George Loizou
email: george.loizou@hsl.gov.uk

Martin Spendiff

Hugh A Barton

Jos Bessems

Frédéric Y Bois

Michel Bouvier D'yvoire

Harrie Harrie Buist

Harvey J Clewell

Bette Meek

Ursula Gundert-Remy

Walter Schmitt

Development of good modelling practice for physiologically based pharmacokinetic models for use in risk assessment : the first steps

Keywords: Good modelling practice, PBPK, risk Assessment

des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

The increasing use of tissue dosimetry estimated using pharmacokinetic models in chemical risk assessments in multiple countries across the globe is producing greater awareness of the need to develop internationally recognized good modelling practices.

These practices would facilitate sharing of models and model evaluations and consistent applications in risk assessments. Clear descriptions of good practices for, 1. model development (i.e., research and analysis activities), 2. model characterization (i.e., methods to describe how consistent the model is with biology and the strengths and limitations of available model and data, e.g., sensitivity analyses), 3. model documentation, and 4. model evaluation (i.e., independent review), would assist not only risk assessors who need to decide whether and how to use the models, but also model developers who need to know what is expected for various purposes (e.g., research versus application in risk assessment) [START_REF] Cobelli | Validation of simple and complex models in physiology and medicine[END_REF][START_REF] Portier | Practicing safe modeling: GLP for biologically based mechanistic models[END_REF][START_REF] Rescigno | The use and abuse of models[END_REF].

For risk assessors, good modelling practice would describe a path forward when seeking to evaluate the potential for a pharmacokinetic model, particularly a physiologically based pharmacokinetic (PBPK) model, to contribute to a risk assessment. Good modelling practices would only be one element, however, in a broader context that describes risk assessment practices ranging from limited information analyses (often referred to as default or screening analyses) to biological modelling of the toxicokinetics and toxicodynamic processes in animals and humans. The shift from 'default' through 'data-informed' approaches to risk assessment represents a 'continuum' of methods. The transition begins with default approaches which involve empirical observations made from broad databases of information that were not group, species or chemical specific and where pharmacokinetics and dynamics were not explicitly addressed. The next phase is 'categorical' and 'species-specific' approaches where substances and species are placed into categories based on their characteristics. The categorical approach is followed by the IPCS scheme which uses compound-related and/or chemical specific adjustment factors (CSAF) (IPCS 2005), that included chemical-specific pharmacokinetic or pharmacodynamic data. When appropriate, fully data-derived, chemical specific, biologically based dose response risk assessment methods can be employed for chemicals of high concern or with high economic impacts.

Descriptions of known or hypothesized modes of action (i.e., the toxicodynamic process) leading to the toxicity under consideration play key roles throughout much of the continuum of risk assessment methods. A framework for organizing and evaluating evidence supporting modes of action has been described, which is applicable to all toxicity endpoints [START_REF] Seed | Overview: Using mode of action and life stage information to evaluate the human relevance of animal toxicity data[END_REF][START_REF] Sonich-Mullin | IPCS conceptual framework for evaluating a mode of action for chemical carcinogenesis[END_REF]US EPA 2005).

Additional steps to evaluate whether a mode of action would occur in humans based upon what was known from other animal species have also been presented [START_REF] Boobis | IPCS Framework for Analyzing the Relevance of a Cancer Mode of Action for Humans[END_REF][START_REF] Boobis | IPCS framework for analysing the relevance of a non-cancer mode of action for humans[END_REF][START_REF] Seed | Overview: Using mode of action and life stage information to evaluate the human relevance of animal toxicity data[END_REF]. Application of toxicokinetic and toxicodynamic information in estimating values of factors accounting for interspecies extrapolation and human interindividual variability has been described [START_REF] Gundert-Remy | The use of toxicokinetic and toxicodynamic data in risk assessment: an international perspective[END_REF]IPCS 2005). These factors may be addressed using appropriate data without a pharmacokinetic model or they may be replaced by application of PBPK models.

For modellers, GMPs are important because they describe the kinds of model characterization and documentation that will be considered in a model evaluation process when a model is considered for application in risk assessment. The initial creation of models, along with needed laboratory experimentation, can be a creative and unpredictable process that will be minimally altered by GMPs. However, even at this very early stage, awareness of GMPs can be valuable, including recommendations when publishing models in the peer reviewed literature [START_REF] Andersen | Applying simulation modeling to problems in toxicology and risk assessment -a short perspective[END_REF]. For example, modellers often try several alternative structures as they attempt to reconcile the available data and the description of the biology in the model. These alternatives would not be documented to the same degree as a model proposed for use in risk assessment, but documenting these alternatives were considered important in supporting the model structure eventually selected [START_REF] Barton | Characterizing Uncertainty and Variability in Physiologically-based Pharmacokinetic (PBPK) Models: State of the Science and Needs for Research and Implementation[END_REF]).

The International Workshop on the Development of GMP for PBPK models 1 was convened with two overarching themes:

1. The selection and evaluation of an appropriate deterministic 2 model structure.

2. Making PBPK techniques accessible to regulators and risk assessors.

This was the first forum dedicated to review, discuss and promote best practice in deterministic PBPK model building and parameterization, including transparency in documentation with clear audit trails for model components, and to facilitate dialogue and understanding between PBPK practitioners and risk assessors and regulators. By bringing together PBPK modellers, mathematicians, statisticians, risk assessors, regulators and laboratory scientists, the sponsors 3 of this workshop seek international implementation of PBPK modelling in risk assessment, which development of GMP for PBPK should facilitate. This paper presents the results and conclusions of the GMP workshop.

Current Practice -Where do we stand?

To move forward effectively in developing GMPs, it is useful to understand current practices in PBPK modelling and its application in risk assessment. These applications are more widespread than has been commonly recognized. The range of uses of PBPK models also need to be reflected in appropriate practice reflecting the stage of development or application for the particular model. Efforts to develop GMP for other 1 April 26 -April 28, 2007, at the Mediterranean Agronomic Institute of Chania, Crete, Greece. Presentations, and discussion papers are at http://www.hsl.gov.uk/news/news_pbpk.htm. Additional information is available at www.pbpk.org. 2 A "deterministic" model is the mathematical representation of the biological/chemical system (e.g., PBPK model and metabolic scheme) as opposed to a "non-deterministic" model which is the mathematical/statistical representation of the uncertainty, variability, and covariance of the data and parameters of the deterministic model (e.g., statistical model for measurement errors and population variability). 3 Sponsors of the International Workshop on the Development of Good Modelling Practice for PBPK models: The UK Health and Safety Executive (HSE), the UK Health and Safety Laboratory (HSL), Health Canada, The European Chemical Industry Council (CEFIC) and The European Centre for the Validation of Alternative Methods (ECVAM). models applied in environmental regulation can also be informative in terms of their form, content, and processes.

Value of PBPK models in risk assessments

The need for change of the current risk assessment paradigm is due to an increasing demand from risk assessors and regulators for a higher precision of risk estimates, a greater understanding of uncertainty and variability [START_REF] Allen | Investigation of the impact of pharmacokinetic variability and uncertainty on risks predicted with a pharmacokinetic model for chloroform[END_REF][START_REF] Barton | Evaluating human variability in chemical risk assessment: hazard identification and dose-response assessment for noncancer oral toxicity of trichloroethylene[END_REF][START_REF] Clewell | Evaluation of the uncertainty in an oral reference dose for methylmercury due to interindividual variability in pharmacokinetics[END_REF]Clewell, H. J. et al. 2002;[START_REF] Cox | Reassessing benzene risks using internal doses and Monte-Carlo uncertainty analysis[END_REF][START_REF] Delic | The utility of PBPK in the safety assessment of chloroform and carbon tetrachloride[END_REF], a more justifiable means of extrapolating across species, routes, doses and time [START_REF] Clewell | Dose, species and route extrapolation using physiologically-based pharmacokinetic modeling[END_REF], a more effective means of interpreting biological monitoring data [START_REF] Georgopoulos | Reconstruction of short-term multi-route exposure to volatile organic compounds using physiologically based pharmacokinetic models[END_REF]Hays, S. M. et al. 2007) and a reduction in reliance on animal testing [START_REF] Barratt | The Integrated Use of Alternative Approaches for Predicting Toxic Hazard -the Report and Recommendations of Ecvam Workshop-8[END_REF][START_REF] Blaauboer | The Integrated Use of Alternative Methods in Toxicological Risk Evaluation: ECVAM Integrated Testing Strategies Task Force Report 1[END_REF][START_REF] Blaauboer | The use of Biokinetics and In Vitro Methods in Toxicological Risk Evaluation[END_REF][START_REF] Dejongh | An integrated approach to the prediction of systemic toxicity using computerbased biokinetic models and biological in vitro test methods: overview of a prevalidation study based on the ECITTS Project[END_REF].

Incorporating PBPK modelling into the risk assessment process can advance all of these objectives. Further, the shift away from standard setting toward a more cost-benefit analysis approach to risk assessment should also see an increase in the utility of biologically based approaches in the support risk management decisions. These topics were addressed in presentations by Dr. George Loizou of the Health and Safety Laboratory, UK in his introduction to the meeting and Dr. Bette Meek of Health Canada in a talk on "Physiologically based pharmacokinetic models (PBPK): the needs of risk assessors". They are also the subject of many paper in the scientific literature as well as reviews (US EPA 2006).

Dr. Meek elaborated on how hazard and dose-response assessment are being driven by mode of action and data informed approaches to characterise dose-response. These evolving developments in risk assessment form the basis for the IPCS harmonization frameworks that, consequently, should also facilitate the incorporation of PBPK modelling into this process. The IPCS harmonization initiative for the risk assessment of chemicals seeks to improve methods through the pursuit of common principles and approaches by drawing on global expertise. It does not seek to standardize the process but to increase understanding and acceptance by identifying potential areas of convergence and work towards the development of analytical frameworks, guidance and associated training materials and the sharing of assessments to avoid duplication [START_REF] Boobis | IPCS Framework for Analyzing the Relevance of a Cancer Mode of Action for Humans[END_REF][START_REF] Boobis | IPCS framework for analysing the relevance of a non-cancer mode of action for humans[END_REF]IPCS 2005;[START_REF] Meek | Practical application of kinetic data in risk assessment--an IPCS initiative[END_REF][START_REF] Meek | A framework for human relevance analysis of information on carcinogenic modes of action[END_REF]Meek, M. E. and A. Renwick 2006;[START_REF] Meek | Guidelines for application of chemical specific adjustment factors (CSAF) in dose/concentration response assessment[END_REF][START_REF] Meek | Guidelines for application of chemical-specific adjustment factors in dose/concentration-response assessment[END_REF][START_REF] Sonich-Mullin | IPCS conceptual framework for evaluating a mode of action for chemical carcinogenesis[END_REF].

Current status of implementation of PBPK models in risk assessments

Dr. Ursula Gundert-Remy described use of PBPK models in the European Union (EU) Existing Substance Regulation (ESR) programme. Under this programme risk assessments are prepared by Rapporteur Member States (RMS) and reported as draft Risk Assessment Reports (RARs). The European Technical Committee then discusses draft RARs on New and Existing Substances (TC NES). Since the introduction of the ESR programme (1996 -2007), 140 substances have been assessed. Dr. Gundert-Remy briefly reviewed 80 of these chemicals to determine how many risk assessments included the application of PBPK modelling in the 'toxicokinetics' section of the RAR and whether the results of the PBPK analysis influenced the outcome. PBPK modelling was noted in the pharmacokinetic assessment of 8 chemicals (benzene, cyclohexane, acrylic acid, methylmethacrylate, vinyl acetate, 2-butoxyethanol, propylene methyl glycol and styrene). A PBPK model was used and influenced the outcome of the risk assessment in the case of four substances, whereas for another four substances a PBPK model was used but did not influence the outcome. Therefore, PBPK analysis influenced the risk assessment of 5% of chemicals. The PBPK model for benzene was not used because the mode of action in rodent and human was considered different. The dose metric for acrylic acid was predicted to be 3 times lower in people than in rat, but was still not used. There was no explanation as to why the modelling results for cyclohexane and methylmethacrylate were not used. In the case of vinyl acetate, 2-butoxyethanol, and propylene methyl glycol (1-methoxypropan-2-ol) the interspecies uncertainty factor was reduced based upon analyses using the PBPK models. As result of the quantification of the interspecies differences in glutathione depletion in lung cells, the classification of styrene as a category 2 carcinogen was changed to category 3. Dr. Gundert-Remy was unable to discern if the application and acceptance of PBPK models in RARs was dependent upon access to PBPK expertise within any given RMS.

The lack of a standardized procedure for the evaluation of a PBPK models and their output was the main concern that prevented application in other chemical risk assessments. Dr. Gundert-Remy was not aware of the approach to evaluation of the PBPK models used in these risk assessments. However, these examples of the use of PBPK modelling by RMS and acceptance by other Member State delegates indicate a shift in risk assessment practices.

In the UK PBPK modelling has been used by the Health and Safety Executive (HSE) in a number of ways in support of its regulatory activities. A PBPK model for formaldehyde was formulated and used to support the position that formaldehyde should not be regarded as an established cause of leukemia in humans and should be considered unlikely to do so [START_REF] Franks | A mathematical model for the absorption and metabolism of formaldehyde vapour by humans[END_REF]. The validity of a biomarker of exposure for 2butoxyethanol was studied using a PBPK model [START_REF] Franks | Physiologically based pharmacokinetic modelling of human exposure to 2-butoxyethanol[END_REF]) and the robustness of past regulatory decisions were examined using PBPK models and Monte Carlo sampling [START_REF] Delic | The utility of PBPK in the safety assessment of chloroform and carbon tetrachloride[END_REF].

In [START_REF] Us Epa | Update to the Health Assessment Document and Addendum for Dichloromethane (Methylene Chloride): Pharmacokinetics, Mechanism of Action, and Epidemiology[END_REF]. FDA raised concerns about the potential for teratogenicity of dermally applied all-trans retinoic acid that were addressed by PBPK modelling and evaluation of several potential dose metrics for the active morphogen [START_REF] Clewell | A physiologically based pharmacokinetic model for retinoic acid and its metabolites[END_REF][START_REF] Rowland | Physiologically Based Pharmacokinetics in Drug Development and Regulatory Science: A Workshop Report[END_REF]. The EPA has used PBPK models for several chemicals in addition to methylene chloride in IRIS assessment [START_REF] Dewoskin | Pharmacokinetic/Physiologically Based Pharmacokinetic Models in Integrated Risk Information System Assessments[END_REF]). These include the cancer assessment for vinyl chloride and noncancer assessments for vinyl chloride, ethylene glycol monobutyl ether, and xylene. PBPK models have been considered for, but not applied, in the IRIS assessments for acetone, chloroform, and methyl ethyl ketone. IRIS assessments are ongoing for a number of other chemicals that propose to use PBPK models, such as that for trichloroethylene [START_REF] Chiu | Key scientific issues in the health risk assessment of trichloroethylene[END_REF].

What can we learn from other similar experiences?

While use of quantitative modelling for toxicology, particularly for biologically based dose-response analyses, has been limited, modelling has been used extensively for environmental fate and transport in European, Canadian, and US regulatory contexts. In the US (Canada?, Europe?), risk assessments for specific contaminated sites or permitting of industrial facilities also rely heavily on often complex models for exposure pathways including food chains (US EPA 1989). Interest in reading across chemicals to make predictions has also created efforts to describe some GMPs for quantitative structure activity relationship (QSAR) models. These experiences provide perspectives potentially useful for the development of GMPs for PBPK modelling.

Environmental Modelling-Achieving Acceptance in the Regulatory

World

Dr. Gerhard Goerlitz of Bayer Crop Science who had participated in the process in Europe described the development of good practice in environmental fate modelling.

Environmental fate modelling was adopted as a regulatory tool in the European Union (EU) as a result of two major issues. The first was EU legislation in the late 1980s which, set a maximum pesticide residue concentration of 0.1 μg L -1 in both drinking and ground water and the second was the very long time, typically 3 to 4 years, required to conduct lysimeter4 studies. The latter problem meant that decisions on the identification of critical products and their uses as required by agriculture, while avoiding contamination of groundwater resources, could not be made rapidly. Environmental fate modelling was recognized as a promising approach to address these issues, but questions were raised concerning whether model predictions were sufficiently reliable and how the integrity of model calculations could be ensured. Clear divisions in attitudes emerged following initial discussions among environmental fate modellers, regulators, and registrants.

Researchers used the models for the investigation of processes and systems, requiring flexibility and adaptability while maintaining full control of processes and algorithms in the models. Regulators and registrants wanted to predict exceedence or adherence to a regulatory limit, requiring scientific and legal certainty and preferred the use of models for which the code was not subject to alteration, provided complete documentation with clear audit trails for calculations and prevented accidental or intentional misuse. Further conflicts arose because version control and documentation of research models was frequently rudimentary, if it existed, no guidance on the selection of appropriate input parameters was available, and it was rarely properly established whether a model design was really suitable for regulatory purposes. These problems were a reflection of the fact that the typical research model was intended for use by a specialist with specific and intensive training, which at the time was almost totally lacking in regulatory agencies and in companies trying to assess the environmental behavior of plant protection products.

The natural consequence was general confusion with different modellers arriving at completely different results while supposedly using the same models for the same problem.

The first attempt to address this problem was a technical quick fix in the form of software packages comprising models and preconfigured scenarios with a user-friendly graphical interface. As a consequence, non-expert users still produced poor results increasing confusion further. There were two main reasons for this, (i) model processes, algorithms and standard parameters did not appropriately reflect substance properties and (ii) substance data from standard environmental fate studies were conceptually different from the model implementations. This led to a proposal to apply good laboratory practice (GLP) for modelling because GLP was supposed to ensure that all data could be traced and reviewed and accidental input of incorrect data, as well as forgery, could be detected. Also, GLP had just been successfully been transferred from toxicology into the metabolism, environmental fate and residue analysis laboratories. On the other hand, measurements are never perfectly reproducible (especially not for living systems) whereas simulations are and GLP is difficult to apply to electronic data systems and calculations. A solution was found in a short document entitled, "Rules for the correct performance and evaluation of model calculations for simulation of the environmental behavior of pesticides". Later to be known as the 'Codex' this document was produced as a result of cooperation between two German regulatory agencies, a research institute and the German Agrochemical Industry5 . The Codex outlined general principles of GMP, not a detailed "cooking recipe" type of guidance. It focused on leaching models but was generally applicable to other simulation models. It covered the following topics:

• Selection of models

• Documentation of models

• Validation

• Support

• Official recognition and version control

• Selection and treatment of input data

• Consistency of input data and models

• Documentation of simulations

• Reporting

• Interpretation

The "Codex" created a basis for the regulatory acceptance of simulation models on the national scale in Germany, as well as forming a platform to address the requirements of the European directive 91/4146 . After, several informal meetings between modellers, Today the FOCUS reports have achieved a prominent position in the exposure assessment for the registration of plant protection products at the EU level. This is best reflected by the fact that in the present draft of the revision of the EU directive 91/414 on the authorization of plant protection products there are many instances where FOCUS reports are directly referred to as guidance on important decision points. Apart from that many member states use FOCUS outputs and adapted it as guidance in their exposure and risk assessments.

QSAR dossiers

Recent activities within the EU with regard to the development of templates for the various stages in the application of quantitative structure activity relationships (QSARs) potentially provide a blueprint for good PBPK modelling practice8 . Templates for QSAR development, prediction and reporting have been proposed. Each template is relevant to different stages of the risk assessment process. In the development template, information on the training domain, internal validation, cross validation and external validation is stored. In the prediction template the substance-specific prediction is stored. Finally, the reporting template should clearly state how the prediction should be used and applied.

Future Directions -Where do we need to go?

The following sections briefly summarize some of the major issues considered and recommendations from the workshop designed to facilitate the development of GMP for PBPK modelling as well as identify research priorities.

A. Risk Assessors needs and their role in the process

Two possible paradigms were proposed for the involvement of the risk assessor throughout the modelling process: 1) a continuous processwherein one would try to include any issues that the risk assessor might have while the model is being developed and 2) an iterative processthat at appropriate times, would stop and evaluate model fitness for regulatory use. The former process would occur when there is an interdisciplinary team involved in the model development and characterization (Barton et al., 2007), while the latter is typical for models that have already been published.

Risk assessors have important roles to play in mode of action and dosimetry based risk assessments utilizing PBPK models. These include helping to define the goals for using the model in the risk assessment [START_REF] Clewell | A consistent approach for the application of pharmacokinetic modeling in cancer and noncancer risk assessment[END_REF]US EPA 2006) (Other REFs???) and participating in a transparent process that brings together appropriate interdisciplinary expertise to evaluate the model and its proposed risk assessment applications [START_REF] Chiu | Evaluation of physiologically based pharmacokinetic models for use in risk assessment[END_REF][START_REF] Clark | Framework for evaluation of physiologically-based pharmacokinetic models for use in safety or risk assessment[END_REF]. Risk assessors play a pivotal role organizing the dose-response (e.g., critical studies and endpoints) and mode of action information that form the context for applying a dosimetry model. Determining whether a PBPK model is reported to be parameterized for the chemical(s), including metabolites, species and life stages, exposure routes and matrices in the toxicity studies to be used in dose-response analysis or the human exposures relevant for the risk assessment can be accomplished by non-modellers. Identifying the dose metrics relevant to the modes of action under consideration often requires communications among risk assessors, toxicologists, and modellers as does evaluation of the biology captured by the model. Evaluation of the mathematical and computer implementation as well as characterization of its consistency with available data and the model's strengths and weaknesses for the proposed risk assessment applications will generally require involving people with appropriate mathematical, statistical, and computational expertise. However, to ensure a transparent process, communications describing the review process and its conclusions need to be understandable to all parties.

B. Model Development Practices

Model standardization can facilitate intra-and inter-disciplinary communications, but faces the challenges of adapting to a variety of software used to create a wide range of model structures necessary to describe different kinetic behaviors and address varying model purposes. The establishment of a standard lexicon, nomenclature and glossary would facilitate efficient communication of models and avoid confusion in semantics that can hinder understanding. There are significant benefits to the use of generic model structures; this would address the establishment of standard abbreviations or parameter nomenclature and glossary. In addition, the need to justify selected aspects of the model could be eliminated, as is currently done by citing existing literature. To be truly generic, such a model would have to encompass a wide range of physiological compartments and all useful dose metrics. A standard methodology for model building rather than a fixed model form might be an alternative [START_REF] Cobelli | Validation of simple and complex models in physiology and medicine[END_REF]. The use of a hybrid of these approaches, where a simple standard model was used as a starting point and refinements that took place during the modelling workflow would be conducted with a standardized model building methodology was seen as a viable compromise. In discussing the problems caused by model code that is specific to a particular solver package, it was agreed that the use of a standard representation similar to SBML or cellML9 would improve communication between modellers and risk assessors.

This type of representation gives a structured description of the conceptual model free of mathematical equations and confusing syntax. The provision of an intuitive graphical interface such as MEGen 10 could make such standard formats more accessible to nonmodellers by allowing rapid generation of this 'PBPKML' representation.

C. Model Characterization

Models that have been developed sufficiently to obtain reasonable correspondence with available data can be characterized in a variety of ways to demonstrate that they are mathematically and computationally free of errors and to characterize the behavior of the model in the region of parameter space that is biologically plausible and reasonably approximates the available data [START_REF] Barton | Characterizing Uncertainty and Variability in Physiologically-based Pharmacokinetic (PBPK) Models: State of the Science and Needs for Research and Implementation[END_REF][START_REF] Oreskes | Evaluation (not validation) of quantitative models[END_REF]) (OTHER REFS???). Demonstration that a model is mathematically and computationally correctly implemented can involve checks implemented in the model (e.g., mass balance checks), rigorous manual checking of the equations and computer code, and independent recoding of the model in another software. The ease of implementing these options varies with the particular software used. A PBPK model code generator tool such as MEGen 10 could play a role in both by permitting rapid recoding of models.

Roles and methods of sensitivity analysis

Sensitivity analysis is a tool for model characterization that can address a number of issues frequently raised concerning PBPK models. This was the topic of a white paper prepared for the meeting entitled "Global sensitivity analysis and its role in model development" by Dr. Martin Spendiff and Dr. George Loizou of the UK Health and Safety Laboratory, as well as being discussed in the breakout group presentation, "Fit for purpose: a proposed approach to PBPK model evaluation", given by Dr. Spendiff.

Sensitivity analyses can be used through the processes of model development, characterization, and evaluation to address issues including the following:

1. Characterize which parameters are well determined by available data.

10 http://xnet.hsl.gov.uk/megen/default.aspx 2. Iterating with experiments, evaluate the sensitivity of parameters to new data that will be collected. Additional, formal, experimental design methods can also be used [START_REF] Cho | Experimental Design in Systems Biology, Based on Parameter Sensitivity Analysis Using a Monte Carlo Method: A Case Study for the TNF{alpha}-Mediated NF-{kappa} B Signal Transduction Pathway[END_REF][START_REF] Gueorguieva | Optimal design for multivariate response pharmacokinetic models[END_REF][START_REF] Nestorov | Lumping of whole-body physiologically based pharmacokinetic models[END_REF].

3. For dose-response analysis predictions, evaluate the sensitivity of dose metrics predicted under the conditions relevant to the toxicity studies (or epidemiological studies)

to the parameters in the model.

4. For risk assessment, evaluate the predicted dose metrics in humans under relevant environmental exposure conditions to characterize their sensitivity with respect to the model parameters.

The many sensitivity analysis methods that exist can be grouped into two categories: interactions among multiple parameters. Dr. Spendiff presented an example of global sensitivity analysis using Extended Fourier Amplitude Sensitivity Test (FAST). This is a variance-based global sensitivity method that is independent of any assumptions about the model structure and is effective for monotonic (exclusively increasing or decreasing predictions) and non-monotonic models [START_REF] Campolongo | Sensitivity analysis of an environmental model; a worked application of different analysis methods[END_REF]. FAST is preferable over other global methods due to its computational efficiency and capability to consider parameter interactions as well as main effects. Because PBPK models will become more complex over time, for a sensitivity analysis technique to become 'standard' for such models, it must be robust and 'future-proof'. The FAST technique satisfies these criteria. Dr. Spendiff illustrated the process with an example of a PBPK model with 57 parameters and a specified dose metric, which predicted greater than 90% publicly accessible via the internet. The documentation would strive for transparency through the use of diagrams of model structure and metabolic pathways and tables of model state variables and parameters along with the mathematical equations and model code.

local

E. Model Evaluation

'Best practices' allow efficient evaluation of models through standardization, documentation, and transparency. The framework for model evaluation described by [START_REF] Clark | Framework for evaluation of physiologically-based pharmacokinetic models for use in safety or risk assessment[END_REF]) provides a useful outline for activities in a model evaluation process that has also been extended by [START_REF] Chiu | Evaluation of physiologically based pharmacokinetic models for use in risk assessment[END_REF]).

It would be valuable to further specify criteria that would assist reviewers in determining the strengths and limitation of a specific model. More complete specifications are needed for the processes to be used to implement model evaluation. Model evaluation needs to be a transparent, independent review process. While involvement of risk assessors and modellers throughout the steps leading from model development to application in risk assessment is valuable, it can impact the perception of the model evaluation as an independent process. An independent review is essential to identify and correct mistakes and to make judgments on the adequacy of the model and its supporting scientific database for purposes of implementing a model in risk assessment. Such reviews present a challenge for regulatory Agencies with limited PBPK modelling expertise, so it may be necessary to access additional expertise. It would also be valuable to be able to share model evaluations among countries, by agreeing upon a common framework and process even if the final decisions concerning model use might be different, for example due to risk assessment needs.

A major challenge of model evaluation is to provide perspective on the scientific uncertainties (i.e., inexact or incomplete information) identified with the model and its supporting scientific database. The models allow characterization of uncertainty in a way that default analyses cannot. For example, a default value of 10 for interspecies extrapolation is commonly applied, but the uncertainty for any specific chemical with because it can limit the need for in vivo studies in animals or humans. The importance of protein and non-specific binding and partitioning of volatile substrates, in particular, between the liquid and headspace phases are fundamental to improving the utility of in vitro systems and the use of such data in PBPK models. The EU 6 th Framework Project "AcuteTox" is addressing many of these issues 11 . Therefore, in vitro metabolism data currently must be ranked lower than in vivo until more detailed models of in vitro systems demonstrate that they are reliable surrogates. In keeping with the theme of the workshop it was agreed that transparency and clear communication with regard to how in vitro data were generated and used in a model is vital.

Cross chemical extrapolation

Risk assessors are increasingly having to address prioritization and assessment for the large numbers of chemicals in commerce, notably the REACH 12 legislation in Europe or the Categorization & Screening of the Domestic Substances List under the Canadian Environmental Protection Act of 1999 13 . Methods to develop initial PBPK models for chemicals using cross-chemical prediction methods would be valuable. Efforts to date have primarily been directed at predicting tissue:blood or tissue:air partition coefficients [START_REF] Beliveau | Quantitative Structure-Property Relationships for Interspecies Extrapolation of the Inhalation Pharmacokinetics of Organic Chemicals[END_REF], though in vitro to in vivo extrapolation for metabolism and other aspects of pharmacokinetics is also receiving attention as noted in the previous section.

Uncertainty and variability in PBPK models

Much of the focus in the development of PBPK models has been to identify and capture the average behaviour of the key biological processes controlling a chemical's pharmacokinetics. These models have successfully assisted in evaluating biological hypotheses for mode of action (e.g., methylene chloride carcinogenesis described previously) as well as identifying previously unrecognized pharmacokinetic behaviours.

The increasing application of PBPK models in risk assessment has led to a range of efforts to better characterize the relationship between the model and supporting data and of uncertainty and variability concepts in risk assessments using PBPK models have been previously described [START_REF] Barton | Characterizing Uncertainty and Variability in Physiologically-based Pharmacokinetic (PBPK) Models: State of the Science and Needs for Research and Implementation[END_REF].

G. Good modelling practices for PBPK models: Developing a description, case studies, and training materials

The International Programme on Chemical Safety (IPCS) steering group of the World Health Organization (WHO) identified PBPK modelling as an important component of chemical risk assessment that merits international harmonization14 . The ability to review a PBPK model according to accepted criteria would greatly facilitate widespread acceptance, in particular, amongst regulators. While agreement amongst PBPK practitioners is paramount for the development of GMP, the guidelines must also be acceptable to regulators and risk assessors. Development of guidelines for GMP is best achieved through a cross-disciplinary exchange of experience and ideas among laboratory scientists, PBPK modellers, regulators and risk assessors. This workshop will provide input to the ongoing IPCS PBPK working group, which is striving to develop a description of GMP.

The adequacy of the GMP description can be evaluated using case studies. The case studies would then form the basis for training materials on GMPs. Some recommendations for case studies included:

Comparing a case with a dose metric for which data was directly available versus one where it was not.

Examples where PBPK models were accepted and used by regulatory Agencies and ones where they were rejected to insure appropriate documentation.

Comparisons of data rich chemicals with data limited chemicals including not just pharmacokinetic or metabolic data, but also mode of action data such as toxicogenomic or metabolomic data.

Illustrations of different risk assessment applications

Potential chemicals to use as case studies would include those previously noted by participants as PBPK models considered for or applied in risk assessments in Europe,

  France, The French Agency for Environmental and Occupational Health Safety and INERIS are developing the use of PBPK modeling for setting reference values for reproductive toxicants (INERIS, 2007) [full reference: INERIS, 2007, Reprotoxicity of Ethylene Glycol Ethyl Ether (EGEE) in Humans -Development of a Dose-Response Relationship, Report DRC 07-83452-0079A] PBPK modelling has been used in Canada in the development of chemical specific adjustment factors for several chemicals including chloroform, 2-butoxyethanol, … (BETTE MEEK WRITE?) In the United States, PBPK models have been used in safety or risk assessments by several government Agencies including the Food and Drug Administration (FDA), the Consumer Products Safety Commission (CPSC), the Occupational Safety and Health Administration (OSHA), and the U.S. Environmental Protection Agency (EPA). As described in the presentation titled "Reducing uncertainty in risk assessment using PBPK Modelling: An example with methylene chloride" by Dr. Melvin E. Andersen of CIIT at The Hamner Institutes for Health Research, modelling for methylene chloride has involved an iterative hypothesis testing process for the pharmacokinetics and glutathione transferase-mediated mode of action leading to cancers in rodents. The mathematical model gave a quantitative form to the researcher's conception of the biological system, permitting the development of a testable, quantitative hypothesis, the design of informative experiments and the ability to recognize inconsistencies between theory (model) and data. The explicit description of model parameters also led to the ability to study and quantify uncertainty. The PBPK model for methylene chloride has been widely applied in risk assessments by CPSC (Babich, M. A. 1998), OSHA for establishing the permissible exposure level including use of Bayesian statistical parameter estimation and characterization of uncertainty and variability (OSHA 1997), and EPA in the IRIS assessment for inhalation cancer risk (Dewoskin, R. S. 2007; US

  regulators and registrants, the FOrum for the Coordination and Use of Simulation models (FOCUS) was created. The steering committee of FOCUS met under the auspices of the EU Directorate General for Health and Consumer Affairs (DG SANCO) for the first time in 1993 and approved 2 research area themes on groundwater and surface water models. FOCUS decisions are based on consensus. It has equal representation of regulators, researchers and industry. The remit is to offer technical support to the EU registration process (91/414). It has no administrative infrastructure, but DG SANCO provides funds for attendance at meetings for regulatory experts and researchers. The FOCUS committee meets approximately 4 times per year and has 2 permanent institutions: • The FOCUS website 7 provides all the reports of past FOCUS projects, as well as the actual versions of models which are recommended in the FOCUS reports as well as essential scenario data. • The Version Control Group is a technical group. Members are the model developers/supporters. Its responsibilities are the approval of new model versions, and the control of the website content. Generally, no formal meetings of the VC are held.

  methods that consider sensitivities close to a specific set of input parameter values, and global methods, which calculate the contribution of a parameter over the set of all possible input parameters. Currently, gaining insight into a model often involves the adjustment of individual model parameters and observation of the predicted changes in model output, either at a single time or throughout a time course. This useful practice can be supplemented by examining the time-dependent global sensitivities of the chosen dose-metric for dominant parameters. When trying to establish the contribution of a parameter to model predictions, local sensitivity analysis techniques are fairly rapid and simple to implement but can give somewhat misleading results if there are substantial

  Canada, and the United States. Other chemicals could include isopropanol (with acetone metabolite submodel) for noncancer endpoints, styrene as an example of an inaccessible dose metric, acrylamide as an example of great current regulatory interest with multiple proposed modes of action and target sites, butadiene due to the substantial animal modelling and uncertainty in human metabolism resulting in assessment based upon epidemiology.Finally, development of training materials and hiring of personnel with needed expertise will be essential to facilitate implementation of mode of action and dosimetry-based risk assessment by regulatory Agencies. A strategy over a longer term would be to include a more quantitative, computationally based study of toxicology in university courses. The adaptation of a PBPK model generator tool such as MEGen as a teaching tool would be very useful in demonstrating to students how biological knowledge can be applied to solve real-world problems. Training materials are needed so that risk assessors and managers with diverse expertise can successfully interact with modellers to implement PBPK models in risk assessment. Training will also be important for modellers to learn about newer methodologies for characterizing uncertainty and variability in PBPK models or implementing local and global sensitivity analyses at appropriate stages of model maturation.

  uncertainty and variability as was described by Dr. R. Woodrow Setzer in his talk reporting on the International Workshop on Uncertainty and Variability in PBPK

	Models (Research Triangle Park, NC, 31 October -2 November, 2006). Improved
	computing power was essential to more widespread use of distributional analyses to
	characterize human variability with Monte Carlo simulation techniques and methods of
	parameter estimation ranging from optimization of selected chemical specific parameters
	(e.g., metabolic rates) to global parameter estimation using Bayesian statistical
	characterization of uncertainty and variability. Priorities for research and implementation

11 (http://www.acutetox.org/) 12 (http://ec.europa.eu/environment/chemicals/reach/reach_intro.htm ) 13 (http://www.ec.gc.ca/substances) quantify

The measurement of the water percolating through soils and the determination of the materials dissolved in the water.

Federal Biological Research Centre for Agriculture and Forestry (BBA), the Federal Environmental Agency (UBA), the Fraunhofer Institute for Environmental Chemistry and Ecotoxicology (FhG IUCT) and the German Agrochemical Industry (IVA).

http://europa.eu.int/eur-lex/en/consleg/pdf/1991/en_1991L0414_do_001.pdf

http://viso.ei.jrc.it/focus/

http://ecb.jrc.cec.eu.int/documents/REACH/RIP_FINAL_REPORTS/RIP_3.3_INFO_REQUIREMENTS/F INAL_DRAFT_GUIDANCE/RIP3.3_TGD_FINAL_2007-05-02_Part1.pdf

http://sbml.org ; www.cellml.org

http://www.who.int/ipcs/methods/harmonization/areas/pbpk/en/index.html

variability across a population that was almost entirely determined by a small subset (<10) of the parameters. This does not mean that the PBPK model was over-specified as it is essential that the factors responsible for heterogeneity in output across all dose metrics, species and exposure scenarios are preserved. However, once the dose metric has been established, the techniques described can greatly reduce the effort required to perform a population simulation and provide estimates of human variability.

D. Model Documentation

Suggestions for documenting models in publications have been presented previously [START_REF] Andersen | Applying simulation modeling to problems in toxicology and risk assessment -a short perspective[END_REF]. As noted there, model documentation must address a diverse readership. Recommendations from this workshop were to develop a standard, brief model description summary for the broad risk assessment audience and more detailed documentation for specialists. The summary would contain at least seven 7. Source of complete information (e.g., citation)

Recommendations for more complete model documentation need further development, but include the possibility that hyperlinked documents can facilitate easy access to supporting materials, including calculations done to convert published scientific information into the form utilized in the model. This extended model documentation would be utilized by subject experts in the model evaluation process and would ideally be regard to the toxicity it causes in animals ranges from a value close to zero (the effect only occurs in the animals) to a much larger value (the effect only occurs in humans).

While the factor of 10 represents a judgment concerning the general tendency across many chemicals, it cannot describe the uncertainties for a specific chemical as one can start to estimate using biologically based modelling. However, this creates a challenge for considering whether the model adequately captures the science and, thus, should be implemented in the risk assessment.

Improving Science Supporting Models

Efforts to use PBPK models more broadly have also resulted in a range of scientific issues that require additional research. These include improving methods for using in vitro data in order to limit controlled animal and human studies, for model development by extrapolating across chemical to those with limited databases, and for better characterizing uncertainty and variability in PBPK models.

In vitro to in vivo extrapolations

During the presentation by Professor György Csanády of the GSF-Institute of Toxicology in Germany entitled "A physiological toxicokinetic model for inhaled propylene oxide in rat and human with special emphasis on the nose" he discussed the significant capabilities of this model given the complex nature of the toxicity and datasets it was addressing. He noted the apparent inability of the model to predict in vivo rat data using metabolic parameters estimated from in vitro studies [START_REF] Csanady | A physiological toxicokinetic model for inhaled propylene oxide in rat and human with special emphasis on the nose[END_REF][START_REF] Faller | Kinetics of propylene oxide metabolism in microsomes and cytosol of different organs from mouse, rat, and humans[END_REF][START_REF] Lee | Propylene oxide in blood and soluble nonprotein thiols in nasal mucosa and other tissues of male Fischer 344/N rats exposed to propylene oxide vapors-relevance of glutathione depletion for propylene oxide-induced rat nasal tumors[END_REF][START_REF] Osterman-Golkar | Dosimetry by means of DNA and hemoglobin adducts in propylene oxide-exposed rats[END_REF]. Introduction of extrahepatic metabolism or reduction of the Michaelis-Menten K M constant was required, while a reduction in pulmonary ventilation rate was need at propylene oxide concentrations above 100 ppm likely reflecting respiratory irritant effects. The discussion stimulated by Professor Csanády's presentation led to agreement that the issue of in vitro to in vivo extrapolation, particularly, with regards to metabolism requires further detailed study [START_REF] Blaauboer | The Integrated Use of Alternative Methods in Toxicological Risk Evaluation: ECVAM Integrated Testing Strategies Task Force Report 1[END_REF][START_REF] Blaauboer | The use of Biokinetics and In Vitro Methods in Toxicological Risk Evaluation[END_REF][START_REF] Dejongh | An integrated approach to the prediction of systemic toxicity using computerbased biokinetic models and biological in vitro test methods: overview of a prevalidation study based on the ECITTS Project[END_REF]. Ideally, in vitro data should be readily usable in PBPK models


Tanguy Amodeo
email: tanguy.amodeo@ineris.fr

Christophe Dutouquet
email: christophe.dutouquet@ineris.fr

François Tenegal
email: francois.tenegal@cea.fr

Benoît Guizard
email: benoit.guizard@cea.fr

Hicham Maskrot
email: hicham.maskrot@cea.fr

Olivier Le Bihan
email: olivier.le-bihan@ineris.fr

Emeric Fréjafon
email: emeric.frejafon@ineris.fr

On-line monitoring of composite nanoparticles synthesized in a pre-industrial laser pyrolysis reactor using Laser-Induced Breakdown Spectroscopy (LIBS)

Keywords: LIBS, on-line monitoring, process control, laser pyrolysis, composite nanoparticles

Laser-Induced Breakdown Spectroscopy (LIBS) was employed for on-line and real time process monitoring during nanoparticle production by laser pyrolysis. Laser pyrolysis has proved to be a reliable and versatile method for nanoparticle production. However, an on-line and real time monitoring system could greatly enhance the process optimization and accordingly improve its performances. For this purpose, experiments aiming at demonstrating the feasibility of an on-line monitoring system for silicon carbide nanoparticle production using the LIBS technique were carried out. Nanosecond laser pulses were focused into a cell through which part of the nanoparticle flux diverted from the production process was flowed for LIBS analysis purposes. The nanoparticles were vaporized within the laser-induced plasma created in argon used as background gas in the process. Temporally-resolved emission spectroscopy measurements were performed in order to monitor nanoparticle stoichiometry. Promising results were obtained and on-line Si/C X stoichiometry was successfully observed. These results put forward the possibility of real time correction of the nanoparticle stoichiometry during the production process.

Introduction

Over the last years, research in the field of nanostructured materials synthesis has become increasingly important. Such materials designed at a nanometric scale are expected to show new properties of great interest to a wide range of industrial applications. In this context, the development of nanoparticle-based materials has advanced rapidly and along with it the need for generic and safe nanoparticle production systems. Producing and commercializing nanoparticle-based materials for different applications (composites, cosmetics, catalysis, optics,…) require industrial equipments permitting large-scale production of a wide range of nanoparticles. Furthermore, these industrial lines are expected to produce nanoparticles with constant quality (size distribution, chemical composition).

For this purpose, the development of an on-line monitoring system allowing a real time analysis, both qualitative and quantitative, of some characteristics of the nanoparticles such as the chemical composition and the size is highly desirable. The monitoring of some of these characteristics will thus provide quasi instantaneous feedback for process control. This is of primary importance while the development of composite nanoparticles arouses a growing interest. These nanoparticles are elaborated from several elements with the aim of creating materials with advanced functionalities. The large number of elements these nanoparticles consist of has actually emphasized the need for on-line stoichiometry control. In addition to process control, production safety requirements could be satisfied by utilizing online monitoring systems to secure the installations producing, handling and integrating nanoparticles. A tool devised both for in-situ and real time chemical and physical identification of nanoparticles could detect their release in the ambient air and therefore could assure safety for the operating personnel and the surrounding environment. Among all the methods known, the continuous nanoparticle synthesis processes are very interesting for nanoparticle production at industrial scale. Indeed, with these techniques, nanoparticles are produced continuously by injecting a continuous flow of reactants in a reactor leading to large daily production rates. Some of these methods are already developed at industrial scale for the production of metals or oxides nanoparticles (for example TiO 2 nanoparticles by the Aerosil® process of DEGUSSA) and the development of industrial processes for non-oxide nanoparticles is currently in progress. Laser pyrolysis is one of these promising techniques. Basically, its principle rests on the interaction between a continuous high-power CO 2 laser beam and a continuous flux of gaseous or liquid reactants. Reactant molecules absorb laser radiation which results in their dissociation. Afterwards, molecules recombine with one another within the pyrolysis flame to form nanoparticles. Thereby, a wide range of nanoparticles can be produced. The process yields a continuous high production rate of nanoparticles with a good control of their size, structure and chemical composition [START_REF] Cannon | Sinterable Ceramic Powders from Laser-Driven Reactions: II, Powder Characteristics and Process Variables[END_REF]. Furthermore, nanoparticles produced in this way are usually weakly agglomerated. In addition, the method is very efficient to produce composite nanoparticles which characteristics can be easily adjusted by varying some processing parameters such as the laser power density, the flow rate, the composition and dilution of the reactants.

Though this method assures a good control of compound stoichiometry, stoichiometry variations in the course of nanoparticle production are not to be neglected. Thus, the production rate of nanoparticle compounds with the right stoichiometry could be enhanced with an on-line and real time quality control.

With this aim in view, the LIBS technique was selected. LIBS has been demonstrated to be a versatile, reliable analytical method for multi-elemental analysis of solid [START_REF] Fichet | Determination of impurities in uranium and plutonium dioxides by Laser-Induced Breakdown Spectroscopy[END_REF], liquid [START_REF] Fichet | Quantitative elemental determination in water and oil by laser-induced breakdown spectroscopy[END_REF], gas [START_REF] Dudragne | Time-resolved laser-induced breakdown spectroscopy: application for qualitative and quantitative detection of fluorine, chlorine, sulfur and carbon in air[END_REF][START_REF] Cremers | Detection of chlorine and fluorine in air by laser-induced breakdown spectroscopy[END_REF] and aerosols [START_REF] Hahn | Detection and analysis of aerosol particles by laser-induced breakdown spectroscopy[END_REF][START_REF] Radziemski | Time-resolved laser-induced breakdown spectrometry of aerosols[END_REF] both for qualitative and quantitative measurements. It has the advantage of being non-intrusive and no sampling is required. Moreover, measurements can be performed at remote distances, in real time and insitu without sample preparation. All these characteristics make LIBS a particularly well adapted tool to analyze materials which are not easily accessible or for which confinement is required.

The LIBS analysis method has already been utilized in diverse industrial applications. Its potential in the steel industry at different stages of the production chain has already been demonstrated [START_REF] Noll | Laser-induced breakdown spectrometryapplications for production control and quality assurance in the steel industry[END_REF][START_REF] Gruber | Rapid in-situ analysis of liquid steel by laser-induced breakdown spectroscopy[END_REF]. For instance, the LIBS technique has been applied for on-line monitoring of molten steel in order to improve the steel quality and to obtain a better production gain. In the nuclear industry [START_REF] Whitehouse | Remote materials analysis of nuclear power station steam generator tubes by laser-induced breakdown spectroscopy[END_REF], LIBS has been selected as the most adequate technique when in-situ material analyses were required. It was the most practical method to implement in a hostile environment with high temperatures and level of radiation. Recently, concerns have been raised regarding particles of micrometric and submicrometric sizes emitted in ambient air (from different sources such as plants, engines….) and their possible hazard towards human health and environment. Several studies have demonstrated the LIBS potentialities for particle detection [START_REF] Hahn | Discrete particle detection and metal emissions monitoring using laser-induced breakdown spectroscopy[END_REF]. LIBS was therefore used for on-line monitoring of metal laden aerosols in industrial production, combustion processes [START_REF] Neuhauser | On-line monitoring of chromium aerosols in industrial exhaust streams by laser-induced plasma spectroscopy (LIPS)[END_REF][START_REF] Cheng | Field measurement comparison of aerosl metals using aerosol beam focused laser-induced plasma spectrometer and reference methods[END_REF][START_REF] Buckley | Implementation of laser-induced breakdown spectroscopy as a continuous emission monitor for toxic metals[END_REF], environmental purposes [START_REF] Carranza | On-line analysis of ambient air aerosols using laser-induced breakdown spectroscopy[END_REF][START_REF] Lithgow | Ambient measurements of metal-containing PM2.5 in an urban environement using laser-induced breakdown spectroscopy[END_REF] and promising results were obtained. All these studies point out the advantages of LIBS for process control and particle detection both on-line and in real time.

In this paper, we report the results of a study coupling for the first time the LIBS technique and a laser pyrolysis reactor. The LIBS equipment was integrated into a laser pyrolysis unit developed by the French Atomic Energy Commission (CEA -Saclay, France) with the aim of monitoring on-line and in real time SiC X composite nanoparticle stoichiometry. These nanoparticles are synthesized prior to nanostructured silicon carbide elaboration, the latter being a promising material for high temperature applications in the nuclear energy industry. Our main objective was centred on nanoparticle stoichiometry determination while the process was being operated. Stoichiometry calculation was achieved using temporally-resolved emission spectroscopy measurements followed by plasma analysis. The results obtained are reported and discussed.

Experimental set-up: LIBS & laser pyrolysis

Instrumentation

Laser pyrolysis experimental set-up

The experimental set-up (figure 1) consists of a reactor equipped with an inlet nozzle at its bottom, an outlet duct at its top and many optical windows among which those for the laser beam passage and the pyrolysis flame monitoring. Reactants, namely SiH 4 and C 2 H 2 for SiC X nanoparticle synthesizing are admitted in the pyrolysis reactor through the inlet nozzle, the latter being flat or cylindrical. Reactants can be injected with different flow rates. A continuous high-power CO 2 laser beam is expanded to twice its starting diameter with an optical system and focused in the vertical direction using a 500 mm focal length zinc-selenium (ZnSe) cylindrical lens. The resulting 30 mm width elliptical cross-section beam passes through the reactor perpendicularly to the above mentioned reactant jet coming out from the nozzle. A wide range of laser power density values for reactant irradiation are accessible by adjusting the distance between the cylindrical lens and the nozzle. The position of maximum focusing is attained for a distance between the cylindrical focusing lens and the nozzle matching the lens focal length and corresponds to the maximum irradiance of 50 kW/cm 2 . A co-flow of argon gas is injected around the reactants in order to confine the reaction. At the same time, a stream of argon sweeps all the inner window surfaces to avoid particle deposition. Eventually, the nanoparticles are evacuated from the reactor with argon as a carrier gas. Then, they are directed towards the nanoparticle collector thanks to a pump placed downstream of the collector and connected to an exhaust-treatment system constituted of a scrubber and a washer. It should be noted that the production rate of this unit can go beyond 1 kg of silicon carbide (SiC) nanoparticles per hour.

LIBS experimental set-up

The experimental set-up of the LIBS system mounted on the laser pyrolysis process is depicted on figure 2. In order to carry out the LIBS experiment, a metallic flow cell devoted to LIBS analysis through which part of the nanoparticle flow was diverted was installed on the process. A bypass line was specially designed to achieve this diversion and to enable an on-line continuous sampling from the main stream of nanoparticles. Using the bypass line, the inlet end of the cell was connected to the nanoparticle production conduit upstream of the nanoparticle collector. The outlet end was linked to the exhaust conduit downstream of the collector. The pre-existent pressure difference between the two connection points sufficed to make the nanoparticles carried by the argon gas stream circulate through the cell for LIBS analysis purposes. A small filter collector was placed downstream of the cell outlet to prevent the particles from being released in the main line. A laser pulse of 5 ns duration and 50 mJ energy originating from a Q-switched Nd-Yag laser ( = 1064 nm) operated at 20 Hz was focused inside the cell using a fused silica plano-convex lens of 50 mm focal length the cell was fitted with. The resulting beam waist value was about 100 µm leading to an estimated fluence value of 150 J cm -2 at the focal point. The LIBS signal originating from the plasma created in the argon-nanoparticle mixture was collected through a quartz window perpendicularly to the LIBS laser beam with a telescope of 150 mm focal length. The plasma emission was imaged onto the entrance a 50 µm diameter core optical fiber. The fiber was linked to an Echelle spectrometer (Mechelle 5000, resolution of 0.1 nm at 500 nm) equipped with a fast intensified CCD camera (Andor Istar, model DH734-18F-03).

Nanoparticle synthesizing parameters

The LIBS experiments planned required nanoparticle stoichiometry and concentration to vary.

Table 1 reports the parameters used to produce nanoparticles for this study. The experiments were carried out with a defocused beam (-150 mm) corresponding to an energy of 420 W/cm 2 . The relative flow rates of SiH 4 and C 2 H 2 were set so that nanoparticles having the following theoretical stoichiometries SiC, SiC 2 , SiC 4 and SiC 8 could be synthesized, the chemical yield for laser pyrolysis experiments using mixtures of SiH 4 and C 2 H 2 being close to 1 (all Si and C atoms are recovered in a condensed form) [START_REF] Cauchetier | Laser synthesis of ultrafine powders[END_REF].

The mass concentration was varied from 1.6 to 11.2 g per cm 3 by changing the ratio between the reactants flow rate and the argon flow rate. The production rate in mass was calculated considering a chemical yield of 1 for all the conditions.

Foreword: stoichiometry

Stoichiometry determination requires to establish a relationship between the measured spectroscopic intensities and the densities of the different elements the nanoparticles consisted of prior to vaporization. A simple equation links the measured intensity ratios to the elemental density ratios provided several conditions are fulfilled. The plasma has to be in local thermal equilibrium state (LTE) and optically thin for the transitions observed. If these conditions are met, the equation yielding stoichiometry S by relating the integrated intensity ratio to the density ratio writes Basically, stoichiometry determination consists of measuring the ratio of the total number of atoms of each element. With the above formula, only ratios of total number of atoms in a given ionization stage is accessible. Nevertheless, stoichiometry can be inferred from ratio of total number of atoms in the neutral state on the condition that the ion density be negligible compared to that of neutral density.

Temporally-resolved emission spectroscopy allows the determination of the right temporal window matching this criterion and the other two: LTE and optically thin plasma. In the paragraphs to follow, a brief description of our time-resolved spectroscopic measurements will be presented. LTE validity will be discussed and the electronic temperature calculated. Finally, plasma optical thickness will be examined and stoichiometry results will be presented and discussed.

Results

Lines identification

In order to identify all the emitting species of interest vaporised within the plasma and to determine the best recording parameters for stoichiometry

Plasma analysis

Electronic density

The temporal evolution of the electronic density was obtained from Stark broadening measurements. The electronic density was calculated by fitting computed spectra with experimental data.

In the early times of the laser spark event, the plasma is highly ionized and Stark broadening dominates over other broadening effects. Spectral line shapes were then simulated using Lorentzian profiles for these functions are well suited to approximate Stark broadened lines [START_REF] Hans | Principles of plasma spectroscopy[END_REF]. For usual LIBS conditions, one N . The apparatus width of the spectrometer was measured using a mercury-argon calibration lamp. The 750.386 and 751.465 nm argon lines and the H line at 656.279 nm were chosen for electronic density determination for Stark widths of these lines have well known tabulated values [START_REF] Konjevic | Experimental stark widths and shifts for spectral lines of neutral and ionized atoms[END_REF][START_REF] Wiese | Detailed study of the Stark Broadening of Balmer Lines in a High-density Plasma[END_REF]. Figure 4 shows an experimental spectrum recorded with a 4.2 µs time delay and a 2 µs gate width and compared with a simulated profile. The computed spectrum fits the experimental profile very well for an electronic density value of 9 10 16 cm -3 . Seventy percent of the values measured using the H line fall within the range of 20 to 50 percent agreement with those obtained from argon lines. The rest of the points scores badly within the range of 50 to 90 percent agreement. Figure 5 presents the temporal evolution of the electronic density during SiC and SiC 8 nanoparticle vaporization. No significant differences of electronic densities were observed for both stoichiometries. Electronic density values of SiC and SiC 8 are almost identical for time delays ranging from 2 µs to 10 µs. For time delays inferior to 2 µs, the slight density difference could be accounted for by the difficulty encountered when simulating strongly widened experimental lines appearing in the early times of the plasma formation.

Electronic temperature

The electronic temperature was calculated using Boltzmann plot method based on the following equation

C T k E A g I e n nm n nm nm ln (3)
n and m being the upper and lower levels of the selected transition respectively, I nm the integrated intensity of the transition, nm its wavelength, g n statistical weight of the upper state level, A nm transition probability, E n energy of the upper state level, T e the electronic temperature, k Boltzmann constant, and C a constant. The 17 strong argon lines which were chosen for the Boltzmann plots are listed in table 2. In order to carry out calculations using different spectral regions, the Echelle / ICCD system was calibrated with a deuterium tungsten-halogen light source (DH-2000-CAL, Ocean Optics). The blackbody radiation emitted from the lamp allowed calibration of the acquired spectra. The calculations are therefore more precise for this calibration gives access to a wide range of lines with different upper energy levels. One of the Boltzmann plot used for temperature determination is displayed in figure 6. The evolution of the plasma temperature is shown in figure 7 as a function of time delay when irradiating SiC nanoparticles. It should be noted that the electronic temperature did not show a great sensitivity to stoichiometry and concentration variations within the ranges investigated.

LTE validity

Plasmas with high electronic densities (namely, electronic densities superior to 10 16 cm -3 ) such as laser-induced plasmas are very close to the local thermal equilibrium (LTE). The assumption of LTE is usually made for laser generated plasmas obtained in air or argon at atmospheric pressure [START_REF] Cremers | Handbook of laser-induced breakdown spectroscopy[END_REF]. Usually, the LTE validity verification consists of calculating the lower limit of electron density N L for the LTE to be established according to a criterion such as the Mac Whirter criterion:

3 12 10 6 . 1 E T N N e L e (4)
where E is the highest energy difference between upper and lower energy levels (eV) populated according to LTE conditions and between which a transition is possible. For the calculation of N L , the chosen electronic temperature value corresponded to the maximum time delay of 10 µs for which electronic density could be measured, that is to say, a value of around 12300 K according to figure 7. The upper and lower energy levels of the 419.10 nm argon line match the above criteria for the evaluation of E which value equals therefore 2.95 eV according to table 2. Eventually, N L value was found to be 4.5 10 15 cm -3 . According to the electronic density graph of figure 5, LTE validity is therefore established for time delay below 10 µs. Beyond this delay range, electronic density measurement became uneasy as Stark broadening effect was diminishing.

Temporal window choice

Equation ( 1) allowing stoichiometry calculation only applies under LTE conditions. The LTE criterion was found to be fulfilled for time delay values under 10 µs. For reasons explained previously [START_REF] Fichet | Quantitative elemental determination in water and oil by laser-induced breakdown spectroscopy[END_REF], ion density must be negligible compared to that of neutrals for equation ( 1) to be applied. Temporal evolution of lines emitted from the plasma showed that no ion emission was detected for time delays higher than 2µs. Total number of Si and C ions are therefore assumed to be negligible and stoichiometry determination can be achieved using equation [START_REF] Cannon | Sinterable Ceramic Powders from Laser-Driven Reactions: II, Powder Characteristics and Process Variables[END_REF]. The best Boltzmann's plots were obtained for a time delay of 4.2 µs which eventually resulted in our selecting this value for all our stoichiometry measurements.

Stoichiometry calculation. Discussion

Stoichiometry was evaluated in accordance with equation (1) taking into account the above plasma analysis results. Two lines had to be chosen for stoichiometry calculation. As explained in section (4.1), carbon line at 247 nm was the only neutral carbon line available on all the spectra recorded and was therefore automatically chosen. The Si line at 288.157 nm was selected for two reasons. It had a better signal-to-background ratio than the other detected transitions. Furthermore, its repeatability proved to be higher than the other recorded lines. Spectroscopic characteristics of these lines are reported in table 3.

The strong transition probability of the aforementioned Si line makes it prone to self absorption. With the aim of verifying whether it was subject to self absorption, a flow of nanoparticles containing only the Si element was produced and analyzed. The LIBS intensity of this Si line was studied as a function of nanoparticle concentration. To this end, the spectra were recorded with the above selected delay of 4.2 µs but with an extended gate width of 20 µs. A fast analysis of all the Si spectra did not reveal any signs of strong self absorption. No self-reversed lines were observed. Figure 8 shows a graph representing the 288.157 nm Si line intensity evolution as a function of concentration. Its intensity proved to be linear within the concentration range investigated. This result points out that self absorption was virtually nonexistent under our experimental conditions. Self absorption of C line was assumed to be negligible as its transition probability was weaker than that of Si at 288.157 nm. These results account for the optically thin plasma hypothesis necessary for equation ( 1) to be applied.

Plotting such graph also aimed at detecting possible saturation of the LIBS signal which may have occurred in case of incomplete nanoparticle vaporization within the plasma. Usually, the grain size of nanoparticles varies within a range of 20 to 100 nanometers when produced by laser pyrolysis.

Carranza and Hahn demonstrated incomplete vaporization of micrometric-sized particles even for high laser irradiance and concluded the existence of an upper particle size of 2.1 µm beyond which vaporization was not completed [START_REF] Carranza | Assessment of the upper particle size limit for quantitative analysis of aerosols using laser-induced breakdown spectroscopy[END_REF]. In addition, possible depletion of nanometric-sized particles from the plasma core was also reported [START_REF] Hohreiter | Calibration effects for laser-induced breakdown spectroscopy of gaseous sample streams: analyte response of gas-phase species versus solid-phase species[END_REF]. Previous experiments carried out in our laboratory at INERIS showed linear dependence of LIBS intensity on particle sizes ranging from 40 nm to 500 nm while irradiating NaCl particles under comparable experimental conditions. In the present work, the detected LIBS intensity showed a linear dependence both on concentration and particle size.

Eventually, all the criteria necessary for proper use of equation (1) were fulfilled. Stoichiometry was calculated with a 4.2 µs time delay recorded spectra and a 2 µs gate width as stated in 4.2.4. The results obtained are reported in table 4 and are in good accordance with the known theoretical values.

However, calculations performed with equation (1) suggest that stoichiometry determination can be strongly temperature dependent. This is evidenced by writing stoichiometry relative uncertainty The first term in equation ( 5) is related to relative errors on line ratio measurements including implicitly different uncertainties such as spectra repeatability and potential self-absorption underestimation. These experiments pointed out that repeatability increased with increasing of number of accumulated spectra, as often verified in LIBS experiments. However our experiments had to be carried out in the allotted time corresponding to the reactor availability. For this first laser pyrolysis reactor -LIBS coupling, temporally-resolved emission spectroscopy measurements were performed. These experiments carried out for different stoichiometry and concentration were time consuming and did not lead us to record spectra with a large number of accumulations. Spectra corresponding to the selected temporal window and recorded with a larger number of accumulations could have enhanced our results.

One characteristic of the LIBS technique applied to particle analysis is the particle presence probability within the laser-induced plasma. For low micrometric-sized particle concentration values such as a few micrograms per cubic meters, it has been demonstrated that a particle hit (a particle vaporized within the plasma and leading to LIBS detection) could be modeled with a Poisson probability law [START_REF] Carranza | Plasma volume considerations for analysis of gaseous and aerosol samples using laser-induced breakdown spectroscopy[END_REF].

Thus, the laser-induced plasma corresponding to one shot may be ignited without any particle of the analyte passing through the plasma volume. No spectroscopic signal is then recorded for that shot lowering thereby the signal-to-background ratio of the resulting spectrum and its repeatability. Under our experimental conditions, the concentrations were of the order of a few micrograms per cubic centimeters, that is to say, 10 6 times higher than the aforementioned low concentrations (Table l) with particle sizes ranging from 20 to 100 nm. An average particle number of more than one thousand was always present within the plasma volume assuring LIBS detection for each laser shot and justifying ensemble averaging (i.e. accumulation of LIBS spectra) [START_REF] Hahn | Discrete particle detection and metal emissions monitoring using laser-induced breakdown spectroscopy[END_REF]. Within the concentration range probed during our LIBS experiment, the discrete nature of particles did not occasion particle miss (absence of analyte particles of the plasma) improving thereby signal-to-background ratio and repeatability. In future experiments, depending on particle concentration, further investigations could be necessary for spectra optimization either by increasing the number of accumulated spectra (in accordance with the time resolution wanted for process monitoring) or by improving data processing.

The second term of equation ( 5) evidences that stoichiometry relative uncertainty value strongly depends on a correct temperature determination and increases with increasing energy difference between the two upper state levels. The impact of the temperature uncertainty on stoichiometry determination is displayed on figure 9. This graph indicates that an error of around 1500 K in the temperature calculation induces a variation in stoichiometry determination of about 25 % (taking into account the variations of the partition functions with temperature). Yet, an accurate determination of the plasma temperature remains difficult. The use of the Boltzmann's plot method for temperature calculation implies that all the lines used be not self-absorbed. This condition is not always easily achieved, though it is necessary to avoid calculation errors. These results actually highlight the need for a reliable tool allowing plasma characteristics determination with a better accuracy than by using Boltzmann's plot method or by measuring Stark broadening directly on a spectrum.

On the basis that laser-induced plasmas generated at atmospheric pressure are very close to the LTE state, the spectra emitted during LIBS experiments could actually be simulated using a LTE model.

By comparing recorded spectra with synthetic spectra, characteristics such as electronic temperature, electronic density, plasma composition and subsequently stoichiometry can be determined using a model including diverse functionalities such as self-absorption modeling [START_REF] Hermann | Diagnostics of the early phase of an ultraviolet laser-induced plasma by spectral line analysis considering self-absorption[END_REF][START_REF] Hermann | Local thermal equilibrium plasma modeling for analyses of gas-phase reactions during reactive-laser ablation[END_REF]. A correct fit guarantees plasma characteristic determination with uncertainties lower than with any other methods. Such model could be of great interest for future and advanced experiments concerning nanoparticle process control using LIBS.

As the nanoparticle sampling flow concentration passing through the analysis cell can be varied at will by designing the right bypass line, the best laser-plasma-particle coupling should be investigated to approach as close as possible of the LTE state. Thus, stoichiometry of composite nanoparticles containing more than two elements could be determined with the best accuracy.

The results obtained in the course of our experiments point out the difficulty met when trying to obtain quantitative results without using calibration standards. In the framework of these experiments, a calibration free method was favored for this first attempt. The possibility of using a nanoparticle sampling flow as a reference for calibration is not ruled out. However, the method to be applied leading to the elaboration of a reliable nanoparticle sampling flow usable as a calibration standard remains to be determined.

Conclusion

Experiments aiming at demonstrating the feasibility of an on-line LIBS monitoring system allowing real time stoichiometry measurements during nanoparticle production by laser pyrolysis have been carried out.

Part of the nanoparticle flow produced by the process was diverted through a flowing cell where the LIBS plasma was created. Time-resolved spectroscopic study of the laser-induced plasma was performed with the aim of determining nanoparticle stoichiometry. Temporally-resolved electronic density and temperature measurements were obtained from Stark Broadening analysis and Boltzmann plot respectively. LTE validity was established and subsequently, stoichiometry was calculated using an equation relating line intensities to Boltzmann's law. Stoichiometry values found were in good agreement with the expected values. However, stoichiometry calculation proved to be sensitive to the uncertainty related to electronic temperature determination. A deeper study of the laser / plasma / nanoparticle coupling combined with LTE modeling could greatly enhance these promising results.

  a and b are the elements studied, ij and kl the selected transitions for a and b elements respectively, a ij I and b kl I the measured integrated intensities, a ijl and b kl the wavelength of the selected transitions, a Z and b Z the partition functions of a and b elements in a given ionization stage, i g et k g statistical weight of upper states with the corresponding a i E and b k E energies, a ij A and b kl A transition probabilities, a N and b N total number of a and b atoms in a given ionization stage and T e the electronic temperature.

  generally assumes ion contribution to Stark broadening is negligible which results in the simplifying of the Stark width expression[START_REF] Miziolek | Laser-Induced Breakdown Spectroscopy[END_REF]. A linear dependence of the Stark width on the electronic density N e was then assumed for the calculations according to the well known formula equation, Stark is the measured Stark width (FWHM), W ref is the half width at half-maximum Stark parameter for a given electron density ref e

Figure caption Figure 1 .

 caption1 Figure caption

Figure 2 .

 2 Figure 2. LIBS experimental set-up integrated into the laser pyrolysis process.

Figure 3 .

 3 Figure 3. Spectrum recorded during vaporization of SiC 2 nanoparticles within the laser-induced plasma (time delay of 4.2 µs and an ICCD gate width of 2 µs). The line on the left is C I at 247.86 nm. The six other lines correspond to Si I at, from left to right: 250.69 nm, 251.43 nm, 251.61 nm, 251.92 nm, 252.41 nm and 252.85 nm.

Figure 4 .

 4 Figure 4. Example of simulated and experimental profiles of Stark-Broadened Ar lines at 750.386 and 751.465 nm used for electronic density measurements.

Figure 5 .

 5 Figure 5. Temporal evolution of the electronic density during vaporization of SiC and SiC 8 nanoparticles within the laser-induced plasma generated in the argon background gas.

Figure 6 .

 6 Figure 6. Example of Boltzmann plot obtained using argon lines (spectra recorded for time delay of 4.2 µs and gate width of 2µs) during SiC 2 Nanoparticle vaporization by the laser-induced plasma generated in the argon background gas.

Figure 7 .

 7 Figure 7. Temporal evolution of the electronic temperature during vaporization of SiC nanoparticles within the laser-induced plasma generated in the argon background gas.

Figure 9 .

 9 Figure 9. Influence of a 10% error in temperature determination on calculated stoichiometry.

  determination, time-resolved spectroscopic measurements were carried out. Series of spectra were recorded as a function of time delay (t d ) and ICCD gate width. Two series of measurements corresponding to two ranges of time delay were performed. In Singly ionised Ar and Si ions (Ar II at 480.602 nm and Si II at 385.602 nm, 386.26 nm, 412.807 nm, 413.089 nm, 504.103 nm, 505,598 nm, 634.71 nm and 637.13 nm) were identified. Line intensities of these ions decreased rapidly due to plasma recombination. These ions were no longer visible beyond 2 µs time delay. No carbon ions were seen in the recorded spectra. Many neutral Ar and Si lines were detected with a good signal-to-background ratio and their intensity lines increased with decreasing ion concentration. Neutral carbon is usually not easily detected because of its low transition probability values. Thus, only the strongest C line at 247.85 nm was observed during our measurements. Figure3shows a spectrum recorded (time delay 4.2 µs, ICCD gate width 2 µs) during vaporisation of SiC 2 nanoparticles within the plasma created in argon as background gas. The nanoparticle concentration value was calculated to be 5.3 µg per cubic centimetres according to table 1. The carbon line at 247.85 nm and six Si lines were clearly identified. Many argon lines were identified and used for plasma analysis purposes both for electronic temperature and density measurements. For time delay beyond approximately 8 µs, molecule formation of C 2 was observed. No other molecule formation was detected.

the first series a 100 ns ICCD gating time was chosen with time delay varying from 200 ns to 2.2 µs. In the second series, the time delay was varied from 2.2 µs to 60 µs using a gate width of 2 µs. In order to enhance the signal-to-background ratio, spectra were accumulated over several laser-induced spark events. Each spectrum consisted of an accumulation of 400 and 200 laser shots for the first and second series respectively. The same recording parameters were used during the vaporisation of nanoparticles with different stoichiometry (Si, SiC, SiC 2 , SiC 4 and SiC 8 ). Lines identification was performed using the NIST [18] database.

In the early times of the laser spark event (200 ns < t d < 2 µs), many widened argon lines were detected up to approximately 2 µs delay time. The strongly widened H line at 656.279 nm originating from the laser pyrolysis reaction also appeared in the spectra. The strong line widening observed is characteristic of Stark broadening and suggests a high electronic density in the early times of plasma expansion.

Table 1 .

 1 Reactants and argon flow rates, pressure within the chamber and laser power utilized during SiC x nanoparticle synthesizing by laser pyrolysis. The theoretical nanoparticle-mass production values are indicated.

	Theoretical	SiH 4	C 2 H 2	Ar	Theoretical	Pressure	Laser
	compound	Flow rate	Flow rate	Flow rate	Production	(mbar)	Power (W)
		(l/min)	(l/min)	(l/min)	(g/h)
	SiC	0,8	0,4	170	86	950	1000
	SiC 2	0,4	0,4	170	56	950	1000
	SiC 4	0,2	0,4	170	41	970	1000
	SiC 8	0,1	0,4	170	33	970	1000

Table 2 .

 2 Wavelengths , transition probabilities A ij10 7 , upper and lower level energies E i and E j and statistical weights of the upper states g i of the 17 argon lines selected for the Boltzmann plots.

	(nm)	A ij *10 7 (s -1 )	E i (eV)	E j (eV)	g i
	696,54	0,639	13,3279	11,5484	3
	706,72	0,38	13,3022	11,5484	5
	714,7	0,0625	13,2826	11,5484	3
	727,29	0,183	13,3279	11,6236	3
	738,39	0,847	13,3022	11,6236	5
	763,51	2,45	13,1718	11,5484	5
	794,81	1,86	13,2826	11,7232	3
	801,47	0,928	13,0949	11,5484	5
	810,36	2,5	13,1531	11,6236	3
	811,53	3,31	13,0757	11,5484	7
	826,45	1,53	13,3279	11,8281	3
	840,82	2,23	13,3022	11,8281	5
	842,46	2,15	13,0949	11,6236	5
	419,10	0,0539	14,6806	11,7232	5
	420,06	0,0967	14,4991	11,5484	7
	426,62	0,0312	14,5289	11,6236	5
	430,01	0,057	14,5061	11,6236	5

Table 3 .

 3 Wavelengths , transition probabilities A ij 10 7 , upper level energies E i , lower level energies E j , statistical weights of the upper states g i , of the Si and C selected lines for stoichiometry calculation. Z are the partition functions of Si and C in the neutral state calculated for temperature value T of 14800 K.

	Element	(nm)	A ij *10 7 (s -1 )	E i (eV)	E j (eV)	g j	Z	T (K)
	Si	288,16	18,9	5,082	0,780	3	16,87	14800
	C	247,85	3,4	7,684	2,684	3	11,51	14800

Table 4 .

 4 Stoichiometry values calculated from equation (1).

	Compound	Calculated
		Stoichiometry
	SiC	1.1
	SiC2	2.5
	SiC4	3.7
	SiC8	8.7

Acknowledgements

This work was supported by the Picardie Regional Council (France), the French Ministry of Environment and the FP6-Nanosafe2 European project.