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NIST Pubs

Certification of Standard Reference Material® 2386 Avocado Powder

Author(s)
Melissa M. Phillips, Laura J. Wood, George Caceres, Grace Hahm, Karen E. Murphy, Rick L. Paul, Benjamin Place, James H. Yen
The National Institute of Standards and Technology (NIST) recently released SRM 2386 Avocado Powder which has value assignment for over 70 analytes. This...

Low-GWP Alternative Refrigerant Blends for HFC-134a: Interim Report

Author(s)
Piotr A. Domanski, Mark O. McLinden, Valeri I. Babushok, Ian Bell, Tara Fortin, Michael Hegetschweiler, Mark A. Kedzierski, Dennis Kim, Lingnan Lin, Gregory T. Linteris, Stephanie L. Outcalt, Richard A. Perkins, Aaron Rowane, Harrison M. Skye
This project addresses the objectives of the Statement of Need number WPSON-17-20 "No/Low Global Warming Potential Alternatives to Ozone Depleting Refrigerants...

Spectral Data for Gallium: Ga I through Ga XXXI

Author(s)
T Shirai, Joseph Reader, Alexander Kramida, J Sugar
The energy levels and spectral lines of gallium atom in all stages of ionization have been compiled. Experimental data on spectral lines and energy levels exist...

A Summary of Heat-Flux Sensor Calibration Data

Author(s)
A V. Murthy, Gerald T. Fraser, D P. DeWitt
This paper presents a statistical evaluation of the responsivity data on a number of heat-flux sensors, calibrated using an electrical substitution radiometer...

Recertification of the SRM 1483a, a Polyethylene

Author(s)
Kathleen M. Flynn, William R. Blair
The recertification of a narrow molecular weight distribution poly(ethylene) Standard Reference Material, SRM 1483a, is described. Studies by size exclusion...

Nano/Bioscience and Technology Education for the 21st Century

Author(s)
J Jacobs, Winnie Wong-Ng, Gale A. Holmes, Richard F. Kayser
NEW:Update 2005, the 20th annual workshops for science and technology educators with the theme, NANO/BIOSCIENCE AND TECHNOLOGY EDUCATION FOR THE 21ST CENTURY...

Learning to Recognize Distributional Functions

Author(s)
J E. Devaney, L Hunter, James J. Filliben
This paper introduces a novel methodology that enables recognition of distributional functions without doing a fit of the data to the distribution. This...
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