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RbNO₃, ⁸⁷Rb (I=3/2) STMAS¶
⁸⁷Rb (I=3/2) satellite-transition off magic-angle spinning simulation.
The following is an example of the STMAS simulation of \(\text{RbNO}_3\). The \(^{87}\text{Rb}\) 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 ST1_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.
Rb87_1 = Site(
isotope="87Rb",
isotropic_chemical_shift=-27.4, # in ppm
quadrupolar=SymmetricTensor(Cq=1.68e6, eta=0.2), # Cq is in Hz
)
Rb87_2 = Site(
isotope="87Rb",
isotropic_chemical_shift=-28.5, # in ppm
quadrupolar=SymmetricTensor(Cq=1.94e6, eta=1.0), # Cq is in Hz
)
Rb87_3 = Site(
isotope="87Rb",
isotropic_chemical_shift=-31.3, # in ppm
quadrupolar=SymmetricTensor(Cq=1.72e6, eta=0.5), # Cq is in Hz
)
sites = [Rb87_1, Rb87_2, Rb87_3] # all sites
spin_systems = [SpinSystem(sites=[s]) for s in sites]
Step 2: Select a satellite-transition variable-angle spinning method. The following ST1_VAS method correlates the frequencies from the two inner-satellite transitions to the central transition. Note, STMAS measurements are highly suspectable to rotor angle mismatch. In the following, we show two methods, first set to magic-angle and the second deliberately miss-sets by approximately 0.0059 degrees.
angles = [54.7359, 54.73]
method = []
for angle in angles:
method.append(
ST1_VAS(
channels=["87Rb"],
magnetic_flux_density=7, # in T
rotor_angle=angle * 3.14159 / 180, # in rad (magic angle)
spectral_dimensions=[
SpectralDimension(
count=256,
spectral_width=3e3, # in Hz
reference_offset=-2.4e3, # in Hz
label="Isotropic dimension",
),
SpectralDimension(
count=512,
spectral_width=5e3, # in Hz
reference_offset=-4e3, # in Hz
label="MAS dimension",
),
],
)
)
# A graphical representation of the method object.
plt.figure(figsize=(5, 2.5))
method[0].plot()
plt.show()
Create the Simulator object, add the method and spin system objects, and run the simulation.
The plot of the simulation.
dataset = [sim.methods[0].simulation, sim.methods[1].simulation]
fig, ax = plt.subplots(1, 2, figsize=(8.5, 3), subplot_kw={"projection": "csdm"})
titles = ["STVAS @ magic-angle", "STVAS @ 0.0059 deg off magic-angle"]
for i, item in enumerate(dataset):
cb1 = ax[i].imshow(item.real / item.real.max(), aspect="auto", cmap="gist_ncar_r")
ax[i].set_title(titles[i])
plt.colorbar(cb1, ax=ax[i])
ax[i].invert_xaxis()
ax[i].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="50 Hz", dim_index=0),
sp.apodization.Gaussian(FWHM="50 Hz", dim_index=1),
sp.FFT(dim_index=(0, 1)),
]
)
processed_dataset = []
for item in dataset:
processed_dataset.append(processor.apply_operations(dataset=item))
processed_dataset[-1] /= processed_dataset[-1].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[1].real, cmap="gist_ncar_r", aspect="auto")
plt.colorbar(cb)
ax.invert_xaxis()
ax.invert_yaxis()
plt.tight_layout()
plt.show()
Total running time of the script: ( 0 minutes 1.302 seconds)