to characterize the inhomogeneity in the soil. This knowledge can also be beneficial, among other things, for the creation of problem-adapted sampling strategies. The element distribution at one sampling point, the so-called microinhomogeneity considerably affects the representativeness of pollution assessment for a whole investigation area. Methods (or Main Features)The case under investigation was a small area of 1 m2 uncultivated pasture, covered by grass and not specifically polluted. The distribution of 13 elements in the topsoil has been investigated. The samples were taken at the 25 nodes of a regular grid from the upper layer (sampling depth: 10 cm) which covered the tested area. After drying and sieving, the soil was digested by using aqua regia. The elements were determined by different techniques of atomic absorption spectroscopy: flame (Cu, Fe, K, Mn, Na, Zn), graphite furnace (Cd, Cr, Cu, Ni, Pb) and FI-hydride (As, Se). The local gradient method, multivariate statistics and mapping of the element distribution are used for quantitative assessment of the inhomogeneity of the element distribution in course of investigation. Results and DiscussionThe contents of the elements are measured in a small area of 1 m2, and the mean and some important parameters are determined. The contents are highly variable and scatter between the minimum and the maximum, with the standard deviation ranging between 11% and 51%. The observed concentrations were used in formulation of ‘local’ polynomial models which approximated element distributions inside the squares of the grid. Together, the local distribution formed a distribution map for the element over the 1 m2 area which was tested. Also, some global (mean, averaging) characteristics of distribution inhomogeneity were used. These values of global characteristics show no gradient type distribution of the elements over the whole tested area under investigation. Additional information about the inhomogeneity of the investigated area can be obtained by multivariate statistical methods (cluster analysis and principal components analysis) and some selected methods of data presentation (2D and 3D sequential diagrams). ConclusionThe advantages and disadvantages of the approach are discussed. The mapping and visualization of the element distribution together with the global characteristics of inhomogeneity is a useful and comfortable way of presenting and collecting data of environmental monitoring. The mapping appears to be a most impressive and user-friendly presentation of element distribution. The local inhomogeneity is more ‘intensive’ if more isolines cross a subsquare. Recommendation and OutlookThe investigation will be continued considering another case study. This particular case study is accentuated by a strong dustlike immisssion and subsequently characterized by a gradient of soil pollution.
    Journal of Soils and Sediments 08/2004; 4(3):170-176. · 1.97 Impact Factor
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    ABSTRACT: From 1991 to 1996 intensive investigations (“Leitprojekt Elbe 2000”) were performed on the water and sediment quality of the German Elbe tributaries. Schwarze Elster, the Mulde river system (Freiberger Mulde, Zwickauer Mulde, andVereinigte Mulde), Saale and its tributaries Ilm and Unstrut, Weiße Elster, and Havel and Spree were considered. These important tributaries have a catchment area of approximately 86 000 km2 and they are together more than 2 500 km long.The concentrations of different metals (e.g., Cd, Cr, Cu, Fe, Hg, Pb, or Zn) were determined. Furthermore, the alkali and alkaline earth elements and the concentrations of anions such as Cl—, NO, and SO were analyzed in the water samples. In addition, conductivity, pH, redox potential, and temperature were measured directly at the sampling location. Together 21 700 pieces of data for water samples and 8 300 pieces of data for sediment samples formed the base for the statistical evaluation and interpretation.The water samples of the German Elbe tributaries are characterized by high salt loads (Ca, Mg, Na, Cl—, and conductivity), resulting from mining in the southern Harz region. In the sediments the concentrations of As, Cd, Pb, and Zn were identified as the most important anthropogenic parameters. The elements Co, Fe, Mn, and Ni were detected as typical background elements.Die Situation der deutschen Elbenebenflüsse — Entwicklung der Belastung in den letzten 10 JahrenIm Zeitraum von 1991 bis 1996 ermöglichte das „Leitprojekt Elbe 2000” eine in diesem Umfang erstmalige Bestandsaufnahme der aktuellen Belastungssituation der deutschen Elbenebenflüsse. Dabei wurden die Schwarze Elster, das Muldensystem, die Saale einschließlich Ilm, Unstrut und Weißer Elster sowie die Havel und die Spree beprobt. Das Einzugsgebiet dieser Flüsse umfasst eine Flche von ca. 86 000 km2 und die Gesamtfließstrecke summiert sich auf mehr als 2 500 km.Innerhalb dieser Untersuchung erfolgte die Bestimmung einer Vielzahl von Schwermetallen (z.B. Cd, Cr, Cu, Hg, Pb und Zn) sowohl im Sediment als auch im Filtrat. Weiterhin wurden die Gehalte an Alkali- und Erdalkalielementen sowie verschiedener Anionen, wie z.B. Cl—, NO, uand SO, im Filtrat ermittelt. Am Ort der Probennahme wurden sowohl die Leitfhigkeit, der pH-Wert, das Redoxpotential als auch die Temperatur gemessen. Insgesamt bilden 21 700 Filtrat- sowie 8 300 Sedimentdaten die Grundlage für die weiterführende statistische Auswertung und Interpretation.Die Filtrate der deutschen Elbenebenflüsse sind insbesondere durch hohe Salzfrachten (Ca, Mg, Na, Cl—, Leitfhigkeit), bedingt vor allem durch die Kaliindustrie in der Region des Südharzes, charakterisiert. Innerhalb der Sedimente ließen sich zum einen die überwiegend anthropogenen Elemente As, Cd, Pb und Zn sowie die in erster Linie geogenen Elemente Co, Fe, Mn und Ni als typische Einflussgrößen identifizieren.
    Acta Hydrochimica et Hydrobiologica 02/2004; 31(4‐5):334 - 345. · 0.89 Impact Factor
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    ABSTRACT: Zusammenfassung Ziel und Absicht Geografische Informationssysteme (GIS) werden in Verbindung mit geostatistischen Methoden mit Erfolg zur Beseitigung von Nutzungskonflikten und Investitionshemmnissen bei großräumigen Kontaminationen mit heterogenen Datenbeständen eingesetzt. Methoden Grundlage bildet zunächst die Kartierung relevanter Rahmenkriterien für die Konfliktlösung wie Nutzungsarten der Flurstücke, Bebauungspläne, Schutzzonen, Überflutungsgebiete u.v.m. sowie deren Georeferenzierung im GIS. Die kritische Auseinandersetzung mit Herkunft und Qualität der Daten und die geostatistische Auswertung mit Semivariogrammanalysen und der Kriging-Schätzung schaffen die Voraussetzung für die Abgrenzung nutzungsbezogener prüfwertüberschreitender Areale nach BBodSchV. Ergebnisse und Schlussfolgerungen Im Ergebnis kann die Gefahrensituation relativiert werden und es können fundierte Handlungsempfehlungen für eine optimierte Gefahrenbeseitigung gegeben werden. Dies geschieht oft im Rahmen vorgesehener städtebaulicher Maßnahmen. Ausblick Weitere Untersuchungs- oder Monitoringarbeiten können auf der Grundlage der Ergebnisse deutlich optimiert werden.
    Umweltwissenschaften und Schadstoff-Forschung 01/2004; 16(2):99-104.
  • Umweltwissenschaften und Schadstoff-Forschung 01/2004; 16(2):99-104.
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    ABSTRACT: This environmetric study deals with modeling and interpretation of river water monitoring data from the basin of the Saale river and its tributaries the Ilm and the Unstrut. For a period of one year of observation between September 1993 and August 1994 a data set from twelve campaigns at twenty-nine sampling sites from the Saale river and six campaigns from the river Ilm at seven sampling sites and from river Unstrut at ten sampling sites was collected. Twenty-seven chemical and physicochemical properties were measured to estimate the water quality. The application of cluster analysis, principal components analysis, and apportioning modeling on absolute principal components scores revealed important information about the ecological status of the region of interest:identification of two separate patterns of pollution (upper and lower stream of the rivers);identification of six latent factors responsible for the data structure with different content for the two identified pollution patterns; anddetermination of the contribution of each latent factor (source of emission) to the formation of the total concentration of the chemical burden of the river water. As a result more objective ecological policy and decision making is possible.
    Analytical and Bioanalytical Chemistry 12/2002; 374(5):898-905. · 3.66 Impact Factor
  • Jürgen W Einax, Jörg Kraft
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    ABSTRACT: Soil pollution data is also strongly scattering at small scale. Sampling of composite samples, therefore, is recommended for pollution assessment. Different statistical methods are available to provide information about the accuracy of the sampling process. Autocorrelation and variogram analysis can be applied to investigate spatial relationships. Analysis of variance is a useful method for homogeneity testing. The main source of the total measurement uncertainty is the uncertainty arising from sampling. The sample mass required for analysis can also be estimated using an analysis of variance. The number of increments to be taken for a composite sample can be estimated by means of simple statistical formulae. Analytical results of composite samples obtained from different fusion procedures of increments can be compared by means of multiple mean comparison. The applicability of statistical methods and their advantages are demonstrated for a case study investigating metals in soil at a very small spatial scale. The paper describes important statistical tools for the quantitative assessment of the sampling process. Detailed results clearly depend on the purpose of sampling, the spatial scale of the object under investigation and the specific case study, and have to be determined for each particular case.
    Environmental Science and Pollution Research 02/2002; 9(4):257-61. · 2.76 Impact Factor
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    ABSTRACT: Geostatistical and multivariate methods of data analysis are used to describe patterns of soil pollution with inorganic contaminants in Celje County, Slovenia. Groups of contaminants and polluted sites were identified using cluster analysis and confirmed with multidimensional variance and discriminant analysis. Factor analysis yields an identification of not directly observable relationships between the contaminants. The spatial structure and distribution of contaminants were assessed by applying semivariogram analysis and kriging interpolation method. Zinc, Cd and Cu were identified as a pollutant emitted from the zinc smelter, Pb also from other sources, and Cr and Ni mostly from geological parent material.
    Environmental Science and Pollution Research 02/2000; 7(2):89-96. · 2.76 Impact Factor