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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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3 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Implementation of a self-consistent slab model of bilayer structure in the SasView suite

Slab models are simple and useful structural descriptions which have long been used to describe lyotropic lamellar phases, such as lipid bilayers. Typically, slab models assume a midline symmetry and break a bilayer structure into three pieces, a central solvent-free core and two symmetric outer layers composed of the soluble portion of the amphiphile and associated solvent. This breakdown matches reasonably well to the distribution of neutron scattering length density and therefore is a convenient and common approach for the treatment of small-angle scattering data. Here, an implementation of this model within the SasView software suite is reported. The implementation is intended to provide physical consistency through the area per amphiphile molecule and number of solvent molecules included within the solvent-exposed outer layer. The proper use of this model requires knowledge of (or good estimates for) the amphiphile and solvent molecule volume and atomic composition, ultimately providing a self-consistent data treatment with only two free parameters: the lateral area per amphiphile molecule and the number of solvent molecules included in the outer region per amphiphile molecule. Here, the use of this code is demonstrated in the fitting of standard lipid bilayer data sets, obtaining structural parameters consistent with prior literature and illustrating the typical and ideal cases of fitting for neutron scattering data obtained using single or multiple contrast conditions. While demonstrated here for lipid bilayers, this model is intended for general application to block copolymers, surfactants, and other lyotropic lamellar phase structures for which a slab model is able to reasonably estimate the neutron scattering length density/electron-density profile of inner and outer layers of the lamellae.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Classifying and analyzing small-angle scattering data using weighted k nearest neighbors machine learning techniques

A consistent challenge for both new and expert practitioners of small-angle scattering (SAS) lies in determining how to analyze the data, given the limited information content of said data and the large number of models that can be employed. Machine learning (ML) methods are powerful tools for classifying data that have found diverse applications in many fields of science. Here, ML methods are applied to the problem of classifying SAS data for the most appropriate model to use for data analysis. The approach employed is built around the method of weighted k nearest neighbors (wKNN), and utilizes a subset of the models implemented in the SasView package (https://www.sasview.org/) for generating a well defined set of training and testing data. The prediction rate of the wKNN method implemented here using a subset of SasView models is reasonably good for many of the models, but has difficulty with others, notably those based on spherical structures. A novel expansion of the wKNN method was also developed, which uses Gaussian processes to produce local surrogate models for the classification, and this significantly improves the classification accuracy. Further, by integrating a stochastic gradient descent method during post-processing, it is possible to leverage the local surrogate model both to classify the SAS data with high accuracy and to predict the structural parameters that best describe the data. The linking of data classification and model fitting has the potential to facilitate the translation of measured data into results for both novice and expert practitioners of SAS.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Modeling the partitioning of amphiphilic molecules and co-solvents in biomembranes

We report amphiphilic co-solvents can have a significant impact on the structure, organization and physical properties of lipid bilayers. Describing the mutual impact of partitioning and induced structure changes is therefore a crucial consideration for a range of topics such as anesthesia and other pharmacokinetic effects, as well as microbial solvent tolerance in the production of biofuels and other fermentation products, where molecules such as ethanol, butanol or acetic acid might be generated. Small-angle neutron scattering (SANS) is a key method for studying lipid and polymer bilayer structures, with many models for extracting bilayer structure (thickness, area per lipid etc.) from scattering data in use today. However, the molecular details of co-solvent partitioning are conflated with induced changes to bilayer structure, making interpretation and modeling of the scattering curves a challenge with the existing set of models. To address this, a model of a bilayer structure is presented which invokes a two-term partition constant accounting for the localization of the co-solvent within the bilayer. This model was validated using a series of SANS measurements of lipid vesicles in the presence of the co-solvent tetrahydrofuran (THF), showing several strategies of how to deploy the two-parameter partition constant model to describe scattering data and extract both structure and partitioning information from the data. Molecular dynamics simulations are then used to evaluate assumptions of the model, provide additional molecular scale details and illustrate its complementary nature to the data fitting procedure. This approach results in estimates of the partition coefficient for THF in 1,2-dimyristoyl-sn-glycero-3-phosphocholine at 35°C, along with an estimate of the fraction of THF residing in the hydrophobic core of the membrane. The authors envision that this model will be applicable to a wide range of other bilayer/amphiphile interactions and provide the associated code needed to implement this model as a fitting algorithm for scattering data in the SasView suite.

59 BASIC BIOLOGICAL SCIENCES↗