Sort by
Refine Your Search
-
materials, (2) the preferred binding sites of adsorbate species in nanoporous solids and predicted experimental signals (e.g., infrared spectra), and (3) the development of DFT-based force field models
-
information. Our group performs research and development to extend the accuracy, wavelength range, power range, robustness, and portability of radiometric standards. We use advanced nanfabrication techniques
-
technologies. Research interests include (1) development of novel approaches for the non-target screening of complex chemical systems; (2) fundamental research of HRAM-MS technologies and affiliated hyphenated
-
being able to address these needs, though significant research gaps still exist in technique development, chemometrics, spectral interpretation, and standards development.Development of new or modified
-
within the Radioactivity Group at NIST addresses some of these hurdles in an effort to provide the foundations for absolute quantitation in imaging. NIST pioneered the development of long-lived calibration
-
DeCost brian.decost@nist.gov 301.975.5160 Description Trustability and physical interpretability are critical requirements for the development of robust and sustainable machine learning systems needed
-
LC-MS/MS instruments and custom software tools. Individuals with a background in proteomic sample preparation, mass spectrometry, and software development are encouraged to apply as well those who have
-
, speciation). The project will focus on the design and development of front-end techniques, devices, and platforms with the potential to directly sample, or separate, analytes from complex matrices (e.g., dirt
-
will have opportunities for participation in instrument/technique development projects. key words Radionuclide metrology; Digital data acquisition; Si(Li) detector; HPGe detector; Coincidence counting
-
been in development over the past 15+ years and their capabilities have grown significantly. An important effort within the LPBF community is the use of high-fidelity multiphysics models to predict melt