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dc.contributor.authorTervonen, T.
dc.contributor.authorLinkov, Igor
dc.contributor.authorFigueira, José Rui
dc.contributor.authorSteevens, J.
dc.contributor.authorChappell, M.
dc.contributor.authorMerad, Myriam
dc.date.accessioned2020-05-25T14:25:36Z
dc.date.available2020-05-25T14:25:36Z
dc.date.issued2008
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/20776
dc.language.isoenen
dc.subjectNanotechnologyen
dc.subjectRisk assessmenten
dc.subjectToxicologyen
dc.subjectDecision analysisen
dc.subjectGovernanceen
dc.subject.ddc003en
dc.titleRisk-based classification system of nanomaterialsen
dc.typeArticle accepté pour publication ou publié
dc.description.abstractenVarious stakeholders are increasingly interested in the potential toxicity and other risks associated with nanomaterials throughout the different stages of a product's life cycle (e.g., development, production, use, disposal). Risk assessment methods and tools developed and applied to chemical and biological materials may not be readily adaptable for nanomaterials because of the current uncertainty in identifying the relevant physico-chemical and biological properties that adequately describe the materials. Such uncertainty is further driven by the substantial variations in the properties of the original material due to variable manufacturing processes employed in nanomaterial production. To guide scientists and engineers in nanomaterial research and application as well as to promote the safe handling and use of these materials, we propose a decision support system for classifying nanomaterials into different risk categories. The classification system is based on a set of performance metrics that measure both the toxicity and physico-chemical characteristics of the original materials, as well as the expected environmental impacts through the product life cycle. Stochastic multicriteria acceptability analysis (SMAA-TRI), a formal decision analysis method, was used as the foundation for this task. This method allowed us to cluster various nanomaterials in different ecological risk categories based on our current knowledge of nanomaterial physico-chemical characteristics, variation in produced material, and best professional judgments. SMAA-TRI uses Monte Carlo simulations to explore all feasible values for weights, criteria measurements, and other model parameters to assess the robustness of nanomaterial grouping for risk management purposes.en
dc.relation.isversionofjnlnameJournal of Nanoparticle Research
dc.relation.isversionofjnlvol11en
dc.relation.isversionofjnlissue4en
dc.relation.isversionofjnldate2009-05
dc.relation.isversionofjnlpages757–766en
dc.relation.isversionofdoi10.1007/s11051-008-9546-1en
dc.identifier.urlsitehttps://hal-ineris.archives-ouvertes.fr/ineris-00963157en
dc.subject.ddclabelRecherche opérationnelleen
dc.relation.forthcomingnonen
dc.relation.forthcomingprintnonen
dc.description.ssrncandidatenonen
dc.description.halcandidatenonen
dc.description.readershiprechercheen
dc.description.audienceInternationalen
dc.relation.Isversionofjnlpeerreviewednonen
dc.relation.Isversionofjnlpeerreviewednonen
dc.date.updated2020-05-25T14:21:43Z
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