A temperature-induced degenerate p-type graphene nanopore/reduced graphene oxide (GNP/rGO) heterojunction-based nanodevice was prepared and studied for the first time, showing a robust negative differential resistance (NDR) feature. In this regard, cellulose-based perforated graphene foams (PGFs), containing numerous nanopores (with an average size of ∼2 nm surrounded by nearly six-layer rGO walls) were synthesized using bagasse as a green starting material. The PGFs with an essential p-type semiconducting property showed a band gap energy of ∼1.8 eV. The observed two-terminal NDR peak could present stable and reversible features at high temperatures of 586–592 K. It was demonstrated that the O2 gas of the ambient would be involved in a crucial activity in water-based degeneration of the initial p-type regions around the GNPs and, consequently, the appearance of an intensive quantum tunneling NDR peak. An electron-band structure-based mechanism is proposed to describe the lateral quantum tunneling current within the degenerate p-type GNP and rGO heterojunction that is induced by the energized water molecules (EWMs). These results can shed light on more investigations regarding lateral quantum tunneling-based NDR features in upcoming and highly desired two-dimensional electronic nanodevices.

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There is an increasing importance of memory technologies in our ever-digitalizing society, which is characterized by the generation and use of a tremendous amount of real-time data. Beyond traditional performance requirements, mechanical flexibility of memory systems becomes therefore critical to enabling emerging applications such as the Internet of things. Graphene, now an established nanomaterial platform, is a promising element for building such high-performance memories with an unconventional form factor because of its exceptional electrical conductivities, outstanding mechanical properties, and processing versatility desirable for hybrid integration. Here, we provide an overview of recently developed flexible memory devices based on graphene and its functionalized counterparts. A defining feature of this review is that it exclusively compares and analyzes the devices that meet the following two criteria: (i) an explicit demonstration of working devices on a flexible substrate is reported; (ii) graphene is employed as an active channel rather than as an electrode. Our primary focus is to systematically classify various types of memories, in view of their materials, structural, and functional characteristics. For this, the shapes and compositions of the conductive channel, the key operational mechanisms underlying the electrical functionalities, and the major characteristics of representative device structures and materials systems are carefully evaluated. Furthermore, the applicability of flexible graphene memories to the neuromorphic computing area is discussed. Finally, we address several remaining issues that need to be solved for future technological advancements.