Imaging and analysis of nanowires
Abstract
We used vapor-liquid-solid (VLS) methods to synthesize discrete single-element semiconductor nanowires and multicomposition nanowire heterostructures, and then characterized their structure and composition using high-resolution electron microscopy (HRTEM) and analytical electron microscopy techniques. Imaging nanowires requires the modification of the established HRTEM imaging procedures for bulk material to take into consideration the effects of finite nanowire width and thickness. We show that high-resolution atomic structure images of nanowires less than 6 nm in thickness have lattice "streaking" due to the finite crystal lattice in two dimensions of the nanowire structure. Diffraction pattern analysis of nanowires must also consider the effects of a finite structure producing a large reciprocal space function, and we demonstrate that the classically forbidden 1/3 [422] reflections are present in the [111] zone axis orientation of silicon nanowires due to the finite thickness and lattice plane edge effects that allow incomplete diffracted beam cancellation. If the operating conditions are not carefully considered, we found that HRTEM image delocalization becomes apparent when employing a field emission transmission electron microscope (TEM) to image nanowires and such effects have been shown to produce images of the silicon lattice structure outside of the nanowire itself. We show that pseudo low-dose imaging methods are effective in reducing nanowire structure…
Read the paper
Related studies
- The Matrisome: In Silico Definition and In Vivo Characterization by Proteomics of Normal and Tumor Extracellular Matrices
- The Escherichia coli MG1655 in silico metabolic genotype: Its definition, characteristics, and capabilities
- In silico pharmacology for drug discovery: methods for virtual ligand screening and profiling
- In silico prediction of splice-altering single nucleotide variants in the human genome
- In silico clinical trials: how computer simulation will transform the biomedical industry
- In silico optimization of a guava antimicrobial peptide enables combinatorial exploration for peptide design
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.