Elemental partitioning and microstructural development in duplex stainless steel weld metal

A thermodynamic analysis which is capable of estimating the austenite/ferrite equilibria in duplex stainless steels has been carried out using the sublattice thermodynamic model. The partitioning of alloying elements between the austenite and ferrite phases has been calculated as a function of temperature. The results showed that chromium partitioning was not influenced significantly by the temperature. The molybdenum, on the other hand, was found to partition preferentially into ferrite phase as the temperature decreases. A strong partitioning of nickel into the austenite was observed to decrease gradually with increasing temperature. Among the alloying elements, average nitrogen concentration was found to have the most profound effect on the phase balance and the partitioning of nitrogen into the austenite. The partitioning coefficient of nitrogen (the ratio of the mole fraction of nitrogen in the austenite to that in the ferrite) was found to be as high as 7.0 around 1300 K. Consequently, the volume fraction of austenite was influenced by relatively small additions of nitrogen. The results are compared with the experimentally observed data in a duplex stainless steel weld metal in conjunction with the solid state δ → δ + γ phase transformation. Particular attention was given to the morphological instability of grain boundary austenite allotriomorphs. A compariso between the experimental results and calculations indicated that the instability associated with irregular austenite perturbations results from the high degree of undercooling. The results suggest that the model can be used successfully to understand the development of the microstructure in duplex stainless steel weld metals.

Kinetic study of the pyrolysis of miscanthus and its acid hydrolysis residue by thermogravimetric analysis

The kinetic parameters of the pyrolysis of miscanthus and its acid hydrolysis residue (AHR) were determined using thermogravimetric analysis (TGA). The AHR was produced at the University of Limerick by treating miscanthus with 5 wt.% sulphuric acid at 175 °C as representative of a lignocellulosic acid hydrolysis product. For the TGA experiments, 3 to 6 g of sample, milled and sieved to a particle size below 250 μm, were placed in the TGA ceramic crucible. The experiments were carried out under non-isothermal conditions heating the samples from 50 to 900 °C at heating rates of 2.5, 5, 10, 17 and 25 °C/min. The activation energy (EA) of the decomposition process was determined from the TGA data by differential analysis (Friedman) and three isoconversional methods of integral analysis (Kissinger–Akahira–Sunose, Ozawa–Flynn–Wall, Vyazovkin). The activation energy ranged from 129 to 156 kJ/mol for miscanthus and from 200 to 376 kJ/mol for AHR increasing with increasing conversion. The reaction model was selected using the non-linear least squares method and the pre-exponential factor was calculated from the Arrhenius approximation. The results showed that the best fitting reaction model was the third order reaction for both feedstocks. The pre-exponential factor was in the range of 5.6 × 1010 to 3.9 × 10+ 13 min− 1 for miscanthus and 2.1 × 1016 to 7.7 × 1025 min− 1 for AHR.

Laser-induced breakdown spectroscopy quantitative analysis method via adaptive analytical line selection and relevance vector machine regression model

Abstract A new LIBS quantitative analysis method based on analytical line adaptive selection and Relevance Vector Machine (RVM) regression model is proposed. First, a scheme of adaptively selecting analytical line is put forward in order to overcome the drawback of high dependency on a priori knowledge. The candidate analytical lines are automatically selected based on the built-in characteristics of spectral lines, such as spectral intensity, wavelength and width at half height. The analytical lines which will be used as input variables of regression model are determined adaptively according to the samples for both training and testing. Second, an LIBS quantitative analysis method based on RVM is presented. The intensities of analytical lines and the elemental concentrations of certified standard samples are used to train the RVM regression model. The predicted elemental concentration analysis results will be given with a form of confidence interval of probabilistic distribution, which is helpful for evaluating the uncertainness contained in the measured spectra. Chromium concentration analysis experiments of 23 certified standard high-alloy steel samples have been carried out. The multiple correlation coefficient of the prediction was up to 98.85%, and the average relative error of the prediction was 4.01%. The experiment results showed that the proposed LIBS quantitative analysis method achieved better prediction accuracy and better modeling robustness compared with the methods based on partial least squares regression, artificial neural network and standard support vector machine.

A laser induced breakdown spectroscopy quantitative analysis method based on the robust least squares support vector machine regression model

Data fluctuation in multiple measurements of Laser Induced Breakdown Spectroscopy (LIBS) greatly affects the accuracy of quantitative analysis. A new LIBS quantitative analysis method based on the Robust Least Squares Support Vector Machine (RLS-SVM) regression model is proposed. The usual way to enhance the analysis accuracy is to improve the quality and consistency of the emission signal, such as by averaging the spectral signals or spectrum standardization over a number of laser shots. The proposed method focuses more on how to enhance the robustness of the quantitative analysis regression model. The proposed RLS-SVM regression model originates from the Weighted Least Squares Support Vector Machine (WLS-SVM) but has an improved segmented weighting function and residual error calculation according to the statistical distribution of measured spectral data. Through the improved segmented weighting function, the information on the spectral data in the normal distribution will be retained in the regression model while the information on the outliers will be restrained or removed. Copper elemental concentration analysis experiments of 16 certified standard brass samples were carried out. The average value of relative standard deviation obtained from the RLS-SVM model was 3.06% and the root mean square error was 1.537%. The experimental results showed that the proposed method achieved better prediction accuracy and better modeling robustness compared with the quantitative analysis methods based on Partial Least Squares (PLS) regression, standard Support Vector Machine (SVM) and WLS-SVM. It was also demonstrated that the improved weighting function had better comprehensive performance in model robustness and convergence speed, compared with the four known weighting functions.

The discrimination and association of float glass and the quantitative analysis of liquids from aerosols and microdrops using laser induced breakdown spectroscopy

Glass is a common form of trace evidence found at many scenes of crimes in the form of small fragments. These glass fragments can transfer to surrounding objects and/or persons and may provide forensic investigators valuable information to link a suspect to the scene of a crime. Since the elemental composition of different glass sources can be very similar, a highly discriminating technique is required to distinguish between fragments that have originated from different sources. ^ The research presented here demonstrates that Laser Induced Breakdown Spectroscopy (LIBS) is a viable analytical technique for the association and discrimination of glass fragments. The first part of this research describes the optimization of the LIBS experiments including the use of different laser wavelengths to investigate laser-material interaction. The use of a 266 nm excitation laser provided the best analytical figures of merit with minimal damage to the sample. The resulting analytical figures of merit are presented. The second part of this research evaluated the sensitivity of LIBS to associate or discriminate float glass samples originating from the same manufacturing plants and produced at approximately the same time period. Two different sample sets were analyzed ranging in manufacturing dates from days to years apart. Eighteen (18) atomic emission lines corresponding to the elements Sr, K, Fe, Ca, Al, Ba, Na, Mg and Ti, were chosen because of their detection above the method detection limits and for presenting differences between the samples. Ten elemental ratios producing the most discrimination were selected for each set. When all the ratios are combined in a comparison, 99% of the possible pairs were discriminated using the optimized LIBS method generating typical analytical precisions of ∼5% RSD. ^ The final study consisted of the development of a new approach for the use of LIBS as a quantitative analysis of ultra-low volume solution analysis using aerosols and microdrops. Laser induced breakdown spectroscopy demonstrated to be an effective technique for the analysis of as low as 90 pL for microdrop LIBS with 1 pg absolute LOD and 20 µL for aerosol LIBS with an absolute LOD of ∼100 fg.^

Seawater carbonate chemistry and particulate organic particles during a semicontinuous batch culture experiment with Trichodesmium IMS101, 2007

Diazotrophic (N2-fixing) cyanobacteria provide the biological source of new nitrogen for large parts of the ocean. However, little is known about their sensitivity to global change. Here we show that the single most important nitrogen fixer in today's ocean, Trichodesmium, is strongly affected by changes in CO2 concentrations. Cell division rate doubled with rising CO2 (glacial to projected year 2100 levels) prompting lower carbon, nitrogen and phosphorus cellular contents, and reduced cell dimensions. N2 fixation rates per unit of phosphorus utilization as well as C:P and N:P ratios more than doubled at high CO2, with no change in C:N ratios. This could enhance the productivity of N-limited oligotrophic oceans, drive some of these areas into P limitation, and increase biological carbon sequestration in the ocean. The observed CO2 sensitivity of Trichodesmium could thereby provide a strong negative feedback to atmospheric CO2 increase.

(Table 1) Elemental mean values of the upper Paleocene and lower Eocene in IODP Hole 302-M0004A

We reconstruct the latest Paleocene and early Eocene (~57-50 Ma) environmental trends in the Arctic Ocean and focus on the Paleocene-Eocene thermal maximum (PETM) (~55 Ma), using strata recovered from the Lomonosov Ridge by the Integrated Ocean Drilling Program Expedition 302. The Lomonosov Ridge was still partially subaerial during the latest Paleocene and earliest Eocene and gradually subsided during the early Eocene. Organic dinoflagellate cyst (dinocyst) assemblages point to brackish and productive surface waters throughout the latest Paleocene and early Eocene. Dinocyst assemblages are cosmopolitan during this time interval, suggesting warm conditions, which is corroborated by TEX86'-reconstructed temperatures of 15°-18°C. Inorganic geochemistry generally reflects reducing conditions within the sediment and euxinic conditions during the upper lower Eocene. Spectral analysis reveals that the cyclicity, recorded in X-ray fluorescence scanning Fe data from close to Eocene thermal maximum 2 (~53 Ma, presence confirmed by dinocyst stratigraphy), is related to precession. Within the lower part of the PETM, proxy records indicate enhanced weathering, runoff, anoxia, and productivity along with sea level rise. On the basis of total organic carbon content and variations in sediment accumulation rates, excess organic carbon burial in the Arctic Ocean appears to have contributed significantly to the sequestration of injected carbon during the PETM.

An experimental geochemical characterization analysis of archaeological iron from the contact period of Newfoundland and Labrador

This research uses scanning electron microscopy-energy dispersive X-ray spectrometry (SEM-EDX) and inductively coupled plasma-mass spectrometry (ICP-MS) on cross-sections of iron artifacts sectioned from along shafts to determine the elemental constituents of a collection of Inuit and European artifacts from along the coast of Labrador. Hand-wrought iron nails from early historic period (16th – 18th centuries CE) Inuit sites in Labrador were originally manufactured by and acquired from early whalers and fishers of various European nationalities. The purpose of this research was to assess if the elements in different samples are sufficiently homogeneous to be viable for a provenience analysis to discern which Inuit nails were originally derived from which European groups; the Basque, English or French. The consistent relationships between the geochemical signatures of iron nails found in Inuit sites and historic nails derived from specific European groups could provide insights into the prevalence, activity and the nature of indigenous interactions of different European nationalities in the region over time. The results show that the methods applied to evaluate the geochemistry of the nails was not sufficient to detect meaningful patterns because the nails did not demonstrate the necessary degree of chemical uniformity among different samples in the same artifacts.