# Correlating Disorder Microstructure and Magnetotransport of Carbon Nanowalls
## 1 Introduction  
The special magnetotransport (MT) properties of graphene-based nanostructures have received considerable attention in the last years. The uncommon Dirac point [1], chiral nature [2] and Berry's phase [3] lead to different MT properties compared to the conventional two-dimensional (2D) systems. The graphene presents an unconventional weak antilocalization (WAL) due to the Berry's phase \(\pi\) in a valley, which adds a quantum phase when two electron waves circulate in a reverse closed path, producing a suppression of backscattering [4]. The magnetotransport properties in graphene materials can be modified by the arrangement of sheets, particle size [5], disorder such as curvature, topological defects [6], geometry of the graphene edges, localized magnetic moment [7] and boundaries and interfaces [8]. Large 3D graphene networks, in contrast to 1D and 2D graphene-based nanostructures, easily allow us to modify the trajectories of carriers in a magnetic field to obtain a specific desirable MT behavior. There are not many studies related to the MT properties of 3D carbon nanowalls (CNWs), although these structures have other remarkable applications, such as catalyst supports, high sensitivity gas detection [9], biosensors [10] and supercapacitors [11, 12]. Thus, completing the range of applications with those related to MT is a new direction for the exploration of the applicative potential of CNW materials. In MT research, we can mention [PERSON] et al. [13], who obtained control over the magnetoresistance (MR) changing the morphology of the CNWs; and [PERSON] et al. [14] in aligned walls. The CNWs consist of stacks of a few vertical graphene layers in a self-supported network [15]. As a consequence, the sheets behave like freestanding ones [16], with a weak influence of the substrate. This advantage allows rigorously identify the intrinsic MT mechanism at difference of the graphene attached over a supporting substrate. Specifically, the quantum interference correction to the magnetotransport and the influences of the nature of disorder are currently lacking. The deposition of CNWs is not straightforward and only a few methods are available. [PERSON]'s group [17] obtained the CNWs via RF sputtering, with the disadvantage of a long deposition time (3 h). [PERSON]'s group [18], using the hot filament chemical vapor deposition (HFCVD) technique, needed catalysts or surface pretreatment. However, the Plasma-Enhanced Chemical Vapor Deposition (PECVD) is a typical and promising technique that does not require a catalyst [19] to grow with huge surface-to-mass ratio the CNWs [20] in a relatively low temperature (600-700 \({}^{\circ}\)C) and short time (\(\sim\)1 h). [PERSON] et al. [21] stated that the deposition temperature is small compared to other synthesis methods, and numerous substrates (Ni, Ti, Pt, Cu, Ge, W, Ta, SiO\({}_{2}\), Al\({}_{2}\)O\({}_{3}\), Cu, Si, SiO\({}_{2}\), stainless steel, carbon paper and others) can be used. [PERSON] et al. [22] showed that the morphology of CNWs does not change significantly when they are synthetized on substrates with different characteristics. In addition, the surface morphology and internal structure of the sample are effectively controlled. The PECVD technique can generate CNWs with different morphologies (including petal-like, cauliflower-like and maze-like) depending on the deposition parameters, such as: temperatures and gaseous plasma (containing hydrogenated carbon precursors) [23]. The growth rate of CNWs (around tens of nanometers per minute) creates a difficulty for practical applications; however, [PERSON] et al. [23] improved it by increasing the RF power and CH\({}_{4}\) flow rate. In this sense, the morphology and microstructure of the CNWs can be easily tunable through the PECVD method. In addition, CNWs could offer a new perspective for the reproducibility of MR devices, in contrast to the few-layer graphene grown by CVD, where the thickness is not controllable yet.