RESUMEN
Multiplex graph representation learning has attracted considerable attention due to its powerful capacity to depict multiple relation types between nodes. Previous methods generally learn representations of each relation-based subgraph and then aggregate them into final representations. Despite the enormous success, they commonly encounter two challenges: 1) the latent community structure is overlooked and 2) consistent and complementary information across relation types remains largely unexplored. To address these issues, we propose a clustering-enhanced multiplex graph contrastive representation learning model (CEMR). In CEMR, by formulating each relation type as a view, we propose a multiview graph clustering framework to discover the potential community structure, which promotes representations to incorporate global semantic correlations. Moreover, under the proposed multiview clustering framework, we develop cross-view contrastive learning and cross-view cosupervision modules to explore consistent and complementary information in different views, respectively. Specifically, the cross-view contrastive learning module equipped with a novel negative pairs selecting mechanism enables the view-specific representations to extract common knowledge across views. The cross-view cosupervision module exploits the high-confidence complementary information in one view to guide low-confidence clustering in other views by contrastive learning. Comprehensive experiments on four datasets confirm the superiority of our CEMR when compared to the state-of-the-art rivals.
RESUMEN
The rational design of composite electrodes that may take full advantage of pseudocapacitive metal oxides and graphene is still challenging. Herein, nickel cobaltate (NiCo2O4) nanoparticle-anchored crumpled graphene microspheres (CGMs) were fabricated through a simple spray-assisted self-assembly process and used as a composite electrode for aqueous supercapacitors. Due to the porous spherical architecture and well-dispersed NiCo2O4 nanoparticles on graphene, the NiCo2O4/CGM electrode displays ideal electrochemical performance, including a specific capacitance of 369.8 F g-1 (at 1 A g-1), good rate performance of 85% capacitance retention even at 10 A g-1 and intriguing cycling stability. An aqueous asymmetric supercapacitor (ASC) with an operating voltage of 1.6 V was then assembled using the NiCo2O4/CGM composite and nitrogen-doped CGM (N-CGM) as the positive and negative electrodes in KOH electrolyte, respectively. The ASC device exhibited an excellent energy density of 24.7 W h kg-1 at a power density of 799.6 W kg-1, and an ultralong cycling life with a capacitance retention of 85% after 50 000 cycles. The satisfactory electrochemical performance and ultralong cycling stability indicate that the NiCo2O4/CGM electrode has promising applications in advanced supercapacitors.
RESUMEN
Ti3C2Tx, as novel members of the two-dimensional material family, hold great promise for electrochemical energy storage and catalysis, however, the electrochemical performance of Ti3C2Tx is largely limited by the self-restacking of their layers due to van der Waals forces. In this study, we report a high-performance electrode material, Ti3C2Tx supported Fe3O4 nanoplates (denoted as MXene-Fe), synthesized by a simple in situ wet chemistry method in a solvothermal system. The mesoporous MXene-Fe material as a supercapacitor electrode exhibits a high specific capacitance of 368.0 F g-1 at 1.0 A g-1 and long cycling stability with about 81% capacitance retention after 10 000 cycles at 10.0 A g-1. Moreover, the optimized MXene-Fe also displays high electrocatalytic activity and stability toward the oxygen evolution reaction in alkaline solution (1.0 M KOH) with a low overpotential of 290 mV at 10 mA cm-2 and a small Tafel slope of 65.1 mV dec-1. This work provides an effective strategy for developing novel Ti3C2Tx-based functional materials with outstanding electrochemical performance for supercapacitors and electrocatalysis.
