Articles producció científica> Enginyeria Química

A computational view on nanomaterial intrinsic and extrinsic features for nanosafety and sustainability

  • Datos identificativos

    Identificador: imarina:9322145
    Handle: http://hdl.handle.net/20.500.11797/imarina9322145
  • Autores:

    Mancardi G
    Mikolajczyk A
    Annapoorani VK
    Bahl A
    Blekos K
    Burk J
    Çetin YA
    Chairetakis K
    Dutta S
    Escorihuela L
    Jagiello K
    Singhal A
    van der Pol R
    Bañares MA
    Buchete NV
    Calatayud M
    Dumit VI
    Gardini D
    Jeliazkova N
    Haase A
    Marcoulaki E
    Martorell B
    Puzyn T
    Agur Sevink GJ
    Simeone FC
    Tämm K
    Chiavazzo E
  • Otros:

    Autor según el artículo: Mancardi G; Mikolajczyk A; Annapoorani VK; Bahl A; Blekos K; Burk J; Çetin YA; Chairetakis K; Dutta S; Escorihuela L; Jagiello K; Singhal A; van der Pol R; Bañares MA; Buchete NV; Calatayud M; Dumit VI; Gardini D; Jeliazkova N; Haase A; Marcoulaki E; Martorell B; Puzyn T; Agur Sevink GJ; Simeone FC; Tämm K; Chiavazzo E
    Departamento: Enginyeria Química
    Autor/es de la URV: Escorihuela Martí, Laura / Martorell Masip, Benjamí
    Palabras clave: Safe and sustainability-by-design(ssbd) Physicochemical descriptors Nanosafety Nanoinformatics Multiscale modeling Materials modeling Machine learning Grouping approaches Engineered nanomaterials
    Resumen: In recent years, an increasing number of diverse Engineered Nano-Materials (ENMs), such as nanoparticles and nanotubes, have been included in many technological applications and consumer products. The desirable and unique properties of ENMs are accompanied by potential hazards whose impacts are difficult to predict either qualitatively or in a quantitative and predictive manner. Alongside established methods for experimental and computational characterisation, physics-based modelling tools like molecular dynamics are increasingly considered in Safe and Sustainability-by-design (SSbD) strategies that put user health and environmental impact at the centre of the design and development of new products. Hence, the further development of such tools can support safe and sustainable innovation and its regulation. This paper stems from a community effort and presents the outcome of a four-year-long discussion on the benefits, capabilities and limitations of adopting physics-based modelling for computing suitable features of nanomaterials that can be used for toxicity assessment of nanomaterials in combination with data-based models and experimental assessment of toxicity endpoints. We review modern multiscale physics-based models that generate advanced system-dependent (intrinsic) or time- and environment-dependent (extrinsic) descriptors/features of ENMs (primarily, but not limited to nanoparticles, NPs), with the former being related to the bare NPs and the latter to their dynamic fingerprinting upon entering biological media. The focus is on (i) effectively representing all nanoparticle attributes for multicomponent nanomaterials, (ii) generation and inclusion of intrinsic nanoform properties, (iii) inclusion of selected extrinsic properties, (iv) the necessity of considering distributions of structural advanced features rather than only averages. This review enables us to identify and highlight a number of key challenges associated with ENMs’ data generation, curation, representation and use within machine learning or other advanced data-driven models to ultimately enhance toxicity assessment. Finally, the set up of dedicated databases as well as the development of grouping and read-across strategies based on the mode of action of ENMs using omics methods are identified as emerging methodologies for safety assessment and reduction of animal testing.
    Áreas temáticas: Medicina ii Mechanics of materials Mechanical engineering Materials science, multidisciplinary Materials science (miscellaneous) Materials science (all) General materials science Condensed matter physics
    Acceso a la licencia de uso: https://creativecommons.org/licenses/by/3.0/es/
    Direcció de correo del autor: benjami.martorell@urv.cat laura.escorihuela@urv.cat
    Identificador del autor: 0000-0002-6350-2396 0000-0002-6350-2396
    Fecha de alta del registro: 2023-07-09
    Versión del articulo depositado: info:eu-repo/semantics/publishedVersion
    Enlace a la fuente original: https://www.sciencedirect.com/science/article/pii/S1369702123001803
    Referencia al articulo segun fuente origial: Materials Today.
    Referencia de l'ítem segons les normes APA: Mancardi G; Mikolajczyk A; Annapoorani VK; Bahl A; Blekos K; Burk J; Çetin YA; Chairetakis K; Dutta S; Escorihuela L; Jagiello K; Singhal A; van der P (2023). A computational view on nanomaterial intrinsic and extrinsic features for nanosafety and sustainability. Materials Today, (), -. DOI: 10.1016/j.mattod.2023.05.029
    URL Documento de licencia: http://repositori.urv.cat/ca/proteccio-de-dades/
    DOI del artículo: 10.1016/j.mattod.2023.05.029
    Entidad: Universitat Rovira i Virgili
    Año de publicación de la revista: 2023
    Tipo de publicación: Journal Publications
  • Palabras clave:

    Condensed Matter Physics,Materials Science (Miscellaneous),Materials Science, Multidisciplinary,Mechanical Engineering,Mechanics of Materials
    Safe and sustainability-by-design(ssbd)
    Physicochemical descriptors
    Nanosafety
    Nanoinformatics
    Multiscale modeling
    Materials modeling
    Machine learning
    Grouping approaches
    Engineered nanomaterials
    Medicina ii
    Mechanics of materials
    Mechanical engineering
    Materials science, multidisciplinary
    Materials science (miscellaneous)
    Materials science (all)
    General materials science
    Condensed matter physics
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