Science Inventory

Robustness analysis of a green chemistry-based model for the classification of silver nanoparticles synthesis processes

Citation:

Cinelli, M., S. Coles, M. Nadagouda, J. Błaszczyński, R. Słowiński, R. Varma, AND K. Kirwan. Robustness analysis of a green chemistry-based model for the classification of silver nanoparticles synthesis processes. JOURNAL OF CLEANER PRODUCTION. Elsevier, AMSTERDAM, Netherlands, 162:938-948, (2017). https://doi.org/10.1016/j.jclepro.2017.06.113

Impact/Purpose:

The comprehensive assessment of the implementation of green chemistry principles (GCP) during nano synthesis processes is a complex decision-making problem, requiring the consideration of multiple evaluation parameters. To achieve credible and sound decision support, the uncertainties inherent in these evaluations as well as the resulting modeling strategies need to be accounted for. This paper proposes a methodology that considers these requirements by employing a decision support method from the Multiple Criteria Decision Aiding (MCDA) research domain. This research can be used by researchers and private industry interested in sustainable green chemistry.

Description:

This paper proposes a robustness analysis based on Multiple Criteria Decision Aiding (MCDA). The ensuing model was used to assess the implementation of green chemistry principles in the synthesis of silver nanoparticles. Its recommendations were also compared to an earlier developed model for the same purpose to investigate concordance between the models and potential decision support synergies. A three-phase procedure was adopted to achieve the research objectives. Firstly, an ordinal ranking of the evaluation criteria used to characterize the implementation of green chemistry principles was identified through relative ranking analysis. Secondly, a structured selection process for an MCDA classification method was conducted, which ensued in the identification of Stochastic Multi-Criteria Acceptability Analysis (SMAA). Lastly, the agreement of the classifications by the two MCDA models and the resulting synergistic role of decision recommendations were studied. This comparison showed that the results of the two models agree between 76% and 93% of the simulation set-ups and it confirmed that different MCDA models provide a more inclusive and transparent set of recommendations. This integrative research confirmed the beneficial complementary use of MCDA methods to aid responsible development of nanosynthesis, by accounting for multiple objectives and helping communication of complex information in a comprehensive and traceable format, suitable for stakeholders and/or decision-makers with diverse backgrounds.

URLs/Downloads:

https://doi.org/10.1016/j.jclepro.2017.06.113   Exit

Robustness Analysis of a Green Chemistry-based Model   Exit

Record Details:

Record Type: DOCUMENT (JOURNAL/PEER REVIEWED JOURNAL)
Product Published Date: 09/20/2017
Record Last Revised: 05/15/2018
OMB Category: Other
Record ID: 337403

Organization:

U.S. ENVIRONMENTAL PROTECTION AGENCY

OFFICE OF RESEARCH AND DEVELOPMENT

NATIONAL RISK MANAGEMENT RESEARCH LABORATORY

SUSTAINABLE TECHNOLOGY DIVISION

CLEAN PROCESSES BRANCH