• Delivering analytical and characterisation excellence in nanomaterial risk assessment: a tiered approach

    Delivering analytical and characterisation excellence in nanomaterial risk assessment: a tiered approach

  • Introducing confidence, adaptability and clarity into nanomaterial risk assessment

    Introducing confidence, adaptability and clarity into nanomaterial risk assessment

  • Facilitating decision-making in choice of techniques and SOPs

    Facilitating decision-making in choice of techniques and SOPs

ACEnano introduces external training events at NanoSAFE 2018

The H2020 project ACEnano aims to introduce confidence, adaptability and clarity into nanomaterial risk assessment, by developing a widely implementable and robust tiered approach to nanomaterials physicochemical characterisation that will simplify and facilitate contextual (hazard or exposure) description and its transcription into a reliable nanomaterials grouping framework.

ACEnano will introduce its plan for external training events during the NanoSAFE 2018 conference in Grenoble, France, from 9 am on 6th November (Chrome1 meeting room).

A half-day session, titled ‘ACEnano: developing robust methodologies for nanomaterial characterisation – methods, data, decision trees’ will outline the approach that the project will take towards training external stakeholders in the use of the developing tools (Decision Tree and Knowledge Warehouse) to improve robustness in decision making on the use of nanomaterial analytical techniques to probe the characteristics of both pristine nanomaterials and nanomaterials within complex biological and environmental matrices. A set of presentations will cover the following:

  • The overall aims of the ACEnano project, including the development and refinement of analytical methodologies for nanomaterial characterisation;
  • The current status and use of the decision tree concept as a means to guide users through the range of characterisation techniques, the currently available tools for aiding users in this process, and the steps being taken within ACEnano to improve the robustness and usability of existing tools;
  • The ACEnano Knowledge Warehouse as a central platform to access harmonised and standardised methods as well as quality data applicable for physicochemical characterisation of nanomaterials, and how it can support making better decision–making by improving confidence in the characterization methods and application of decision trees within the context of nanomaterial characterisation;
  • Insights into the public/external ACEnano training events.

No registration is required to attend this event. There will be the opportunity to ask questions, provide feedback and influence the direction and planning of the external training events.

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