Implementation of the NANoREG Safe-by-Design approach for different nanomaterial applications

Micheletti, Christian; Roman, Marco; Tedesco, Erik; Olivato, I; Benetti, Federico

doi:10.1088/1742-6596/838/1/012019

Cited by 7 publications

(4 citation statements)

References 4 publications

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“…Nanotechnology has emerged at the forefront of science and technology due to its enormous potential to produce revolutionary advances in material science and in several fields of application, including microelectronics, energy storage units, smart therapeutics, optical detection systems, and many others. Design of safe engineered nanomaterials (ENMs) is perhaps the most important challenge in the field, [ 1 ] because due to their small size, ENMs may result in the modulation of pathways and mechanisms of toxic action that may endanger human health and the environment. Nanoinformatics approaches have gained popularity over the last few years as novel tools to address several challenges in nanotechnology [ 2 ] including design of safer ENMs, based on computational and data analysis methodologies, with the goal of reducing to the greatest possible extent the need for traditional hazard and risk assessment methodologies that are based on animal testing.…”

Section: Introductionmentioning

confidence: 99%

Development of Deep Learning Models for Predicting the Effects of Exposure to Engineered Nanomaterials on Daphnia magna

Karatzas

Melagraki

Ellis

et al. 2020

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This study presents the results of applying deep learning methodologies within the ecotoxicology field, with the objective of training predictive models that can support hazard assessment and eventually the design of safer engineered nanomaterials (ENMs). A workflow applying two different deep learning architectures on microscopic images of Daphnia magna is proposed that can automatically detect possible malformations, such as effects on the length of the tail, and the overall size, and uncommon lipid concentrations and lipid deposit shapes, which are due to direct or parental exposure to ENMs. Next, classification models assign specific objects (heart, abdomen/claw) to classes that depend on lipid densities and compare the results with controls. The models are statistically validated in terms of their prediction accuracy on external D. magna images and illustrate that deep learning technologies can be useful in the nanoinformatics field, because they can automate time‐consuming manual procedures, accelerate the investigation of adverse effects of ENMs, and facilitate the process of designing safer nanostructures. It may even be possible in the future to predict impacts on subsequent generations from images of parental exposure, reducing the time and cost involved in long‐term reproductive toxicity assays over multiple generations.

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Section: Introductionmentioning

confidence: 99%

Development of Deep Learning Models for Predicting the Effects of Exposure to Engineered Nanomaterials on Daphnia magna

Karatzas

Melagraki

Ellis

et al. 2020

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“…The advances in nanotechnology have favored an increase in the use of coatings to modify the core NM surface. These coatings are used as part of the safe-by-design strategy to improve, for instance, the reactivity of NMs with a target organism or their dispersion and stability or to reduce their hazard or fate persistence [ 1 , 2 ].…”

Section: Introductionmentioning

confidence: 99%

Toxic Effects of Different Coating-Related Functionalized Nanoparticles on Aquatic Organisms

Hernández-Moreno,

Fernández-Díaz,

Rucandio

et al. 2024

Toxics

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The peculiar physico-chemical characteristics of nanomaterials (NMs) and the use of different coatings to improve their expected properties result in a huge amount of nanoforms, which vary in chemical composition, size, shape and surface characteristics. This makes it almost impossible to test all the nanoforms available, and efforts have been made to establish grouping or read-across strategies. The aim of this work was to find a behavior pattern of effect among nanoforms of different metallic core nanoparticles (NPs) (TiO2, CeO2 and Ag NP) with the same coatings (sodium citrate, poly (ethylene glycol), dodecylphosphonic acid or oleylamine). Daphnia magna, rainbow trout and two fish cell lines (PLHC-1 and RTH-149) were exposed to a range of concentrations (up to 100 mg/L) of the uncoated or coated NPs. Ag NPs were the most toxic, followed by CeO2 NPs and finally by TiO2 NPs. The results show that a clear pattern of toxicity in the studied species could not be established related to the coatings. However, it was possible to confirm different inter-species sensitivities. RTH-149 was the most sensitive cell line, and Daphnia magna was more sensitive than fish. Moreover, some differences in coating-core interactions were found between the metal oxide and the metal NPs in Daphnia magna.

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“…In 2015, the European Commission initiated a Circular Economy Action Plan to help the transition from the classic linear economic model to a circular one [8], with the overall aim of fostering sustainable products and reducing waste generation. In the same year, the European NANoREG project summarized earlier approaches related to the Safe by Design (SbD) concept and their application in nanotechnology [9][10][11]. Subsequently, the European ProSafe project suggested a roadmap towards using the SbD concept as a means of including safety in an integrated way as early as possible in stage-gate product innovation processes [12].…”

Section: Introductionmentioning

confidence: 99%

Development of a Benefit Assessment Matrix for Nanomaterials and Nano-enabled Products—Toward Safe and Sustainable by Design

2023

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Industry and scientists develop new nanomaterials and nano-enabled products to make use of the specific properties that the nanoscale can bring. However, the benefit of a nano-enabled product over a conventional product is not always a given. This paper describes our development of a Benefit Assessment Matrix (BAM) that focuses on the functional, health and environmental benefits of nanomaterials, nano-enabled manufacturing and nano-enabled products. The BAM is an Excel spreadsheet-based tool to help researchers and small and medium-sized enterprises assess these potential benefits throughout their product's life cycle while they are still in the early phase of the innovation process. Benefit indicators were developed based on a review of the literature on the life cycles and intrinsic properties of nanomaterials, nano-enabled manufacturing and nano-enabled products. Assessing the benefits of a nano-enabled product involves a comparative approach, contrasting them against the benefits of a conventional reference product. To help users understand the reliability of the benefits, the BAM identifies the evidence of the benefit claimed. The BAM provides a different action plan for each phase of the stage–gate product innovation process. The tool's applications and potential are presented using three case studies, focusing at different phases of the innovation process: nano-clays used in internal automobile body-panels, nano-TiO2 used in outdoor facade coatings and nano-Ag used in T-shirts. Using these cases studied, we highlight how the results from the BAM can be used to give recommendations for moving towards the concept of safe and sustainable by design in nanotechnology development.

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Implementation of the NANoREG Safe-by-Design approach for different nanomaterial applications

Cited by 7 publications

References 4 publications

Development of Deep Learning Models for Predicting the Effects of Exposure to Engineered Nanomaterials on Daphnia magna

Development of Deep Learning Models for Predicting the Effects of Exposure to Engineered Nanomaterials on Daphnia magna

Toxic Effects of Different Coating-Related Functionalized Nanoparticles on Aquatic Organisms

Development of a Benefit Assessment Matrix for Nanomaterials and Nano-enabled Products—Toward Safe and Sustainable by Design

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