PERIODATE OXIDATION OF PEG–600, AN ESSENTIAL PHARMACEUTICAL POLYMER

Objective: To study the kinetics of periodate oxidation of polyethylene glycol-600 (PEG-600), a familiar non-toxic polymer used in pharmaceutical and other fields of industry. Methods: Reactions were carried out in alkaline medium and measured the kinetics by iodometry. One oxygen atom loss or two electrons transfer was observed per each molecule of periodate i.e., the rate of reaction was measured periodate converts to iodate because the formed iodate species is unable to oxidize the substrate molecules. Results: Based on log (a-x) versus t plots, order w. r. t. oxidant (periodate) is unity. Reactions were found to be independent of substrate (PEG-600) concentration. A decrease in rate with an increase in alkali concentration [OH–] was found and order was inverse fractional. Temperature dependence of reaction rate was studied and then calculated the corresponding Arrhenius parameters. Conclusion: An appropriate rate law was proposed by considering the above experimental results

Quenching of fluorescence of aromatic molecules by graphene due to electron transfer

Investigations on the fluorescence quenching of graphene have been carried out with two organic donor molecules, pyrene butanaoic acid succinimidyl ester (PyBS, I) and oligo(p-phenylenevinylene) methyl ester (OPV-ester, II). Absorption and photoluminescence spectra of I and II recorded in mixture with increasing the concentrations of graphene showed no change in the former, but remarkable quenching of fluorescence. The property of graphene to quench fluorescence of these aromatic molecules is shown to be associated with photo-induced electron transfer, on the basis of fluorescence decay and time-resolved transient absorption spectroscopic measurements.Comment: 18 pages, 6 figure

Object Detection using Deep Learning with Hierarchical Multi Swarm Optimization

Till now there is a huge research had in the field of visual information retrieval, but with the growth of data and with less processing speed we are not meeting the needs of current problem. The main focus of this paper is to identify the objects with salient features and object highlighting. Till now object identification is done with the pixel based or with the region based. Different methodologies are compared in this work and they will be processed with the learning work. Multi scale contrast is one of the pixel based technology where object borders are identified but not the object. This can be done with the histogram contrast. Still it is not covering all the features of the object and it is not clear in identifying the objects at high contrast regions. To solve this issue region based contrasting method is used which is the better solution for all this object identification. After extracting the features and identifying the object, now auto classification or identification of the object should be done. The other part of the work mainly concentrates on the learning system which uses most popular neural network algorithms. Identifying the drawbacks of neural network algorithms and proposing the new methodology identify the objects is done in this paper

Tool and work piece vibrations measurement - a review

Tool condition monitoring is one of the important aspects in machining process to improve tool life. It comprises three important steps namely machining data acquisition, data analysis and decision making. Vibration in metal cutting has direct impact on the tool life as well as surface roughness. The present study focused on measurement of vibration during the machining process. Data acquisition is made by using various types of sensors. A wide variety of technologies like contact and non contact sensors have been used for real time data acquisition of tool or work piece vibrations. Research works carried out by many authors is highlighted in measurement of cutting tool and machine tool vibrations using different sensors. Influence of various input parameters like tool geometry, feed, speed and depth of cut on the magnitude of vibrations is discussed. Influence of vibration on surface roughness, tool life and power consumption is reviewed. Three dimensional vibration measurement with single Laser Doppler Vibrometer is also covered for precise analysis of vibration

Multi-objective optimization approach for cost management during product design at the conceptual phase

The effective cost management during the conceptual design phase of a product is essential to develop a product with minimum cost and desired quality. The integration of the methodologies of quality function deployment (QFD), value engineering (VE) and target costing (TC) could be applied to the continuous improvement of any product during product development. To optimize customer satisfaction and total cost of a product, a mathematical model is established in this paper. This model integrates QFD, VE and TC under multi-objective optimization frame work. A case study on domestic refrigerator is presented to show the performance of the proposed model. Goal programming is adopted to attain the goals of maximum customer satisfaction and minimum cost of the product

Efficient removal of methyl orange using magnesium oxide nanoparticles loaded onto activated carbon

ABSTRACT. In this work, an activated carbon composite made with magnesium oxide nanoparticles (MgONP-AC) was effectively utilized for methyl orange (MO) adsorption. The effect of pH (6-10), mass of MgONP-AC (0.1-0.3 g/L), initial MO concentration (10-30 mg/L), and temperature (283-323 K) on MO removal was investigated using a central rotatable composite experimental design based on the response surface technique (RSM) at an equilibrium agitation period of 60 min. The studies predicted the optimal MO removal of 98.99% at pH 7.68, MgONP-AC dosage of 0.24 g/L, and starting MO concentration of 15 mg/L, and temperature of 313 K. Furthermore, an artificial neural network (ANN) was utilized to simulate MO adsorption, and it properly predicted MO removal using mean squared error (MSE) and R2 for the testing data. The ANN predicts a maximum removal of 99.63% with ANN with R2 = 0.9926. The kinetic results suited the pseudo-second order kinetic equation, and the data from the equilibrium investigations corresponded well with the Langmuir isotherm (maximum uptake capacity qmax = 346 mg/g). Endothermic, spontaneous, and physical adsorption were discovered during the thermodynamic investigations.   KEY WORDS: Adsorption, Artificial neural network, Experimental design, isotherms, Kinetics, Methyl orange, MgONP-AC   Bull. Chem. Soc. Ethiop. 2022, 36(3), 531-544.                                                                DOI: https://dx.doi.org/10.4314/bcse.v36i3.

The minimum mean monopoly energy of a graph

The motivation for the study of the graph energy comes from chemistry, where the research on the so-called total pi - electron energy can be traced back until the 1930s. This graph invariant is very closely connected to a chemical quantity known as the total pi - electron energy of conjugated hydro carbon molecules. In recent times analogous energies are being considered, based on Eigen values of a variety of other graph matrices. In 1978, I.Gutman [1] defined energy mathematically for all graphs. Energy of graphs has many mathematical properties which are being investigated. The ordinary energy of an undirected simple finite graph G is defined as the sum of the absolute values of the Eigen values of its associated matrix. i.e. if mu(1), mu(2), ..., mu(n) are the Eigen values of adjacency matrix A(G), then energy of graph is Sigma(G) = Sigma(n)(i=1) vertical bar mu(i)vertical bar Laura Buggy, Amalia Culiuc, Katelyn Mccall and Duyguyen [9] introduced the more general M-energy or Mean Energy of G is then defined as E-M (G) = Sigma(n)(i=1)vertical bar mu(i) - (mu) over bar vertical bar, where (mu) over bar vertical bar is the average of mu(1), mu(2), ..., mu(n). A subset M subset of V (G), in a graph G (V, E), is called a monopoly set of G if every vertex v is an element of (V - M) has at least d(v)/2 neighbors in M. The minimum cardinality of a monopoly set among all monopoly sets in G is called the monopoly size of G, denoted by mo(G) Ahmed Mohammed Naji and N.D.Soner [7] introduced minimum monopoly energy E-MM [G] of a graph G. In this paper we are introducing the minimum mean monopoly energy, denoted by E-MM(M) (G), of a graph G and computed minimum monopoly energies of some standard graphs. Upper and lower bounds for E-MM(M) (G)are also established.Publisher's Versio