12 research outputs found
Iridium-decorated multiwall carbon nanotubes and its catalytic activity with Shell 405 in hydrazine decomposition
No full textIridium-functionalized multiwalled carbon nanotubes (Ir-MWNT) are the future catalyst support material for hydrazine fuel decomposition. The present work demonstrates decoration of iridium particle on iron-encapsulated multiwalled carbon nanotubes (MWNT) by wet impregnation method in the absence of any stabilizer. Electron microscopy studies reveal the coated iridium particle size in the range of 5-10 nm. Elemental analysis by energy dispersive X-ray diffraction confirms 21 wt% of Ir coated over MWNT. X-ray photoelectron spectroscopy (XPS) shows 4f(5/2) and 4f(7/2) lines of iridium and confirms the metallic nature. The catalytic activity of Ir-MWNT/Shell 405 combination is performed in 1 N hydrazine micro-thrusters. The thruster performance shows increase in chamber pressure and decrease in chamber temperature when compared to Shell 405 alone. This enhanced performance is due to high thermal conducting nature of MWNTs and the presence of Ir active sites over MWNTs
An efficient protocol for the synthesis of six-membered N, O-heterocycles via a 1,3-dipolar (3+3) cycloaddition between nitrile oxide and α-diazo esters
No full textUnprecedented cyclization of α -diazo hydrazones upon N-H functionalization: A Et 3 N base promoted one-step synthetic approach for the synthesis of N-amino-1,2,3-triazole derivatives from α -diazo hydrazone
No full textRhodium-catalyzed synthesis of 2,3 – Disubstituted N -methoxy pyrroles and furans via [3+2] cycloaddition between metal carbenoids and activated olefins
No full textN‐Promoted Stereoselective One‐Pot Synthesis of α‐Diazo Oxime Ethers via Diazo Transfer Reaction.
No full textNaOH/Et<sub>3</sub>N-Promoted Stereoselective One-Pot Synthesis of <i>α</i>-Diazo Oxime Ethers via Diazo Transfer Reaction
No full text<div><p></p><p>For the first time, we have observed a combined effect of two bases NaOH/Et<sub>3</sub>N to promote the diazo transfer reaction of β-oximino esters. This unusual synergistic effect has been employed to obtain <i>α</i>-diazo oxime ethers directly from <i>β-</i>keto esters by one-pot process. This method is simple and cost-effective and the reagents are readily available.</p></div
Conformational structure of propranolol: A β-adrenergic blocking drug studied by NMR and PCILO methods
No full textConformational structure of propranolol: A β-adrenergic blocking drug studied by NMR and PCILO methods
No full textThe conformational structure of propranolol, a β-adrenergic blocking drug, has been investigated by pcilo calculations and 270-MHz proton nuclear magnetic resonance in D2O solution. The molecules coexist in at least two conformational states in solution with a low energy barrier. Both preferred conformations have extended structures which allow a three-point drug-receptor binding involving the aromatic moiety, the β-hydroxyl group, and -NH+2 groups of propranolol. The previously postulated "rigid" bicyclic structure does not exist to an appreciable extent in D2O solution
Improving energy consumption prediction for residential buildings using Modified Wild Horse Optimization with Deep Learning model.
No full textThe consumption of a significant quantity of energy in buildings has been linked to the emergence of environmental problems that can have unfavourable effects on people. The prediction of energy consumption is widely regarded as an effective method for the conservation of energy and the improvement of decision-making processes for the purpose of lowering energy use. When it comes to the generation of positive results in prediction tasks, the Machine Learning (ML) technique can be considered the most appropriate and applicable strategy. This article presents a Modified Wild Horse Optimization with Deep Learning approach for Energy Consumption Prediction (MWHODL-ECP) model in residential buildings. The MWHODL-ECP method that has been provided places an emphasis on providing an up-to-date and precise forecast of the amount of energy that residential buildings consume. The MWHODL-ECP algorithm goes through several phases of data preprocessing in order to achieve this goal. These steps include merging and cleaning the data, converting and normalising the data, and converting the data. A model known as deep belief network (DBN) is used here for the purpose of predicting energy consumption. In the end, the MWHO algorithm is utilised for the hyperparameter tuning procedure. The results of the experiments demonstrated that the MWHODL-ECP approach is superior to other existing DL models in terms of its performance. The MWHODL-ECP model has improved its performance, with effective prediction results of MSE-1.10, RMSE-1.05, MAE-0.41, R-squared-96.28, and Training time-1.23
