Improving performance and efficiency of Graph Neural Networks by injective aggregation

Aggregation functions are regarded as the multiplication between an aggregation matrix and node embeddings, based on which a full rank matrix can enhance representation capacity of Graph Neural Networks (GNNs). In this work, we fill this research gap based on the full rank aggregation matrix and its functional form, i.e., the injective aggregation function, and state that injectivity is necessary to guarantee the rich representation capacity to GNNs. To this end, we conduct theoretical injectivity analysis for the typical feature aggregation methods and provide inspiring solutions on turning the non-injective aggregation functions into injective versions. Based on our injective aggregation functions, we create various GNN structures by combining the aggregation functions with the other ingredient of GNNs, node feature encoding, in different ways. Following these structures, we highlight that by using our injective aggregation function entirely as a pre-processing step before applying independent node feature learning, we can simultaneously achieve satisfactory performance and computational efficiency on the large-scale graph-based traffic data for traffic state prediction tasks. Through comprehensive experiments on standard node classification benchmarks and practical traffic state data (for Chengdu and Xi'an cities), we show that the representation capacity of GNNs can be improved by using our injective aggregation functions just by changing the model in simple operations

Hollow Mesoporous Co(PO₃)₂@Carbon Polyhedra as High Performance Anode Materials for Lithium Ion Batteries

The hollow mesoporous Co­(PO3)2@carbon nanocomposite (H–Co­(PO3)2@C) was synthesized using ZIF-67 as the template by a facile one-step thermal decomposition reaction. As an anode for lithium ion batteries, its reversible capacity remains up to 601 mAh g–1 at 1 C after 500 cycles. Such a high reversible capacity along with the excellent rate capability and long-term cycling stability benefits from the hollow mesoporous structure and uniform carbon framework encapsulated active nanocrystals. These results render the as-prepared H–Co­(PO3)2@C to be a promising anode material for high performance lithium ion batteries

Highly Strained Au Nanoparticles for Improved Electrocatalysis of Ethanol Oxidation Reaction

Au is an ideal noble metal for use as an electrocatalyst for the ethanol oxidation reaction owing to its high performance-to-cost ratio. The catalyst usually exists as nanoparticles (NPs) for high surface area-to-volume ratio. In the present work, a nontraditional physical approach has been developed to fabricate ultrasmall and homogeneous single-crystalline Au NPs by ion bombardment in a precision ion polishing system. Transmission electron microscopy characterizations show that the Au NPs produced with 5 keV Ar+ are highly strained to form twinned crystals, which accumulate a large amount of surface energy, and this was found to be an underlying reason causing strong catalysis. Electrochemistry tests reveal that in alkaline medium the C1 pathway occurs much more preferentially with the strained Au NPs than the normal Au NPs. The surface area-to-volume ratio is no longer the only factor that affects the performance; instead, surface energy might play a more important role in enhancing the catalytic activities

Nanostructured CuO/C Hollow Shell@3D Copper Dendrites as a Highly Efficient Electrocatalyst for Oxygen Evolution Reaction

Adoption of bare metal oxides as catalytic materials shows inferior electrochemical activity because of their poor electrical conductivity. Although synthetic strategies for the employment of conductive substrates are well-established, the rational design and fabrication of hollow metal oxides nanostructures on the robust matrix with a high surface area and conductivity remains challenging. In the present research work, a strategy that transforms a metal–organic framework thin layer into a nanostructured CuO/C hollow shell to coat on the 3D nano-dendritic Cu foams as an electrode was successfully developed. This electrode is claimed to provide an extraordinary electrocatalysis for oxygen evolution reaction (OER) in alkaline media. The hierarchical complex presents fast electronic transmission networks and rich redox sites, leading to the significant enhancement in electrocatalytic OER efficiency. Furthermore, the spherical porous structure and robust architecture facilitate the high-speed diffusion of O₂ bubbles in a long-term operation. The results of this study may serve as a reference for the designing of novel class 3D metal/metal oxide hierarchical structures for gas-involved (i.e., O₂, H₂, and CO₂) electrocatalytic applications and beyond

Additional file 1 of Bufalin induces mitochondrial dysfunction and promotes apoptosis of glioma cells by regulating Annexin A2 and DRP1 protein expression

Additional file 1. Additional figures and tables