EXTRACTION OF MATERIALS FROM E-WASTE
Journal: International Journal of Engineering Sciences & Research Technology (IJESRT) (Vol.6, No. 7)Publication Date: 2017-07-30
Authors : Aljo Anand; Seju Thomas; Deeksha Aggarwal;
Page : 574-584
Keywords : e-waste; pollution; cadmium; lithium; nickel; salts and batteries.;
Abstract
Electronic waste (E-waste) is the term used to describe old, end-of-life or discarded applications. It includes computers, consumer electronics, batteries etc. which have been disposed of by their original users. It is one of the fastest – growing pollution problems worldwide given the presence of a variety of toxic substances (heavy and toxic metals like Cd, Pb, Ni, Li,…….) which can contaminate the environment and threaten human health, if disposal protocols are not meticulously followed. The current study presents an overview of toxic substances present in e-waste, which can be extracted using standard methods. These extractions can be done in laboratories and also in common household equipped with certain safety measures. E-waste solutions were analyzed for presence of various metals qualitatively and those present were estimated quantitatively. From the eight Ni-Cd batteries containing a total of 11.342 g of source material from which 5.986 g of Graphite and 2.109 g of Cadmium were obtained. Eight AA alkaline batteries contain 16.325 g of source material which yielded 6.859 g of graphite, 5.439 g of Manganese and 2.380g of Nickel. From six Lithium Laptop batteries yielded a source of 15.714 g out of which 2.989 g of Lithium were extracted. These salts or compounds extracted can be recycled and purified. It also can be used as a substituent or potential replacement in the laboratories.
Other Latest Articles
- CORROSION ANALYSIS OF METALLIZED FILMS AFTER PRINTING
- A SURVEY OF URBAN PEOPLE AWARENESS ABOUT NEW INDIAN CURRENCY SECURITY FEATURES AFTER DEMONETIZATION
- AN INTEGRATED APPROACH FOR TRACEABLE FOOD SUPPLY CHAIN MANAGEMENT
- FAULT ANALYSIS IN SELF ALIGNING BALL BEARING BY WAVELET TRANSFORM BASED FEATURE EXTRACTION USING NEURAL NETWORKS
- ENHANCE LABOUR PRODUCTIVITY THROUGH APPLICATIONS OF WORK STUDY PRINCIPLES FOR A RESIDENTIAL SITE
Last modified: 2017-07-19 20:55:19