A method that can be used to predict the growth of earthquake faults also aids prediction of the tiniest of phenomena–how arrays of “artificial atoms,” or quantum dots, assemble and stack themselves on semiconductor materials, National Institute of Standards and Technology (NIST) researchers report in the July 15 issue of Physical Review B.
From National Institute of Standards and Technology (NIST) :Dot, dot, dot . . . How quantum dots line up
A method that can be used to predict the growth of earthquake faults also aids prediction of the tiniest of phenomena–how arrays of “artificial atoms,” or quantum dots, assemble and stack themselves on semiconductor materials, National Institute of Standards and Technology (NIST) researchers report in the July 15 issue of Physical Review B.
The insight could aid development of more reliable methods for fabricating lasers, sensors and other devices that exploit quantum dots’ special electronic properties — the result of confining electrons in the space of a few nanometers. The minuscule structures already are the basis for some lasers. Yet, difficulties in making quantum dots of uniform size and precisely positioning them on a substrate remain formidable. These obstacles stand in the way of an array of faster, more powerful electronic and photonic devices that require only small inputs of energy to spring into action.
NIST’s Bo Yang and Vinod Tewary borrowed a mathematical concept that explains how cracks grow in a solid, such as the Earth’s crust or an airplane wing. The concept, called the elastic energy release rate, accounts for how energy is apportioned as a crack advances. The scientists found that the rate also accounts for how self-assembling quantum dots, which strain the system’s lattice-like atomic geometry, will position and align themselves among their neighbors–those next door and those living below. For cube-shaped quantum dots, at least, the equation predicts the most “energetically favorable” location for a quantum dot. The NIST pair says their theory can be used, for example, to predict the optimal depth for embedding quantum dots that will be overlain by another array of dots.
