Carbon Nanotube Fiber-Based Flexible Microelectrode for Electrochemical Glucose Sensors.

Electrochemical sensors are gaining significant demand for real-time monitoring of health-related parameters such as temperature, heart rate, and blood glucose level. A fiber-like microelectrode composed of copper oxide-modified carbon nanotubes (CuO@CNTFs) has been developed as a flexible and wearable glucose sensor with remarkable catalytic activity. The unidimensional structure of CNT fibers displayed efficient conductivity with enhanced mechanical strength, which makes these fibers far superior as compared to other fibrous-like materials. Copper oxide (CuO) nanoparticles were deposited over the surface of CNT fibers by a binder-free facile electrodeposition approach followed by thermal treatment that enhanced the performance of non-enzymatic glucose sensors. Scanning electron microscopy and energy-dispersive X-ray analysis confirmed the successful deposition of CuO nanoparticles over the fiber surface. Amperometric and voltammetric studies of fiber-based microelectrodes (CuO@CNTFs) toward glucose sensing showed an excellent sensitivity of ∼3000 µA/mM cm2, a low detection limit of 1.4 µM, and a wide linear range of up to 13 mM. The superior performance of the microelectrode is attributed to the synergistic effect of the electrocatalytic activity of CuO nanoparticles and the excellent conductivity of CNT fibers. A lower charge transfer resistance value obtained via electrochemical impedance spectroscopy (EIS) also demonstrated the superior electrode performance. This work demonstrates a facile approach for developing CNT fiber-based microelectrodes as a promising solution for flexible and disposable non-enzymatic glucose sensors.

An AI-empowered affect recognition model for healthcare and emotional well-being using physiological signals.

Affective Computing is one of the central studies for achieving advanced human-computer interaction and is a popular research direction in the field of artificial intelligence for smart healthcare frameworks. In recent years, the use of electroencephalograms (EEGs) to analyze human emotional states has become a hot spot in the field of emotion recognition. However, the EEG is a non-stationary, non-linear signal that is sensitive to interference from other physiological signals and external factors. Traditional emotion recognition methods have limitations in complex algorithm structures and low recognition precision. In this article, based on an in-depth analysis of EEG signals, we have studied emotion recognition methods in the following respects. First, in this study, the DEAP dataset and the excitement model were used, and the original signal was filtered with others. The frequency band was selected using a butter filter and then the data was processed in the same range using min-max normalization. Besides, in this study, we performed hybrid experiments on sash windows and overlays to obtain an optimal combination for the calculation of features. We also apply the Discrete Wave Transform (DWT) to extract those functions from the preprocessed EEG data. Finally, a pre-trained k-Nearest Neighbor (kNN) machine learning model was used in the recognition and classification process and different combinations of DWT and kNN parameters were tested and fitted. After 10-fold cross-validation, the precision reached 86.4%. Compared to state-of-the-art research, this method has higher recognition accuracy than conventional recognition methods, while maintaining a simple structure and high speed of operation.

Binder-Free Fabrication of Prussian Blue Analogues Based Electrocatalyst for Enhanced Electrocatalytic Water Oxidation.

Developing a cost-effective, efficient, and stable oxygen evolution reaction (OER) catalyst is of great importance for sustainable energy conversion and storage. In this study, we report a facile one-step fabrication of cationic surfactant-assisted Prussian blue analogues (PBAs) Mx[Fe(CN)5CH3C6H4NH2]∙yC19H34NBr abbreviated as SF[Fe-Tol-M] (where SF = N-tridecyl-3-methylpyridinium bromide and M = Mn, Co and Ni) as efficient heterogeneous OER electrocatalysts. The electrocatalysts have been characterized by Fourier transform infrared (FT-IR) spectroscopy, powder X-ray diffraction (PXRD), scanning electron microscopy (SEM) coupled with energy dispersive X-ray (EDX) analysis, and X-ray photoelectron spectroscopy (XPS). In the presence of cationic surfactant (SF), PBAs-based electrodes showed enhanced redox current, high surface area and robust stability compared to the recently reported PBAs. SF[Fe-Tol-Co] hybrid catalyst shows superior electrochemical OER activity with a much lower over-potential (610 mV) to attain the current density of 10 mA cm-2 with the Tafel slope value of 103 mV·dec-1 than that for SF[Fe-Tol-Ni] and SF[Fe-Tol-Mn]. Moreover, the electrochemical impedance spectroscopy (EIS) unveiled that SF[Fe-Tol-Co] exhibits smaller charge transfer resistance, which results in a faster kinetics towards OER. Furthermore, SF[Fe-Tol-Co] offered excellent stability for continues oxygen production over extended reaction time. This work provides a surface assisted facile electrode fabrication approach for developing binder-free OER electrocatalysts for efficient water oxidation.

DFT-Net: Deep Feature Transformation Based Network for Object Categorization and Part Segmentation in 3-Dimensional Point Clouds.

Unlike 2-dimensional (2D) images, direct 3-dimensional (3D) point cloud processing using deep neural network architectures is challenging, mainly due to the lack of explicit neighbor relationships. Many researchers attempt to remedy this by performing an additional voxelization preprocessing step. However, this adds additional computational overhead and introduces quantization error issues, limiting an accurate estimate of the underlying structure of objects that appear in the scene. To this end, in this article, we propose a deep network that can directly consume raw unstructured point clouds to perform object classification and part segmentation. In particular, a Deep Feature Transformation Network (DFT-Net) has been proposed, consisting of a cascading combination of edge convolutions and a feature transformation layer that captures the local geometric features by preserving neighborhood relationships among the points. The proposed network builds a graph in which the edges are dynamically and independently calculated on each layer. To achieve object classification and part segmentation, we ensure point order invariance while conducting network training simultaneously-the evaluation of the proposed network has been carried out on two standard benchmark datasets for object classification and part segmentation. The results were comparable to or better than existing state-of-the-art methodologies. The overall score obtained using the proposed DFT-Net is significantly improved compared to the state-of-the-art methods with the ModelNet40 dataset for object categorization.

AI inspired EEG-based spatial feature selection method using multivariate empirical mode decomposition for emotion classification.

Classification of human emotions based on electroencephalography (EEG) is a very popular topic nowadays in the provision of human health care and well-being. Fast and effective emotion recognition can play an important role in understanding a patient's emotions and in monitoring stress levels in real-time. Due to the noisy and non-linear nature of the EEG signal, it is still difficult to understand emotions and can generate large feature vectors. In this article, we have proposed an efficient spatial feature extraction and feature selection method with a short processing time. The raw EEG signal is first divided into a smaller set of eigenmode functions called (IMF) using the empirical model-based decomposition proposed in our work, known as intensive multivariate empirical mode decomposition (iMEMD). The Spatio-temporal analysis is performed with Complex Continuous Wavelet Transform (CCWT) to collect all the information in the time and frequency domains. The multiple model extraction method uses three deep neural networks (DNNs) to extract features and dissect them together to have a combined feature vector. To overcome the computational curse, we propose a method of differential entropy and mutual information, which further reduces feature size by selecting high-quality features and pooling the k-means results to produce less dimensional qualitative feature vectors. The system seems complex, but once the network is trained with this model, real-time application testing and validation with good classification performance is fast. The proposed method for selecting attributes for benchmarking is validated with two publicly available data sets, SEED, and DEAP. This method is less expensive to calculate than more modern sentiment recognition methods, provides real-time sentiment analysis, and offers good classification accuracy.

Electrochemically Deposited Amorphous Cobalt-Nickel-Doped Copper Oxide as an Efficient Electrocatalyst toward Water Oxidation Reaction.

Production of hydrogen through water splitting is one of the green and the most practical solutions to cope with the energy crisis and greenhouse effect. However, oxygen evolution reaction (OER) being a sluggish step, the use of precious metal-based catalysts is the main impediment toward the viability of water splitting. In this work, amorphous copper oxide and doped binary- and ternary-metal oxides (containing CoII, NiII, and CuII) have been prepared on the surface of fluorine-doped tin oxide by a facile electrodeposition route followed by thermal treatment. The fabricated electrodes have been employed as efficient binder-free OER electrocatalysts possessing a high electrochemical surface area due to their amorphous nature. The cobalt-nickel-doped copper oxide (ternary-metal oxide)-based electrode showed promising OER activity with a high current density of 100 mA cm-2 at 1.65 V versus RHE that escalates to 313 mA cm-2 at 1.76 V in alkaline media at pH 14. The high activity of the ternary-metal oxide-based electrode was further supported by a smaller semicircle in the Nyquist plot. Furthermore, all metal-oxide-based electrodes offered high stability when tested for continuous production of oxygen for 50 h. This work highlights the synthesis of efficient and cost-effective amorphous metal-based oxide catalysts to execute electrocatalytic OER employing an electrodeposition approach.

An Innovative Multi-Model Neural Network Approach for Feature Selection in Emotion Recognition Using Deep Feature Clustering.

Emotional awareness perception is a largely growing field that allows for more natural interactions between people and machines. Electroencephalography (EEG) has emerged as a convenient way to measure and track a user's emotional state. The non-linear characteristic of the EEG signal produces a high-dimensional feature vector resulting in high computational cost. In this paper, characteristics of multiple neural networks are combined using Deep Feature Clustering (DFC) to select high-quality attributes as opposed to traditional feature selection methods. The DFC method shortens the training time on the network by omitting unusable attributes. First, Empirical Mode Decomposition (EMD) is applied as a series of frequencies to decompose the raw EEG signal. The spatiotemporal component of the decomposed EEG signal is expressed as a two-dimensional spectrogram before the feature extraction process using Analytic Wavelet Transform (AWT). Four pre-trained Deep Neural Networks (DNN) are used to extract deep features. Dimensional reduction and feature selection are achieved utilising the differential entropy-based EEG channel selection and the DFC technique, which calculates a range of vocabularies using k-means clustering. The histogram characteristic is then determined from a series of visual vocabulary items. The classification performance of the SEED, DEAP and MAHNOB datasets combined with the capabilities of DFC show that the proposed method improves the performance of emotion recognition in short processing time and is more competitive than the latest emotion recognition methods.

Synthesis, characterization and biological evaluation of novel benzimidazole derivatives.

In search of achieving less toxic and more potent chemotherapeutics, three novel heterocyclic benzimidazole derivatives: 2-(1H-benzo[d]imidazol-2-yl)-4-chlorophenol (BM1), 4-chloro-2-(6-methyl-1H-benzo[d]imidazol-2-yl)phenol (BM2) and 4-chloro-2-(6-nitro-1H-benzo[d]imidazol-2-yl)phenol (BM3) with DNA-targeting properties, were synthesized and fully characterized by important physicochemical techniques. The DNA binding properties of the compounds were investigated by UV-Visible absorption titrations and thermal denaturation experiments. These molecules exhibited a good binding propensity to fish sperm DNA (FS-DNA), as evident from the high binding constants (Kb) values: 1.9 × 105, 1.39 × 105 and 1.8 × 104 M‒1 for BM1, BM2 and BM3, respectively. Thermal melting studies of DNA further validated the absorption titration results and best interaction was manifested by BM1 with ΔTm = 4.96 °C. The experimental DNA binding results were further validated theoretically by molecular docking study. It was confirmed that the molecules (BM1-BM3) bind to DNA via an intercalative and groove binding mode. The investigations showed a correlation between binding constants and energies obtained experimentally and through molecular docking, indicating a binding preference of benzimidazole derivatives with the minor groove of DNA. BM1 was the preferential candidate for DNA binding because of its flat structure, π-π interactions and less steric hindrance. To complement the DNA interaction, antimicrobial assays (antibacterial & antifungal) were performed. It was observed that compound BM2 showed promising activity against all bacterial strains (Micrococcus luteus, Staphylococcus aureus, Enterobacter aerogenes and Escherichia coli) and fungi (Aspergillus flavus, Aspergillus fumigatus and Fusarium solani), while rest of the compounds were active against selective strains. The MIC values of BM2 were found to be in the range of 12.5 ± 2.2-25 ± 1.5 µg/mL. Thus, the compound BM2 was found to be the effective DNA binding antimicrobial agent. Furthermore, the preliminary cytotoxic properties of synthesized compounds were evaluated by brine shrimps lethality assay to check their nontoxic nature towards healthy normal cells.Communicated by Ramaswamy H. Sarma.

EEG-Based Multi-Modal Emotion Recognition using Bag of Deep Features: An Optimal Feature Selection Approach.

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.