Nitrogen-doped carbon aerogel synthesis by solvothermal gelation for supercapacitor application

Journal of Solid State Electrochemistry - Nitrogen-doped mesoporous carbon aerogel having high surface area and high nitrogen content was synthesized by sol–gel method for supercapacitor...     

Validation of a Stability Indicating LC Method for Amiodarone HCL and Related Substances

A simple, rapid and sensitive isocratic high performance liquid chromatographic (HPLC) method has been developed for the estimation of purity and quantitative determination of Amiodarone HCl active pharmaceutical ingredient (API).The method describes a quantitative estimation of five process related impurities of Amiodarone HCl with a resolution of more than or near to 3.0 between each impurity. These five known related substances are estimated by a simple, rapid and accurate reverse phase isocratic HPLC method. The method has been validated for the determination of assay and related substances in Amiodarone HCl API, using a C₈ column. The elution is carried out using a mobile phase consisting of water-methanol-acetic acid with a pH 5.8. For the quantitative determination of these relative substances, a relative response factors have been determined for all five related substances with respect to Amiodarone HCl. The precision (system precision, method precision and intermediated precision) is demonstrated for both the assay as well as related substances on six independent sample preparations. Accuracy of the method (recovery) is demonstrated for both Amiodarone and each of the five related substances. Specificity of the method is demonstrated by forced degradation study of Amiodarone HCl API under various stress conditions. The method is found to be stability indicating and useful for the analysis of assay and related substances of Amiodarone HCl API in a routine quality control laboratory and for the stability studies of drug substance.     

Hybrid system identification using a structural approach and its model based control: An experimental validation

For implementation of various model based techniques such as in control and fault diagnosis, data-driven identification is key for enabling cheap and rapid development of models of hybrid systems of industrial interest. In the present work, a novel identification method is proposed for a class of hybrid systems which are linear and separable in the discrete variables (that is discrete states and discrete inputs). The method takes cognizance of the fact that the separable structure of the hybrid system constrains the evolution of system dynamics. In particular, the proposed method identifies models corresponding to a certain number of modes, far fewer than the total possible modes of the system. It then generates the models for the remaining modes without any further requirement for input–output data by exploiting the separable structure of the hybrid system. We experimentally validate the method by identifying the model for a three tank benchmark hybrid system followed by model predictive control using the identified model.