Molecular Modeling of Fluid Thermophysical Properties
This project uses computer simulations to predict fluid properties like vapor pressure and density for various mixtures, including water and atmospheric gases.
This grant is no longer accepting proposals
NRC Research Associateship Programs has archived this opportunity.
Funder: NRC Research Associateship Programs
Due Dates: February 1, 2025 | May 1, 2025 | August 1, 2025 | November 1, 2025
Funding Amounts: Base stipend approximately $82,764/year with $3,000 travel allowance; typical appointment duration 2 years.
Summary: Postdoctoral fellowship supporting molecular modeling research to predict fluid thermophysical properties relevant to industry, metrology, and environmental applications.
Key Information: Open to U.S. citizens holding a doctoral degree within the last 5 years; research conducted onsite at NIST in Boulder, CO; requires prior contact with Research Adviser.
Description
This fellowship opportunity supports postdoctoral research in molecular modeling of fluid thermophysical properties at the National Institute of Standards and Technology (NIST), specifically within the Material Measurement Laboratory's Applied Chemicals and Materials Division in Boulder, Colorado. The research focuses on using statistical mechanics tools such as Monte Carlo and molecular dynamics simulations, and calculations of virial coefficients from intermolecular potentials, to predict and analyze fluid properties where experimental data are limited or unavailable.
Key fluid properties of interest include vapor-liquid and solid-fluid equilibria, fugacity and activity coefficients, virial coefficients describing vapor nonideality, transport properties, dielectric constants, and refractive indices. The systems studied encompass water and aqueous mixtures (including electrolytes), atmospheric gases, carbon dioxide mixtures, molecules differing in size and polarity, and fluids where quantum or multibody effects significantly influence thermophysical behavior.
This research aims to provide data with accuracy potentially exceeding current experimental capabilities, supporting industrial, metrological, and environmental applications.
