Laboratory Safety and Nanotechnology Workers: an Analysis of Current Guidelines in the USA

Although some regulatory frameworks for the occupational health and safety of nanotechnology workers have been developed, worker safety and health issues in these laboratory environments have received less attention than many other areas of nanotechnology regulation. In addition, workers in nanotechnology labs are likely to face unknown risks and hazards because few of the guidelines and rules for worker safety are mandatory. In this article, we provide an overview of the current health and safety guidelines for nanotechnology laboratory workers by exploring guidelines from different organizations, including the Department of Energy Nanoscale Science Research Centers (DOE-NSRC), Massachusetts Institute of Technology (MIT), the National Institutes of Health (NIH), the National Institute for Occupational Safety and Health (NIOSH), the Occupational Safety and Health Administration (OSHA), Texas A&M University (TAMU), and University of Massachusetts-Lowell (UML). After discussing these current guidelines, we apply an ethical framework to each set of guidelines to explore any gaps that might exist in them. By conducting this gap analysis, we are able to highlight some of the weaknesses that might be important for future policy development in this area. We conclude by outlining how future guidelines might address some of these gaps, specifically the issue of workers’ participation in the process of establishing safety measures and the development and enforcement of more unified (and mandatory) guidelines.     

Scientists’ Ethical Obligations and Social Responsibility for Nanotechnology Research

Scientists’ sense of social responsibility is particularly relevant for emerging technologies. Since a regulatory vacuum can sometimes occur in the early stages of these technologies, individual scientists’ social responsibility might be one of the most significant checks on the risks and negative consequences of this scientific research. In this article, we analyze data from a 2011 mail survey of leading U.S. nanoscientists to explore their perceptions the regarding social and ethical responsibilities for their nanotechnology research. Our analyses show that leading U.S. nanoscientists express a moderate level of social responsibility about their research. Yet, they have a strong sense of ethical obligation to protect laboratory workers (in both universities and industry) from unhealthy exposure to nanomaterials. We also find that there are significant differences in scientists’ sense of social and ethical responsibility depending on their demographic characteristics, job affiliation, attention to media content, risk perceptions and benefit perceptions. We conclude with some implications for future research.     

Divergence Behaviour of the Successive Geometric Mean Method of Pairwise Comparison Matrix Generation for a Multiple Stage,Multiple Objective Optimization Problem

This paper focuses on the divergence behaviour of the successive geometric mean (SGM) method used to generate pairwise comparison matrices while solving a multiple stage, multiple objective (MSMO) optimization problem. The SGM method can be used in the matrix generation phase of our three‐phase methodology to obtain pairwise comparison matrix at each stage of an MSMO optimization problem, which can be subsequently used to obtain the weight vector at the corresponding stage. The weight vectors across the stages can be used to convert an MSMO problem into a multiple stage, single objective (MSSO) problem, which can be solved using dynamic programming‐based approaches. To obtain a practical set of non‐dominated solutions (also referred to as Pareto optimal solutions) to the MSMO optimization problem, it is important to use a solution approach that has the potential to allow for a better exploration of the Pareto optimal solution space. To accomplish a more exhaustive exploration of the Pareto optimal solution space, the weight vectors that are used to scalarize the MSMO optimization problem into its corresponding MSSO optimization problem should vary across the stages. Distinct weight vectors across the stages are tied directly with distinct pairwise comparison matrices across the stages. A pairwise comparison matrix generation method is said to diverge if it can generate distinct pairwise comparison matrices across the stages of an MSMO optimization problem. In this paper, we demonstrate the SGM method's divergence behaviour when the three‐phase methodology is used in conjunction with an augmented high‐dimensional, continuous‐state stochastic dynamic programming method to solve a large‐scale MSMO optimization problem. Copyright © 2013 John Wiley & Sons, Ltd.