The development of vanadium redox flow batteries (VRFBs) is partly limited by the sluggishness of the electrochemical reactions at conventional carbon-based electrodes. The VO 2+ /VO 2 + redox reaction is particularly sluggish and improvements in battery performance require the development of new electrocatalysts for this reaction. In this study, synergistic Electrochemical analysis shows that the electrocatalytic activity of the composite material is significantly higher than those of the individual components due to synergism between the Mn 3 O 4 nanoparticles and the carbonaceous support material. The electrocatalytic activity is highest when the Mn 3 O 4 loading is ~24% but decreases at lower and higher loadings.Furthermore, electrocatalysis of the redox reaction is only observed when nitrogen is present within the support framework, demonstrating that the metal-nitrogen-carbon coupling is key to the performance of this electrocatalytic composite for VO 2+ /VO 2 + electrochemistry.

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Pyroelectric multi-walled carbon nanotubes:polyvinylidene fluoride (PVDF:MWCT) composite films have been fabricated by the solution casting technique. The pyroelectric and dielectric properties of the composite films were examined for their use in uncooled infrared detectors. The properties measured include: 1) dielectric constants and 2) pyroelectric coefficient as a function of temperature. From the foregoing parameters, materials Figures-of-merit, for infrared detection and thermal-vidicons, were calculated. The results indicated Figures-of-merit of composite film were higher than pristine polyvinylidene fluoride films.

Photogalvanic effect produces actuation of periodic motion of macroscopic LiNbO 3 crystal. This effect was applied to the development of an all-optical moving-grating interferometer usable for optical trapping and transport of algae chlorella microorganisms diluted in water with a concentration of 27ϫ 10 4 ml −1 .

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We experimentally demonstrate an on-chip electro-optic circuit for realizing arbitrary nonlinear activation functions for optical neural networks (ONNs). The circuit operates by converting a small portion of the input optical signal into an electrical signal and modulating the intensity of the remaining optical signal. Electrical signal processing allows the activation function circuit to realize any optical-to-optical nonlinearity that does not require amplification. Such line shapes are not constrained to those of conventional optical nonlinearities. Through numerical simulations, we demonstrate that the activation function improves the performance of an ONN on the MNIST image classification task. Moreover, the activation circuit allows for the realization of nonlinearities with far lower optical signal attenuation, paving the way for much deeper ONNs.

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