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Fractal Pt-based materials with hierarchical structures and high self-similarity have attracted more and more attention due to their bioinspiring maximum optimization of energy utilization and mass transfer. However, their high-efficiency design of the mass-and electron-transfer still remains to be a great challenge. Herein, fractal PtPdCu hollow sponges (denoted as PtPdCu-HS) facilitating both directed mass-and electron-transfer are presented. Such directed transfer effects greatly promote electrocatalytic activity, regarded as 3.9 times the mass activity, 7.3 times the specific activity, higher poison tolerance, and higher stability than commercial Pt/C for the methanol oxidation reaction (MOR). A new "directed mass-and electron-transfer" concept, characteristics, and mechanism are proposed at the micro/nanoscale to clarify the structural design and functional enhancement of fractal electrocatalyst. This work displays new possibilities for designing novel nanomaterials with high activity and superior stability toward electrocatalysis or other practical applications.

The objective of this study is to develop improved polyamide (nylon) 11 (PA11) and 12 (PA12) polymers with enhanced flame retardancy, thermal, and mechanical properties for selective laser sintering rapid manufacturing. PA11 and PA12 were melt-blended, dispersing low concentrations of nanoparticles, namely nanoclays (NCs), carbon nanofibers (CNFs), and nanosilicas (NSs) via twin-screw extrusion. To enhance their thermal and flammability properties, an intumescent flame retardant (FR) was added to the mechanically superior NC and CNF PA11 formulations. NC or CNF additions to either PA11 or PA12 generally increased its tensile strength and modulus, but sharply reduced its elongation at rupture. FR additives reduced PA11’s properties considerably. This substitution, however, only exacerbated the already steep drop in elongation at rupture due to FR additives alone; while elongation dropped 58% with the addition of 30 wt% FR, it dropped 98% with the addition of 25 wt% FR/5 wt% CNF.

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Abstract. Advanced false data injection attack in targeted malware intrusion is becoming an emerging severe threat to the Supervisory Control And Data Acquisition (SCADA) system. Several intrusion detection schemes have been proposed previously [21,3]. However, designing an effective real-time detection system for a resource-constraint device is still an open problem for the research community. In this paper, we propose a new relation-graph-based detection scheme to defeat false data injection attacks at the SCADA system, even when injected data may seemly fall within a valid/normal range. To balance effectiveness and efficiency, we design a novel detection model, alternation vectors with state relation graph. Furthermore, we propose a new inference algorithm to infer the injection point(s), i.e., the attack origin, in the system. We evaluate SRID with a real-world power plant simulator. The experiment results show that SRID can detect various false data injection attacks with a low false positive rate at 0.0125%. Meanwhile, SRID can dramatically reduce the search space of attack origins and accurately locate most of attack origins.