The stability of polyaniline in strongly alkaline or acidic aqueous media

Polyaniline (PANI) base has been suspended in 9 M potassium hydroxide at 20 °C or 90 °C for various time intervals extending to 4 months. The fraction of acetone-soluble material increased from 1.2 wt.% to 4.5 wt.% after exposure to an alkaline medium for 60 days at 20 °C. Gel-permeation chromatography indicates that the aggregation of PANI is reduced, while the chain degradation itself is negligible. FTIR spectroscopy confirms this trend and the absence of hydrolytic changes in the PANI structure. Polyaniline retains the ability to be reprotonated with a 1 M sulfuric acid to a conducting form. No marked changes in the molecular structure have been found, even after suspension of PANI in 9 M KOH at 90 °C for 60 days.Similar immersion of PANI salt in 5 M sulfuric acid at 20 °C was responsible for changes in the protonation, and the mass increased by 11 wt.%. This was explained by the exchange of the original sulfate or chloride counter-ions for hydrogen sulfate anions or by the protonation of secondary amine sites in PANI in addition to imine ones. The changes in the molecular structure are discussed on the basis of FTIR spectra. The conductivity decreased from 1.2 S cm⁻¹ to ∼10⁻³ S cm⁻¹ but no time-dependence of conductivity was observed. There was no fraction of PANI soluble in acetone. PANI in the protonated state is thus stable also in the strongly acidic medium.The study is supplemented by the assessment of the thermal stability of PANI base, which is of importance for the processing of PANI. Loss of moisture has been observed after exposure to 250 °C for 10 h in both nitrogen atmosphere and in air. Good stability was found at 350 °C only in the nitrogen atmosphere, while a marked mass loss in weight was registered in air.     

Automated resolution of overlapping peaks in chromatographic data

Parallel factor analysis 2 (PARAFAC2) has been shown to be a powerful tool for resolution of complex overlapping peaks in chromatographic analyses. It is particularly useful because of its ability to handle shifts in the elution time mode and peak shape changes. Like all curve resolution techniques, PARAFAC2 will only find chemically meaningful parameters (elution time profiles and mass spectra) if the correct number of factors are determined. So far, the primary way to determine an appropriate number of factors, when using PARAFAC2, is to calculate models with different number of factors and then inspect the models manually. This approach is time consuming, and the result may be biased because of the manual assessment of the model quality, making PARAFAC2 inaccessible for analytical chemists in general. Here, we develop a method that can determine an appropriate number of factors in an automated way. The automation is based on a number of model diagnostics (quality criteria) collected from models with different numbers of factors. Combining these diagnostics, it is possible to assess what the appropriate number of components is. In this work, only gas chromatography–mass spectrometry data are considered. However, it will most likely be fairly straightforward to expand the work to also cover liquid chromatography data (with a multivariate detector). Automating the model quality evaluation of the PARAFAC2 model enables both the inexperienced and trained user to perform comprehensive and advanced analysis of chromatographic data with a minimum of manual work. © 2013 The Authors. Journal of Chemometrics Published by John Wiley & Sons Ltd.     

Mixtures poly((<Emphasis Type="Italic">R</Emphasis>)-3-hydroxybutyrate) and poly(<Emphasis Type="SmallCaps">l</Emphasis>-lactic acid) subjected to DSC

Blends of bacterial poly((R)-3-hydroxybutyrate) (PHB) and poly(l-lactic acid) (PLLA) synthesized by polycondensation of l-lactic acid or by ring-opening polymerization of l-lactide were studied. Miscibility was investigated through both conventional differential scanning calorimetry (DSC) and temperature-modulated DSC (TMDSC). PHB and low-molar mass PLLA were miscible in a whole concentration range, and a single glass transition temperature was observed. On the other hand, PHB/high-molar mass PLLA mixtures phase separate, giving rise to two glass transition temperatures corresponding to PHB and PLLA. A treatment of blends at 190 °C leads to formation of block/multiblock/random copolymers, and blends become miscible.     

Olive oil quantification of edible vegetable oil blends using triacylglycerols chromatographic fingerprints and chemometric tools

The present work studies the effectiveness of the use of triacylglycerols (TAGs) for the quantification of olive oil in blends with vegetable oils. The determinations were obtained using high-performance liquid chromatography (HPLC) coupled to a Charged Aerosol Detector (CAD), in combination with Partial Least Squares (PLS) regression and using interval PLS (iPLS) for variable selection.Results revealed that PLS models can predict olive oil concentrations with reasonable errors. Variable selection through iPLS did not improve predictions significantly, but revealed the chemical information important in the chromatogram to quantify olive oil in vegetable oil blends.     

Core consistency diagnostic in PARAFAC2

PARAFAC2 is applied in multiple research areas, for example, where data containing shifts are analysed, but it is a challenge to determine the appropriate number of components in the model. In this paper, it is hypothesized that the core consistency diagnostic, which is currently applied in, for example, PARAFAC1 can be used to determine model complexity in PARAFAC2. Theoretically, a PARAFAC1 model is fitted ‘inside’ the PARAFAC2 algorithm, and it should therefore be possible to apply the core consistency diagnostic from PARAFAC1 in PARAFAC2. To support this hypothesis, three different datasets, as well as simulated datasets, have been evaluated by means of PARAFAC2, and the core consistencies have been investigated. There is a general trend that if the core consistency is low, the model is overfitted as in PARAFAC1. Also, core consistency captures the true variation in the data, whereas small peaks are easily overlooked by visual inspection of noisy models. However, for determining the number of components in a PARAFAC2 model, we suggest usage of the core consistency in combination with other model parameters such as residuals, loadings, and split‐half analysis. Copyright © 2013 John Wiley & Sons, Ltd.     

Handling within run retention time shifts in two-dimensional chromatography data using shift correction and modeling

The use of PARAFAC for modeling GC × GC-TOFMS peaks is well documented. This success is due to the trilinear structure of these data under ideal, or sufficiently close to ideal, chromatographic conditions. However, using temperature programming to cope with the general elution problem, deviations from trilinearity within a run are more likely to be seen for the following three cases: (1) compounds (i.e., analytes) severely broadened on the first column hence defined by many modulation periods, (2) analytes with a very high retention factor on the second column and likely wrapped around in that dimension, or (3) with fast temperature program rates. This deviation from trilinearity is seen as retention time-shifted peak profiles in subsequent modulation periods (first column fractions). In this report, a relaxed yet powerful version of PARAFAC, known as PARAFAC2 has been applied to handle this shift within the model step by allowing generation of individual peak profiles in subsequent first column fractions. An alternative approach was also studied, utilizing a standard retention time shift correction to restore the data trilinearity structure followed by PARAFAC. These two approaches are compared when identifying and quantifying a known analyte over a large concentration series where a certain shift is simulated in the successive first column fractions. Finally, the methods are applied to real chromatographic data showing severely shifted peak profiles. The pros and cons of the presented approaches are discussed in relation to the model parameters, the signal-to-noise ratio and the degree of shift.     

Reduction of silver nitrate by polyaniline nanotubes to produce silver-polyaniline composites

Polyaniline (PANI) nanotubes were prepared by oxidation of aniline in 0.4 M acetic acid. They were subsequently used as a reductant of silver nitrate in 1 M nitric acid, water or 1 M ammonium hydroxide at various molar ratios of silver nitrate to PANI. The resulting PANI-silver composites contained silver nanoparticles of 40–60 nm size along with macroscopic silver flakes. Under these experimental conditions, silver was always produced outside the PANI nanotubes. Changes in the molecular structure of PANI were analyzed by FTIR spectroscopy. Silver content in the composites was determined as a residue by thermogravimetric analysis, and confirmed by density measurements. The highest conductivity of a composite, 68.5 S cm⁻¹, was obtained at the nitrate to PANI molar ratio of 0.67 in water. Also, the best reaction yield was obtained in water. Reductions performed in an acidic medium gave products with conductivity of 10⁻⁴–10⁻² S cm⁻¹, whereas the reaction in alkaline solution yielded non-conducting products.     

Classification of GC‐MS measurements of wines by combining data dimension reduction and variable selection techniques

Different classification methods (Partial Least Squares Discriminant Analysis, Extended Canonical Variates Analysis and Linear Discriminant Analysis), in combination with variable selection approaches (Forward Selection and Genetic Algorithms), were compared, evaluating their capabilities in the geographical discrimination of wine samples. Sixty‐two samples were analysed by means of dynamic headspace gas chromatography mass spectrometry (HS‐GC‐MS) and the entire chromatographic profile was considered to build the dataset. Since variable selection techniques pose a risk of overfitting when a large number of variables is used, a method for coupling data dimension reduction and variable selection was proposed. This approach compresses windows of the original data by retaining only significant components of local Principal Component Analysis models. The subsequent variable selection is then performed on these locally derived score variables. The results confirmed that the classification models achieved on the reduced data were better than those obtained on the entire chromatographic profile, with the exception of Extended Canonical Variates Analysis, which gave acceptable models in both cases. Copyright © 2008 John Wiley & Sons, Ltd.