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The corrosion inhibitive capabilities of some ferrocene-based Schiff bases on aluminium alloy AA2219-T6 in acidic medium were investigated using Tafel polarization, electrochemical impedance spectroscopy (EIS), weight loss measurement, FT-IR spectroscopy and scanning electron microscopic (SEM) techniques. The influence of molecular configuration on the corrosion inhibition behavior has been explored by quantum chemical calculation. Ferrocenyl Schiff bases 4,4′-((((ethane-1,2-diylbis(oxy))bis(4,1-phenylene))bis(methaneylylidene))bis(azaneylylidene))bisferrocene (Fcua), 4,4′-((((ethane-1,2-diylbis(oxy))bis(2-methoxy-1,4-phenylene))bis(methaneylylidene))bis(azaneylylidene))bisferrocene (Fcub) and 4,4′-((((ethane-1,2-diylbis(oxy))bis(2-ethoxy-1,4-phenylene))bis(methaneylylidene))bis(azaneylylidene))bisferrocene (Fcuc) have been synthesized and characterized by FT-IR, 1H and 13C NMR spectroscopic studies. These compounds showed a substantial corrosion inhibition against aluminium alloy in 0.1 M of HCl at 298 K. Fcub and Fcuc showed better anticorrosion efficiency as compared with Fcua due to the electron donating methoxy and ethoxy group substitutions, respectively. Polarization curves also indicated that the studied biferrocenyl Schiff bases were mixed type anticorrosive materials. The inhibition of the aluminium alloy surface by biferrocenyl Schiff bases was evidenced through scanning electron microscopy (SEM) studies. Semi-empirical quantum mechanical studies revealed a correlation between corrosion inhibition efficiency and structural functionalities. Validerad;2020;Nivå 2;2020-02-24 (johcin)

Much attention has been paid to the recognition of human emotions with the help of electroencephalogram (EEG) signals based on machine learning technology. Recognizing emotions is a challenging task due to the non-linear property of the EEG signal. This paper presents an advanced signal processing method using the deep neural network (DNN) for emotion recognition based on EEG signals. The spectral and temporal components of the raw EEG signal are first retained in the 2D Spectrogram before the extraction of features. The pre-trained AlexNet model is used to extract the raw features from the 2D Spectrogram for each channel. To reduce the feature dimensionality, spatial, and temporal based, bag of deep features (BoDF) model is proposed. A series of vocabularies consisting of 10 cluster centers of each class is calculated using the k-means cluster algorithm. Lastly, the emotion of each subject is represented using the histogram of the vocabulary set collected from the raw-feature of a single channel. Features extracted from the proposed BoDF model have considerably smaller dimensions. The proposed model achieves better classification accuracy compared to the recently reported work when validated on SJTU SEED and DEAP data sets. For optimal classification performance, we use a support vector machine (SVM) and k-nearest neighbor (k-NN) to classify the extracted features for the different emotional states of the two data sets. The BoDF model achieves 93.8% accuracy in the SEED data set and 77.4% accuracy in the DEAP data set, which is more accurate compared to other state-of-the-art methods of human emotion recognition.

Oxygen evolution reaction (OER) has significant impact on the overall electrochemical water splitting. We introduce, for the first time, a facile approach towards the fabrication of versatile electrode composed of free-standing multiwalled carbon nanotubes (MWCNTs) as electrocatalyst for the water splitting reaction. Directly extracted MWCNTs as sheets from vertically grown arrays transferred over the glass substrate, are used without any post treatment as a working electrode for OER. Onset potential of 1.60 V was achieved for MWCNTs which is significantly reduced as compared to platinum based metal electrode (1.72 V) with excellent current density. No surface modification, metal-free nature, flexibility and low cost with excellent catalytic activity proved this material as a promising candidate for the replacement of metal based electrodes in electrochemical water splitting. (C) 2017 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.