Strain gradient plasticity analysis of transformation induced plasticity in multiphase steels

“To what extent do plastic strain gradients affect the strengthening resulting from the transformation of small metastable inclusions into hard inclusions within a plastically deforming matrix?” is the central question addressed here. Though general in the approach, the focus is on the behavior of TRIP-assisted multiphase steels. A two-dimensional embedded cell model of a simplified microstructure composed of a single metastable austenitic inclusion surrounded by a soft ferritic matrix is considered. The cell is inserted in a large homogenized medium. The transformation of a fraction of the austenite into a hard martensite plate is simulated, accounting for a transformation strain, and leading to complex elastic and plastic accommodation. The size of a transforming plate in real multiphase steels is typically between 0.1 and 2 μm, a range of size in which plastic strain gradient effects are expected to play a major role. The single parameter version of the Fleck–Hutchinson strain gradient plasticity theory is used to describe the plasticity in the austenite, ferrite and martensite phases. The higher order boundary conditions imposed on the plastic flow have a large impact on the predicted strengthening. Using realistic values of the intrinsic length parameter setting the scale at which the gradients effects have an influence leads to a noticeable increase of the strengthening on top of the increase due to the transformation of a volume fraction of the retained austenite. The geometrical parameters such as the volume fraction of retained austenite and of the transforming zone also bring significant strengthening. Strain gradient effects also significantly affect the stress state inside the martensite plate during and after transformation with a potential impact on the damage resistance of these steels.     

Class identity assignment for amphetamines using neural networks and GC–FTIR data

An exploratory analysis was performed in order to evaluate the feasibility of building of neural network (NN) systems automating the identification of amphetamines necessary in the investigation of drugs of abuse for epidemiological, clinical and forensic purposes. A first neural network system was built to distinguish between amphetamines and nonamphetamines. A second, more refined system, aimed to the recognition of amphetamines according to their toxicological activity (stimulant amphetamines, hallucinogenic amphetamines, nonamphetamines). Both systems proved that discrimination between amphetamines and nonamphetamines, as well as between stimulants, hallucinogens and nonamphetamines is possible (83.44% and 85.71% correct classification rate, respectively). The spectroscopic interpretation of the 40 most important input variables (GC–FTIR absorption intensities) shows that the modeling power of an input variable seems to be correlated with the stability and not with the intensity of the spectral interaction. Thus, discarding variables only because they correspond to spectral windows with weak absorptions does not seem be not advisable.     

Validation of the CALUX bioassay for PCDD/F analyses in human blood plasma and comparison with GC-HRMS

Following the dioxin crisis of 1999, several studies were conducted to assess the impact of this crisis on the dioxin body burden in the Belgian population. The Scientific Institute of Public Health identified a population from whom plasma samples were available and from whom, during the follow up survey, plasma samples were obtained in 2000. In total, 496 samples were collected for GC-HRMS and CALUX analyses to verify statistical assessment conclusions. This study was seen as an opportunity to validate the CALUX bioassay for biological sample analysis and to compare toxic equivalency (TEQ) values obtained by the reference GC-HRMS technique and by the screening method. This article focuses on the validation results of the CALUX bioassay for the analyses of the dioxin fractions of blood plasma. The sample preparation is based on a liquid–liquid extraction, followed by an acid silica in series with an activated carbon clean-up. A good recovery (82%) and reproducibility (coefficient of variation less than 25%) were found for this method. Based on 341 plasma samples, a significant correlation was established between the bioassay and chemical method (R = 0.64). However, a proportional systematic error was observed when the results obtained with the CALUX bioassay were regressed with the results from the GC-HRMS analyses. The limit of quantification (LOQ) used to calculate TEQ values from the GC-HRMS determinations, the use of the relative potency values instead of the toxic equivalent factor and the potential of CALUX bioassay to measure all compounds with affinity for the AhR may partly explain this proportional systematic error. Nevertheless, the present results suggest that the CALUX bioassay could be a promising valid screening method for human blood plasma analyses.     

Inverse calibration predicts better than classical calibration

The study shows that in the classical calibration situation inverse calibration, i.e. concentration = f(measurement) yields more reliable predictions than classical calibration, i.e. measurement = f(concentration). The theoretical proof is verified by Monte Carlo simulations and by two practical examples. The improvement due to the use of inverse calibration increases with decreasing precision of the measurements. Received: 27 October 1997 / Revised: 9 January 1998 / Accepted: 15 January 1998     

Activation analysis of rare earths : Separation of rare earths from accompanying elements

A scheme for the separation of rare-earth elements from gadolinite and tantalocolumbite minerals is discussed. The possibility of interference from traces of nonseparated elements in the subsequent separation of rare-earth elements from each other by cation exchange with α-hydroxyisobutyrate as the eluting agent is investigated.     

Retention behaviour of acidic, neutral and basic drugs on a CN column using phosphate buffers in the mobile phase

The retention behaviour of a set of sixteen more or less drugs on a CN column with methanol-phosphate buffer as eluent was studied. The influence of the volume percentage of methanol, pH and the ionic strength on the capacity factors (k') of the drugs was determined using an experimental design consisting of three pH values (3, 5 and 7), four ionic strengths (0.025, 0.05, 0.075 and 0.1) and three volume percentages of methanol (10, 30 and 50%). The selected drugs were basic, acidic and neutral compounds with different polarity properties. The number of carbon atoms, considered here as reflecting the hydrophobic characteristics of the solutes, varied from 7 to 25. The influence on the retention of drugs on a CN column of the different parameters studied was evaluated. The volume percentage of organic modifier and the pH are the most important factors. A change in ionic strength is important only when large molecules are chromatographed. As the interaction between pH, ionic strength and volume percentage of methanol is small, optimization of the three parameters separately is possible.     

Rapid screening for chiral separations by short-end injection capillary electrophoresis using highly sulfated cyclodextrins as chiral selectors

Two series of amino acid derivatives and phenylamines were used to evaluate the potential of highly sulfated cyclodextrins (HS-CDs) for the screening for chiral separations by capillary electrophoresis (CE). HS-CDs showed to be very versatile and to exhibit very high enantioselectivity. The use of short-end injection allowed to reduce dramatically the analysis time. From the results obtained, a scheme for the rapid screening of enantiomeric molecules was developed and applied to various chiral drugs. Results are very satisfying as almost all compounds (62 out of 67) could be baseline-resolved. Usually, less than three experiments were necessary to obtain very good separation.     

Feature selection for hierarchical clustering

Feature selection is a valuable technique in data analysis for information-preserving data reduction. This paper describes a feature selection approach for hierarchical clustering based on genetic algorithms using a fitness function that tries to minimize the difference between the dissimilarity matrix of the original feature set and the one of the reduced feature sets. Clustering trees based on reduced feature sets are comparable with those based on the complete feature set. Special measures to favor small reduced feature sets are discussed.