Getting to Green - Research Progress on Environmental Health and Safety Aspects of Engineered Nanomaterials - page 57

ENMs in relevant biologic and environmental media. It is also a prerequisite to development of
appropriate models for predicting ENM behavior in complex systems (such as biouptake models) and
effects. It is an extremely challenging task, especially in complex media, and will probably require new
instrumentation with spatial resolution adequate for focusing on single particles and initial development
in well-characterized systems before application in more complex media.
Another important component of this research is the ability to determine critical release points
along the value chain and to identify exposed populations. Therefore, characterization in relevant
complex matrices requires methods for characterizing ENMs and transformations in the matrix in which
the ENMs are used. The matrix may affect the ENM properties that are used to measure pristine ENMs
(such as fluorescence or absorption at a specific wavelength); therefore, development of new methods or
validation of existing methods is needed to detect and characterize ENMs released from their matrices.
It is important that the measured properties and characteristics of transformed ENMs be captured
in the knowledge commons. That requires an ontology for describing such properties as the adsorbed
macromolecular layer. Placing such data in the knowledge commons will allow the community to share
them and to develop and update models for describing the behavior of the ENMs in complex
environments.
INFORMATICS: THE KNOWLEDGE COMMONS
In Figure 4-1, the knowledge commons performs three functions. The first is to broaden
participation in the development and validation of predictive models, particularly risk models. To
accomplish that, more effective communication is needed among those engaged in reductionist science
(the laboratory world) at the left of the figure, those engaged in integrative science (the real world) at the
right, and the information on materials at the top of the figure. Model development via the knowledge
commons would be hosted in a collaborative environment with access to both processed and raw
experimental data and data from other, lower-level computations and simulations. The iterative model
validation process would lead to publication of validated models with any run-time parameters, files,
sample data, baseline results, and metadata regarding the range of validity of the model. Such information
would help to accelerate the use and improvement of the model.
The second function of the knowledge commons is to provide a collaborative environment for
methods development, including access to the results of ruggedness testing and interlaboratory testing for
a method, amplification regarding sample preparation and additional controls for different ENM types,
and comments regarding modifications to improve reproducibility. Additional benefits for collaborative
methods development include better understanding of the method, its range of validity, available
instrumentation, and user facilities supporting the method. In addition, the knowledge commons would
establish a means to publish, access, and annotate issues regarding analytic methods and their
reproducibility.
The third function of the knowledge commons is to establish a means of collaboratively designing
new ENMs by using models to encapsulate and quantify a material's characteristics and effects and
potential risks associated with different manufacturing processes and controls. Because this function
would provide useful results for manufacturers, regulators, and users of the materials, additional
governance would be required to allow collaboration for precompetitive projects and continued use of
modeling tools in a secure environment.
Although the knowledge commons would provide a new mechanism and environment for
collaborative development of methods, models, and materials, many of the core functions have been
initiated elsewhere. The Nanomaterial Registry (Nanomaterialregistry 2013) and NanoHUB
(NanoHUB.org 2013) are two examples—the registry for sharing and annotating nanomaterial data and
the NanoHUB for providing facilities for accessing, running, and annotating models. The underlying
strength of the knowledge commons would be in linking these existing capabilities and others in a new
environment focused on providing quantitative, reliable estimates of uncertainty for risk estimation and
method validation and for establishing a vital missing link between the reductionist and integrative