Nanomaterials are man-made materials of a size thousands of times smaller than the width of a human hair. They have fascinated scientists and industry with their unique and unpredictable properties, which have given rise to an endless variety of new applications in every sector of technology and medicine. As a result, an ever-increasing number of nanomaterials are entering the market in everyday products spanning from healthcare and leisure to electronics, cosmetics and foodstuffs. However, the novelty in exploitable properties may be mirrored by new hazards and, in order to manage these, a well-founded and robust legislative framework that will ensure safe development of nano-enabled products is needed.
The development of such a framework has proven particularly challenging; at the heart of the challenge lies the difficulty in the reliable and reproducible characterisation of nanomaterials given their novelty, variety in properties and forms and dynamic nature, particularly in complex conditions, such as within different biological, environmental and technological compartments.
To resolve this, the ACEnano project, coordinated by the University of Birmingham (UK), is working towards introducing confidence, adaptability and clarity into nanomaterial risk assessment by developing a widely implementable and robust tiered approach to nanomaterials physicochemical characterisation that will simplify the choice of characterisation methods, and facilitate contextual (hazard or exposure) description and its transcription into a reliable nanomaterials grouping framework.
ACEnano is structured into three phases of instrument and method development: innovation to develop new methods, new means of instrument hyphenation, novel sample introduction, or miniaturised equipment; optimisation of workflows and sample introduction systems; and benchmarking of both existing methods and those developed and optimised within ACEnano. In all cases, full protocols and SOPs, and training materials (video protocols, online tutorials, libraries of representative results…) will be provided, alongside a decision tree to support users (industry, regulators…) in the selection of the most appropriate combination of methods to address their specific analysis or characterisation need. The decision tree approach for method selection will be based on an online platform consisting of a set of pre-designed questions (based on case studies from previous projects, such as NanoFASE or NanoDefine which specifically addressed the question of whether a material is “nano” or not).
The first year of the project (which runs from January 2017 to December 2020) focused on partner integration and on addressing the project’s technical challenges. In particular, excellent progress is being made on methods that enable more precise characterisation of complex multi-component matrices. Also, in order to achieve a major project impact via improved technologies, a selection of methods to be benchmarked and initial training efforts were made. Work mainly related to information and knowledge gathering related to decision-tree building was also carried out.
ACEnano’s impact will thus be achieved through new characterisation tools and services that are robust, reliable, more user-friendly and fit for purpose for risk assessment and regulation, enabling greatly increased confidence in datasets, and identification of quantitative nanomaterials structure-activity relationships (SARs) as the basis for grouping and read-across.
The multi-disciplinary project consortium consists of 26 partners from UK, Austria, Switzerland, Germany, the Netherlands, Sweden, Belgium, Korea and China. Partner expertise spans chemistry and materials science, statistical, engineering, physical, mathematical, environmental and biological sciences. These are complemented by industry partners who specialize in instrumentation and integration at various technology readiness levels and various scales from SMEs to global leaders, knowledge management, risk assessment, innovation management and regulatory consultancy. This combination of partners, working together, is bringing the needed experimental, analytical, modelling and dissemination skills required for successful delivery of the project work plan.
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 720952.
This text reflects only the author's view and the Commission is not responsible for any use that may be made of the information it contains.