Dysprosium doped copper oxide (Cu1-xDyxO) nanoparticles enabled bifunctional electrode for overall water splitting

The production of hydrogen, a favourable alternative to an unsustainable fossil fuel remains as a significant hurdle with the pertaining challenge in the design of proficient, highly productive and sustainable electrocatalyst for both oxygen evolution reaction (OER) and hydrogen evolution reaction (HER). Herein, the dysprosium (Dy) doped copper oxide (Cu_1-xDy_xO) nanoparticles were synthesized via solution combustion technique and utilized as a non-noble metal based bi-functional electrocatalyst for overall water splitting. Due to the improved surface to volume ratio and conductivity, the optimized Cu_1-xDy_xO (x = 0.01, 0.02) electrocatalysts exhibited impressive HER and OER performance respectively in 1 M KOH delivering a current density of 10 mAcm^?2 at a potential of ?0.18 V vs RHE for HER and 1.53 V vs RHE for OER. Moreover, the Dy doped CuO electrocatalyst used as a bi-functional catalyst for overall water splitting achieved a potential of 1.56 V at a current density 10 mAcm^?2 and relatively high current density of 66 mAcm^?2 at a peak potential of 2 V. A long term stability of 24 h was achieved for a cell voltage of 2.2 V at a constant current density of 30 mAcm^?2 with only 10% of the initial current loss. This showcases the accumulative opportunity of dysprosium as a dopant in CuO nanoparticles for fabricating a highly effective and low-cost bi-functional electrocatalyst for overall water splitting.     

Review of electrospun hydrogel nanofiber system: Synthesis,Properties and Applications

Hydrogel-based nanofibers or vice versa are a relatively new class of nanomaterials, in which hydrogels are structured in nanofibrous form. Structure and size of the material directly governs its functionality, therefore, in hydrogel science, the nanofibrous form of hydrogels enables its usage in targeted applications. Hydrogel nanofiber system combines the desirable properties of both hydrogel and nanofiber like flexibility, soft consistency, elasticity, and biocompatibility due to high water content, large surface area to volume ratio, low density, small pore size and interconnected pores, high stiffness, tensile strength, and surface functionality. Swelling behavior is a critical property of hydrogels that is significantly increased in hydrogel nanofibers due to their small size. Electrospinning is the most popular method to fabricate “hydrogel nanofibers,” while other processes like self-assembly, solution blowing and template synthesis also exist. Merging the characteristics of both hydrogels and nanofibers in one system allows applications in drug delivery, tissue engineering, actuation, wound dressing, photoluminescence, light-addressable potentiometric sensor (LAPS), waterproof breathable membranes, and enzymatic immobilization. Treatment of wastewater, detection, and adsorption of metal ions are also emerging applications. In this review paper, we intend to summarize in detail about electrospun “hydrogel nanofiber” in relation to its synthesis, properties, and applications.     

Nano fibrillated cellulose-based foam by Pickering emulsion: Preparation,characterizations, and application as dye adsorbent

An ecofriendly and biodegradable porous structure was prepared from drying aqueous foams based on nano fibrillated cellulose (NFC), extracted from softwood pulp by subcritical water/CO₂ treatment (SC-NFC). The primary aim of this work was to use the modified SC-NFC as stabilizer for a water-based Pickering emulsion which upon drying, yielded porous cellulosic materials, a good dye adsorbent. In order to exploit the carboxymethylated SC-NFC (CMSC-NFC, with a degree of substitution of 0.35 and a charge density of 649 μeqv/g) as a stabilizer for water-based Pickering emulsion in subsequent step, an optimized quantity of octyl amine (30 mg/g of SC-NFC) was added to make them partially hydrophobic. A series of dry foam structures were prepared by varying the concentrations of treated CMSC-NFCs and 4 wt% was found to be the optimum concentration to yield foam with high porosity (99%) and low density (0.038 g/cc) along with high compression strength (0.24 MPa), superior to the conventionally extracted NFC. The foams were applied to capture as high as 98% of methylene blue dyes, making them a potential green candidate for treating industrial effluent. In addition, the dye adsorption kinetics and isotherms were found to be well suited with second order kinetics and Langmuir isotherm models.     

Effect of spheroidized microstructure on the impact toughness and electrochemical performance of a high-carbon steel

The influence of cementite spheroidization on the impact toughness and electrochemical properties of a high-carbon steel has been thoroughly investigated in this study. Heavy warm rolling, followed by 2 h of annealing, has resulted in near-complete spheroidization, leading to a microstructure consisting of nano-cementite globules dispersed in the ultrafine-grained ferritic matrix. The Charpy impact test exhibited superior impact toughness with increased spheroidization. It is validated by the presence of abundant dimples in the fractographs of spheroidized specimens, in contrast to the as-received one that experienced a brittle failure due to its lamellar pearlitic structure. Potentiodynamic polarization and electrochemical impedance spectroscopy (EIS) carried out in a 3.5% NaCl solution revealed that the corrosion resistance of the alloy gets improved with the increase in the degree of spheroidization. This is attributed to the lower susceptibility of the spheroidized specimen to microgalvanic corrosion owing to the minimum area of contact between nano-spheroidized cementite and ferrite, as elucidated with the help of EIS results aided by equivalent electrical circuit model.     

Electrical and magnetic properties of double perovskite: Y2CoMnO6

In this communication, the structural, micro-structural, dielectric, electrical, magnetic, and leakage-current characteristics of a double perovskite (Y₂CoMnO₆) ceramic material have been reported. The material was synthesized via a high-temperature mixed-oxide route. The compound crystallizes in a monoclinic structure which is confirmed from preliminary X-ray structural study. The morphological study by using scanning electron micrograph reveals the almost homogeneous distribution of grains throughout the surface of the sample. The nature of frequency-dependence of dielectric constant has been described by the Maxwell-Wagner model. The occurrence of a dielectric anomaly in the temperature dependence of dielectric permittivity study demonstrates the ferroelectric-paraelectric phase transition in the material. From the Nyquist plots, we found the existence of both grain and grain boundary effects. The frequency dependence of conductivity was studied by the Jonscher’s Power law, and the conduction phenomenon obeys the large overlapping polaron tunneling model. By using the Arrhenius equation, the activation energy has been calculated which is nearly equal to the energy required for the hoping of the electron. Both impedance and conductivity analysis demonstrate that the sample exhibits negative temperature coefficient of resistance (NTCR) properties indicating the semiconducting type of material at high temperatures. The anti-ferromagnetic character of the material is observed from the nature of magnetic hysteresis loop. The leakage current analysis suggests that the conduction process in the material follows the space charge limited conduction phenomenon. Such material will be helpful for modern electronic devices and spintronic applications.     

An automated patient monitoring using discrete‐time wireless sensor networks

In recent years, Internet of Things (IoT) devices are used for remote health monitoring. For remotely monitoring a patient, only the health information at different time points are not sufficient; predicted values of biomarkers (for some future time points) are also important. In this article, we propose a powerful statistical model for an efficient dynamic patient monitoring using wireless sensor nodes through Bayesian Learning (BL). We consider the setting where a set of correlated biomarkers are measured from a patient through wireless sensors, but the sensors only report the ordinal outcomes (say, good, fair, high, or very high) to the sink based on some prefixed thresholds. The challenge is to use the ordinal outcomes for monitoring and predicting the health status of the patient under consideration. We propose a linear mixed model where interbiomarker correlations and intrabiomarker dependence are modeled simultaneously. The estimated and the predicted values of the biomarkers are transferred over the internet so that health care providers and the family members of the patient can remotely monitor the patient. Extensive simulation studies are performed to assess practical usefulness of our proposed joint model, and the performance of the proposed joint model is compared to that of some other traditional models used in the literature.     

Impact of Climate Change on Future Flood Susceptibility: an Evaluation Based on Deep Learning Algorithms and GCM Model

Floods are common and recurring natural hazards which damages is the destruction for society. Several regions of the world with different climatic conditions face the challenge of floods in different magnitudes. Here we estimate flood susceptibility based on Analytical neural network (ANN), Deep learning neural network (DLNN) and Deep boost (DB) algorithm approach. We also attempt to estimate the future rainfall scenario, using the General circulation model (GCM) with its ensemble. The Representative concentration pathway (RCP) scenario is employed for estimating the future rainfall in more an authentic way. The validation of all models was done with considering different indices and the results show that the DB model is most optimal as compared to the other models. According to the DB model, the spatial coverage of very low, low, moderate, high and very high flood prone region is 68.20%, 9.48%, 5.64%, 7.34% and 9.33% respectively. The approach and results in this research would be beneficial to take the decision in managing this natural hazard in a more efficient way.