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Wollastonite, ²⁹Si (I=1/2)¶
²⁹Si (I=1/2) spinning sideband simulation.
Wollastonite is a high-temperature calcium-silicate, \(\beta−\text{Ca}_3\text{Si}_3\text{O}_9\), with three distinct \(^{29}\text{Si}\) sites. The \(^{29}\text{Si}\) tensor parameters were obtained from Hansen et al. [1]
import matplotlib.pyplot as plt
from mrsimulator import Simulator, SpinSystem, Site
from mrsimulator import signal_processor as sp
from mrsimulator.method.lib import BlochDecaySpectrum
from mrsimulator.spin_system.tensors import SymmetricTensor
from mrsimulator.method import SpectralDimension
Create sites and spin systems. We create three single-site spin systems for better performance.
Si29_1 = Site(
isotope="29Si",
isotropic_chemical_shift=-89.0, # in ppm
shielding_symmetric=SymmetricTensor(zeta=59.8, eta=0.62), # zeta in ppm
)
Si29_2 = Site(
isotope="29Si",
isotropic_chemical_shift=-89.5, # in ppm
shielding_symmetric=SymmetricTensor(zeta=52.1, eta=0.68), # zeta in ppm
)
Si29_3 = Site(
isotope="29Si",
isotropic_chemical_shift=-87.8, # in ppm
shielding_symmetric=SymmetricTensor(zeta=69.4, eta=0.60), # zeta in ppm
)
spin_systems = [
SpinSystem(sites=[Si29_1]),
SpinSystem(sites=[Si29_2]),
SpinSystem(sites=[Si29_3]),
]
Create a Bloch decay spectrum method.
method = BlochDecaySpectrum(
channels=["29Si"],
magnetic_flux_density=14.1, # in T
rotor_frequency=1500, # in Hz
spectral_dimensions=[
SpectralDimension(
count=2048,
spectral_width=25000, # in Hz
reference_offset=-10000, # in Hz
label=r"$^{29}$Si resonances",
)
],
)
# A graphical representation of the method object.
plt.figure(figsize=(4, 2))
method.plot()
plt.show()
Create the Simulator object and add method and spin system objects, and run.
Simulate the spectrum.
sim.run()
# The plot of the simulation before signal processing.
plt.figure(figsize=(4.25, 3.0))
ax = plt.subplot(projection="csdm")
ax.plot(sim.methods[0].simulation.real, color="black", linewidth=1)
ax.invert_xaxis()
plt.tight_layout()
plt.show()
Add post-simulation signal processing.
processor = sp.SignalProcessor(
operations=[sp.IFFT(), sp.apodization.Exponential(FWHM="70 Hz"), sp.FFT()]
)
processed_dataset = processor.apply_operations(dataset=sim.methods[0].simulation)
# The plot of the simulation after signal processing.
plt.figure(figsize=(4.25, 3.0))
ax = plt.subplot(projection="csdm")
ax.plot(processed_dataset.real, color="black", linewidth=1)
ax.invert_xaxis()
plt.tight_layout()
plt.show()
Total running time of the script: (0 minutes 1.133 seconds)