Throughout the project, the ACEnano consortium delivered technological innovations, optimisations and benchmarking, as appropriate, and addressed many other peripheral challenges, such as moving forward with technology implementation, when appropriate via interlaboratory testing and standardization, dissemination and training. A very exciting aspect of ACEnano was the strong collaborative element among technologies developed, interlaboratory comparisons (ILCs) performed, and data and training solutions delivered, including multi-industry partner solutions.
Many ACEnano innovations advanced the state-of-the-art in NM characterization and these have been described in detail in the project deliverable reports. The work in ACEnano also identified 6 new exploitable technologies:
• Single particle-ICP-TOF-MS hyphenated to AF4: TOF pilot workflows to run single particles and cells analysis: this is a software tool for nanoparticle detection in liquid samples and biological tissues, using laser ablation technology;
• The ACENano Knowledge Warehouse and Data Management System (software): a software tool integrating quality assured NM characterisation into a risk assessment framework based on grouping, read-across and safe-by-design strategies;
• Automated sample delivery systems and hyphenated instruments for NM characterisation: Robot-based station for automated preparation of NM suspension: this is a prototype robotic station that enables reproducible automated preparation of NM suspensions for characterisation (Figure 1);
• Analytics and Instruments for Air-Liquid-Interface Systems (ALI): a prototype miniaturised system of the air-liquid interface cell culture model (Figure 2);
• Automated on-chip assays for measuring the reactivity of NMs: this is a prototype of an automated on-a-chip assay (a microfluidic well plate) for the detection of reactivity of NMs (Figure 3);
• A one-stop solution to NM characterisation (ACEnano toolbox): a decision support tool, providing advice on the selection of NM characterisation methods for different scenarios (REACH, risk assessment, labelling etc) and directing users to relevant information resources in the Knowledge Infrastructure.