Characterization and analysis of the interlaminar behavior of thermoplastic composites considering fiber bridging and R-curve effects

Thermoplastic composites can enable the development of new manufacturing techniques to make the aviation industry more sustainable while at the same time greatly benefit cost-efficient and high-volume production. One of the thermoplastic composite materials that can enable this transition is AS4D/PEKK-FC. In this work, the interlaminar properties of AS4D/PEKK-FC thermoplastic composite are characterized and analyzed by means of Mode I, II and Mixed Mode I/II at 50:50 tests, while considering fiber bridging and R-curve effects. In order to achieve stable crack propagation the test configurations are adjusted to account for the large fracture process zone ahead of the crack tip and an appropriate data reduction method is selected. The experimental data is reduced using an inverse methodology to extract cohesive laws based on only the load–displacement curves. Additionally, the use of this methodology provides new insights into the validity of two different mode II tests and the influence of fiber bridging on the mixed-mode interlaminar behavior. The interlaminar damage mechanisms are investigated by means of scanning electron microscopy. The resulting cohesive laws are implemented in commercial finite element software in tabular form, without the need for user-subroutines. All experimental test configurations are analyzed using a single material card and it is shown that fiber bridging and R-curve effects are well captured

Molecular Events behind the Selectivity and Inactivation Properties of Model NaK-Derived Ion Channels

Y55W mutants of non-selective NaK and partly K+-selective NaK2K channels have been used to explore the conformational dynamics at the pore region of these channels as they interact with either Na+ or K+. A major conclusion is that these channels exhibit a remarkable pore conformational flexibility. Homo-FRET measurements reveal a large change in W55–W55 intersubunit distances, enabling the selectivity filter (SF) to admit different species, thus, favoring poor or no selectivity. Depending on the cation, these channels exhibit wide-open conformations of the SF in Na+, or tight induced-fit conformations in K+, most favored in the four binding sites containing NaK2K channels. Such conformational flexibility seems to arise from an altered pattern of restricting interactions between the SF and the protein scaffold behind it. Additionally, binding experiments provide clues to explain such poor selectivity. Compared to the K+-selective KcsA channel, these channels lack a high affinity K+ binding component and do not collapse in Na+. Thus, they cannot properly select K+ over competing cations, nor reject Na+ by collapsing, as K+-selective channels do. Finally, these channels do not show C-type inactivation, likely because their submillimolar K+ binding affinities prevent an efficient K+ loss from their SF, thus favoring permanently open channel states.This work was partly supported by grants PGC2018-093505-B-I00 from the Spanish “Ministerio de Ciencia e Innovación”/FEDER, UE, and FCT-Fundação para a Ciência e a Tecnologia, I.P., under the scope of the project UIDB/04565/2020 and UIDP/04565/2020 of the Research Unit Institute for Bioengineering and Biosciences—iBB and the project LA/P/0140/2020 of the Associate Laboratory Institute for Health and Bioeconomy—i4HB

Motif Statistics and Spike Correlations in Neuronal Networks

Motifs are patterns of subgraphs of complex networks. We studied the impact of such patterns of connectivity on the level of correlated, or synchronized, spiking activity among pairs of cells in a recurrent network model of integrate and fire neurons. For a range of network architectures, we find that the pairwise correlation coefficients, averaged across the network, can be closely approximated using only three statistics of network connectivity. These are the overall network connection probability and the frequencies of two second-order motifs: diverging motifs, in which one cell provides input to two others, and chain motifs, in which two cells are connected via a third intermediary cell. Specifically, the prevalence of diverging and chain motifs tends to increase correlation. Our method is based on linear response theory, which enables us to express spiking statistics using linear algebra, and a resumming technique, which extrapolates from second order motifs to predict the overall effect of coupling on network correlation. Our motif-based results seek to isolate the effect of network architecture perturbatively from a known network state