Brian Antalek

Direct exponential curve resolution algorithm (DECRA): A novel application of the generalized rank annihilation method for a single spectral mixture data set with exponentially decaying contribution profiles

Abstract The generalized rank annihilation method (GRAM) is a powerful method for calibration and self-modeling curve resolution. The mathematical treatment requires two data sets, which implies two experiments. The required relation between the two data sets is strict and minor differences, such as in retention times for hyphenated techniques, violate the mathematical requirements leading to erroneous results. It will be shown in this paper that only one experiment is needed in the case where the contribution of the components in the mixture spectra is of a decaying exponential character. Examples are given of pulsed gradiet spin echo (PGSE) nuclear magnetic resonance (NMR) data. The MATLAB function to reproduce the results is given and is available through Chemolabs archives, together with the data files. The method is called DECRA (direct exponential curve resolution algorithm).

The viscosity of polymer–surfactant mixtures in water

The viscosity of aqueous solutions of poly(ethyleneoxide) (PEO) in the dilute and semidilute regimes exhibits a maximum as a function of sodium dodecyl sulfate (SDS) concentration. The maximum is shown to correspond to the point at which the polymer coils are saturated with surfactant. From intrinsic viscosity and small angle neutron scattering (SANS), we obtain a value of 0.65 for the excluded volume exponent of PEO at saturation. Carbon‐13 nuclear magnetic resonance (NMR) relaxation measurements indicate that the correlation time for local segmental motion for polymer segments attached to micelles is reduced from about 10−11 to 10−9 s. However, only a relatively small fraction of the total coil is in direct contact with the micelles at saturation. A model for the polymer–surfactant complex is presented based on these considerations.

A study of the microstructure of four-component sucrose ester microemulsions by SAXS and NMR

Sucrose esters form a class of surfactants with the important properties of being biodegradable, non-toxic and capable of forming temperature-insensitive microemulsions. Such microemulsions would be expected to suit a variety of food-based and pharmaceutical applications; however to date little is known about their structure and stability. In this study, the Winsor IV microemulsion systems composed of sucrose esters (SE)/1-butanol/water and oils such as n-dodecane, n-hexadecane and medium chain triglyceride (MCT), have been investigated using small angle X-ray scattering (SAXS), pulsed gradient spin echo (PGSE) NMR and viscosity measurements. The SAXS results for the sucrose monostearate (S1570) system at SE/MCT/1-butanol=1.5:1.1 clearly indicate that the periodicity d increases with increase in water content and is not sensitive to the nature of the oil. From the amphiphilicity factor, fa, and the correlation length, ξ, one can conclude that the n-dodecane-based microemulsion system is the most ordered. Microstructure investigation by PGSE NMR gives evidence of structural changes as the water content in the system increases. The oil self-diffusion remains unchanged when MCT serves as the oil phase. However, when the oil is paraffinic in nature (n-dodecane and n-hexadecane) the self-diffusion coefficient indicates participation of the oil molecules at the interface. Surfactant self-diffusion is only weakly affected by the water content. The shorter chain oils (n-dodecane and MCT) solubilize a maximum of 40 and 47 wt.% of water and cannot invert, while the long chain paraffinic (n-hexadecane-based system) inverts into an O/W microemulsion. The viscosity of these microemulsions decreases with increasing water content. The absence of a yield stress in any of the samples studied, together with the linearity of the flow curves, is evidence that there are no relaxation processes in these microemulsions which show a non-Newtonian flow behavior.

Applications and new developments of the direct exponential curve resolution algorithm (DECRA). Examples of spectra and magnetic resonance images

Recently, a new multivariate analysis tool was developed to resolve mixture data sets, where the contributions (‘concentrations’) have an exponential profile. The new approach is called DECRA (direct exponential curve resolution algorithm). DECRA is based on the generalized rank annihilation method (GRAM). Examples will be given of resolving nuclear magnetic resonance spectra resulting from a diffusion experiment, spectra in the ultraviolet/visible region of a reaction and magnetic resonance images of the human brain. Copyright

Microstructure analysis at the percolation threshold in reverse microemulsions

Time domain dielectric spectroscopy of reverse water/acrylamide/Aerosol-OT (AOT)/toluene microemulsions shows that percolation induced by increasing cosurfactant concentration (increasing cosurfactant chemical potential) obeys scaling above and below a percolation threshold. This scaling analysis suggests that the observed percolation is close to static percolation limits. Self-diffusion measurements derived from nuclear magnetic resonance pulsed-gradient spin-echo experiments reveal an increase in water proton diffusion above the percolation threshold. This increase is assigned to water transport through fractally chained assemblies of microemulsion droplets. The diffusion of water, cosurfactant, and surfactant (AOT) below threshold is modeled quantitatively taking into account the chemical partitioning equilibria between the microemulsion droplets and the toluene continuous pseudophase. Above threshold, the apparent increasing water and cosurfactant partitioning into the toluene (continuous) pseudophase suggests facilitated transport through fractal aggregates. A dynamic partitioning model is used to estimate the volume of percolating fractal clusters, and yields an order parameter for water-in-oil to percolating cluster microstructural transitions. This same order parameter is also illustrated to derive from self-diffusion data wherein percolation and transformation to sponge phase microstructure are driven by increases in temperature and in disperse phase volume fraction. For microstructural transitions driven by three different field variables, chemical potential, temperature, and disperse phase volume fraction, this order parameter shows that the onset of percolation corresponds to the onset of increasing water proton self-diffusion, and that the onset of increasing surfactant self-diffusion corresponds to the formation of bicontinuous microstructures and the onset of transformation to middle phase microemulsion.

Organic Solvent-Dispersed TiO2 Nanoparticle Characterization

Anatase titanium dioxide nanoparticles are derivatized with the polymerizable reagent (3-methacryloxypropyl)trimethoxysilane to provide dispersions in organic solvent. The titania core particles are characterized by transmission electron microscopy (TEM) and X-ray diffraction (XRD). The organic component structures and thickness are elucidated using nuclear magnetic resonance (NMR), quasielastic light scattering (QELS), and size-exclusion chromatography (SEC). Thin, high-refractive-index coatings prepared from the organic dispersions are characterized by atomic force microscopy (AFM). The combination of microscopies, spectroscopy, light scattering, and separation techniques provides unique information on the structure, thickness, morphology, and size distributions of the surface-treated nanoparticles that is difficult to obtain by any single technique. The findings indicate titania platelets with a modal diameter of 9.8 nm and a thickness of approximately 1.5 nm. The particles are coated with a 1.5-1.9 nm thick organic ligand layer, and a substantial population of 2 nm siloxane oligomers is detected. The analytical methodology presented may also be useful for characterizing other anisotropic organic-inorganic nanoparticles and their dispersions.

Applications of the direct exponential curve resolution algorithm (DECRA) to solid state nuclear magnetic resonance and mid-infrared spectra

DECRA (direct exponential curve resolution algorithm) is a fast multivariate method used to resolve spectral data with concentration profiles that are linear combinations of exponential functions. DECRA has been previously applied to a wide variety of spectroscopies. Results are presented in this paper for two new application areas: solid state nuclear magnetic resonance spectra of polymorphic crystal mixtures and mid‐infrared spectroscopy of chemical reactions. Furthermore, the paper will show the effect of the way the data set is split, which is a part of the algorithm, on the results. Copyright

Resolving nuclear magnetic resonance data of complex mixtures by three-way methods:: Examples of chemical solutions and the human brain

Abstract Despite the use of hyphenated and/or high-resolution instruments in analytical spectroscopy, the resulting spectral data often represent mixtures of several components. When no reference data in the form of reference spectra or concentration profiles are available, self-modeling mixture analysis techniques can be utilized to obtain the spectra of the pure components and their concentration profiles. There are many different algorithms to resolve mixture spectra, and the mathematical procedures involved are not always simple. This paper will discuss some of the aspects and problems of self-modeling mixture analysis, with the focus on the three-way method and without going into the mathematical details. Practical examples will be shown of methods applied to nuclear magnetic resonance data. The techniques discussed can also be applied to magnetic resonance images and an example will be shown of the human brain.

Cosurfactant facilitated transport in reverse microemulsions

Faradaic electron transfer in reverse microemulsions of water, AOT, and toluene is strongly influ- enced by cosurfactants such as primary amides. Cosurfactant con- centration, as a field variable, drives redox electron transfer processes from a low-flux to a high-flux state. Thresholds in this electron-transport phenomenon correlate with perco- lation thresholds in electrical conduc- tivity in the same microemulsions and are inversely proportional to the interfacial activity of the cosurfactants. The critical exponents derived from the scaling analyses of low-frequency conductivity and dielectric spectra suggest that this percolation is close to static percolation limits, implying that percolative transport is along the extended fractal clusters of swollen micellar droplets. 1H and 13C NMR spectra show that surfactant packing transitions are also driven by changes in cosurfactant concentration. These packing transitions provide a physical basis for these electron transfer and conductivity percolation phenomena. Self-diffusion measurements derived from NMR pulsed gradient spin echo experiments show that water proton diffusion increases at the onset of electrical conductivity percolation and is transported along extended clusters. A dynamic partitioning model provides a direct measure of the volume fraction of these percolating clusters and an order parameter for quantifying water-in-oil droplet to percolating cluster micro- structural transitions.

Reverse micelle to sponge phase transition

A pseudoternary system exhibiting a single-phase domain extending from water-in-decane to sponge phase to decane-in-water microemulsions as temperature and decane weight fraction (α; relative to brine+decane) are varied has been studied by Chen, Chang, and Strey [J. Chem. Phys. 93, 1907 (1990)]. Conductivity and self-diffusion measurements at 45 °C show that the reverse micelle to sponge phase transition proceeds in a sequence of two continuous transitions defined quantitatively by an order parameter. This is the first study to quantify the onset of sponge phase formation within a single-phase domain, as a function of composition, and to quantify the amount of sponge phase.