Albite, ²⁷Al (I=5/2) 3QMAS¶

²⁷Al (I=5/2) triple-quantum magic-angle spinning (3Q-MAS) simulation.

The following is an example of \(^{27}\text{Al}\) 3QMAS simulation of albite \(\text{NaSi}_3\text{AlO}_8\). The \(^{27}\text{Al}\) tensor parameters were obtained from Massiot et al. [1].

import matplotlib.pyplot as plt

from mrsimulator import Simulator, SpinSystem, Site
from mrsimulator.method.lib import ThreeQ_VAS
from mrsimulator import signal_processor as sp
from mrsimulator.spin_system.tensors import SymmetricTensor
from mrsimulator.method import SpectralDimension

Generate the site and spin system objects.

site = Site(
    isotope="27Al",
    isotropic_chemical_shift=64.7,  # in ppm
    quadrupolar=SymmetricTensor(Cq=3.25e6, eta=0.68),  # Cq is in Hz
)

spin_systems = [SpinSystem(sites=[site])]

Select a Triple Quantum variable-angle spinning method. You may optionally provide a rotor_angle to the method. The default rotor_angle is the magic-angle.

method = ThreeQ_VAS(
    channels=["27Al"],
    magnetic_flux_density=7,  # in T
    spectral_dimensions=[
        SpectralDimension(
            count=256,
            spectral_width=1e4,  # in Hz
            reference_offset=-3e3,  # in Hz
            label="Isotropic dimension",
        ),
        SpectralDimension(
            count=512,
            spectral_width=1e4,  # in Hz
            reference_offset=4e3,  # in Hz
            label="MAS dimension",
        ),
    ],
)

Create the Simulator object, add the method and spin system objects, and run the simulation.

sim = Simulator()
sim.spin_systems = spin_systems  # add the spin systems
sim.methods = [method]  # add the method.
sim.run()

The plot of the simulation.

dataset = sim.methods[0].simulation

plt.figure(figsize=(4.25, 3.0))
ax = plt.subplot(projection="csdm")
cb = ax.imshow(dataset.real / dataset.real.max(), aspect="auto", cmap="gist_ncar_r")
plt.colorbar(cb)
ax.invert_xaxis()
ax.invert_yaxis()
plt.tight_layout()
plt.show()

Add post-simulation signal processing.

processor = sp.SignalProcessor(
    operations=[
        # Gaussian convolution along both dimensions.
        sp.IFFT(dim_index=(0, 1)),
        sp.apodization.Gaussian(FWHM="0.2 kHz", dim_index=0),
        sp.apodization.Gaussian(FWHM="0.2 kHz", dim_index=1),
        sp.FFT(dim_index=(0, 1)),
    ]
)
processed_dataset = processor.apply_operations(dataset=sim.methods[0].simulation)
processed_dataset /= processed_dataset.max()

The plot of the simulation after signal processing.

plt.figure(figsize=(4.25, 3.0))
ax = plt.subplot(projection="csdm")
cb = ax.imshow(processed_dataset.real, cmap="gist_ncar_r", aspect="auto")
plt.colorbar(cb)
ax.set_xlim(75, 25)
ax.set_ylim(-15, -65)
plt.tight_layout()
plt.show()

Total running time of the script: ( 0 minutes 0.750 seconds)

Gallery generated by Sphinx-Gallery