Search

To increase the effectiveness of both the cathodic hydrogen evolution reaction (HER) and the anodic oxygen evolution reaction (OER), significant efforts are made to produce bifunctional water‐splitting electrocatalysts. In this study, bismuth oxide/nickel oxide (Bi2O3/NiO)‐based composite is supported over the nickel foam (Bi2O3/NiO‐NF) and carbon nanotube fiber (Bi2O3/NiO‐CNTF) to develop two different electrode systems for water‐splitting studies. For OER, Bi2O3/NiO‐NF and Bi2O3/NiO‐CNTF require overpotential of 401 and 369 mV, respectively, to achieve the current density of 50 mA cm−2 in alkaline media, while the current density of 50 mA cm−2 is achieved at overpotential of 398 and 304 mV, respectively, in neutral media. For HER, Bi2O3/NiO‐NF and Bi2O3/NiO‐CNTF achieve the current density of 50 mA cm−2 at overpotential of 301 and 268 mV, respectively, in neutral media. For overall water splitting in neutral media, Bi2O3/NiO‐CNTF achieves the current density of 50 mA cm−2 at cell voltage of 1.654 V. The promising performance of synthesized electrocatalysts reveals that the mixed metal oxides (p‐block and d‐block elements)‐based electrocatalysts can be potential candidates for practical water splitting in basic (1 M KOH) as well as neutral media (1 M phosphate‐buffered saline). [ABSTRACT FROM AUTHOR]

This study focuses on the large-scale production of a high-energy-density supercapacitor through the cost-effective polymerization of a PANI/Cr2O3/graphene nanocomposite, utilizing commercially available flexible substrate and cost-effective chemicals. To explore the synergistic effect, a PANI/Cr2O3/graphene nanocomposite is evaluated in a hybrid gel electrolyte with varying polyvinyl alcohol (PVA) concentrations. The investigation is aimed at enhancing the electrochemical performance of electrode material for supercapacitors. Two symmetric devices are fabricated to assess the specific capacitance and energy density of the prepared nanocomposite. In the PVA2g/Na2SO4 electrolyte, the energy density is increased to 61.71 Wh kg−1, accompanied by an enhanced potential window of 1.95 V. Furthermore, the prepared composite demonstrates good electrochemical stability, retaining 94% of its initial capacitance after 2000 cycles at 2 Ag−1 in 2 g PVA/Na2SO4 gel electrolyte. The remarkable electrochemical properties of the composite can be attributed to the effective contact and synergistic effect among PANI, Cr2O3, and graphene, making it a promising candidate for supercapacitor applications. Moreover, this facile process is easily scalable to meet commercial demands and can be utilized to enhance the energy density of various carbon-based materials with limited initial performance. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Polyoxometalates (POMs), as carbon-free metal-oxo-clusters with unique structural properties, are emerging water-splitting electrocatalysts. Herein, we explore the development of cobalt-containing polyoxometalate immobilized over the carbon nanotube fiber (CNTF) (Co4POM@CNTF) towards efficient electrochemical oxygen evolution reaction (OER). CNTF serves as an excellent electron mediator and highly conductive support, while the self-activation of the part of Co4POM through restructuring in basic media generates cobalt oxides and/or hydroxides that serve as catalytic sites for OER. A modified electrode fabricated through the drop-casting method followed by thermal treatment showed higher OER activity and enhanced stability in alkaline media. Furthermore, advanced physical characterization and electrochemical results demonstrate efficient charge transfer kinetics and high OER performance in terms of low overpotential, small Tafel slope, and good stability over an extended reaction time. The significantly high activity and stability achieved can be ascribed to the efficient electron transfer and highly electrochemically active surface area (ECSA) of the self-activated electrocatalyst immobilized over the highly conductive CNTF. This research is expected to pave the way for developing POM-based electrocatalysts for oxygen electrocatalysis. [ABSTRACT FROM AUTHOR]

We report a cost-effective, chemical polymerization route to synthesize PANI/Co3O4/graphene (PCG) ternary nanocomposite for supercapacitor applications to achieve enhanced energy density in a cost-effective, non-toxic, and non-corrosive neutral aqueous electrolyte of 1M Na2SO4. These properties are advantageous for a supercapacitor assembling procedure, being favorably facile and inexpensive. The electrochemical analysis was carried out to check analyte performance with cyclic voltammetry (CV), galvanostatic charge–discharge (GCD), and electrochemical impedance spectroscopy (EIS). The PCG ternary nanocomposite exhibits a specific capacitance of 183 F g−1 at 1 A g−1 in a two-electrode device, and retains its 93% of initial specific capacitance after 2000 cycles. The energy density of PCG ternary nanocomposite is 49.81 Wh Kg−1 with a power density of 697.72 W Kg−1. The excellent electrochemical properties of the PCG ternary nanocomposite are credited to the good contact and the synergistic effect between the PANI nanofibers, Co3O4 nanoparticles, and graphene nanosheets. This indicates that this synthesized ternary nanocomposite material could be used as a promising candidate for supercapacitor electrode material. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Two new non‐centrosymmetric biferrocenyl Schiff bases, that is, N‐(ferrocenylmethylene)‐4‐(4‐(ferrocenylmethyleneamino)phenoxy)benzenamine (FeL1) and 4,4′‐([1,1′‐biferrocenyl]‐4,4′‐diylbis(methylene)bis(N‐benzylideneaniline) (FeL2), have been synthesized and studied by density functional theory (DFT) to understand their nonlinear optical (NLO) behavior. The synthesized compounds were characterized by FTIR, NMR, and UV–visible spectroscopic techniques. DFT studies were performed to obtain the polarizability and first‐order hyperpolarizability (β) parameters useful in determining the NLO response. Results indicate that the substituted biferrocenyl Schiff bases show a substantial increase in first‐order hyperpolarizability (44 times greater than the reference urea) compared with unsubstituted ferrocene. The enhanced NLO behavior follows the trend FeL2 > FeL1 > urea. Bader's atoms in molecules (AIM) theory has been employed to obtain topological parameters and critical points to evaluate the nature and strength of different types of intramolecular interactions. Charge delocalization in the complexes was investigated through natural bond order (NBO) analysis. It was observed that intramolecular charge transfer (ICT) is responsible for the optical nonlinearity of the biferrocenyl Schiff bases, with hydrogen bonding playing a prominent role in the bonding networks leading to increased hyperpolarizability. Furthermore, molecular electrostatic potential (MEP) surfaces of the molecular systems have also been analyzed. From an application viewpoint, the interaction of the biferrocenyl derivatives with fish‐sperm DNA was studied through spectrophotometric and electrochemical measurements. Results indicate major groove binding with DNA, which is also predicted by molecular docking studies. [ABSTRACT FROM AUTHOR]

Gladiolus is one of the most cultivated cut flowers globally, but its short vase life reduces its marketability. The researchers find a lot of chemicals that enhance the vase life of this cut flower. In the current investigation, natural (organic) products are used to check their effects on the vase life of gladiolus. Lemon juice has concentrations of 10 ml L-1, 30 ml L-1, 50 ml L-1 concentration, and Moringa leaf extract with concentrations of 10 ml L-1, 30 ml L-1, and 50 ml L-1 along with 2 and 3% of sucrose. The experiment was arranged according to Complete Randomized Design (CRD) with ten plants per replication of each treatment. The present study was comprised of 6 treatments with three replications. The collected data were statistically analyzed using ANOVA techniques, using Statistix 8.1 analytical software. The comparison of treatment means was done using the Least Significant Difference (LSD) test at a 5 % probability level. Results indicated that using 30 ml L-1 lemon juice + 2% sucrose increased the vase life by 24.53 days, solution uptake by 78 ml, maximum of 4.2 number of florets were open during the period of basal flower senescence, while-4.83 g weight gained by this treatment. Minimum ion leakage %, the maximum number of days for basal flower senescence, and an average life of florets were greater in T1 when 10 ml L-1 lemon juice + 3 % sucrose was used. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Photocatalytic hydrogen production using solar irradiation is the best solution for existing energy crisis and ecological issues. Various efforts have been made to design a stable, proficient and visible light driven photocatalyst for hydrogen generation. It has been revealed that numerous factors e.g., surface area, morphology, band structure, charge transference and crystallinity affect the solar to hydrogen conversion ability of photocatalyst. Currently, many modification strategies including anion/cation doping, composite formation and alloy fabrication have been advised for semiconductor catalysts to harvest solar light to maximum extent. Moreover, a progression of novel engineering techniques that introduces dye sensitizers, quantum dots and co-catalysts, seems to enhance the photocatalytic efficiency for hydrogen production. In this perspective, we present a summary of various factors that can enhance the effectiveness of hydrogen generation and outline current advancement of frequently used fabricating strategies that look for greater yield of hydrogen. Lastly, emergence of surface plasmon resonance and significance of photocatalyst recycling for hydrogen generation is discussed. It is expected that this perspective will help researchers in designing an efficient photocatalyst for industrial scale hydrogen production. • Role of surface plasmon resonance in H 2 production is of significant importance. • Strategies to enhance semiconductor photocatalytic performance is discussed. • Effect of doping and alloy fabrication over water splitting is discussed. • Role of surface engineering and defects over water splitting is described. [ABSTRACT FROM AUTHOR]

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 cm 2 , 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., Competing Interests: The authors declare no competing financial interest., (© 2023 The Authors. Published by American Chemical Society.)

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 Co II , Ni II , and Cu II ) 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., Competing Interests: The authors declare no competing financial interest., (© 2021 The Authors. Published by American Chemical Society.)