In this study, we demonstrate a partially transparent nonvolatile resistive random access memory (ReRAM) device based on Cu2O-graphene core–shell quantum dots (QDs) dispersed in a poly(methyl methacrylate) (PMMA) matrix. The Cu2O-graphene QDs were synthesized through a solvothermal route and exhibited an average diameter of approximately 5 nm. The graphene shell modifies the local interfacial electronic environment and facilitates interfacial charge redistribution around the Cu2O cores. XRD, Raman, FTIR, XPS, and HRTEM analyses collectively support the formation of the Cu2O–graphene hybrid structure, while TEM observations indicate its dispersion within the PMMA matrix. The fabricated devices exhibited clear bipolar resistive switching with an ON/OFF current ratio of approximately 103, stable operation over repeated switching cycles, and experimentally measured retention exceeding 105 s. Extrapolation of the measured retention data suggests that the ON/OFF states would remain distinguishable at 10 years. Detailed current–voltage analysis indicated a transition from ohmic and space-charge-limited conduction to a high-field transport regime consistent with field-assisted tunneling through the PMMA barrier. These results demonstrate that the Cu2O–graphene core–shell QD/PMMA composite is a promising switching material for organic nonvolatile ReRAM and offers a useful platform for transparent and solution-processable next-generation memory systems.
The ISO 19100 series provides a conceptual framework for supporting semantic interoperability in geographic information systems through a structured set of standards. Within this framework, meta-level standards define shared conceptual foundations and modeling rules intended to guide the consistent development of application schemas across domains. However, these standards are not always used as direct design references in practice, and application schemas are frequently constructed through selective interpretation or intermediate specifications, indicating a discrepancy between their intended and actual roles. This study investigates the structural causes of this discrepancy by analyzing the architecture of the ISO 19100 standards framework from a multilevel modeling perspective. It examines the limitations of the Conceptual Schema modeling Facility (CSMF), particularly its reliance on fixed abstraction levels and type–instance relationships that do not adequately represent complex dependencies among standards. A dependency analysis based on normative references further shows that relationships among standards form a network rather than a strict hierarchy. Using ISO 19109:2025 as an exploratory case study, the paper derives structural requirements for stabilizing the role of meta-level standards from the dependency analysis and examines how these requirements are reflected in the revision of a core meta-level standard.
A 10B-coated cathode double-GEM neutron detector (BGEM) was developed as a 3He-free cold-neutron beamline detector using a single 10B4C converter cathode and a 512-channel APV25 orthogonal-strip readout over 10 & times; 10 cm2. The detector was tested at the HANARO Bio-REF beamline with a monochromatic 4.5 angstrom beam (En = 4.03 meV). The detection efficiency relative to a 6Li-based Ce:LiCAF reference detector was epsilon BGEM = (8.69 +/- 0.20) % (stat.). The pulse-height spectrum was qualitatively consistent with Geant4 energy-deposition simulations, and Cd-mask imaging yielded sigma = (555 (R) 102) mu m, corresponding to FWHMLSF = (1.31 (R) 0.24) mm. These results establish a cold-neutron beamline benchmark for a single-converter BGEM detector with full-strip APV25 readout.
Thermoelectric materials require precise doping to adjust the Fermi level in order to fully realize their thermoelectric potential. The effective mass (EM) model is commonly used to predict the maximum thermoelectric figure-of-merit (zT) and optimal carrier concentration, but its application is limited by the need for numerical Fermi integral solutions and Hall effect measurements. Since the thermopower (magnitude of the Seebeck coefficient, |S|) is effectively a measure of the Fermi level, it can be used as a direct descriptor of doping level in heavily doped semiconductors such as good thermoelectric materials. Here, we present a simple method to analyze thermoelectric transport using only the typical thermoelectric measurements: Seebeck coefficient, electrical conductivity and thermal conductivity. This enables evaluation of weighted mobility, quality factor (B), theoretical maximum zT, optimal thermopower, identification of anomalous scattering behavior; as well as a full prediction of zT, as a function of |S| given by
This study presents a graph-based framework for managing urban transport infrastructure and identifying potential mobility hubs within the Seoul metropolitan rail network in the Republic of Korea. The approach integrates traditional network centrality analysis with graph neural networks to capture both structural influence and flow mediation. Using operational schedule data, Bonacich power and random-walk betweenness centrality were embedded into a graph learning model to evaluate node importance beyond the limitations of conventional shortest-path assumptions. The results reveal that top nodes exhibit high multimodal potential, acting as strategic connectors within the metropolitan transit structure. By linking network-derived hub scores with public bicycle usage data, the analysis identifies spatial overlaps between structural centrality and micromobility demand. These findings support road space reallocation and multimodal integration strategies that enhance sustainable and inclusive accessibility. The proposed framework expands the methodological scope of transport planning by combining network science and machine learning. It provides a data-driven basis for infrastructure management and policy development toward low carbon dioxide, human-centred and resilient mobility systems. The proposed framework expands transport planning by combining network science and machine learning, providing a data-driven basis for infrastructure management, mobility hub planning and policy development toward low carbon dioxide and resilient urban transport systems.
