To conveniently obtain one-dimensional MnO2 nanowires (NWs) with controlled structure and unique properties for electron transfer, the genetically engineered M13 phages were used as templates for precise nucleation and growth of MnO2 crystals in filamentous phage scaffolds, via the spontaneous oxidation of Mn(2+) in alkaline solution. It was found that the morphology of NWs could be tailored by the surface charge of M13 mutants. MnO2 crystals were uniformly distributed on the surface of negatively charged tetraglutamate-fused phage (M13-E4), significantly different from irregular MnO2 agglomeration on the weakly negatively charged wild-type phage and positively charged tetraarginine-fused phage. The as-synthesized M13-E4@MnO2 NWs could catalyze the electro-oxidation of H2O2 at neutral pH. To demonstrate the superiority of the electrocatalytic activity in the solution containing plenty of chloride ions at neutral pH, both glucose oxidase and as-prepared MnO2 NWs were used for fabricating the glucose biosensor. The proposed biosensor showed a wide linear range (5 μM to 2 mM glucose), a low limit of detection of 1.8 μM glucose (S/N = 3), good interassay and intra-assay reproducibility and satisfactory storage stability. Due to the superiorities of synthesis and electrochemical performance, the as-prepared MnO2 NWs are promising for applications in electrocatalysis, electrochemical sensor, and supercapacitor.
Ceramics are commonly used as high-temperature structural materials which are easy to fracture because of the propagation of thermal shock cracks. Characterizing and controlling crack propagation are significant for the improvement of the thermal shock resistance of ceramics. However, observing crack morphology, based on macro and SEM images, costs much time and potentially includes subjective factors. In addition, complex cracks cannot be counted and will be simplified or omitted. Fractals are suitable to describe complex and inhomogeneous structures, and the multifractal spectrum describes this complexity and heterogeneity in more detail. This paper proposes a crack characterization method based on the multifractal spectrum. After thermal shocks, the multifractal spectrum of alumina ceramics was obtained, and the crack fractal features were extracted. Then, a deep learning method was employed to extract features and automatically classify ceramic crack materials with different strengths, with a recognition accuracy of 87.5%.
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