As the number of engineered nanomaterials (ENM) used in research increases with an incredible speed, health and safety specialists are continuously faced with the challenge of evaluating the risks involved with these materials. Nowadays there is not enough information about their toxicology and new materials are continuously being developed. Preliminary scientific results indicate that ENM might have a damaging impact on human health, which makes it even more important to have the right mitigation measures in place. To address this challenge a methodology of iterative nature has been developed, and this paper demonstrates how the iterative cycle is applied in practice and the added value this adds to the university in terms of economic value, protection of researcher health and identifying knowledge-gaps. The methodology is adaptive and learning based, and it takes into account both the hazard level of the ENM and the exposure. The usefulness and completeness of the methodology is demonstrated with an extensive classification of the activities involving ENM at the EPFL research campus. This classification allowed for a complete hazard portfolio and a simplified risk mapping, which facilitates resource allocation decision-making.

As the number of engineered nanomaterials (ENM) used in research increases with an incredible speed, health and safety specialists are continuously faced with the challenge of evaluating the risks involved with these materials. Nowadays there is not enough information about their toxicology and new materials are continuously being developed. Preliminary scientific results indicate that ENM might have a damaging impact on human health, which makes it even more important to have the right mitigation measures in place. To address this challenge, a practical risk management procedure for working with ENM is presented. The task of choosing preventive and protective measures is largely simplified with a schematic decision tree approach that allows for a simple determination of the hazard level and Nano classification of a laboratory with three control bands. The methodology is adaptive and learning based, and it takes into account both the hazard level of the ENM and the exposure. The usefulness and completeness of the methodology is demonstrated with an extensive classification of the activities involving ENM in one of the EPFL research units. The research group handles inorganic nanomaterials both in powder form and in suspension. This classification allowed for a complete hazard and risk mapping, which facilitates resource allocation decision-making. This was demonstrated with the proposition of a set of technical, organizational and personal mitigation measures that has since then been implemented in the laboratories.

BACKGROUND:It is still unknown which types of nanomaterials and associated doses represent an actual danger to humans and environment. Meanwhile, there is consensus on applying the precautionary principle to these novel materials until more information is available. To deal with the rapid evolution of research, including the fast turnover of collaborators, a user-friendly and easy-to-apply risk assessment tool offering adequate preventive and protective measures has to be provided.RESULTS:Based on new information concerning the hazards of engineered nanomaterials, we improved a previously developed risk assessment tool by following a simple scheme to gain in efficiency. In the first step, using a logical decision tree, one of the three hazard levels, from H1 to H3, is assigned to the nanomaterial. Using a combination of decision trees and matrices, the second step links the hazard with the emission and exposure potential to assign one of the three nanorisk levels (Nano 3 highest risk; Nano 1 lowest risk) to the activity. These operations are repeated at each process step, leading to the laboratory classification. The third step provides detailed preventive and protective measures for the determined level of nanorisk.CONCLUSIONS:We developed an adapted simple and intuitive method for nanomaterial risk management in research laboratories. It allows classifying the nanoactivities into three levels, additionally proposing concrete preventive and protective measures and associated actions. This method is a valuable tool for all the participants in nanomaterial safety. The users experience an essential learning opportunity and increase their safety awareness. Laboratory managers have a reliable tool to obtain an overview of the operations involving nanomaterials in their laboratories; this is essential, as they are responsible for the employee safety, but are sometimes unaware of the works performed. Bringing this risk to a three-band scale (like other types of risks such as biological, radiation, chemical, etc.) facilitates the management for occupational health and safety specialists. Institutes and school managers can obtain the necessary information to implement an adequate safety management system. Having an easy-to-use tool enables a dialog between all these partners, whose semantic and priorities in terms of safety are often different.

Since the rise of occupational safety and health research on nanomaterials a lot of progress has been made in generating health effects and exposure data. However, when detailed quantitative risk analysis is in question, more research is needed, especially quantitative measures of workers exposure and standards to categorize toxicity/hazardousness data. In the absence of dose-response relationships and quantitative exposure measurements, control banding (CB) has been widely adopted by OHS community as a pragmatic tool in implementing a risk management strategy based on a precautionary approach. Being in charge of health and safety in a Swiss university, where nanomaterials are largely used and produced, we are also faced with the challenge related to nanomaterials' occupational safety. In this work, we discuss the field application of an in-house risk management methodology similar to CB as well as some other methodologies. The challenges and issues related to the process will be discussed. Since exact data on nanomaterials hazardousness are missing for most of the situations, we deduce that the outcome of the analysis for a particular process is essentially the same with a simple methodology that determines only exposure potential and the one taking into account the hazardousness of ENPs. It is evident that when reliable data on hazardousness factors (as surface chemistry, solubility, carcinogenicity, toxicity etc.) will be available, more differentiation will be possible in determining the risk for different materials. On the protective measures side, all CB methodologies are inclined to overprotection side, only that some of them suggest comprehensive protective/preventive measures and others remain with basic advices. The implementation and control of protective measures in research environment will also be discussed.

There is a need for a risk analysis technique specific for academic research laboratories. Since accurate accident data, normally required for quantitative risk analysis, are not available for this environment, expert judgements are often used to describe risks. However, these judgements are afflicted with linguistic, lexical or informal uncertainties. As a consequence, analyses made by different experts can lead to different results, which make risks incomparable. The purpose of this work is to analyse the effect of these uncertainties and to test strategies to improve the accuracy of the risk estimation based on expert judgements. Different calculation methods were used to compare the obtained risk scores. Results show that a multiplication-based formula, as used, for example, in the Failure Mode, Effects and Criticality Analysis (FMECA), has an inconsistent variance of the risk score distribution. Another approach, using a logarithm-sum-based formula, gives more consistent results but introduces other drawbacks. An estimation method based on Bayesian networks is giving more consistent variances, which are crucial for the risk estimation. With a higher precision of the risk score results, the prioritization of risks can be enhanced and resources can be better allocated to improve the level of occupational safety in academic research laboratories.

In this work, we present a practical and engineering risk management procedure for a university-wide safety and health management of nanomaterials, developed as a multi-stakeholder effort (government, accident insurance, researchers and experts for occupational safety and health). It provides the identification and evaluation of potential hazards and establishes effective control mechanisms to ensure protection of the employee and the environment. The process, similar to control banding approach, starts using a schematic decision tree that allows classifying the nano laboratory into three hazard classes (from Nano 3 - highest hazard to Nano1 - lowest hazard). The first differentiation in the decision tree for hazard class determination regards the environment, whether the process is carried out in a closed (complete process confinement) or open system. In case the process is not fully enclosed (glove box or completely sealed environment), different types of activities with nanomaterials are discussed (activity with nanofibers, powders, suspensions and activity with nanoobjects in solid matrix). For each determined hazard level we then propose a list of required risk mitigation measures (technical, organizational, personal, reception and storage, shipping and handling, medical survey and cleaning facilities). The target ‘users’ of this safety and health methodology are researchers and safety officers in the first place. They can rapidly access the precautionary hazard class of their activities and the corresponding adequate protective and preventive measures.

Available risk analysis techniques are well adapted to industry since they were developed for its purpose. All hazards met in industry are also present in research/academia (although quantities of some hazardous substances are smaller). Still, because of its characteristics (high turnover of collaborators, rapid reorientation of research programs, freedom of research, equipment often in development stage, difficulty to obtain accidents statistics, not well-established processes, etc.), research/academia is an environment whose risks are difficult to analyze by conventional techniques. In this paper, we discuss various risk analysis methods in the light of their adequacy for an academic environment. Finally, we propose the bases for the development of a new risk analysis methodology for complex areas such as academia/research. This method should be fast, intuitive, semi quantitative, and easy to use. It should lead to risk-ranking providing the identification of critical areas and prioritization of safety actions. It is suggested to estimate this ranking as a combination of severity, probability of accident, hazard detectability, and worsening factors (where academia/research specificities are considered). The formula, in which risk is a nonlinear function of the constituting elements, will have a general form allowing getting a risk index for all hazard categories in the same manner.

The effect of an Excimer laser treatment on the corrosion resistance of friction stir welds in AA2024-T351 has been studied by means of electrochemical techniques and immersion tests. The results show a decrease in anodic and cathodic reactivity in the weld region after the laser treatment, achieved due to the formation of a 3-5 mu m thick corrosion resistant layer. Immersion tests in 0.1M NaCl confirm that the presence of the treatment decreases the severity of corrosion attack. However they also show that, if the laser layer is damaged by a scratch, corrosion occurs preferentially in the scratch area. Delamination of the laser treated layer can also occur as a consequence of corrosion propagation during the exposure of laser treated samples.

Keywords: LARA ; Risque Reference EPFL-CONF-207844 Record created on 2015-05-12, modified on 2017-11-13

Current available risk analysis techniques are well adapted to industry needs since they were developed for its purpose. All hazards present in industry are also met in research/academia, although quantities of some hazardous substances are smaller. Still, because of its characteristics e.g., high turnover of collaborators, rapid reorientation of research programs, freedom of research, equipment often in development stage, difficulty to obtain accidents statistics, not well described processes, etc., research/academia milieu is an environment whose risks are difficult to assess by available risk analysis techniques. In the present paper, a new methodology, Laboratory Assessment and Risk Analysis - LARA, for research and/or complex environment is proposed. When multiple hazards are analyzed, the result of assessment is a risk ranking calculated using a Lab Criticity Index - LCI, providing identification of critical areas and prioritization of safety actions. LCI is conceived through two approaches: the Risk Priority Number - RPN and the Analytic Hierarchy Process - AHP. It is suggested to estimate risk as a combination of severity, probability, detectability, worsening factors and research specificities. (C) 2011 Elsevier Ltd. All rights reserved.

A new methodology for risk analysis. namely Laboratory Assessment and Risk Analysis - LARA, is proposed in the companion paper to assess risks in research/academia environment. The core of this methodology relies on defining the adequate role player factors to assess risks in research environment and their mathematical combination to quantify and assess the risk. Quantitative outcome of the analysis results in a Lab Criticity Index - LCI, constructed as a rather comprehensive function of probability, severity, risk worsening factors, research specificities and Hazard Detectability. Even though the LCI model can be used at this stage, its "surjective" and "linear" properties were outlined; the non differentiation between LCI factors remains to be solved. The present article addresses this problematic, bringing a solution based on a Multicriteria Decision Making - MCDM modeling, namely Analytic Hierarchy Process - AHP. This leads to a refined criticity index being "bijective" and unique for every combination of factors. A preliminary risk assessment based on the LARA methodology is discussed for a research lab working with lasers. (C) 2010 Elsevier Ltd. All rights reserved.

The corrosion protection afforded by laser surface melting (LSM) AA7449-T7951 friction stir welds was investigated. LSM produced melting of the constituent particles and formation of a homogeneous 3-5 mu m thick layer. Electrochemical tests showed a reduction in cathodic reactivity after LSM. The breakdown potentials, however, did not change significantly, indicating an anodically reactive surface. In situ and ex situ observation after immersion in 0.1 M NaCl showed that LSM reduced the depth attack in the weld region (particularly the HAZ), affording sacrificial protection to the substrate. Delamination of the treatment can occur during corrosion propagation. (C) 2011 Elsevier Ltd. All rights reserved.

Despite numerous discussions, workshops, reviews and reports about responsible development of nanotechnology, information describing health and environmental risk of engineered nanoparticles or nanomaterials is severely lacking and thus insufficient for completing rigorous risk assessment on their use. However, since preliminary scientific evaluations indicate that there are reasonable suspicions that activities involving nanomaterials might have damaging effects on human health; the precautionary principle must be applied. Public and private institutions as well as industries have the duty to adopt preventive and protective measures proportionate to the risk intensity and the desired level of protection. In this work, we present a practical, 'user-friendly' procedure for a university-wide safety and health management of nanomaterials, developed as a multi-stakeholder effort (government, accident insurance, researchers and experts for occupational safety and health). The process starts using a schematic decision tree that allows classifying the nano laboratory into three hazard classes similar to a control banding approach (from Nano 3 - highest hazard to Nano1 - lowest hazard). Classifying laboratories into risk classes would require considering actual or potential exposure to the nanomaterial as well as statistical data on health effects of exposure. Due to the fact that these data (as well as exposure limits for each individual material) are not available, risk classes could not be determined. For each hazard level we then provide a list of required risk mitigation measures (technical, organizational and personal). The target 'users' of this safety and health methodology are researchers and safety officers. They can rapidly access the precautionary hazard class of their activities and the corresponding adequate safety and health measures. We succeed in convincing scientist dealing with nano-activities that adequate safety measures and management are promoting innovation and discoveries by ensuring them a safe environment even in the case of very novel products. The proposed measures are not considered as constraints but as a support to their research. This methodology is being implemented at the Ecole Polytechnique de Lausanne in over 100 research labs dealing with nanomaterials. It is our opinion that it would be useful to other research and academia institutions as well.

Keywords: risk ; research Reference EPFL-CONF-165626 Record created on 2011-05-06, modified on 2017-11-13

The interfaces: K/Cu(115) and CO/Cu(115) have been characterized using surface sensitive techniques, including low energy electron diffraction and photoelectron spectroscopy. K adatoms show tendency to occupy the sites close to the step edges. At low temperature (near 125 K), after completion of two layers, potassium grows in 3D islands (the Stranski-Krastanov mode). At higher temperature, e.g. at room temperature, potassium introduces reconstruction of the substrate even at low coverages. Calibration of the alkali coverage, up to completion of the first layer, using the work function changes curve has been confirmed as a very convenient and precise procedure. The adsorbed state of CO at 130 K has been identified by registration of core levels obtained by the use synchrotron radiation photoelectron spectroscopy. The characteristics of the main is and satellite peaks have been analyzed in context of substrate geometry and compared with the ones of other copper planes. There are no indications of dissociative adsorption of CO, only residual carbon and oxygen were found after adsorbate desorption around 220 K. CO molecules show a strong tendency to "on top" adsorption in sites far from the step edges of the Cu(115) surface.