Advanced Algorithms for PALS Analysis of Self-Assembled Amphiphiles

Abstract

Self-assembled amphiphile systems are utilized in a wide variety of applications including drug delivery and energy storage. Nano-scale physical and chemical interactions govern the packing of self-assembled amphiphilic molecules, resulting in thermodynamically stable phases of defined geometries. Possible phases include micellar, hexagonal, cubic, lamellar and sponge phases. The internal nano-structure of the amphiphile self-assembly materials plays an important role in the properties of these systems and their application. To date small angle x-ray scattering (SAXS) has been the most common technique used to characterise their structure. We explore positron annihilation lifetime spectroscopy (PALS) as an alternative and/or complementary technique for this purpose, using the phytantriol/water system. While PALS is a well established technique for characterising many materials, the coexistence of aqueous and hydrophobic regions in a soft self-assembled amphiphile material poses a challenge to the analysis and interpretation of the results. In order to alleviate these difficulties we developed a computer program for general-purpose PALS data analysis called PAScual. Amongst the most salient features of this new code are the possibility to perform bounded fits and the option of using advanced algorithms to provide a more robust and unbiased fit: on the one hand, it incorporates a global nonlinear optimisation routine based on the Simulated Annealing algorithm and, on the other hand it gives information on the reliability of the results by means of a Markov Chain Monte-Carlo Bayesian Inference method. In this work we present the newly developed PALS data analysis techniques as well as the results for the phytantriol/water system, comparing them with additional data obtained from complementary techniques.

Positron annihilation lifetime spectroscopy (PALS): a probe for molecular organisation in self-assembled biomimetic systems

“…Several PALS data analysis programs exist with CONTIN, 33 MELT, 34 and RESOLUTION 37 used to transform the experimental data into a continuous lifetime contribution function (deconvolution) whilst PositronFit, 35 PALSfit, 36 LT, 32 and PAScual 38,39 employ mathematical modelling (convolution). The different analysis programs employ various regularisation strategies such as the maximum entropy principle (MELT), whereas CONTIN uses the principle of parsimony, and both PositronFit and LT perform a least squares fitting.…”