Facility acknowledgment for publications
All users are asked to submit information about their resulting publications to the FRIB Manager for User Relations (useroffice@frib.msu.edu). In the future, publication information will be entered on the FRIB Scientific User Portal.
To the extent possible, an acknowledgment of Federal support must appear in the publication of any material, whether copyrighted or not, based on or developed using FRIB resources, as follows:
- Acknowledgment: "This material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of Nuclear Physics and used resources of the Facility for Rare Isotope Beams (FRIB) Operations, which is a DOE Office of Science User Facility under Award Number DE-SC0023633.”
Users must submit their publication information to the DOE Energy Link (E-Link) System.
Observation of the Exotic Isotope 13F Located Four Neutrons Beyond the Proton Drip Line
doi: 10.1103/PhysRevLett.126.132501
First simultaneous acceleration of multiple charge states of heavy ion beams in a large-scale superconducting linear accelerator
doi: 10.1103/PhysRevLett.126.114801
129I and 247Cm in meteorites constrain the last astrophysical source of solar r-process elements
doi: 10.1126/science.aba1111
Thermal-Hydraulic Characterization of Shell-Side Flow in a Cryogenic Coiled Finned-Tube Heat Exchanger
doi: 10.1115/1.4049961
Advances in modeling nuclear matrix elements of neutrinoless double beta decay
doi: 10.1016/j.scib.2020.09.031
Three-Dimensional Network Adjustment of Laser Tracker Measurements for Large-Scale Metrology Applications
doi: 10.1061/(ASCE)SU.1943-5428.0000332
K-Athena: A Performance Portable Structured Grid Finite Volume Magnetohydrodynamics Code
doi: 10.1109/TPDS.2020.3010016
Quenching of single-particle strength from direct reactions with stable and rare-isotope beams
doi: 10.1016/j.ppnp.2021.103847
Detailed study of the decay of 32Ar
doi: 10.1140/epja/s10050-020-00341-3
Exciting prospects for detecting late-time neutrinos from core-collapse supernovae
doi: 10.1103/PhysRevD.103.023016
Optimization and supervised machine learning methods for fitting numerical physics models without derivatives
doi: 10.1088/1361-6471/abd009
Recent advances in the quantification of uncertainties in reaction theory
doi: 10.1088/1361-6471/abba72
