Lateral force processing¶
This notebook opens an image with lateral force data and separates friction from topography effects. Friction turns the
tip the opposite way in the forward and backward scans, so it shows in the difference of the two. What both have in
common, such as slopes of the surface, shows in their mean. Run it from this folder, next to SBS-PMMA.nid.
from NSFopen.process import process
import matplotlib.pyplot as plt
afm = process("SBS-PMMA.nid")
print(afm.channels["Forward"])
channel = "Friction force"
['Deflection', 'Z-Axis', 'Friction force', 'Z-Axis Sensor']
The offset between the scans¶
The forward and backward images lie a few pixels apart. It is best measured on the height, which should be the same in
both scans. find_shift returns the shift in pixels and how well the images match, 1 being a perfect match.
shift_x, shift_y, match = afm.find_shift(("Forward", "Z-Axis"), ("Backward", "Z-Axis"))
print(f"The backward scan lies {shift_x:.2f} pixels to the side and {shift_y:.2f} lines up, match {match:.2f}")
The backward scan lies 3.75 pixels to the side and 0.04 lines up, match 0.97
Friction and mean¶
combine shifts the backward scan by that amount and stores each result as a new channel, with the unit of the
original. Only the shift along the scan line is used. half_diff is (forward - backward) / 2. The outermost columns
have no partner in the other scan after the shift, so ignore a few pixels at the left and right edges.
forward, backward = ("Forward", channel), ("Backward", channel)
afm.combine(forward, backward, "half_diff", shift_x=shift_x, name="Friction")
afm.combine(forward, backward, "mean", shift_x=shift_x, name="Mean")
fig, axes = plt.subplots(1, 3, figsize=(14, 4.2), layout="constrained", dpi=150)
afm.flatten()
afm.image(ax=axes[0], color_range="p1", title="Height")
afm.image(ax=axes[1], channel="Mean", cmap="viridis", color_range="p1", title="Mean of the two scans")
afm.image(ax=axes[2], channel="Friction", cmap="viridis", color_range="p1", title="Friction: half the difference")
plt.show()
The new channels work like any other, for example for the distribution of friction, which separates the two polymers.
fig = afm.plot_histogram_fit(channel="Friction", model="gauss2")
plt.show()
fit = afm.histogram_fit(channel="Friction", model="gauss2")
print(fit.summary(1e3, "mV"))
2 Gaussians, reduced χ² 17 1: center -65.49 ± 0.071 mV, σ 12.33 ± 0.048, FWHM 29.03, share 82.7 % 2: center -31.81 ± 0.097 mV, σ 6.238 ± 0.079, FWHM 14.69, share 14.8 % separation 33.68 ± 0.12 mV