Glossary

A nanoparticle (NP) is defined as a material having a diameter of 1–100nm with any external dimension (Dunphy Guzman et al., 2006).

A nanomaterial (NM) is a natural, incidental or manufactured material containing particles, in an unbound state or as an aggregate or as an agglomerate and where, for 50% or more of the particles in the number size distribution, one or more external dimensions is in the size range 1nm – 100nm, as recommended by the EU since October 2011.

SbD is the process of addressing safety issues in both the R&D (Research & Development) and design phases of technologies (e.g., nanotechnology), through the integration of hazard identification and risk assessment methods early in the design process in order to eliminate or minimise the risks of harm through the construction and life of the product being designed. It is a modifying process through received scientific evidence in order that the products where SbD is applied are safer and do not endanger the public. The main belief behind the SbD is to engineer out the unwanted impact of nanomaterials on the bio-environmental system by using the existing knowledge of NMs adversities and hazardous effects. 

A main target of SABYDOMA project for Cnano, besides the reduction of wasted and shift to more safer processes, is the improvement of coating’s quality. Already based on the preliminary results, a significant improvement of our nano-composite coatings has been observed based on the following SEM images. On the left side, the surface of a Ni/SiC nano-composite coating with the SoA method. On the right side, the surface of the corresponding coating produced from the SABYDOMA plating apparatus, in which a better distribution of nanoparticles is evident 

Technology Readiness Levels (TRLs) are indicators of the maturity level of particular technologies. This measurement system provides a common understanding of technology status and addresses the entire innovation chain. There are nine technology readiness levels; TRL 1 being the lowest and TRL 9 the highest.

Technology Readiness Levels (according to EU H2020 guideline):

TRL 1 – Basic principles observed

TRL 2 – Technology concept formulated

TRL 3 – Experimental proof of concept

TRL 4 – Technology validated in lab

TRL 5 – Technology validated in relevant environment (industrially relevant environment in the case of key enabling technologies)

TRL 6 – Technology demonstrated in relevant environment (industrially relevant environment in the case of key enabling technologies)

TRL 7 – System prototype demonstration in operational environment

TRL 8 – System complete and qualified

TRL 9 – Actual system proven in operational environment (competitive manufacturing in the case of key enabling technologies; or in space)

EBD is a safety philosophy which aims to develop refined and appropriate decisions on the basis of scientific evidence. It refers to Evidence Based Decision where the scientific evidence is used to modify the production of  a nanomaterial and/or chemical to render it more safe and/or to improve its functionality

MPC is an advanced method of process control that is used to control a process while satisfying a set of constraints. It uses a model of the system that is being analysed, to make predictions about its future behaviour. 

The philosophy of MPC can be described simply as follows: Predict future behaviour using a system model, given measurements or estimates of the current state of the system and a hypothetical future input trajectory or feedback control policy.  

In this framework future inputs are characterised by a finite number of degrees of freedom, which are used to optimise a predicted cost. Only the first control input of the optimal control sequence is implemented, and, to introduce feedback into this strategy, the process is repeated at the next time instant using newly available information on the system state.  

This repetition is instrumental in reducing the gap between the predicted and the actual system response (in closed-loop operation). It also provides a certain degree of inherent robustness to the uncertainty that can arise from imperfect knowledge or unknown variations in the model parameters (referred to as multiplicative uncertainty), as well as to model uncertainty in the form of disturbances appearing additively in the system dynamics (referred to as additive uncertainty). 

(with minor adaptations from: Kouvaritakis, Basil, and Mark Cannon. “Introduction.” In Model Predictive Control: Classical, Robust and Stochastic, edited by Basil Kouvaritakis and Mark Cannon, 1–9. Cham: Springer International Publishing, 2016. https://doi.org/10.1007/978-3-319-24853-0_1) 

SPOP refers to Screening at Point of Production whereby the screening is located as close as possible to the nanomaterial/chemical production line to enable the screening result to be as accurate and precise as possible so that it is able to directly modify the nanomaterial/chemical design.

ToxScore is applied for the prediction of the toxicity of nanomaterials. Results from five assays in cell line models are combined to generate the prediction of toxicity expressed ultimately as a score of toxicity, abbreviated as ToxScore. Misvik’s HT-Tox5 score is an in vitro high-throughput toxicity testing method that serves to compare, group and potency rank the nanomaterial products studied in SABYDOMA.

Sensing elements or, as we define them sensor elements, are elements which are sensitive to and responsive to a specific analyte in a medium where the response is transduced by electrical and/or optical means to a monitoring station.

Biological sensing elements are screening devices which are sensitive to biologically active species and whose response to that sensitivity is transduced by electronic and/or optical devices. The sensing elements such as lipid bilayer of plasma membrane, proteins, antibodies, DNA etc. are hypersensitive to specific analytes/substrates and their interactions could be interpreted through optical/electronic detection systems.

Quantitative structure-activity relationship (QSAR) is a computational modelling method for revealing relationships between structural properties of chemical compounds and biological activities.  

The evaluation of nanoparticles (NPs) biological activity and toxicity by in vitro and in vivo studies is costly and time consuming and therefore alternative novel techniques that are fast, inexpensive and reduce the animal testing are required. To date, a great number of QSAR models have been proposed in literature. These models usually cover the biological profile of small organic molecules and have been proven accurate in predicting the biological effect for a wide range of molecular scaffolds. This is not the case for NPs that have recently emerged as important chemical structures with a wide range of significant properties and applications in different areas of interest. Although ‘classic’ QSAR models own a great proportion of their success in the presence of organised databases, only a few databases are available for NPs with limited well-organised datasets. Experimental data are scarce and produced by different groups of scientists following different protocols and it is often difficult to select and combine the available information from different sources. On top of that, the structural characteristics of NPs cannot be encoded by the “conventional” widely used 2D and 3D molecular descriptors. NPs include organic as well as inorganic elements with sometimes unknown composition and highly complex structures that demand new approaches for developing molecular descriptors. These hurdles have already been recognized and now international efforts are being organised towards the development of large datasets for NPs and the computational exploration of these results.