Spectroscopic chemical characterization of atomic-scale interfaces is a challenging scientific problem. In the search for new spectroscopic capabilities, we investigate nonresonant Raman interactions that occur at the interface between organic adsorbates and inorganic surfaces. Our system is a trans-1,2-bis(4-pyridyl)ethylene molecule adsorbed to a semiconductor PbSe surface. We employ first-principles density functional methods to investigate the vibrational dynamics and Raman spectra of this absorbate−surface motif, and use an external electric bias to tune the degree of the interfacial chemical coupling. As a result, changes in the Raman spectra reveal a continuous transition between the weak and the strong coupling regimes. The strong coupling is associated with the vibration-induced charge transfer, which appears to be a damping mechanism that caps the chemical enhancement (CE) of the Raman spectra. This effect reduces the potential of CE to be used as merely an enhancing mechanism, but shows the potential of Raman scattering to probe changes in the interfacial electronic structure.
A ceramic-metal composite (cermet) of uranium nitride (UN) particles embedded in a tungsten-molybdenum (W/Mo) alloy matrix is being considered as reactor fuel for nuclear thermal propulsion (NTP). One possible issue is the loss of fissile uranium atoms during reactor operation. We begin by reviewing historical data that suggest that a likely mechanism for fuel loss is transport of free uranium along metal grain boundaries and through cracks to free surfaces. We then employ simple two-dimensional (2D) mesoscale simulations to provide insights into crack formation and free uranium transport in a W/ Mo-UN cermet. Phase-field fracture simulations show that cracks form during cooling at the fuel-matrix interface and then within the fuel particles. Transport simulations show that cracks at the fuel-matrix interface and within the fuel accelerate fuel loss. Mechanical and diffusion data are needed for UN and W/Mo alloys to make these preliminary predictions more accurate.
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