Structural Feature Based Computational Approach Of Toxicity Prediction | Ionic Liquids toxicity: Effect of Anions and Cations

Md Abdus Salam (u. a.)

ISBN 10: 3330050616 / ISBN 13: 9783330050617

Published by LAP LAMBERT Academic Publishing, 2017

About this Item

Structural Feature Based Computational Approach Of Toxicity Prediction | Ionic Liquids toxicity: Effect of Anions and Cations | Md Abdus Salam (u. a.) | Taschenbuch | 64 S. | Englisch | 2017 | LAP LAMBERT Academic Publishing | EAN 9783330050617 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.

Seller Inventory # 108769707

Bibliographic Details

• Title: Structural Feature Based Computational Approach Of Toxicity Prediction | Ionic Liquids toxicity: Effect of Anions and Cations
• Publisher: LAP LAMBERT Academic Publishing
• Publication Date: 2017
• Language: English
• ISBN 10: 3330050616
• ISBN 13: 9783330050617
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 5D millimeters 220 millimeters width by 150 millimeters height by 5 millimeters depth
• Weight: 113 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

Ionic liquids(IL's) toxicity database is very important now a days due to its diversified application. An economic IL's toxicity prediction method support in enlarging the toxic IL's database and a tool of a safe and sustainable environment.There is no DFT based IL's toxicity prediction method so far. The contents of the book present a novel investigation using the density functional theory (DFT) in predicting the toxicity of ionic liquids. Seventeen DFT based reactivity descriptors were investigated and detailed out in chapter 2.Electrophilic indices (ω),the energy of highest occupied (EHOMO) and lowest unoccupied molecular orbital,(ELUMO) and energy gap (∆E) were selected as the best toxicity descriptors of IL's via Pearson correlation and multiple linear regression analyses that discussed in chapter 4.The principle components analysis(PCA) demonstrated the distribution and interrelation of descriptors of the model. A multiple linear regression (MLR) analysis on selected descriptors derived the model equation for toxicity prediction of ionic liquids. The toxicity perdition method help researchers, toxicologist and for industrial application in knowing the level of their toxicity.

About the Author

Dr Md Abdus Salam is a scientist of BCSIR and Ex-faculty member of UTP, Malaysia. He has extensive experiences in strengthening industry-relevant research and academic excellence. He has obtained a PhD in Chemical Eng. from Univ. Teknologi PETRONAS, Malaysia. His research area is design and development of advanced materials and chemical processes.