The fast development of the Internet of things (IoT) promises to deliver convenience to human life. However, a huge amount of the data is constantly generated, transmitted, processed, and stored, posing significant security challenges. The currently available security protocols and encryption techniques are mostly based on software algorithms and pseudorandom number generators that are vulnerable to attacks. A true random number generator (TRNG) based on devices using stochastically physical phenomena has been proposed for auditory data encryption and trusted communication. In the current study, a Bi₂O₂Se-based memristive TRNG is demonstrated for security applications. Compared with traditional metal–insulator–metal based memristors, or other two-dimensional material-based memristors, the Bi₂O₂Se layer as electrode with non-van der Waals interface, high carrier mobility, air stability, extreme low thermal conductivity, as well as vertical surface resistive switching shows intrinsic stochasticity and complexity in a memristive true analogue/digital random number generation. Moreover, those analogue/digital random number generation processes are proved to be resilient for machine learning prediction.

N-doped graphene with low intrinsic defect densities was obtained by combining a solid source doping technique and chemical vapor deposition (CVD). The solid source for N-doping was embedded into the copper substrate by NH₃ plasma immersion. During the treatment, NH₃ plasma radicals not only flattened the Cu substrate such that the root-mean-square roughness value gradually decreased from 51.9 nm to 15.5 nm but also enhanced the nitrogen content in the Cu substrate. The smooth surface of copper enables good control of graphene growth and the decoupling of height fluctuations and ripple effects, which compensate for the Coulomb scattering by nitrogen incorporation. On the other hand, the nitrogen atoms on the pre-treated Cu surface enable nitrogen incorporation with low defect densities, causing less damage to the graphene structure during the process. Most incorporated nitrogen atoms are found in the pyrrolic configuration, with the nitrogen fraction ranging from 1.64% to 3.05%, while the samples exhibit low defect densities, as revealed by Raman spectroscopy. In the top-gated graphene transistor measurement, N-doped graphene exhibits n-type behavior, and the obtained carrier mobilities are greater than 1100 cm²·V⁻¹·s⁻¹. In this study, an efficient and minimally damaging n-doping approach was proposed for graphene nanoelectronic applications.