https://www.selleckchem.com/products/monomethyl-auristatin-e-mmae.html Although large-radius carbon nanotubes (CNTs) are now available in macroscopic quantities, little is known about their condensed phase. Large-scale density functional theory calculations predict a low energy phase in which the same-diameter "dog-bone" collapsed CNTs form a graphite-like phase with complex, anomalous grain boundaries (GBs). The excess GB volume does not prevent the strong van der Waals coupling of the flattened CNT sides into AB stacking. The associated GB energetics is dominated by the van der Waals energy penalty and high curvature bending of the loop CNT edges, which exhibit reactivity and flexoelectricity. The large density and superior mechanical rigidity of the proposed microstructural organization as well as the GB flexoelectricity are desirable properties for developing ultra-strong composites based on large-radius CNTs.DNA molecules can electrophoretically be driven through a nanoscale opening in a material, giving rise to rich and measurable ionic current blockades. In this work, we train machine learning models on experimental ionic blockade data from DNA nucleotide translocation through 2D pores of different diameters. The aim of the resulting classification is to enhance the read-out efficiency of the nucleotide identity providing pathways toward error-free sequencing. We propose a novel method that at the same time reduces the current traces to a few physical descriptors and trains low-complexity models, thus reducing the dimensionality of the data. We describe each translocation event by four features including the height of the ionic current blockade. Training on these lower dimensional data and utilizing deep neural networks and convolutional neural networks, we can reach a high accuracy of up to 94% in average. Compared to more complex baseline models trained on the full ionic current traces, our model outperforms. Our findings clearly reveal that the use of the