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Transient Nuclear Induction and Double Nuclear Resonance in Solids
176
Citations
29
References
1956
Year
SpintronicsEngineeringNuclear PhysicsPhysicsTransient Nuclear InductionMarkoff ProcessNatural SciencesSpin Echo SignalsNuclear Quadrupole ResonanceMagnetic ResonanceCoherence ResonanceAtomic PhysicsDouble ResonanceCoherent ProcessStochastic ResonanceEcho SignalsSpin DynamicNuclear Engineering
The behavior of transient nuclear induction signals from solids is described by a stochastic model based on a Markoff process. The model assumes the presence of local dipolar field fluctuations in a crystal due to a set $B$ of coupled nuclei. These fluctuations can destroy or enhance the observed precessional coherence of a different set $A$ of nuclei in the crystal. Coupling among $A$ spins is assumed negligible. The spin echo of $A$ formed at a given time $t$ has an amplitude determined by the magnitude and rate $R$ of field fluctuations. For values of $\mathrm{Rt}$ between zero and the order of unity the echo amplitude decreases, reaches a minimum at $\mathrm{Rt}\ensuremath{\sim}1$, and increases for $\mathrm{Rt}>1$. For $R$ larger than the spin $A$ static line width (when $R=0$) in units of frequency, line narrowing becomes effective, and is reflected in terms of increased lifetime and amplitudes of echo signals. The effect of $B$ spin (${\mathrm{Na}}^{23}$) continuous wave resonance upon the echo relaxation of $A({\mathrm{Cl}}^{35})$ is studied in NaCl${\mathrm{O}}_{3}$, a nuclear quadrupole system. For sufficiently weak cw rf excitation of ${\mathrm{Na}}^{23}$, the behavior of ${\mathrm{Cl}}^{35}$ echoes roughly follows the behavior predicted by the stochastic model for changes in local field fluctuation rate $R$. The effect of coherent oscillations of local fields at larger cw rf excitation is discussed. Zeeman splittings of the ${\mathrm{Na}}^{23}$ quadrupole resonance are studied by the double resonance method. The decay of spin echo signals in liquids, as a result of molecular diffusion, is conveniently described by the stochastic model.
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