Simulation of Defects, Flexibility and Rupture in Biopolymer Networks

Networks of biopolymers occur often in nature, and are vulnerable to damage over time. In this work, a coarse grained model of collagen IV molecules is applied in a 2D hexagonal network and the mechanisms by which these networks can rupture are explored. The networks are stretched linearly in order to study their structural limits and mechanism of rupture over timescale of up to 100 microseconds. Metrics are developed to track the damage networks suffer over time, and qualitatively analyse ruptures that occur. Further simulations repeatedly stretch the networks sinusoidally to mimic the in vivo strains. Defects of increasing levels of complexity are introduced into an ordered network, and their effect on the rupturing behaviour of the biopolymer networks studied. The effect of introducing holes of varying size in the network, as well as strips of finite width to mimic surgical damage are studied. These demonstrate the importance of the flexibility of the networks to preventing damage.

Dominating Number on Icosahedral-Hexagonal Network

In this paper, we deal with the dominating set and the domination number on an icosahedral-hexagonal network. We will consider all cases of successive halving of the edges of triangles that are the sides of icosahedrons and thus obtain icosahedral-hexagonal networks.

Egg morphology of Mydaea lateritia (Rondani, 1866) (Diptera: Muscidae)

Several specimens of Mydaea lateritia (Rondani, 1866) were collected during studies of arthropod succession on pig carrions in western Poland. This is the first record of this species in Poland and the northernmost occurrence of the species. Scanning electron microscopy documentation of egg morphology of M. lateritia is presented for the first time. Hexagonal chorionic network, foliate hatching pleats, short lateral respiratory horns, dorsomedian flange and median area with smooth hexagonal network and craters are described. The egg could be classified as Mydaea-type with short respiratory horns.

Some computational aspects of carbon nanocone using Q(G) operator, hexagonal network and probabilistic neural network

In this article, we first find closed forms of M-polynomials of carbon nanocones using Q(G) operator, hexagonal networks and probabilistic neural network. We also reckon closed forms of various degree-based topological indices of these structures. These indices are numerical tendencies that generally interprit quantitative structural activity/property/toxicity relationships and correlate certain physico-chemical properties, such as boiling point, stability, and strain energy, of respective nanomaterial.

Sterically crowded hydrogen-bonded hexagonal network frameworks

TpMeandTpFwith substituents at theortho-positions of carboxy groups were synthesized and revealed substituent effects on molecular assembly.

Transformation of self-assembly of a TTF derivative at the 1-phenyloctane/HOPG interface studied by STM—from a nanoporous network to a linear structure

When dissolved in 1-phenyloctane, EDTTF could co-assemble with TCDB to form a hexagonal network and would transform into a linear structure.