Tomasz Derda

Statistics of Critical Avalanches in Vertical Nanopillar Arrays

Nanopillar arrays are encountered in numerous areas of nanotechnology such as bio-medical and chemical sensing, nanoscale electronics, photovoltaics or thermoelectrics. Especially arrays of nanopillars subjected to uniaxial microcompression reveal the potential applicability of nanopillars as components for the fabrication of electro-mechanical sense devices. Thus, it is worth to analyze the failure progress in such systems of pillars. Under the growing load pillars destruction forms an avalanche and when the load exceeds a certain critical value the avalanche becomes self-sustained until the system is completely destroyed. In this work we have explored the distributions of such catastrophic avalanches appearing in overloaded systems. Specifically, we analyze the relations between the size of an avalanche being the numbers of instantaneously crushed pillars and the size of the corresponding array of nanopillars using different load transfer protocols.

Statistics of Critical Load in Arrays of Nanopillars on Nonrigid Substrates

Multicomponent systems are commonly used in nano-scale technology. Specifically, arrays of nanopillars are encountered in electro-mechanical sense devices. Under a growing load weak pillars crush. When the load exceeds a certain critical value the system fails completely. In this work we explore distributions of such a critical load in overloaded arrays of nanopillars with identically distributed random strength-thresholds (\(\sigma _{th}\)). Applying a Fibre Bundle Model with so-called local load transfer we analyse how statistics of critical load are related to statistics of pillar-strength-thresholds. Based on extensive numerical experiments we show that when the \(\sigma _{th}\) are distributed according to the Weibull distribution, with shape and scale parameters k, and \(\lambda = 1\), respectively, then the critical load can be approximated by the same probability distribution. The corresponding, shape and scale, parameters K and \(\varLambda \) are functions of k.