Science Inventory

A green chemistry-based classification model for the synthesis of silver nanoparticles

Citation:

Cinelli, M., S. R. Coles, M. Nadagouda, J. Błaszczyński, R. Słowiński, R. Varma, AND K. Kirwan. A green chemistry-based classification model for the synthesis of silver nanoparticles. GREEN CHEMISTRY. Royal Society of Chemistry, Cambridge, Uk, 17(5):2825-2839, (2015).

Impact/Purpose:

Sent for publication in the Royal Society of Chemistry (RSC) journal, Green Chemistry.

Description:

The assessment of implementation of green chemistry principles in the synthesis of nanomaterials is a complex decision-making problem that necessitates integration of several evaluation criteria. Multiple Criteria Decision Aiding (MCDA) provides support for such a challenge. One of its methods - Dominance-based Rough Set Approach (DRSA) - was used in this research to develop a model for the green chemistry-based classification of silver nanoparticles synthesis protocols into preference-ordered performance classes. DRSA allowed integration of knowledge from both peer-reviewed literature and experts (decision makers, DMs) in the field, resulting in a model composed of decision rules, being logical statements in the form: “if conditions, then decision”. The approach poses the basis for the design of rules for the greener synthesis of silver nanoparticles. Decision rules are supported by synthesis protocols that enforce the principles of green chemistry at various extents, resulting in robust recommendations for the development and assessment of silver nanoparticles synthesis that perform at a one of five pre-determined levels. The DRSA-based approach is transparent, structured and can be easily updated. New perspectives and criteria could be added in the model if relevant data would be available and domain-specific experts would collaborate through the MCDA procedure.

Record Details:

Record Type:DOCUMENT( JOURNAL/ PEER REVIEWED JOURNAL)
Product Published Date:05/01/2015
Record Last Revised:05/15/2015
OMB Category:Other
Record ID: 307958