Phages recognizing the Indium Nitride semiconductor surface via their peptides.

Considerable advances in materials science are expected via the use of selected or designed peptides to recognize material, control their growth, or to assemble them into elaborate novel devices. Identifying specific peptides for a number of technologically useful materials has been the challenge of many research groups in recent years. This can be accomplished by using affinity-based bio-panning methods such as phage display technologies. In this work, a combinatorial library including billions of clones of genetically engineered M13 bacteriophage was used to select peptides that could recognize improved indium nitride (InN) semiconductor (SC) material. Several rounds of biopanning were necessary to select the phage with the higher affinity from the low variant library. The DNA of this specific phage was extracted and sequenced to set up the related specific adherent peptide. Atomic force microscopy (AFM) is used to demonstrate the real affinity of a selected phage for the InN surface. Due to the possibility of its functionalization with biomolecules and its important physical properties, InN is a promising candidate for developing affinity-based optical and electrical biosensors and/or for biomimetic applications.

Strain Relief Analysis of InN Quantum Dots Grown on GaN.

We present a study by transmission electron microscopy (TEM) of the strain state of individual InN quantum dots (QDs) grown on GaN substrates. Moiré fringe and high resolution TEM analyses showed that the QDs are almost fully relaxed due to the generation of a 60° misfit dislocation network at the InN/GaN interface. By applying the Geometric Phase Algorithm to plan-view high-resolution micrographs, we show that this network consists of three essentially non-interacting sets of misfit dislocations lying along the directions. Close to the edge of the QD, the dislocations curve to meet the surface and form a network of threading dislocations surrounding the system.