4 Search Results

Stabilizing oil-water interfaces in emulsions by plant-based proteins provides sustainable and tunable ways for designing emulsions with specific properties, for food, healthcare, and pharmaceuticals. Cruciferin, a protein from rapeseed, has great potential as green emulsifier, but details about its structure and mobility at oil-water interfaces are largely unknown. Here, these properties are studied with small angle neutron and x-ray scattering, and neutron spin echo spectroscopy, analyzed by atomistic modelling of scattering curves and coarse-grained modelling, to gain insight into interface coverage, and molecular conformation and mobility at the interface. Cruciferin assumes trimeric conformations at the interface, as in solution, butmore » with its protrusions from the central core of the subunits (“arms”) more compressed. Interfacial mobility is only marginally lower than in solution, indicating the arms still transiently extend and preserve a network, for the first time revealing the mechanism how cruciferin forms highly elastic 2d gel-like oil-water interfaces, as observed in macroscopic rheology. The high interfacial mobility may help in self-repairing non-stabilized interfacial fractions, reducing coalescence. These findings provide a deeper molecular level understanding of proteins at oil-water interfaces, which can stimulate development of new plant-based emulsion products, and contribute to the global protein transition.« less

The ability of methylammonium lead triiodide (MAPbI₃) to achieve photoelectric conversion efficiency that is on par with crystalline silicon has led to a surge of interest in perovskite photovoltaics. However, an in-depth understanding of how the ubiquitous coupling between the fast rovibrational movements of the organic cations and the phonon vibrations of the inorganic framework affects the relaxation and recombination of hot carriers remains largely elusive. Access to such knowledge is critical to guide design and increase efficiency of new classes of perovskite solar cells. In this report, we report a time-domain ab initio investigation of temperature dependent excited statemore » dynamics in MAPbI₃, with particular emphasis on nuclear anharmonic effects. The observed strong anharmonicity is attributed to softness of the material and unusual dynamical coupling between the organic and inorganic components. At an elevated temperature, the hydrogen bonding between MA and iodines is weakened, enhancing rotation of MA cations, which become more dynamically disordered. As a result, thermal vibrations of the inorganic Pb–I sublattice are suppressed, and the lattice anharmonicity is decreased. Thermal fluctuations of the electronic energy levels are found to follow the trend of anharmonic motions of Pb and I atoms, with holes relaxing faster to the band edges than electrons, due to higher density of the hole states. While elevated temperature accelerates intraband carrier cooling, it slows nonradiative carrier recombination. The latter result is important for performance, since solar cells and other devices heat up during operation. The reported signatures of coupled structural dynamics of the organic cations and inorganic framework unravel the interplay between anomalous structural fluctuations and charge carrier dynamics, which is of particular importance for fundamental understanding of the structure–property relationships in hybrid metal halide perovskites.« less

The sixth blind test of organic crystal structure prediction (CSP) methods has been held, with five target systems: a small nearly rigid molecule, a polymorphic former drug candidate, a chloride salt hydrate, a co-crystal and a bulky flexible molecule. This blind test has seen substantial growth in the number of participants, with the broad range of prediction methods giving a unique insight into the state of the art in the field. Significant progress has been seen in treating flexible molecules, usage of hierarchical approaches to ranking structures, the application of density-functional approximations, and the establishment of new workflows and `bestmore » practices' for performing CSP calculations. All of the targets, apart from a single potentially disorderedZ' = 2 polymorph of the drug candidate, were predicted by at least one submission. Despite many remaining challenges, it is clear that CSP methods are becoming more applicable to a wider range of real systems, including salts, hydrates and larger flexible molecules. The results also highlight the potential for CSP calculations to complement and augment experimental studies of organic solid forms.« less

Comprehensive evaluation of common genetic variations through association of single nucleotide polymorphisms (SNPs) with complex human diseases on the genome-wide scale is an active area in human genome research. One of the fundamental questions in a SNP-disease association study is to find an optimal subset of SNPs with predicting power for disease status. To find that subset while reducing study burden in terms of time and costs, one can potentially reconcile information redundancy from associations between SNP markers. We have developed a feature selection method named Supervised Recursive Feature Addition (SRFA). This method combines supervised learning and statistical measures formore » the chosen candidate features/SNPs to reconcile the redundancy information and, in doing so, improve the classification performance in association studies. Additionally, we have proposed a Support Vector based Recursive Feature Addition (SVRFA) scheme in SNP-disease association analysis. We have proposed using SRFA with different statistical learning classifiers and SVRFA for both SNP selection and disease classification and then applying them to two complex disease data sets. In general, our approaches outperform the well-known feature selection method of Support Vector Machine Recursive Feature Elimination and logic regression-based SNP selection for disease classification in genetic association studies. Our study further indicates that both genetic and environmental variables should be taken into account when doing disease predictions and classifications for the most complex human diseases that have gene-environment interactions.« less