4
achieved by directly exfoliating the 2D materials onto the plasma-treated surface immediately after
taking the substrate out of the chamber, ensuring the cleanest interface between the 2D material and
substrate and high surface reactivity of the substrate’s surface (Fig. 1c). The longer the SiO2 is re-
exposed to ambient conditions, the more environmental adsorbates are redeposited onto the surface
and limits interactions with the exfoliated 2D flakes (Fig. 1d)4,9. The adhesion energies between
monolayer graphene and SiO2 varies tremendously in the literature between 100 and 450 mJ/m2 in the
literature2,10, which is possibly a manifestation of these discrepancies with substrate cleaning and
ambient exposure. We choose to control adhesion by varying the time the plasma-treated substrates
are in ambient prior to exfoliation.
To observe this variation in graphite adhesion to plasma-treated substrates, we conduct
ultrasonic delamination threshold testing. Here, we take the several graphite/SiO2 samples that vary in
the amount of time the plasma-treated SiO2 was left in ambient prior to exfoliation between 1 minute
and 60 minutes. We image several regions with graphite flakes over the samples (Fig. 1f,h), then place
them into an IPA ultrasonic bath for 5 minutes, and finally repeat imaging over those exact regions (Fig.
1g,i). From this procedure, we find almost 90% of the graphite flakes survive the ultrasonic bath when
the exfoliation is immediate (< 1 minute), which falls off quickly to only 15% after a 60 minute ambient
exposure time (Fig. 1e). These results suggest that adhesion is changing rapidly with the substrates’
exposure to ambient, more importantly we can infer that exfoliating under 1 minute and after 60
minutes of ambient exposure will guarantee either graphite flakes with high or low adhesion
respectively. We can clearly observe how fixed graphite flakes are preserved from the ultrasonic bath
test (Fig. 2a,b), while the free graphite flakes almost entirely are no longer on the substrate after (Fig.
2c,d).
Using a free and readily available image analysis software (ImageJ), we can quantitatively
extract the area of graphite flakes that survive the ultrasonic bath testing presented in Fig. 1e. The
optical micrographs of these areas were converted into binary images using ImageJ, where the
substrate is highlighted in white and the graphite flakes in black (Fig. 2). To construct the binary
images, we utilize two methods (Fig. 2a,b and Fig. 2c,d). Fig. 2a,b presents a method which outlines the
edges of the graphite flakes, through an edge detection algorithm (a plugin provided by ImageJ). The
algorithm calculates intensity gradients throughout the original image (Fig. 2e), where the gradient
values are presented in Fig. 2a. Once the graphite edges are identified from the calculated gradient
values, they are “filled in” to construct the final binary image in Fig. 2b. Fig. 2c,d presents a second
method, where image thresholding is employed instead. Most graphite flakes within this thickness
range have RGB values below the substrate value11, therefore the background (substrate) of the
original image can be brought to saturation (RGB = 255) by increasing the overall RGB value of the
image (Fig. 2c). If any of the graphite flakes have RGB values above that of the substrate mean, the
overall image’s RGB value can be decreased such that the substrate is all black (RGB = 0), then the
image can be inverted. Once the background is saturated, image thresholding will similarly yield a
binary image where the graphite flakes are highlighted in black (Fig. 2d). Finally, these two images can
be overlaid to extract the final binary image (Fig. 2f), two methods are used to affirm most of the
exfoliated graphite features in the optical micrograph are accounted for. We also note, because
monolayer graphene’s contrast is extremely close to that of the substrate (|RGB1L - RGBsubstrate| < 5),
these methods are optimized to specifically capture these monolayer graphene features. Using this