Concepts of non-volatile memory to replace conventional flash memory have suffered from low material reliability and high off-state current, and the use of a thick, rigid blocking oxide layer in flash memory further restricts vertical scale-up. Here, we report a two-terminal floating gate memory, tunnelling random access memory fabricated by a monolayer MoS2/h-BN/monolayer graphene vertical stack. Our device uses a two-terminal electrode for current flow in the MoS2 channel and simultaneously for charging and discharging the graphene floating gate through the h-BN tunnelling barrier. By effective charge tunnelling through crystalline h-BN layer and storing charges in graphene layer, our memory device demonstrates an ultimately low off-state current of 10−14 A, leading to ultrahigh on/off ratio over 109, about ∼103 times higher than other two-terminal memories. Furthermore, the absence of thick, rigid blocking oxides enables high stretchability (>19%) which is useful for soft electronics.

Background Modeling abilities play an important role in engineering. The creation and use of representations is a central aspect of modeling, and students who are learning to model often use a variety of representations to express, test, revise, and communicate their own thinking. Consequently, model development often depends on representational fluency and the ability to translate between and within different representational forms. Purpose This study investigates the role that representations and representational fluency play in conceptual understanding during a complex modeling task related to heat transfer. Design/Method This study involved 16 teams of 3 or 4 college students in a first‐semester heat transfer course participating in a complex modeling task. The task of the student teams was to develop a model to predict the interface temperature and the sensation felt by human skin when touching a utensil made of a given material at a given temperature. Data sources included audio recordings of student teams, as well as student‐generated artifacts. Results The results show teams thinking about their model through multiple representations and through translations within and among representations. Students' early ways of thinking used a variety of interacting representations but were often unstable and involved incomplete notions of the system to be modeled. Model development involved increasing representational fluency as well as parallel and interacting progress along a variety of dimensions. Conclusions This study furthers the understanding of representational fluency in undergraduate engineering students in a heat transfer setting and how representational fluency contributes to conceptual and application understanding.

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Piezoelectricity of transition metal dichalcogenides (TMDs) under mechanical strain has been theoretically and experimentally studied. Powerful strain sensors using Schottky barrier variation in TMD/metal junctions as a result of the strain-induced lattice distortion and associated ion-charge polarization were demonstrated. However, the nearly fixed work function of metal electrodes limits the variation range of a Schottky barrier. We demonstrate a highly sensitive strain sensor using a variable Schottky barrier in a MoS2/graphene heterostructure field effect transistor (FET). The low density of states near the Dirac point in graphene allows large modulation of the graphene Fermi level and corresponding Schottky barrier in a MoS2/graphene junction by strain-induced polarized charges of MoS2. Our theoretical simulations and temperature-dependent electrical measurements show that the Schottky barrier change is maximized by placing the Fermi level of the graphene at the charge neutral (Dirac) point by applying gate voltage. As a result, the maximum Schottky barrier change (ΔΦSB) and corresponding current change ratio under 0.17% strain reach 118 meV and 978, respectively, resulting in an ultrahigh gauge factor of 575 294, which is approximately 500 times higher than that of metal/TMD junction strain sensors (1160) and 140 times higher than the conventional strain sensors (4036). The ultrahigh sensitivity of graphene/MoS2 heterostructure FETs can be developed for next-generation electronic and mechanical–electronic devices.