The Proposal and Necessity of the Numerical Description of Nano- and Microplastics’ Surfaces (Plastisphere)

The constantly growing amount of synthetic materials < 5 mm, called microplastics (MPs), is fragmented in the environment. Thus, their surface, Plastisphere, is substantially increasing forming an entirely new ecological niche. It has already been extensively studied by microbiologists observing the biofilm and by material scientists interested in the weathering of polymer materials. This paper aims to construct a bridge between the physical and chemical description of the Plastisphere and its microbiological and ecological significance. Various algorithms, based on the analysis of pictures obtained by scanning electron microscopy (SEM), are proposed to describe in detail the morphology of naturally weathered polymers. In particular, one can study the size and distribution of fibres in a standard filter, search the synthetic debris for mapping, estimate the grain size distribution, quantitatively characterize the different patterns of degradation for polymer spheres and ghost nets, or calculate the number of pores per surface. The description and visualization of a texture, as well as the classification of different morphologies present on a surface, are indispensable for the comprehensive characterization of weathered polymers found inside animals (e.g., fishes). All these approaches are presented as case studies and discussed within this work.

Extended connectivity interaction features: improving binding affinity prediction through chemical description

Motivation Machine-learning scoring functions (SFs) have been found to outperform standard SFs for binding affinity prediction of protein–ligand complexes. A plethora of reports focus on the implementation of increasingly complex algorithms, while the chemical description of the system has not been fully exploited. Results Herein, we introduce Extended Connectivity Interaction Features (ECIF) to describe protein–ligand complexes and build machine-learning SFs with improved predictions of binding affinity. ECIF are a set of protein−ligand atom-type pair counts that take into account each atom’s connectivity to describe it and thus define the pair types. ECIF were used to build different machine-learning models to predict protein–ligand affinities (pKd/pKi). The models were evaluated in terms of ‘scoring power’ on the Comparative Assessment of Scoring Functions 2016. The best models built on ECIF achieved Pearson correlation coefficients of 0.857 when used on its own, and 0.866 when used in combination with ligand descriptors, demonstrating ECIF descriptive power. Availability and implementation Data and code to reproduce all the results are freely available at https://github.com/DIFACQUIM/ECIF. Contact [email protected] Supplementary information Supplementary data are available at Bioinformatics online.

Hydrogen order in hydrides of Laves phases

Many Laves phases AM2 takes up hydrogen to form interstitial hydrides in which hydrogen atoms partially occupy A2M2, AM3, and/or M4 tetrahedral interstices. They often exhibit temperature-driven order-disorder phase transitions, which are triggered by repulsion of hydrogen atoms occupying neighboring tetrahedral interstices. Because of the phase widths with respect to hydrogen a complete ordering, i.e., full occupation of all hydrogen positions is usually not achieved. Order-disorder transitions in Laves phase hydrides are thus phase transitions between crystal structures with different degrees of hydrogen order. Comparing the crystal structures of ordered and disordered phases reveals close symmetry relationships in all known cases. This allows new insights into the crystal chemical description of such phases and into the nature of the phase transitions. Structural relationships for over 40 hydrides of cubic and hexagonal Laves phases ZrV2, HfV2, ZrCr2, ZrCo2, LaMg2, CeMg2, PrMg2, NdMg2, SmMg2, YMn2, ErMn2, TmMn2, LuMn2, Lu0.4Y0.6Mn2 YFe2, and ErFe2 are concisely described in terms of crystallographic group-subgroup schemes (Bärnighausen trees) covering 32 different crystal structure types, 26 of which represent hydrogen-ordered crystal structures.

Effects of Composition Heterogeneities on Flame Kernel Propagation: A DNS Study

In this study, a new set of direct numerical simulations is generated and used to examine the influence of mixture composition heterogeneities on the propagation of a premixed iso-octane/air spherical turbulent flame, with a representative chemical description. The dynamic effects of both turbulence and combustion heterogeneities are considered, and their competition is assessed. The results of the turbulent homogeneous case are compared with those of heterogeneous cases which are characterized by multiple stratification length scales and segregation rates in the regime of a wrinkled flame. The comparison reveals that stratification does not alter turbulent flame behaviors such as the preferential alignment of the convex flame front with the direction of the compression. However, we find that the overall flame front propagation is slower in the presence of heterogeneities because of the differential on speed propagation. Furthermore, analysis of different displacement speed components is performed by taking multi-species formalism into account. This analysis shows that the global flame propagation front slows down due to the heterogeneities caused by the reaction mechanism and the differential diffusion accompanied by flame surface density variations. Quantification of the effects of each of these mechanisms shows that their intensity increases with the increase in stratification’s length scale and segregation rate.