Project ACEnano, acronym for “Analytical and Characterisation Excellence in nanomaterial risk assessment: A tiered approach”, is a Horizon2020 project which recently completed its 4.5-year course. The project partnership comprised of 28 members and included universities, research institutes, government bodies, multinational companies and SMEs, from across Europe but also South Korea and China. ACEnano had the ambitious goal to introduce confidence, adaptability and clarity into nanomaterial risk assessment by developing a widely implementable and robust tiered approach to nanomaterials physicochemical characterisation that would simplify and facilitate their description and underpin the development of a reliable nanomaterials grouping framework.
But why is the physicochemical characterisation of nanomaterials such a challenge? Figure 1 provides a visual explanation: whereas for simple bulk materials their composition and concentration are usually enough to provide a good identification and to build a risk assessment framework around it, this is not the case for nanomaterials, whose physicochemical properties vary depending on their size and which may behave very differently, even if just one of the properties shown in Figure 1 is modified. To put it simply, changes in one property may influence -unpredictably!- other properties. For example, if agglomeration occurs, this will also affect the surface area, but the magnitude of the effect will depend on the size and kind (i.e. hard vs. soft) of aggregates formed, which we do not know a priori.
Figure 1: Challenges in detection and characterisation of nanomaterials: for their full and reliable description, all variables shown in the figure need to be known or measured.
To account for such complexity in nanomaterial identification, their characterisation has to be comprehensive, which implies that a number of different analytical techniques need to be used to describe them fully. Additional challenges come from the fact that many standard analytical techniques are not capable of resolving such tiny (i.e. 100nm or less) objects, and also that nanomaterials, which are highly dynamic in nature, may even be changing their properties as they are being measured. An example here would be a nanomaterial that might be dissolving (and therefore becoming smaller in size) or transforming (and thus changing to a structurally more stable form) in the characterisation medium.
This should make the magnitude of the ACEnano project undertaking a little more apparent. To tackle all these challenges, the ACEnano consortium came up with 5 innovation approaches, which are shown in Figure 2, and explained briefly next:
Figure 2: Project ACEnano innovation approaches to drive towards analytical technologies capable of addressing the complex characterisation needs of nanomaterials
Thus ACEnano produced a suite of new analytical solutions, some of which are already available to use (e.g. in mass spectrometry, instrument hyphenation and automated exposure) and others which require further development but have progressed sufficiently to prove their value (e.g. assays and sample introduction system). And the innovation does not stop there. A further ambition of the project was to create a “conceptual toolbox” to hold all of the project’s analytical achievements, some of which were specifically aimed towards demystifying and simplifying analysis, through interlaboratory comparisons, reliable protocol development and making all these accessible via a centralised hub to the community. Importantly these resources are provided freely in open access format via the project website. These services were designed with both industry (especially SMEs) and inexperienced academic (research student) users in mind.
The Toolbox comprises a Decision Tool (DT) for a guided choice of the optimal analytical approach for a user wishing to perform their own characterisation, and a Knowledge Infrastructure (KI) for the storage and retrieval of Standard Operating Protocols, coupled with performance data derived from Interlaboratory Comparison studies and in selected cases, demonstrations of the use of techniques through video protocols. Users may retrieve information directly from the KI, or may use the DT to produce a set of recommended techniques (based on known nanomaterial characteristics and the analytical endpoint required), and be guided to the information on those techniques within the KI. Throughout its lifetime, and now available through the KI, ACEnano generated libraries of techniques and protocols, including video protocols, collections of data, simplified guides to analyses and model analytical workflows. In other words, a literal virtual toolbox for users, which aims at underpinning the future of nanomaterial characterisation, quality control, labelling and safety assessment. A visual example of the ACEnano toolbox is shown in Figure 3.
So, although now we have to say “goodbye” to the active phase of analytical innovation of ACEnano, we can say “hello” to the project’s tools and legacy, available via: ACEnano website. Plans are also afoot to turn the platform into a dedicated service, subject to funding.
Acknowledgment: The work described above was funded through the European Union Horizon 2020 Programme (H2020) under grant agreement nº 720952, project ACEnano (call NMBP-26-2016).
Figure 3. Layout of the ACEnano Toolbox
Éva Valsami-Jones is a Professor of Environmental Nanoscience at the University of Birmingham. Her research focuses on nanoscale processes in the environment and within biota. She has pioneered the development of traceable stable-isotope labelled nanomaterials and is currently working on the development of analytical solutions for the improvement in speed and quality of identification of nanoscale objects in complex matrices. She was the Mineralogical Society’s Distinguished Lecturer for 2015 and the Distinguished Guest Lecturer and Medalist of the Royal Society of Chemistry for 2015. She is currently a Royal Society Wolfson Fellow.
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