Design and Development of Sustainable Cu2 (II)- and Mn2 (III)-Embedded Bifunctional Electrocatalysts: Enhanced Hydrogen and Oxygen Generation.

Nowadays, environmentally friendly, low-cost-effective, and sustainable electrocatalysts used widely for hydrogen and oxygen evolution reactions have come into the limelight as a new research topic for scientists. This study highlights the preparation of two unique and symmetrical dinuclear Cu (II) and Mn (III) bifunctional catalysts by a facile simple slow evaporation and diffusion route. [C32H24Cu2F4N4O4] (1) and [C32H24Mn2F4N4O4] (2) both have monoclinic (C2/c (15)) crystal systems, with oxidation states +2 and +3, respectively. Prominent SPR peaks at 372 and 412 nm indicate an M-L charge transfer transition in both complexes. The synthesized electrocatalysts display exceptional catalytic activity for the hydrogen evolution reaction (HER) and oxygen evolution reaction (OER). Complex 1 exhibits enhanced hydrogen generation in 0.5 M H2SO4 with a small overpotential of 216 mV at -10 mA cm-2 along with a significantly lower Tafel value of 97 mV/dec compared to Complex 2. Moreover, Complex 1 is highly active for the OER in 1 M KOH with a small Tafel slope of 103 mV/dec and a low overpotential of 340 mV to acquire 10 mA cm-2 current density, compared to Complex 2. Complex 1 and Complex 2 remain stable up to 20 h in acidic electrolyte and up to 36 h and 20 h in the basic electrolyte, respectively.

A novel hybrid fuzzy time series model for prediction of COVID-19 infected cases and deaths in India.

World is facing stress due to unpredicted pandemic of novel COVID-19. Daily growing magnitude of confirmed cases of COVID-19 put the whole world humanity at high risk and it has made a pressure on health professionals to get rid of it as soon as possible. So, it becomes necessary to predict the number of upcoming cases in future for the preparation of future plan-of-action and medical set-ups. The present manuscript proposed a hybrid fuzzy time series model for the prediction of upcoming COVID-19 infected cases and deaths in India by using modified fuzzy C-means clustering technique. Proposed model has two phases. In phase-I, modified fuzzy C-means clustering technique is used to form basic intervals with the help of clusters centroid while in phase-II, these intervals are upgraded to form sub-intervals. The proposed model is tested against available COVID-19 data for the measurement of its performance based on mean square error, root mean square error and average forecasting error rate. The novelty of the proposed model lies in the prediction of COVID-19 infected cases and deaths for next coming 31 days. Beside of this, estimation for the approximate number of isolation beds and ICU required has been carried out. The projection of the present model is to provide a base for the decision makers for making protection plan during COVID-19 pandemic.

In Situ Formation of ZrB2 and Its Influence on Wear and Mechanical Properties of ADC12 Alloy Mixed Matrix Composites.

In this work, aluminium alloy ADC12 reinforced with various amounts of ZrB2 (0 wt.%, 3 wt.%, 6 wt.%, 9 wt.%) were synthesized by an in-situ reaction of molten aluminium with inorganic salts K2ZrF6 & KBF4. XRD, EDAX, and SEM techniques are used for the characterization of the fabricated composite. XRD analysis revealed the successful in situ formation of ZrB2 in the composite. From the SEM images, it was concluded that the distribution of reinforcement was homogeneous in the composites. A study of mechanical and tribological properties under the dry sliding condition of ZrB2-reinforced ADC12 alloy has also been carried out. It is seen that there is an increase in tensile strength by 18.8%, hardness by 64.2%, and an increase in wear resistance of the material after reinforcement. The ductility of the material decreased considerably with an increase in the amount of reinforcement. The composite's impact strength decreased by 27.7% because of the addition of hard ZrB2 particulates.