Influence of Mg concentration on structural,morphological and optical properties of nanoceria

Nano crystalline pure and Mg doped ceriaparticles were synthesized by simple chemical co-precipitation method using cerium nitrate hexahydrate as a source material and magnesium nitrate as doping precursor at room temperature. The effect of doping were investigated by X-ray diffraction pattern(XRD), FT-Raman,fourier transform infrared spectroscopy(FTIR), Ultraviolet spectroscopy(UV), photoluminescence spectroscopy(PL), field emission scanning electron microscope(FESEM) and high resolution transmission electron microscopy with energy dispersive spectroscopy (HRTEM &EDS). The X-ray diffraction pattern and FT-Raman studies showed that the prepared samples were nano particulates with cubic fluorite structure. The XRD pattern analysis showed that the size of the particles ranged from 13 to 20?nm, however 4?wt% Mg doping results in reduction of particle size compared with other doping concentrations. The effects of Mg concentration on various structural parameters of the prepared samples were also determined. The slight blue shift observed upon doping in UV–Vis absorption region around 330–360nmrecorded for reduction in particle size. The FTIR unveils the presence of Metal oxygen bonds below 700?cm^?1in the prepared samples. All samples showed a broad emission band at 430?nm with linearly increasing intensity with respect to dopant concentrations. The Spherical morphology with weak agglomeration was identified through FESEM and HRTEM analysis. The elemental analysis of Ce, O and Mg were confirmed through EDS analysis.     

A multiparameter wearable physiologic monitoring system for space and terrestrial applications.

A novel, unobtrusive and wearable, multiparameter ambulatory physiologic monitoring system for space and terrestrial applications, termed LifeGuard, is presented. The core element is a wearable monitor, the crew physiologic observation device (CPOD), that provides the capability to continuously record two standard electrocardiogram leads, respiration rate via impedance plethysmography, heart rate, hemoglobin oxygen saturation, ambient or body temperature, three axes of acceleration, and blood pressure. These parameters can be digitally recorded with high fidelity over a 9-h period with precise time stamps and user-defined event markers. Data can be continuously streamed to a base station using a built-in Bluetooth RF link or stored in 32 MB of on-board flash memory and downloaded to a personal computer using a serial port. The device is powered by two AAA batteries. The design, laboratory, and field testing of the wearable monitors are described.     

Realization of a temperature sensor using both two- and three-dimensional photonic structures through a machine learning technique

A temperature sensor based on photonic crystal structures with two- and three-dimensional geometries is proposed, and its measurement performance is estimated using a machine learning technique. The temperature characteristics of the photonic crystal structures are studied by mathematical modeling. The physics of the structure is investigated based on the effective electrical permittivity of the substrate (silicon) and column (air) materials for a signal at 1200 nm, whereas the mathematical principle of its operation is studied using the plane-wave expansion method. Moreover, the intrinsic characteristics are investigated based on the absorption and reflection losses as frequently considered for such photonic structures. The output signal (transmitted energy) passing through the structures determines the magnitude of the corresponding temperature variation. Furthermore, the numerical interpretation indicates that the output signal varies nonlinearly with temperature for both the two- and three-dimensional photonic structures. The relation between the transmitted energy and the temperature is found through polynomial-regression-based machine learning techniques. Moreover, rigorous mathematical computations indicate that a second-order polynomial regression could be an appropriate candidate to establish this relation. Polynomial regression is implemented using the Numpy and Scikit-learn library on the Google Colab platform.

     

Cover Feature: Superior Proton-Transfer Catalytic Promiscuity of Cytochrome c in Self-Organized Media (7/2021)

Evolutionarily elderly proteins commonly feature greater catalytic promiscuity. Cytochrome c is among the first set of proteins in evolution to have known prospects in electron transport and peroxidative properties. Here, we report that cyt c is also a proficient proton-transfer catalyst and enhances the Kemp elimination (KE; model reaction to show proton transfer catalytic property) by ∼750-fold on self-organized systems like micelles and vesicles. The self-organized systems mimic the mitochondrial environment in vitro for cyt c. Using an array of biophysical and biochemical mutational assays, both acid–base and redox mechanistic pathways have been explored. The histidine moiety close to hemin group (His18) is mainly responsible for proton abstraction to promote the concerted E2 pathway for KE catalysis when cyt c is in its oxidized form; this has also been confirmed by a H18A mutant of cyt c. However, the redox pathway is predominant under reducing conditions in the presence of dithiothreitol over the pH range 6–7.4. Interestingly, we found almost 750-fold enhanced KE catalysis by cyt c compared to aqueous buffer. Overall, in addition to providing mechanistic insights, the data reveal an unprecedented catalytic property of cyt c that could be of high importance in an evolutionary perspective considering its role in delineating the phylogenic tree and also towards generating programmable designer biocatalysts.     

BUBBLE TEMPERATURES OF THE BINARY MIXTURES OF 1,2-DIMETHYLBENZENE WITH 2-PROPANOL, 1-BUTANOL, 2-BUTANOL, 2-METHYLPROPAN-1-OL AND 2-METHYLPROPAN-2-OL AT 95 kPa

Bubble temperatures at 95 kPa over the entire composition range are measured for the five binary systems formed by 1,2-dimethylbenzene with 2-propanol, 1-butanol, 2-butanol, 2- methylpropan-1-ol, and 2-methylpropan- 2-ol. A Swietoslawski - type ebulliometer was used for the measurements. The composition versus temperature measurements are well represented by the Wilson model.     

Effect of organic modifiers on dynamic and static nanomechanical properties and crystallinity of intercalated clay–polycaprolactam nanocomposites

Polymer clay nanocomposites (PCN) of Polyamide6 and sodium montmorillonite are prepared using different organic modifiers (12‐aminolauric acid, n‐dodecylamine, and 1,12‐diaminododecane) to study effect of organic modifiers on structure and nanomechanical properties of PCN. Using X‐ray diffraction and differential scanning calorimetry, crystalline nature of PCNs are evaluated. Nanoscale viscoelastic properties of PCNs are evaluated using nanodynamic mechanical analyzer (NanoDMA). Nanoscale elastic modulus and hardness of PCNs are evaluated using nanoindenter. PCNs show enhancement in elastic modulus, storage modulus, loss modulus, and loss factor by maximum amount of 62.88%, 56.38%, 145.74%, and 71.43%, respectively, and decrease in percentage crystallinity by 16.52% compared to pure polymer. This result indicates that organic modifiers have effect on crystallinity and nanomechanical properties of PCN. To evaluate effect of clay loading on nanomechanical properties of PCN, PCN containing 12‐aminolauric acid is synthesized with different weight percent (3, 6, and 9% of weight of polymer) of organically modified montmorillonite (OMMT), which shows that nanomechanical properties of PCN improves with increase in clay loading. Our study reveals that change in crystallinity of polymer in PCN may have role in the enhancement of nanomechanical properties of PCNs in comparison to pristine polymer. © 2007 Wiley Periodicals, Inc. J Appl Polym Sci 2007