Determination of Rosuvastatin Calcium in its Bulk Drug and Pharmaceutical Preparations by High-Performance Thin-Layer Chromatography

JPC – Journal of Planar Chromatography – Modern TLC - A normal-phase TLC method has been established for the estimation of rosuvastatin calcium in its bulk drug and pharmaceutical...     

Unfolding the Role of Building Units of MOFs with Mechanistic Insight Towards Selective Metal Ions Detection in Water**

The potential emergence of fluorescence-based techniques has propelled research towards developing probes that can sense trace metal ions specifically. Although luminescent metal-organic frameworks (MOFs) are well suited for this application, the role of building blocks towards detection is not fully understood. In this work, a systematic screening by varying number of Lewis basic (pyridyl-N atoms) sites is carried out in a series of isostructural, robust UiO-67 MOFs, and targeting a model metal ion-Fe³⁺. All the three fluorescent MOFs are seen to present quenching response towards Fe³⁺ ions in water. However, UiO-67@N exhibits highly selective and sensitive response, whereas emission of both UiO-67 and UiO-67@NN is quenched by several metal ions. Detailed experimental and theoretical mechanistic investigation is carried out in addition to demonstration of UiO-67@N being able to sense trace amount of Fe³⁺ ions in synthetic biological water sample. Further, UiO-67@N based mixed-matrix membrane (MMM) has been prepared and employed to mimic the real time Fe³⁺ ions detection in water.     

Imidazolium-Functionalized Chemically Robust Ionic Porous Organic Polymers (iPOPs) toward Toxic Oxo-Pollutants Capture from Water

Fabricating new and efficient materials aimed at containment of water contamination, in particular removing toxic heavy metal based oxo-anions (e. g. CrO₄²⁻, TcO₄⁻) holds paramount importance. In this work, we report two new highly stable imidazolium based ionic porous organic polymers (iPOPs) decorated with multiple interaction sites along with electrostatics driven adsorptive removal of such oxo-anions from water. Both the iPOPs (namely, iPOP-3 and iPOP-4) exhibited rapid sieving kinetics and very high saturation uptake capacity for CrO₄²⁻ anions (170 and 141 mg g⁻¹ for iPOP-3 and iPOP-4 respectively) and ReO₄⁻ (515.5 and 350.3 mg g⁻¹ for iPOP-3 and iPOP-4 respectively), where ReO₄⁻ anions being the non-radioactive surrogative counterpart of radioactive TcO₄⁻ ions. Noticeably, both iPOPs showed exceptional selectivity towards CrO₄²⁻ and ReO₄⁻ even in presence of several other concurrent anions such as Br⁻, Cl⁻, SO₄²⁻, NO₃⁻ etc. The theoretical binding energy calculations via DFT method further confirmed the preferential interaction sites as well as binding energies of both iPOPs towards CrO₄²⁻ and ReO₄⁻ over all other competing anions which corroborates with the experimental high capacity and selectivity of iPOPs toward such oxo-anions.     

A surrogate and Lagrangian approach to constrained network problems

This paper presents the use of surrogate constraints and Lagrange multipliers to generate advanced starting solutions to constrained network problems. The surrogate constraint approach is used to generate a singly constrained network problem which is solved using the algorithm of Glover, Karney, Klingman and Russell [13]. In addition, we test the use of the Lagrangian function to generate advanced starting solutions. In the Lagrangian approach, the subproblems are capacitated network problems which can be solved using very efficient algorithms.The surrogate constraint approach is implemented using the multiplier update procedure of Held, Wolfe and Crowder [16]. The procedure is modified to include a search in a single direction to prevent periodic regression of the solution. We also introduce a reoptimization procedure which allows the solution from thekth subproblem to be used as the starting point for the next surrogate problem for which it is infeasible once the new surrogate constraint is adjoined.The algorithms are tested under a variety of conditions including: large-scale problems, number and structure of the non-network constraints, and the density of the non-network constraint coefficients.The testing clearly demonstrates that both the surrogate constraint and Langrange multipliers generate advanced starting solutions which greatly improve the computational effort required to generate an optimal solution to the constrained network problem. The testing demonstrates that the extra effort required to solve the singly constrained network subproblems of the surrogate constraints approach yields an improved advanced starting point as compared to the Lagrangian approach. It is further demonstrated that both of the relaxation approaches are much more computationally efficient than solving the problem from the beginning with a linear programming algorithm.     

Vibration and stability in thermally stressed roatating disks

Paper presents the experimental confirmation of a vibration and stability model of a thermally stressed rotating disk. Attention is directed to the rotating-disk convective-heat-transfer problem. Measured temperature distributions are used in the computation of the theoretical characteristic frequency spectrum which is then verified by the experimental frequency spectrum. The theoretical characteristic spectrum is shown to be effective in predicting the likelihood of a critical speed instability of the thermally stressed rotating disk. Optimal control of disk operation using this model is proposed.     

Sentiment Analysis of Persian Movie Reviews Using Deep Learning

Sentiment analysis aims to automatically classify the subject’s sentiment (e.g., positive, negative, or neutral) towards a particular aspect such as a topic, product, movie, news, etc. Deep learning has recently emerged as a powerful machine learning technique to tackle the growing demand for accurate sentiment analysis. However, the majority of research efforts are devoted to English-language only, while information of great importance is also available in other languages. This paper presents a novel, context-aware, deep-learning-driven, Persian sentiment analysis approach. Specifically, the proposed deep-learning-driven automated feature-engineering approach classifies Persian movie reviews as having positive or negative sentiments. Two deep learning algorithms, convolutional neural networks (CNN) and long-short-term memory (LSTM), are applied and compared with our previously proposed manual-feature-engineering-driven, SVM-based approach. Simulation results demonstrate that LSTM obtained a better performance as compared to multilayer perceptron (MLP), autoencoder, support vector machine (SVM), logistic regression and CNN algorithms.     

Mode identification of a rotating disk

A modal testing method permitting identification of the natural frequencies, the number of nodal diameters and wave motions in a rotating disk is presented in this paper. This method is applicable at arbitrary rotation speed without requiring a priori information about the vibration modes of the stationary disk. The influence of disk rotation speed on the prediction of mode shapes with this method is shown, and experimental predictions of modal parameters are presented for both axisymmetric and asymmetric disks.     

SBA‐15‐Oxynitrides as a Solid‐Base Catalyst: Effect of Nitridation Temperature on Catalytic Activity

Solid bases, such as SBA‐15‐oxynitrides, have attracted considerable interest for potential applications as catalysts in important industrial processes. Reported herein is that by simply tuning the temperature of nitridation (ammonolysis), the catalytic activity of these solid bases can be enhanced. Solid‐state NMR spectroscopy and XPS studies provided the reasoning behind this change in activity.     

Investigating internal resonances and 3:1 modal interaction in an electrostatically actuated clamped-hinged microbeam

Meccanica - In this paper, we investigated the realization of all possible internal resonance conditions between the first three modes of an electrostatically actuated, straight, clamped-hinged...