Sort by
Refine Your Search
-
continues to push patterning to new limits. There are significant needs to understand how the components in these resists are distributed, and critically whether there is aggregation that could contribute
-
of multiscale methods to measure how the marcoscopic behavior of novel graphene devices arises from the microscopic distribution of nanoscale properties involves multiple NIST research efforts linking STM, STS
-
distribution of nanomaterials, and to study the fate of nanomaterials in the environment or in biological systems. key words Buckeyballs; Compositional imaging; Metals in nanomaterials; Nanomaterials
-
, since every nanoparticle produced is not identical, it also utilizes new techniques like First Order Reversal Curves (FORC) to characterize the distributions in these properties. A variety of experimental
-
. These materials systems may have far-reaching applications, extending from neuromorphic computing to compact multiple-input multiple-output antennas. By achieving the aims of this project, this Associate will
-
variability that is due to experimental conditions in metabolomics experiments. The aim of this project is to establish a standard protocol to evaluate the analytical variability associated with liquid
-
RAP opportunity at National Institute of Standards and Technology NIST Mathematical Modeling and Simulation Location Information Technology Laboratory, Applied and Computational Mathematics
-
quantum computing. Their dimensions range from a few to several hundred nanometers. There is special interest in color centers in nanodiamonds, which give them unique photonic and spin characteristics
-
is to measure to high accuracy the SI-traceable spectral energy distribution over the visible and near infrared wavelength range for a set of stars for use as flux standards for astronomy. In
-
experimentally. However, important challenges remain, such as transition-metal compounds and floppy or tautomerizing molecules. Determining the quantitative uncertainties associated with high-level predictions is