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A green chemistry-based classification model for the synthesis of silver nanoparticles
The assessment of the implementation of green chemistry principles in the syntheses of nanomaterials is a complex decision-making problem that necessitates the integration of several evaluation criteria. Multiple Criteria Decision Aiding (MCDA) provides support for such a challenge. One of its metho...
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Published in: | Green chemistry : an international journal and green chemistry resource : GC 2015-05, Vol.17 (5), p.2825-2839 |
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Main Authors: | , , , , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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Summary: | The assessment of the implementation of green chemistry principles in the syntheses of nanomaterials is a complex decision-making problem that necessitates the 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 nanoparticle 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 that are logical statements in the form: "ifconditions, thendecision". The approach provides 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 to various extents, resulting in robust recommendations for the development and assessment of silver nanoparticle synthesis that perform at one of five pre-determined levels. The DRSA-based approach is transparent and structured and can be easily updated. New perspectives and criteria could be added into the model if relevant data were available and domain-specific experts could collaborate through the MCDA procedure. |
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ISSN: | 1463-9262 1463-9270 |
DOI: | 10.1039/c4gc02088j |