We revealed that the yield of SWNTs formed by Nd:YAG laser ablation depends on the target composition with yields following the order C x Ni y Co y > C x Ni y ≫ C x Co z . The SWNT bundles in the web formed when using the C x Ni y Co y target (web-C x Ni y Co y ) is thicker and longer than those in the web-C x Ni y . The diameters of the SWNTs in the web-C x Ni y Co y were larger and more uniform than those of the SWNTs in the web-C x Ni y . The NiCo particles in the web-C x Ni y Co y and the Ni particles in the web-C x Ni y were nanometer sized and were embedded in the amorphous carbon flakes that were dispersed throughout the weblike deposits. Filmlike deposits were formed when using the C x Co z targets, and nanometer-sized Co particles in these deposits were localized within sub-millimeter-sized areas. Examination of the target surfaces revealed that Ni emits from the C x Ni y target more efficiently than NiCo from the C x Ni y Co y target or Co from the C x Co z target during the laser ablation. On the basis of these results, we provide an explanation of how the yield and structure of SWNTs formed by laser ablation depend on the species of the metal catalysts.
Regulation for nanomaterial is urgently needed and the drive to adopt an intelligent testing strategy is evident. The intelligent testing strategy will not only be beneficial from a cost reduction point of view but will also mean the reduction of the moral and ethical concerns related to animal research. In the chemical and legislative world, such an approach is promoted by REACH and in particular the use of (Q)SAR as a tool for the purpose of categorisation. In addition to traditional compounds, (Q)SAR has also been applied to nanomaterials i.e. nano(Q)SAR, useful to correlate toxicological endpoints with physicochemical properties.Although (Q)SAR in chemicals is well established, nano(Q)SAR is still at an early stage of its development and its successful uptake is far from reality. The purpose of this paper is to identify some of the pitfalls and challenges associated with nano-(Q)SARs, in relation for its use to categorise nanomaterials. Our findings show clear gaps in the research framework that must be addressed if we are to have reliable predications from the use of such models. Three major types of barriers were identified: a) the need to improve quality of experimental data in which the models are being developed from in the first place, b) the need to have practical guidelines for the development of the nano(Q)SAR models, c) the need to standardise and harmonise activities for the purpose of regulation. Out of the three barriers, immediate attention is needed for a) as this underpins activities associated in b) and c). It should be noted that the usefulness of data in the context of nano-(Q)SAR modelling is not only about the quantity of data but also about the quality, consistency and accessibility of those data.
A central challenge for the safe design of nanomaterials (NMs) is the inherent variability of NM properties, both as produced and as they interact with and evolve in, their surroundings. This has led to uncertainty in the literature regarding whether the biological and toxicological effects reported for NMs are related to specific NM properties themselves, or rather to the presence of impurities or physical effects such as agglomeration of particles. Thus, there is a strong need for systematic evaluation of the synthesis and processing parameters that lead to potential variability of different NM batches and the reproducible production of commonly utilized NMs. The work described here represents over three years of effort across 14 European laboratories to assess the reproducibility of nanoparticle properties produced by the same and modified synthesis routes for four of the OECD priority NMs (silica dioxide, zinc oxide, cerium dioxide and titanium dioxide) as well as amine-modified polystyrene NMs, which are frequently employed as positive controls for nanotoxicity studies. For 46 different batches of the selected NMs, all physicochemical descriptors as prioritized by the OECD have been fully characterized. The study represents the most complete assessment of NMs batch-to-batch variability performed to date and provides numerous important insights into the potential sources of variability of NMs and how these might be reduced.
Collaborative diversity is, arguably, an intrinsic characteristic of research networks built on the emergence of general-purpose technologies such as nanotechnology. European research policy, epitomised in Framework Programmes, creates arrangements that institutionalise the development of internationally and institutionally diverse research networks. Motivated by concerns that a high degree of collaborative diversity may create managerial challenges for network members in sharing knowledge across national and institutional borders, we study the configurations of collaborative research networks and consider their international and institutional diversity. We also explore the influence of European policy mechanisms on the international and institutional diversity of collaborative research networks. We conclude that nanotechnology research networks are indeed characterised by a significant degree of collaborative diversity, which in turn exposes a need for participating members to develop strategic capabilities to manage research within diverse networks.
There is an increasing recognition that nanomaterials pose a risk to human health, and that the novel engineered nanomaterials (ENMs) in the nanotechnology industry and their increasing industrial usage poses the most immediate problem for hazard assessment, as many of them remain untested. The large number of materials and their variants (different sizes and coatings for instance) that require testing and ethical pressure towards non-animal testing means that expensive animal bioassay is precluded, and the use of (quantitative) structure activity relationships ((Q)SAR) models as an alternative source of hazard information should be explored.(Q)SAR modelling can be applied to fill the critical knowledge gaps by making the best use of existing data, prioritize physicochemical parameters driving toxicity, and provide practical solutions to the risk assessment problems caused by the diversity of ENMs. This paper covers the core components required for successful application of (Q)SAR technologies to ENMs toxicity prediction, and summarizes the published nano-(Q)SAR studies and outlines the challenges ahead for nano-(Q)SAR modelling. It provides a critical review of (1) the present status of the availability of ENMs characterization/toxicity data, (2) the characterization of nanostructures that meets the need of (Q)SAR analysis, (3) the summary of published nano-(Q)SAR studies and their limitations, (4) the in silico tools for (Q)SAR screening of nanotoxicity and (5) the prospective directions for the development of nano-(Q)SAR models.
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