Characterization & testing in extreme, applicable environments

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IMPLEMENTATION OF QBD APPROACH TO DEVELOP AND VALIDATE ANALYTICAL METHOD FOR SIMULTANEOUS ESTIMATION OF DULOXETINE HYDROCHLORIDE AND METHYLCOBALAMIN IN PHARMACEUTICAL DOSAGE FORM BY HPTLC METHOD

Objective: To develop and validate High-Performance Thin Layer Chromatography (HPTLC) analytical method for the determination of Duloxetine Hydrochloride (DUL) and Methylcobalamin (MEC) in the standard mixture and pharmaceutical capsule dosage form by implementing Quality by the Design (QbD) approach.Methods: The chromatographic separation was performed on aluminium plates precoated by silica gel 60 F-254 using propanol: water: 25% v/v ammonia solution (9:2:1, v/v/v) as a mobile phase which was optimized with the help of a design expert. Densitometric analysis was carried out in the absorbance mode at 280 nm.Results: Compact spots for Duloxetine HCl and Methylcobalamin were found at retardation factor (Rf) value of 0.77±0.02 and 0.55±0.03, respectively. The linear regression analysis data for the calibration plots showed correlation coefficient 0.9989 and 0.999 with a concentration range of 1200-3600 ng/spot and 60-180 ng/spot for Duloxetine HCl and Methylcobalamin respectively. Limit of detection (LOD) and limit of quantification (LOQ) was found to be 113.39 and 343.62 ng for Duloxetine HCl and 6.68 and 20.24 ng for Methylcobalamin, respectively. The method was validated for precision, accuracy, robustness, LOD and LOQ according to ICH Q2 R1 guidelines.Conclusion: A new, simple, accurate, and precise HPTLC analytical method has been developed and validated for the determination of Duloxetine HCl and Methylcobalamin in pharmaceutical capsule dosage form by QbD concept in favour of fewer trials and error-free experimentation for the optimization process. The method seems to be suitable for the quality control in the pharmaceutical industry because of its sensitivity, simplicity, and selectivity.Keywords: Duloxetine HCl (DUL), Methylcobalamin (MEC), Quality by Design (QbD), HPTLC, Validatio

Comparison of CrAlN layers obtained with one (CrAl) or two targets (Cr and Al) by magnetron sputtering

The authors would like to thank the Regional Council of Burgundy, France for its funding and Michael Walock for his help in English revisionsThe aim of this study is to compare the properties of CrAlN coatings obtained by magnetron sputtering with one (CrAl) or two targets (Cr and Al). The influence of parameters such as the target bias voltage, the working pressure, the deposition time and the bias voltage applied on the Cr or Al targets on the properties of the layers was studied. We characterized the films by X-ray Diffraction, Scanning Electron Microscopy, coupled with Energy Dispersive Spectroscopy, nanoindentation and their residual stresses were also determined. The optimal films obtained with both methods are well crystallized, well-adherent to the substrate, and contained similar amounts of Al (20–30 at.%). The optimal coatings synthesized with one target presented properties not as good as those realized with two targets. Nevertheless, films made with one target showed a lower frictional coefficient probably due to composition control. The lack of compositional control with the use of one target limits the optimization process. With two targets, we have greater control over the film composition. This leads to higher hardness, lower stresses, and improved Young's modulus over films produced with a single CrAl target. Additionally, the morphologies are different (columnar with CrAl and dense with Cr and Al). To conclude, it seems more justified to work if possible with two independent targets.Regional Council of Burgund

Systems analysis of an astrophysics mission utilizing electric propulsion

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 1995.Includes bibliographical references (p. 199-204).by Bhavesh T. Patel.M.S

Influence of Electrode Material and Process Parameters on Surface Quality and MRR in EDM of AISI H13 using ANN

Electrical Discharge Machining (EDM) is a non conventional machining process where electrically conductive materials are machined by using precisely controlled spark that occurs between an electrode and a work piece in the presence of a dielectric fluid. It has been a demanding research area to model and optimize the EDM process in the present scenario. In the present p aper Artificial Neural Network (ANN) model has been proposed for the prediction of Material Removal Rate (MRR), Surface Roughness (SR) and Tool Wear Rate (TWR) in Electrical Discharge Machining (ED M) of AISI H13 Steel. For this purpose Neural Network Toolbox (nntool) with Matlab 7.1 has been used. The neural network based on process model has been generated to establish relationship between input process conditions ( Gap Voltage, Peak Current, Pulse On Time, Pulse Off Time and Electrode M aterial ) an d process responses (MRR, SR and TWR ). The ANN model has been trained and tested using the d ata generated from a series of experiments on EDM machine. The trained neural network system has been used to predict MRR , SR and TWR for different input conditions. The ANN model has been found efficient to predict EDM process response s for selected process conditions

Physicochemical characterization of seed and seed oil of Jatropha curcas L. Collected from Bardoli (South Gujarat)

The seed of Jatropha curcas was collected from the outskirts region of the Bardoli (Gujarat) and it was utilized for determination of seed characterization. The Jatropha curcas oil was extracted using light petroleum ether (60-80°C) by Soxhlet apparatus. The physicochemical properties of Jatropha curcas oil were evaluated. The result showed that the seeds consist of 46.31% (dry w/w) oil, moisture and volatilities (5.8% v/w) and protein content (22.50%). The physicochemical properties shows acid value (36.46), iodine value (106.00 mg/g) and saponification value (194.70 mg/g). The unsaponifiable matter was 1.02%. Negative Halphen test indicated the absence of cyclopropanoid acids in seed oil. GC analysis of J. curcas oil showed presence of palmitic acid (16.69%), stearic acid (7.67%), oleic acid (40. 39%) and linoleic acid (33.09%)