Sentence examples for of known materials from inspiring English sources

Computerized comparisons of analyte mass spectra with mass spectra of known materials is commonly used to identify an analyte.

It provides a classification tool for recognition and verification of known materials.

The crystallographic structure and grain size are identified by comparing diffraction data against a database of known materials.

The challenge was not limited to spectral data of known materials but also invited unknowns to be submitted.

However, a complete set of experimentally determined elastic properties is only available for a small subset of known materials, and an automatic scheme for the derivations of elastic properties that is adapted to high-throughput computation is much demanding.

Dry particle coating is used to create new-generation materials by combining different powders having different physical and chemical properties to form composites, which show new functionality or improve the characteristics of known materials.

In general, these procedures, like all wet digestions of organic matter, should be attempted only with small test portions of known material, strictly following well-established procedures designed for the specific sample type.

The ability to develop many shows simultaneously is just one of the luxuries available to Disney, a company that also has a stockpile of known material, large budgets and ample cross-marketing opportunities -- it owns, for example, ABC and Miramax, which have also invested in and produced Broadway shows.

Using multivariate analysis and a training set of known material mixes, a regression model can be built for predicting the quantity of illicit drugs in an 'unknown' sample (Luggar 1996b; Speller 2001).

Experimental Evaluation: The purpose of the experimental evaluation is to characterize the impact of known material attribute variability on the CQA of the product and manufacturing process.

Thus, for a nanostructure of known material composition, its bulk RI sensitivity can be predicted simply by characterizing its LSPR peak.