Advanced high aspect ratio and multicomponent materials: towards comprehensive intelligent testing and Safe-by-Design strategies
Public and private researchers spent great efforts on nanosafety research in the past decade. This helped to establish nanotechnology on key markets and it inspired regulatory approaches such as the REACH requirements for nanomaterials. Nevertheless, as risk assessment has focussed predominantly on a few types of monocomponent nanomaterials, current product designs and regulations may be outpaced by the development of next generation multicomponent nanomaterials – a significant cause for concern to the manufacturer as well as to human health and the environment. Complex multi-component, hybrid nanomaterials and High Aspect Ratio Nanoparticles (MCNM & HARNs) exhibit differing rates of fragmentation, degradation and leaching, differing toxicities of the separate and interacting components, and different interactions with biological and environmental systems.
HARMLESS develops a novel, multifaceted Safe Innovation Approach to MCNM & HARNs by integrating a toolbox of New Approach Methodologies, which can test key data according to latest scientific insights into MCNM & HARNs. To ensure that industries operating at differing scale, including SMEs, pick up our approach, we create a user-friendly decision support system and validate it iteratively at scale in different case studies.
To be viable for industry, Safe-by-Design approaches have to predict how the multidimensional design space may affect the functionality for the intended use. Conventional characterisation and testing methods are inefficient in this regard and not flexible enough for different innovation stages and industry sectors. In particular outside large industries, potential users of Safe-by-Design suffer from the complexity and variety of testing methods. To better guide them through intelligent decision choices throughout their entire design cycles and production, we develop a user-friendly Safe-by-Design decision support tool. The tool includes machine/deep learning algorithms that support: i) automatic and intelligent selection of methods/models, ii) fusion of heterogeneous model outputs to predict the single outcome for risk assessment, and iii) knowledge integration for assessing the risk of new materials.
Our long-term vision is to move Safe-by-Design concepts from its current infancy state to a mature state that all companies – including SMEs – should be able to apply routinely. To make our vision come true, we collaborate across the entire nanosafety domain on international level. Our Safe Innovation Approach will be delivered both as guidance and as e-tool.