Florence Nguyen
email: florence_thi_nguyen@hotmail.com

Keywords: Framing effects, Risk communication, Discrete choice experiment Classification JEL: C12, I19, D83

Background How to communicate uncertainty is a major concern in medicine and in health economics. We aimed at studying the framing effects of risk communication on stated preferences in a discrete choice experiment (DCE) performed to elicit women's preferences for Hormone Replacement Therapy. Methods Two versions of the questionnaire were randomly administered to respondents. Multiple risks were expressed as natural frequencies using either a constant reference class (Design 1) or variable reference classes (Design 2). We first tested whether Design 1 would impose a lower cognitive burden than Design 2. We then examined whether the two designs resulted in different utility model estimates. Results Design 1 improved consistency (monotonicity and stability). However, rates of dominance or intransitive responses did not differ across designs. Design 1 decreased women's sensitivity to the risk of fractures and increased their sensitivity to the risk of breast cancer as compared to all other attributes. Discussion Framing effects of risk communication on stated preferences may be a major problem in the design of DCEs. More research is needed to determine whether our findings are replicable and to further investigate the normative question of how to improve risk communication in health-related decision-making.

Titre de l'article

Framing effects of risk communication in health-related decision making. Learning from a discrete choice experiment

Nom des auteurs:

Florence Nguyen 1 , Marie-Odile Carrère 1 , Nora Moumjid

Introduction

Thirty years ago, Tversky and Kahnemann stated that the psychological principles that govern the perception of decision problems produce predictable shifts of preference when the same problem is framed in different ways (Tversky and Kahnemann, 1981). They also specified that "the frame that a decision maker adopts is controlled partly by the formulation of the problem and partly by the norms, habits and personal characteristics of the decision maker". Thus, variations in these factors may result in different decisions on the same problem, a phenomenon which is referred to as the framing effect.

Since then, framing effects have drawn considerable attention in various academic fields such as psychology, economics, sociology and neurosciences, and have been tested in quite different areas of human decision making such as management, finance, environment, law and medicine. A considerable amount of theoretical and empirical literature has explored the impact of framing effects on decision under uncertainty, knowing that uncertainty is a major component of most decision problems. Framing effects, either extrinsic or intrinsic to individuals, challenge their ability to make accurate decisions when they are faced with a decision problem under uncertainty. Considering the formulation of the problem, framing effects raise the essential normative question of how to communicate uncertainty so that a well informed decision can be made.

In decision-making processes involving peoples' health, the framing effects of communication of uncertainty are particularly important to consider [START_REF] Politi | Communicating the uncertainty of harms and benefits of medical interventions[END_REF]. Indeed, different formulations of the same uncertainty, which are logically equivalent, can lead to different decisions regarding life or death. Depending first on the available evidence, which can be very poor (British Medical Journal Clinical Evidence, 2007), uncertainty may be communicated in a number of different ways. Using verbal methods leads to highly variable interpretation [START_REF] Mazur | Patients' interpretations of probability terms[END_REF][START_REF] Bogardus | Perils, pitfalls, and possibilities in talking about medical risk[END_REF][START_REF] Mazur | Patients' interpretations of verbal expressions of probability: implications for securing informed consent to medical interventions[END_REF]. Quantitative information on risk (probabilities or likelihood) using for example absolute risks, relative risks or frequencies, allows for more precision. However, the framing effects of communicating relative risks compared to absolute risks have been shown to affect decisions since using relative risks emphasizes the perception of both benefits and risks [START_REF] Edwards | Explaining risks: turning numerical data into meaningful pictures[END_REF][START_REF] Hux | Communicating the benefits of chronic preventive therapy: does the format of efficacy data determine patients' acceptance of treatment?[END_REF][START_REF] Forrow | Absolutely relative: how research results are summarized can affect treatment decisions[END_REF][START_REF] Bucher | Influence of method of reporting study results on decision of physicians to prescribe drugs to lower cholesterol concentration[END_REF].

Regarding numeracy, i.e. the ability to understand quantitative information, [START_REF] Gigerenzer | How to improve Bayesian reasoning without instruction: Frequency formats[END_REF] have pointed out that it could be improved if the information format was adapted to the cognitive process. They have demonstrated that human Bayesian inference is better performed when risks are presented as natural frequencies rather than as standard probabilities [START_REF] Gigerenzer | How to improve Bayesian reasoning without instruction: Frequency formats[END_REF]. Indeed, the proportion of correct responses strongly increases when natural frequencies are used instead of standard probabilities. Several experiments in medicine and law have confirmed these findings and the authors conclude that "statistics expressed as natural frequencies improve the statistical thinking of experts and non experts alike" [START_REF] Hoffrage | Medicine. Communicating statistical information[END_REF].

It is worth noting that these marked advances in risk communication guidance were based on experiments where respondents' answers could be compared with a right answer. However, in most choice situations, there is by definition no right or wrong answer, since choice is supposed to result from the individual's preferences. In that case, there is a lack of evidence about the best way to communicate risk information, which is all the more true when information is complex. For example, in many cases, multiple risks and benefits must be considered simultaneously. Risk information can then exceed the cognitive capacity of decision makers who may, in turn, use heuristics and other simplifying strategies [START_REF] Tversky | Judgment under uncertainty: heuristics and biases[END_REF] or behave according to ambiguity aversion [START_REF] Einhorn | Decision making under ambiguity[END_REF]. All these strategies may result in biased decisions.

If the question of how to communicate uncertainty is a major concern in medicine, it is also of major importance in health economics, where eliciting the preferences of economic agents who make healthrelated decisions under uncertainty is a key issue. However, the impact of uncertainty communication on stated preferences has received little attention. Regarding the contingent valuation method, we have identified only one published study in which willingness to pay for a longevity benefit has been found sensitive to the framing of the benefit, either as a gain in life expectancy or a reduction in annual mortality risk [START_REF] Morris | Using Life Expectancy to Communicate Benefits of Health-Care Programs in Contingent-Valuation Studies[END_REF]. Overall, the life expectancy format performs better than the risk reduction format regarding the validity of contingent valuation.

Considering the discrete choice experiment (DCE) method, we have also identified only one very recent study testing the effects of different frames of information about risk in colorectal cancer screening [START_REF] Howard | Does attribute framing in discrete choice experiments influence willingness to pay? Results from a discrete choice experiment in screening for colorectal cancer[END_REF]. Risk attributes were framed either as gains or losses (e.g. cancers found versus cancers missed). The authors conclude that framing of attributes significantly influences the estimation of willingness to pay and marginal rates of substitution between attributes.

However, no guidance on risk communication is given. Instead, the authors emphasize that we are at a very early stage of examining framing effects in health-related DCE and firmly encourage further research on this issue.

We aimed at studying the framing effects of risk communication in a health-related DCE. We intended not only to determine whether framing effects are at work, but also to test the impacts of different risk presentations on preference properties, in order to provide some guidance for risk communication in health-related decision making.

Methods

The DCE was carried out to analyse women's preferences for Hormone Replacement Therapy (HRT)

-a treatment used by 20 to 30% French post-menopausal women [START_REF] Allemand | Baisse de l'incidence des cancers du sein en 2005 et 2006 en France : un phénomène paradoxal[END_REF]. HRT alleviates menopausal symptoms such as climacteric troubles, and decreases the risk of fractures due to osteoporosis. However, it is also associated with negative effects, such as an increased risk of developing breast cancer. Besides, HRT may also entail a monetary cost for users, since some treatments are covered by the public health insurance system and others are not. Those characteristics may lead women to make trade-offs and influence their attitude towards treatment.

Choice experiment design

In order to establish which characteristics of HRT are important to women, we first searched the medical literature (WHI [START_REF] Rossouw | Risks and benefits of estrogen plus progestin in healthy postmenopausal women: principal results from the Women's Health Initiative randomized controlled trial[END_REF], MWS [START_REF] Beral | Breast cancer and hormone-replacement therapy in the million women study[END_REF] and E3N [START_REF] Fournier | Breast cancer risk in relation to different types of hormone replacement therapy in the E3N-EPIC cohort[END_REF] studies), then we organized three meetings with a panel of medical experts (three gynaecologists, 1 rheumatologist and 1 cardiologist). Finally, we organized a focus group interview with post-menopausal women, of whom some had already taken HRT and some had not.

Seven attributes were selected, six related to health and one to cost. In order to reduce respondents' cognitive burden, we decided to assign only two levels to each attribute (Table I). For each of the 6 health risks, the two levels corresponded to the situation of post-menopausal French women with or without treatment. More precisely, the levels of risk of climacteric troubles were derived from reports by [START_REF] Grady | Postmenopausal hormones-therapy for symptoms only[END_REF] and by the French Association for the Study of Menopause (AFEM). The levels of other health risks were derived from a report by the French Agency for the Safety of Health Products (AFSSAPS, 2004). The two cost levels were chosen as either 0, corresponding to no treatment, or the mean total cost of treatment in France whoever the payer is.

Table I

Hypothetical scenarios were created by combining attribute levels. There were 128 possible scenarios (2 7 ). We developed a level balanced, orthogonal main effect fractional factorial design of 8 scenarios [START_REF] Zwerina | A general method for constructing efficient choice designs[END_REF]. Applying the rules of minimal overlap and utility balance, we created eight choice sets by pairing each initial scenario with its exact opposite, i.e. the one which differed in all attribute levels.

In order to test for a framing effect of risk communication, two different versions of the survey instrument were developed, then randomly administered to respondents. In the two versions, the 6 health risks were expressed using natural frequencies, according to previously mentioned guidance [START_REF] Hoffrage | Medicine. Communicating statistical information[END_REF]. However, the two versions differed in the choice of reference classes. In the first version (referred to as « Design 1 »), natural frequencies were expressed using a constant reference class. We chose a 100 000 people reference class because (i) two of the 6 health risks involved cancer (breast and colorectal) and (ii) cancer incidence is usually expressed per 100 000 population (International Agency for Research on Cancer, 2008). The second version (referred to as « Design 2 ») slightly differed from the first one: risks were also expressed in natural frequencies but the reference class could vary from one attribute to another. Reference population sizes were still expressed as powers of ten but as small as possible, so that numbers of cases were still expressed as whole numbers. As a consequence, natural frequencies referred to 10, 1 000 or 10 000 people.

An example of a choice set as presented with both DCE designs is given in Table II.

Table II

Survey

The survey was conducted in December, 2006 and January, 2007. Participants randomly selected from the list of telephone subscribers in the Lyon area, France, were recruited by phone. Women who accepted to participate were sent a questionnaire and a prepaid return envelope, and were asked to return their questionnaire once it had been completed.

Of the 669 women who were sent a questionnaire, 470 returned it (70% response rate). After exclusion of 8 respondents with large amounts of missing data, 462 subjects were finally used in the analysis.

Subjects randomly received one of the two versions of the questionnaire: 229 completed the constant reference class survey instrument (Design 1) and 233 completed the variable reference class survey instrument (Design 2). Considering menopausal status, marital status, education, household income, professional or extraprofessional activities and complementary health insurance, Chi-square analyses did not detect any significant differences between respondents of the two groups.

Each respondent was presented the eight choice sets of the two scenarios and was asked which scenario she preferred in each choice set.

Model estimation

The statistical analysis in our DCE consisted in estimating a preference model by comparing two utility functions. Individual utility function is not directly observable and has to be estimated indirectly by analysing the respondents' choices.

McFadden's Random Utility Theory [START_REF] Mcfadden | Conditional logit analysis of qualitative choice behaviour[END_REF] states that the utility of individual i for scenario r consists of an observable systematic component (V ir ) and an unobservable random component (ε ir ).

,

The observable utility depends on the utility associated with each attribute. Assuming a linear and additive utility function, we obtain:

(2)

where X r is a vector of the levels of m HRT attributes in scenario r. Thus:

(3)

Due to the random component, the utility function is probabilistic. The statistical model is based on the probability that individual i chooses scenario A rather than scenario B if:

P Ai = Prob (U Ai > U Bi ) (4) Leading to P Ai ( ) (5)
Assuming a normal distribution of the random component, the calculation of the probability that individual i will choose scenario A is based on a probit specification:

(6)

where f(.) represents the density function and Ф(.) the standardised normal distribution. The scale parameter, σ, is usually set to 1. Nevertheless, when observations are obtained from two different survey instruments as was the case here, possible differences in scale parameters between the two data sets have to be taken into account (cf. infra).

As each woman was presented with eight choice sets, a random effects probit model was applied in order to account for the individual-specific variation, α i :

(7)

This error term eliminates the correlation between ε ir and explanatory variables, and improves the efficiency of the estimates.

Hypotheses and methods

We wanted to determine whether two slightly different ways of communicating risk information through DCE could result in differences in preference properties on the one hand and in preference structure on the other hand. Thus, we used a two-step approach as done by Maddala et al. when comparing DCE designs which differed in overlap of attribute levels [START_REF] Maddala | An experiment on simplifying conjoint analysis designs for measuring preferences[END_REF].

First, we tested the overall hypothesis that the constant reference class survey instrument (Design 1)

would impose a lower cognitive burden on respondents than the variable reference class survey instrument (Design 2). This hypothesis was suggested by Gigerenzer and Edwards who stated that "[…] the ultimate source of confusion is the reference class […]", specifying that "conditional probabilities such as sensitivity and specificity refer to different classes […], which makes their mental combination difficult" [START_REF] Gigerenzer | Simple tools for understanding risks: from innumeracy to insight[END_REF]. We first compared response consistency (i.e. monotonicity and stability), dominance and transitivity across the two survey groups. Second, we examined whether the two different risk presentations resulted in significantly different utility model estimates, which would confirm the hypothesis that risk communication yields framing effects.

Our hypotheses and the methods used to test them are summarized in Table III anddetailed hereafter.

Table III

As said before, the first group of hypotheses relates to whether the constant reference class survey instrument would impose a lower cognitive burden than the variable reference class survey instrument.

Hypothesis 1a: The constant reference class survey instrument results in more consistency

Besides the eight choice sets created to elicit women's preferences, another choice set was added in which scenario A dominated scenario B with better values on 3 attributes (climacteric risk, breast cancer risk, cost) without any difference on the other 4 attributes. Monotonicity was violated when women chose scenario B rather than scenario A.

In order to test stability, one of the eight initial choice sets was repeated at the end of the questionnaire. Stability was violated when women did not give the same answer to the two same questions.

Hypothesis 1b: The constant reference class survey instrument results in fewer dominant responses

Assuming that questionnaire complexity could be one of the reasons for dominant preferences [START_REF] Scott | Identifying and analysing dominant preferences in discrete choice experiments: An application in health care[END_REF], we tested for differences in this property between the two designs. Respondents were asked whether they had taken into account one or several HRT attributes when making their choices. We considered responses as dominant when women who claimed they had taken only one attribute into account had actually always chosen the scenario in which this attribute reached its best level.

Hypothesis 1c: The constant reference class survey instrument results in fewer intransitive responses

As was the case for dominance, we assumed that questionnaire complexity could yield intransitive responses. Transitivity was assessed by creating a choice set based on one of the eight initial choice sets, with scenario A remaining the same but the new scenario B being dominated by the initial one.

Thus, women choosing scenario A in the initial choice set were again expected to choose scenario A in the new choice set.

The second group of hypotheses relates to whether the two different risk presentations resulted in significantly different utility model estimates. We decided to test these hypotheses among women whom responses did not violate consistency, non-dominance and transitivity. Indeed, one may reasonably assume that inconsistent responses may result from limitation in cognitive capacities, then that they may not reflect respondents' preferences. The question is more complex regarding dominance and intransitivity, which can reflect either limitation in cognitive capacities or real preferences. However, we excluded the corresponding answers because, even when they do reflect preferences, they cannot be modelled using a utility function since this requires both non dominance and transitivity.

Hypothesis 2a: The overall vector of choice parameters differs across the two survey instruments

Two separate random parameter probit models were estimated for the two survey instruments. Using a likelihood ratio (LR) test we tested the hypothesis of equal overall vectors of parameters in the two designs, using either constant or variable reference classes. However, pooling the two datasets was problematical as the estimated parameters were confounded with the corresponding scale parameters (σ in Eq.6). That is why we adjusted for a possible difference in scales between the datasets using the Swait and Louvière procedure (Swait and Louvière, 1993). It consisted in setting the scale of the dataset in Design 1, σ 1 , to 1 and searching for the scale of the dataset in Design 2, σ 2 , which would maximise the log-likelihood function of the scaled-pooled model.

A first LR test compared the two separate models and the scaled-pooled model to test the hypothesis of equal overall vectors of parameters in the two datasets. Rejection of this hypothesis would indicate significant differences in preferences between the two survey designs. In case of non rejection of the hypothesis, a further likelihood ratio between the scaled-pooled and the simple pooled model would be required to test the hypothesis of equality between the scale parameters σ 1 and σ 2 , in the two datasets.

Hypothesis 2b: Some choice parameters vary depending on the survey instrument, while others do not

First, estimated parameters could have statistical significance in one of the two separate models and not in the other. Second, in case of difference in the scale parameters, significant coefficients of the two estimated models cannot be compared directly. So we compared marginal rates of substitution (MRS). Since MRS are expressed as the ratios of two coefficients (when significantly different from zero) they do not depend on scale parameters. MRS variances were estimated using the Delta method [START_REF] Greene | Econometric Analysis[END_REF]. Possible MRS differences between the two survey designs were tested using the Student t-test.

Results

Statistically significant differences in consistency were observed between the two survey groups (Table IV). More precisely, rates of non-monotonic and unstable preferences were both higher in the variable reference class survey group (Design 2) than in the constant one (Design 1). Proportions were respectively 6.9% (n=16) versus 2.1% (n=5) for non-monotonicity (p=0.0157, Chi-sq test), and 14.6% (n=34) versus 9.1% (n= 21) for instability (p=0.0720, Chi-sq test).

We observed no statistically significant difference in dominance between the two survey groups (Table IV): 10% (n= 24) in Design 1 and 12% (n=27) in Design 2 (p=0.700, Chi-sq test).

Table IV

Transitivity was studied in the 219 women who had chosen scenario A in the initial choice set. No statistical difference was observed between the two survey groups, since the same number of women made intransitive choices (1.4%, n=3).

As described above, women's preferences were then modelled among the 338 women who did not exhibit inconsistent, dominant or intransitive preferences, of whom 176 had completed the Design 1 questionnaire and 153 the Design 2 questionnaire.

Table V presents the results of separate probit models for each of the two experimental designs, i.e.

the estimated choice parameters in the utility function. Of note, risks had been previously recoded so that they were expressed in the same units in the two data groups. Most of the parameters were significant, except for the risk of fractures in Design 1. As expected, all significant parameters were negative, since attributes were expressed as either risks or costs.

Table V

By setting σ 1 to 1 and maximizing the log-likelihood function of the scaled pooled model, we estimated the scale parameter σ 2 to be 0.95.

Using a likelihood-ratio test, we compared each separate model with a scaled pooling of the two datasets (Table VI). The Swait-Louviere [START_REF] Swait | The Role of the Scale Parameter in the Estimation and Comparison of Multinomial Logit Models[END_REF]) likelihood ratio test statistic was LR= 2 [-3809.77-(-1995.02 + -1816.23)] = 2.96. Knowing that the critical value of the chi-square distribution is 14.06 at the 0.05 significance level on 11 degrees of freedom, the hypothesis that the overall vectors of attribute parameters were equal across the two data sets was not rejected.

To test whether the scale parameters were significantly different across data sets we performed a further likelihood ratio test comparing the scaled pooled and the simple pooled models (Table VI). In the simple pooled model, the relative scale parameter was no longer allowed to differ across data sets. The likelihood-ratio statistic for this test was LR= -2 [-3811.25-(-3809.77)] = 2.96. Knowing that the critical value at the 0.05 significance level is 2.70, this result approaches statistical significance.

However, the hypothesis that the scale parameters do not differ across the two datasets could not be rejected.

Table VI

Whilst the above results indicate no significant difference in the overall vectors of choice parameters across designs, some choice parameters or MRS did vary, while others did not. First, the risk of fractures had no statistically significant weight in Design 1 whereas it had in Design 2 (Table V).

Then, MRS estimates were derived from the two separate models for all combinations of 2 of the 7 attributes, except when the risk of fractures was involved in Design 1 (Table VII). As a result, 15 MRS, could be compared across the two models referring to the 6 remaining attributes.

Five MRS were found significantly different. Hence, preference structures resulting from the two designs were different and these differences were not induced by possible scaling effects. All 5 MRS involved the risk of breast cancer. It seems that Design 1 made women even more sensitive to this risk than to the other attributes, either other health risks or cost.

To achieve a given reduction in the risk of breast cancer, women were willing to accept an increase in all other health risks, and this increase was higher when preferences were elicited using Design 1 rather than Design 2: 1.63 higher for climacteric troubles (10.063/6.149), 2.66 for colorectal cancer (0.568/0.213), 1.81 for thromboembolism (0.886/0.488) and 2.06 for cardiac risk (1.857/0.901).

As far as cost was concerned, women were willing to pay 1.72 times more for a given breast cancer risk reduction when presented with Design 1 rather than Design 2 (0.398/0.231).

While risk communication impacted women's preferences through the risk of fractures according to the significance of choice parameter and through the risk of breast cancer according to the related MRS, it did not seem to have any influence on MRS between the other 5 attributes, i.e. risks of climacteric troubles, colorectal cancer, thromboembolism or cardiovascular disease, and cost.

Table VII

Discussion

Our study aimed at analysing the possible framing effects of risk communication in a health-related DCE. Respondents were randomly assigned to one of two questionnaires which only differed in the way how information on risk was provided.

We first hypothesized that expressing multiple risks using a constant reference class rather than variable reference classes would impose a lower cognitive burden on respondents. This hypothesis was partly confirmed. An important finding was that preference consistency significantly improved when risks were presented using a constant reference class. Indeed, the monotonicity test exhibited 2.1% of non-monotonic preferences when the constant reference class design was used, a proportion which increased to 6.9% with the other design. Moreover, according to the stability test, the constant reference class risk presentation generated 9.1% of unstable preferences, whereas this rate increased to 14.6% when variable reference classes were used.

The pattern of intransitive preferences was not statistically different across survey designs. Rates were very low (1.4%), which can be due to the transitivity test used which compared two choice sets only.

Considering the test of dominant preferences, the corresponding rates were about 10% of respondents and they did not differ across designs. As stated by [START_REF] Scott | Identifying and analysing dominant preferences in discrete choice experiments: An application in health care[END_REF], dominant preferences can have various explanations such as the complexity of the choice task and the use of heuristics in the choice process. But they can also reflect real strong preferences. It was beyond the scope of this study to analyze the reasons for dominant preferences. However, dominant preferences deserve specific analysis given that, as they result in non compensatory decision making, they should not be modeled using a multiattribute utility function.

Considering our hypothesis that the two survey instruments would result in different utility model estimates, it is worth noting that this hypothesis was not tested on the whole sample. Indeed, women whose responses violated consistency (i.e. monotonicity and stability), non-dominance and transitivity had to be excluded from the utility function modelling. First, one may reasonably assume that inconsistent responses may result from limitation in cognitive capacities, then that they may not accurately reflect respondents' preferences. Second, regarding dominance and intransitivity, and whatever the explanations given, corresponding preferences cannot be modelled using a utility function since this would require both non dominance and transitivity.

Our findings confirm that preferences structure depends on the presentation of risk. More precisely, the attribute level utility weights resulting from the two risk presentations did not exhibit significantly different scale parameters at the 0.05 level, which means that the amount of unexplained response variability was the same across the two designs. However, while the overall vectors of choice parameters were not significantly different, significant differences in some choice parameters and/or MRS were observed across designs. Two major changes occurred when risks were presented using a constant reference class instead of variable reference classes. On the one hand, women were no more sensitive to the risk of fractures. On the other hand, women's sensitivity to breast cancer risk increased. More precisely, all MRS involving breast cancer risk changed towards a significantly greater sensitivity to this risk. The front page of the questionnaire presented all possible effects of HRT, either positive such as a decreased risk of fractures, or negative such as an increased risk of breast cancer. According to Fischoff et al. (1978), judgments of risk and benefit are negatively correlated, i.e. the greater the perceived benefit, the lower the perceived risk and vice versa. As highlighted by [START_REF] Slovic | The affect heuristic[END_REF], "this negative relationship […] occurs even when the nature of the gains or benefits from an activity is distinct, and qualitatively different, from the nature of the risks". Our own findings suggest that, since the constant reference class design improved response consistency, the preferences elicited accordingly better conformed to the expected negative relationship. Indeed, the perception of an increase in breast cancer risk, a negative effect of HRT, increased while the perception of a decrease in the risk of fractures, a positive effect of HRT, decreased or even disappeared.

Let us now consider the increased sensitivity to breast cancer risk in more details. In their study of people's perception of the frequency of lethal events, [START_REF] Lichtenstein | Judged frequency of lethal events[END_REF] have shown that cancer, due to sensationalization, is one of the most overestimated causes of death, as opposed to "undramatic, quiet killers" such as stroke and heart disease. Indeed, breast cancer has been greatly sensationalized in France over the past three years, due to a national information campaign for breast cancer screening. As a consequence, since the constant reference class design improves response consistency, preferences elicited accordingly better conform to the expected strong sensitivity to breast cancer risk.

Considering MRS between health risks, women would accept a higher increase in all other health risks in return for a given reduction in breast cancer risk when preferences are elicited using constant reference than when using the other design. While this is obviously true for the risk of fractures to which women are no more sensitive, this is also the case for all other health risks, for which the corresponding MRS are multiplied by a factor ranging from 1.63 to 2.66.

As far as cost is concerned, women would be willing to pay 1.72 times more for a given breast cancer risk reduction when preferences are elicited using the constant reference class risk presentation rather than the other design. Importantly, this finding means that a slight change in risk communication may yield a major change in willingness to pay.

Our results demonstrate that the framing effects of risk communication on stated preferences may be a major problem in the design of DCEs. As mentioned, we have identified only one recent report of framing effects of risk communication in a DCE on colorectal cancer screening [START_REF] Howard | Does attribute framing in discrete choice experiments influence willingness to pay? Results from a discrete choice experiment in screening for colorectal cancer[END_REF]. Risk attributes were presented either as gains or losses but no guidance regarding the best way to proceed was provided. However, other framing effects have been demonstrated in DCE studies. Various levels of overlap of attributes have been compared [START_REF] Maddala | An experiment on simplifying conjoint analysis designs for measuring preferences[END_REF] and a possible ordering effect of the price attribute has been tested [START_REF] Kjaer | Ordering effect and price sensitivity in discrete choice experiments: need we worry?[END_REF].

Analyzing framing effects is not an easy task. But answering the essential normative question of how to overcome framing problems is even more difficult. In the presence of multiple risks, as was the case in our study, our findings are in favor of a presentation using constant reference parameters, given that the stated preferences are more consistent, thus confirming the suggestion made by others regarding risk communication in medicine [START_REF] Gigerenzer | Simple tools for understanding risks: from innumeracy to insight[END_REF]International Patient Decision Aid Standards Collaboration, 2008).

However, more research is needed to determine whether these findings are replicable and, more generally, to investigate the normative question of how to improve risk communication in healthrelated decision-making.

Tables Table I Attributes and levels used in the study

Attributes Levels

Risk of climacteric troubles

Table III. Hypotheses and associated methods

Hypotheses

  two survey instruments result in different utility model estimates (a) The overall vector of choice parameters differs across the two survey instruments (a) Estimation of 2 separate models for the 2 survey instruments, and LR tests of separate and pooled, models (simple and scaled) (b) Some choice parameters vary depending on the survey instrument, while others do not (b) Comparison of choice parameters and marginal rates of substitution between attributes in the two survey instrument groups

Table II Example of a choice set as presented with both DCE designs Design 1

 II

		a	Scenario A	Scenario B
	Risk of climacteric troubles	70 000 per 100 000	20 000 per 100 000
	Risk of fractures	600 per100 000 per year 400 per 100 000 per year
	Risk of colorectal cancer	60 per 100 000 per year	40 per 100 000 per year
	Risk of breast cancer	250 per 100 000 per year 350 per 100 000 per year
	Risk of thromboembolism	150 per 100 000 per year 350 per 100 000 per year
	Risk of cardiovascular disease 200 per 100 000 per year 150 per 100 000 per year
	Cost		0€ per year		250€ per year
	Design 2	a	Scenario A		Scenario B
	Risk of climacteric troubles	7 per 10		2 per 10
	Risk of fractures	6 per 1 000 per year	4 per 1 000 per year
	Risk of colorectal cancer	6 per 10 000 per year	4 per 10 000 per year
	Risk of breast cancer	25 per 10 000 per year	35 per 10 000 per year
	Risk of thromboembolism	15 per 10 000 per year	35 per 10 000 per year
	Risk of cardiovascular disease	20 per 10 000 per year	15 per 10 000 per year
	Cost		0 € per year	250€ per year

a Risks are presented in natural frequencies, using a constant reference class in Design 1 and variable reference classes in Design 2.

Table IV . Tests for consistency and dominance differences between the two survey groups

 IV Risks are presented in natural frequencies, using a constant reference class in Design 1 and variable reference classes in Design 2.

					Hypothesis 1(a)		Hypothesis 1(b)
				Monotonicity		Stability	Dominance
	Risk presentation		Incorrect	Correct	Incorrect	Correct	Yes	No
	Design n=229	1	a	0.02	0.98	0.09	0.91	0.10	0.90
	Design n=233	2	a	0.07	0.93	0.15	0.85	0.12	0.88
	Chi²			5.84	(p=0.0157)	3.24	(p=0.0720)	0.14	(p=0.700)

a

Table VII Differences in marginal rates of substitution (MRS) a between the two designs

 VII MRS of attribute x 1 compared to attribute x 2 is defined as the ratio of absolute compensating level variations with x 1 as numerator and x 2 as denominator.Risks are presented in natural frequencies, using a constant reference class in Design 1 and variable reference classes in Design 2. MRS involving the risk of fractures are not presented since the corresponding parameter is not significantly different from zero in Design 1.

						b
		Design 1	b	Design 2	b
	MRS					Prob (MRS 1 -MRS 2 >0)
	Troubles, fractures	--	c		0.818	--
	Troubles, colorectal cancer	17.726		28.931	0.117
	Troubles, breast cancer	10.063		6.149	0.017
	Troubles, thromboembolism	4.909		5.448	0.302
	Troubles, cardiac risk	9.065		11.384	0.138
	Troubles, cost	2.327		2.446	0.419
	Fractures, colorectal cancer	--	c		35.362	--
	Fractures, breast cancer	--	c		7.516	--
	Fractures, thromboembolism	--	c		6.659	--
	Fractures, cardiac risk	--	c		13.915	--
	Fractures, cost	--	c		2.990	--
	Colorectal cancer, breast cancer	0.568		0.213	0.016
	Colorectal cancer, thromboembolism	0.277		0.188	0.147
	Colorectal cancer, cardiac risk	0.511		0.394	0.267
	Colorectal cancer, cost	0.131		0.085	0.172
	Breast cancer, thromboembolism	0.488		0.886	0.001
	Breast cancer, cardiac risk	0.901		1.857	0.001
	Breast cancer, cost	0.231		0.398	0.028
	Thromboembolism, cardiac risk	1.847		2.090	0.219
	Thromboembolism, cost	0.474		0.449	0.405
	Cardiac risk, cost	0.257		0.215	0.200

a b c

Acknowledgements

The authors gratefully acknowledge financial support from the Fondation de France. We thank Marie-Dominique Reynaud for editorial assistance.


S Ait

Hammou Taleb

D Brown

J Dillet

P Guillemet

J Mainka

O Crosnier

C Douard

L Athouël

T Brousse

O Lottin

Direct Hybridization of Polymer Exchange Membrane Surface Fuel Cell with Small Aqueous Supercapacitors

Keywords: Fuel cell, supercapacitors, direct hybridization, PEMFC, stack, load transients. 1

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Introduction

PEMFC can produce electricity as long as they are fed with oxygen and hydrogen: that is the reason why their energy density can approach that of hydrogen storage systems in the case of the most powerful systems. However, their high power density may be significantly hampered by the size and weight of the ancillaries needed to control air and hydrogen flow rates and humidity [START_REF] Yan | Steady state and dynamic performance of proton exchange membrane fuel cells (PEMFCs) under various operating conditions and load changes[END_REF][START_REF] Kim | The effect of stoichiometry on dynamic behavior of a proton exchange membrane fuel cell (PEMFC) during load change[END_REF]. On top of that, fuel cell dynamics (i.e. their ability to respond to a sudden increase in power demand) may also be limited by the gas supply lines: compressor, flow controllers and/or humidifiers. Indeed, due to usual requirements in terms of electric efficiency and compactness, Gas Diffusion Layers (GDL) that are used to homogenize the gas flow to the reaction sites and conduct the electrons [3 ,4], as well as the flow field channels, can store only a very limited amount of reactants. Thus, an increase in current intensity and electrical power must be accompanied by an immediate increase of the hydrogen and air/oxygen flow rates, which can be difficult to achieve.

In order to overcome these difficulties, a variety of research has been carried out on the development of hybrid systems based on batteries or supercapacitors [START_REF] Azib | An Innovative Control Strategy of a Single Converter for Hybrid Fuel Cell/Supercapacitor Power Source[END_REF][START_REF] Thounthong | Analysis of Supercapacitor as Second Source Based on Fuel Cell Power Generation[END_REF]. Unlike PEMFC, supercapacitors (SCs) are able to supply high power (with reference to their weight) but cannot store an important amount of energy (Figure 1). In these regards, fuel cells and supercapacitors (also called electrochemical capacitors) can be considered as complementary. However, if hybridization with supercapacitors can improve the performance of fuel cell systems, questions remain about the best architectures and control strategies. The complexity and cost of the entire system must remain reasonable and hybridization must not be done at the expense of the fuel cell durability and reliability.

Currently, most of hybrid fuel cell/supercapacitor systems include power management units [START_REF] Azib | Système hybride à pile à combustible et supercondensateur : Structures, contrôlecommande et gestion d'énergie[END_REF] with two possible designs depending on whether one or two DC/DC converters are used:

• Two converters, one for the fuel cell and the other for the supercapacitor bank, make it possible to control independently the power provided by each source…

• … but only one DC/DC converter connected to the fuel cell can be enough to maintain the fuel cell voltage in a given range.

Nevertheless, power management units and DC/DC converters can be avoided by using direct (or passive) hybridization, i.e. by connecting directly the supercapacitors to the cells. In this case, there also exist two possible configurations:

• One SC can be connected to two cells in series [9], which is possible because commercial SCs with organic electrolytes can accept voltages up to 2.7 V.

• Each SC can be connected to an individual cell, which is the choice we have made. This makes it possible to use SCs with aqueous electrolytes since the cell voltage always remains below 1 V. However, in this case, the SC will be subjected to small potential variations, i.e. of the order of a few hundreth of mV.

In this work, we study the effect of passive hybridization to improve the system performance and reliability, more specifically during steep load variations. We show that during the short time needed by the gas supply lines to respond to an increase in power or current density, SC of about 1 F.cm -2 (with reference to the MEA flat area) are required to limit the system voltage drop to acceptable values.

2.

Experimental set-up

Fuel cell

All experimental results described in the following were obtained using a 98 mm× 20 mm = 19.6 cm² cell active area with eleven 1 mm×99 mm parallel channels of 0.4 mm in depth on both the anode and cathode side. Membrane-Electrode Assemblies (MEAs) were purchased from SolviCore GmbH & Co. KG under the commercial name H400E. We assumed a cathode Pt loading of about 0.4 mgPt.cm - 2 , the exact value being not given by the manufacturer. On both anode and cathode sides, the gas diffusion layers (GDL) coated with microporous layer (MPL) were 235 μm thick SGL 24BC (by SGL Carbon) compressed to 200 μm using Teflon gaskets. Both gases flew in the same (vertical) direction.

The cathode compartment was supplied with wet air (70% RH at FC operation temperature and a stoichiometry of 3). Unless otherwise stated, the fuel cell was operated with a dead-ended anode: i.e.

the anode outlet was closed except during 1 s every 60 s to flush the compartment. Hydrogen was not humidified and a pressure regulator kept its pressure to 1.3 bar. In section 4.2, we also present results

obtained with an open anode: in this case, the hydrogen stoichiometry was set to 1.2 and it was humidified to 70% RH (at FC operation temperature).

The FC temperature was kept to 60°C thanks to an external thermostated cooling circuit.

Each new MEA was subjected to a 2 hours conditioning stage consisting of 45 s current steps between open circuit, 0.4 A.cm -2 and 1 A.cm -2 .

Small aqueous supercapacitors

Basics characteristics of SCs

The two principle characteristics of a SC are its high frequency or series resistance R and its (equivalent) capacity C. As illustrated in Figure 2, the simplest SC equivalent circuit consists in setting these two components in series. The response time of the system is then 𝜏 = 𝑅 × 𝐶. four times lower than that of circuit 2 (in red). As can be seen, a higher resistance increases the SC characteristic time, that is to say the charge or discharge time. This clearly shows that in the case of hybridization with a FC, the performance of a SC does not only depend on its capacity but also on its high frequency (or series) resistance. Flat aqueous supercapacitors were also designed and manufactured in the framework of this project: indeed, low maximum voltages of about one volt per cell and a temperature limited to about 80°C allow to use alternative and safe materials such as activated carbon electrodes in PVDF binder (10%), and 5M LiNO3 as electrolyte instead of organic liquids. In order to lower the high frequency resistance as much as possible, the electrodes were set on gold plated nickel collectors. Details about manufacturing techniques and materials are out of the scope of this paper and will be published shortly. We associated SCs of about 20 F to 19.6 cm² fuel cells, i.e. 1 F.cm -2 MEA, which is relatively low compared to the hybrid systems usually encountered in the literature, but the performance remains satisfying due to a very good high frequency resistance of only 18 mΩ (compared to about 50 mΩ for equivalent commercial products, Figure 3). The 100 F commercial SC appearing in Figure 3 was used for comparison purpose -for the hybridization of a single cell-, as well for the hybridization of a small stack described in section 5.

Use of flat supercapacitors

One of the objectives of this project is to reduce the size of the system by using flat SCs that will be set close to the FC stack or even inserted between two adjacent cells inside the stack.

PEMFC behavior during steep load variations

During steep load variations such as Heaviside steps, gas flow rates cannot be adjusted rapidly enough to maintain proper hydrogen and air supplies to the cell because of the limited dynamics of the air compressor, air or hydrogen mass flow controllers or even gas humidifiers. That is the reason why when significant load variations are expected, gas stoichiometries are usually set to values higher due to unnecessarily high consumption of the ancillaries, and many of the system components are oversized, which impacts its cost and energy weight density. Figure 4 shows the cell voltage evolution during a 5 s current step from 0.25 to 0.95 A.cm -2 . Flow rate inputs were changed accordingly at t = 0 s and t = 5 s. During half a second, the voltage decreased because of the lack of oxygen (and possibly hydrogen) in the cathode and anode compartments because the mass flow controllers failed to adjust rapidly enough the flow rates. The voltage came back to its nominal value after about 1 s when the fuel cell was fed again with the right amount of gas (i.e. when the mass flow controllers delivered the appropriate flow rate). To validate this explanation of the initial FC voltage drop, the second graph in Figure 4 shows a comparison with the voltage evolution of an overfed fuel cell (i.e. the air and hydrogen flow rates were adjusted to a current density of 0.95 A.cm -2 before the 5 s increase of the current intensity). As shown in Figure 4, right at the beginning of the step, the voltage is identical (or very close) in both cases and the voltage of the underfed fuel cell (full line in Figure 4) dropped further only after 0.05 s, while that of theinitially-overfed cell remained constant during the whole sequence (dashed line in Figure 4).

These results show that our FC has a "survival time" of about 0.05 s to a Heaviside step current variation from 0.25 to 0.95 A.cm -2 , which corresponds to the time needed to deplete significantly O2 and/or H2 in the cathode and/or anode compartments. This "survival time" is much lower than the response time needed by the mass flow controllers (and gas lines) to provide the FC with the right amount of reactants. The objective of FC/SC hybridization is thus to allow the system to properly operate during these 0.5 s, the fuel cell remaining the main electric source the rest of the time.

As shown in the next section, a SC of only 20 F and 18 mΩ series resistance connected to our 19.6 cm² cell is able to release about 60% of its stored energy during 0.5 s, which considerably limits the fuel cell voltage drop.

Direct hybridization of a single PEMFC with a supercapacitor

Comparison between flat and commercial supercapacitors and impact of fuel cell cathode operating conditions

Figure 5 shows the FC voltage as well as FC and SC current time-evolutions during a total current (i.e. the sum of the SC and FC currents) step of 5 s from 0.25 A.cm -2 to 0.95 A.cm -2 . The 19.6 cm² fuel cell was connected in parallel either with the flat aqueous (20 F -18 mΩ, blue curves)

or a commercial SC (20 F -50 mΩ, orange curves). The data recorded with the FC alone appears in grey.

As expected, hybridization limits the voltage drop and the results were better with the flat 20 F -18 mΩ SC than with the commercial 20 F -50 mΩ SC: this is mostly due to the lower high frequency resistance of the flat SC ( § 2.2), which makes it possible to deliver more current at the beginning of the sequence. This better performance of the SC with a lower resistance remained valid whatever the step duration, i.e. from 0.1 to 10 s in our case, as illustrated in Figure 6. Figure 7 shows with more detail the effect of air stoichiometry on the maximum voltage drop during a current step of more than 1 s (which is the time needed to reach the lowest voltage value). This confirms that the system performance remains much more satisfying with the flat 20 F (18 mΩ) SC than with the commercial 20 F (50 mΩ) SC because of its lower high frequency resistance. Using the 100 F (6 mΩ) commercial SC significantly improved the performance of the hybrid system only for air stoichiometries lower than 2. dead-end mode (dashed lines); the operating conditions are described in section 2.1 for both cases.

The current density and flow rate inputs were changed simultaneously at t = 0 s. In this section, we discuss the response time of the whole system (i.e. mass flow controller(s), humidifiers, pipes and fuel cell) depending on the anode operating conditions. The impact of hybridization is analyzed using commercial (organic) and our flat (aqueous) SC of same capacity (20 F) but different series resistance (50 mΩ and 18 mΩ).

The first graph of Figure 8 shows the voltage evolution of the fuel cell alone (i.e. without hybridization). With an open anode (full grey line) the voltage decreased because of the lack of oxygen and hydrogen in the cathode and anode compartments and started to increase again only after 1.2 s. It came back to its nominal value after about 2 s when the fuel cell was fed again with the proper amount of gas. With a dead-ended anode however (dashed grey line) the system dynamics were better, the full sequence (voltage drop and recovery) lasting only about 1 s, i.e. half as long as with an open anode. The voltage drop was also slightly less pronounced than with an open anode with a difference of 0.02 V.

Interestingly, this improvement in the system dynamics when shifting from open to dead-ended anode occurred with the same magnitude when the fuel was connected to either the 20 F commercial SC or to our lab-designed 20 F flat SC (middle and bottom graphs in Figure 8): in any case, the voltage recovery started after 0.35 s vs. 1.2 s initially, which is more than three times shorter. On top of that, the voltage drop was always less significant with a dead-ended anode, with a difference ranging from

0.1 V (flat Sc) to 0.18 V (commercial SC).
Discussing the details of the physical phenomena entailing such a change in behavior would be out of the scope of the paper. However, there are a few points that may be worth underlining:

• Although the cathode is (rightfully) considered as the limiting electrode, the transients of a PEM fuel cell are also governed by the anode. Indeed, the hydrogen stoichiometry being usually much lower than that of air, any increase in the current intensity will result in significant hydrogen starvation in the anode compartment.

• Such hydrogen starvation events must be considered with care because they can impact the cell durability and reliability. Indeed, many works showed that hydrogen starvation resulted in dramatic local (or even global) anode and cathode potentials surges, along with severe damages to the cathode materials [10 -15].

• In our case, operating the fuel cell with a dead-ended anode always resulted in a better response to an increase in the current intensity than with an open anode, which is probably due to the combining effects of a higher hydrogen pressure (1.3 bar vs. atmospheric pressure with the open anode) and a shorter response time of the pressure regulator than that of the hydrogen mass flow controller.

• Nevertheless, it must be kept in mind that in dead-end mode the hydrogen content in the anode compartment may vary significantly, locally and with time, depending on the moment the last opening of the anode outlet occurred. In our case, the anode outlet was opened for 1 s every 60 s in order to flush entirely the anode compartment, thus preventing local hydrogen starvation. However, without a careful monitoring of the anode local potentials, we cannot affirm that such events did not occur.

• Finally, the three graphs in Figure 8 show that connecting directly a SC in parallel does not modify the dynamics of the FC/ancillary system but only limits or avoids excessive voltage drops following an increase in the current intensity.

Direct hybridization of a small stack

Direct hybridization allows connecting one SC per cell, which can be particularly interesting regarding the non-homogenous operating conditions along a stack, especially during transients. The mini-stack used for the present study was made of 3 cells (Figure 9). Cell #1 is close to the hydrogen inlet while cell #3 is close to the air inlet. Both gases flew in the same direction (i.e. in co-flow) within the cells, from the top to the bottom. Each cell was connected individually to a (100 F -6 mΩ)

commercial SC (see Figure 3) and in the example described below, the stack was subjected to the 5 s current step from 0.25 A.cm -2 to 0.95 A.cm -2 (Figure 4). As described above in the case of single cells, the air flow rate input was changed with the current density, at the beginning and at the end of the sequence. However, when operating a stack the air flow controller needed about 1 s to adapt to the new value. The hydrogen compartment was operated in dead-end mode (dry H2, PH2 = 1.3 bar, anode compartment bled every 60 s during 1 s). As shown in Figure 9 (bottom right), without SC hybridization the stack voltage decreased dramatically during the first second following the current step and the voltage of the individual cells evolved differently. Conversely, when the cells were connected to the SCs, the stack and cell voltage behave as if there was no gas shortage (bottom left in Figure 9). Note furthermore that SC hybridization results in identical or at least very similar voltage evolution of the individual cells.

Although the stack performance and architecture may be improved, the results in Figure 9 clearly

show that direct hybridization of each cell with a small SC can significantly improve the reliability and performance of a stack during transients.

Conclusion

The present work shows that direct hybridization of a PEMFC with small aqueous SC opens perspectives to more efficient, simpler and more reliable FC systems. Indeed, passive hybridization makes it possible to avoid additional electronic components such as inverter(s), not to oversize the mass flow controllers and/or air compressor and eventually to reduce the cost and weight of the system. Furthermore, connecting all cells of a stack individually to a SC may be a convenient way to homogenize their voltage evolution during transients. Such kind of hybrid architecture can be achieved using small SCs of low effective capacity but it is of primary importance to keep their high frequency resistance as low as possible to improve their response time. We also noticed that the system dynamics was better when the anode compartment was operated in dead-end mode (with a 1 s opening every 60 s) than with an open outlet. Using a dead-ended anode can lead to further system simplifications: a pressure regulator can be used instead of a mass flow controller for instance and hydrogen humidifier or recycling loop are not needed anymore.

Figure 1 :

 1 Figure 1 : Ragone plot of various energy devices and their charge times (diagonal strokes) [7].

Figure 2 :

 2 Figure 2: Simplest SC equivalent circuit (left) and the corresponding charge and discharge of a RC circuit (right) with different values of series resistance and identical capacity during 0.5 s current steps of ±1 A.

Figure 2

 2 Figure 2 shows the voltage vs. time charge and discharge curves of two SCs with different resistance values and identical capacities for a 0.5 s current step of ±1 A. The resistance of circuit 1 (in blue) is

Figure 3 :

 3 Figure 3: Commercial (organic) and flat aqueous SC. The latter were designed and assembled during the project. Values of R and C were identified starting from charging curve (time response) at I = 1 A between 0 V and 1 V with uncertainties of about 5 F, 1 mΩ for the 100 F commercial SC and 1 F, 2 mΩ for the two others.

Figure 4 :

 4 Figure 4: Fuel cell voltage during a current step of 5 s from 0.25 A.cm -2 to 0.95 A.cm -2 .

Figure 5 :

 5 Figure 5: System voltage as well as FC and SC currents vs. time during a 5 s load step from 0.25 A.cm -2 to 0.95 A.cm -2 . The capacitance and resistance set in parallel to the single cell stand for the SC equivalent circuit.

Figure 6 :

 6 Figure 6: Left, voltage drop vs. load step duration, air stoichiometry of 3 and right, the voltage drop vs. load step duration, air stoichiometry of 2, current intensity increased from 0.25 to 0.5 A.cm -2 . On the right graph, the maximum drop obtained with the FC alone corresponds to a FC voltage of about 0 V starting from an initial value of 0.7 V.

Figure 7 :

 7 Figure 7: Maximum voltage drop vs. air stoichiometry for an increase of the current intensity from 0.25 to 0.5 A.cm -2 .

4. 2 Impact of fuel cell anode operating conditions 5 . 6 .Figure 8 :

 2568 Voltage time-evolution during Fuel Cell operation with dead-ended or open anode, with and without hybridization using 20F SCs with different series resistances (50 mΩ with the commercial SC and 18 mΩ with the flat SC.

Figure 8

 8 Figure 8 shows the cell voltage evolution during the 5 s current step (still from 0.25 to 0.95 A.cm -2 ), with and without hybridization for different anode operating conditions: open anode (full lines) and

Figure 9 :

 9 Figure 9: Scheme of our 3 cells stack (top left) and picture (top right). Cell dynamics in a small stack with (bottom left) and without (bottom right) hybridization of each cell (with a 100 F commercial SC) during a 5s current step from 0.25 A.cm -2 to 0.95 A.cm -2 (Figure 4).

Acknowledgements:

The authors would like to thanks French DGA/ANR for financial support of SUPERCAPAC project within the framework of ASTRID program.

9.


Anne Piscitelli
email: anne.piscitelli@subatech.in2p3.fr

Verron Thomas

Le Meur Mathieu

Dia Malak

Montavon Gilles

Béchet Béatrice

Highly sensitive quantification of aquatic nanoparticles by LIBD

All natural aquatic systems contain nanoparticles which can be inorganic, organic or even include microorganisms. The quantification of these nanoparticles presents considerable difficulties as they are often present at very low concentrations and with a predominance of the size distribution over small diameters (< 100 nm). The methods traditionally used for their characterization are based on separation and/or fractionation (Ultrafiltration, Ultracentrifugation, Size-Exclusion Chromatography, Flow Field Fractionation…) but also on Static and Dynamic Light Scattering methods.

However, these techniques have important limitations: separation and fractionation interfere with the colloidal system and easily lead to changes in the colloidal population (coagulation, sorption and precipitation) while Light Scattering has relatively high detection limits for particles with an average diameter of less than about 100 nm.

Alongside these techniques, LIBD (originally invented to monitor ultrapure water in the semiconductor industry) was rapidly used and improved for the detection and characterization of synthetic or natural aquatic nano-colloids.

Despite certain drawbacks (partial destruction of nano-colloids, limitation to solids, etc.), LIBD has proved to be a reliable and powerful tool for the detection and characterization of aquatic nanoparticles under "extreme" conditions : it enables highly sensitive detection and characterization of nanoparticles with small diameters (from approx. 5 nm, up to 1000 nm) and very low concentrations (from approx. 1 ng/L, up to mg/L). In addition to sizing and concentration determination, it can also be used for a variety of other purposes, such as kinetics studies, breakthrough curves, colloidal tracings, etc. opening up a vast field of investigation to users. This technique uses a high-energy pulsed laser beam that is focused in a measuring cell to selectively generate a plasma when solid matter (the nano-colloid) interacts with high-intensity light (the laser beam). Absorption of photons by the nanoparticles during the laser pulse leads to the heating and expansion of the plasma, which can then be detected acoustically or optically via, respectively, the propagation of the shock wave in the medium or the emitted light.

For more informations about this study, you can go to see the poster: PA2-4 SOLUBILITY EVALUATION

  laser technical characteristic  = 532 nm, Emax = 10 mJ,  = 10ns, f = 1 to 20 Hz. Control,acquisition and synchronization (LabView) : Calibration Results Conclusion Calibration of the system is made from standard solutions constituted by nano-sphere of polystyrene which sizes vary between 20nm and 1m and for concentration going up to the ng/L. "S" curves are obtained by plotting Breakdown probability (H bd ) versus lasernm for U (new) solutions, the influence of solution aging is visible in the subsequent appearance of colloids Low concentration and size of colloids for U/Th (approx. 20nm -10ng/L) LIBD is used here to detect the presence (or absence) of colloids in standard Ti and Au solutions for SP-ICP-MS experiments Presence of Ti colloids (whereas it's normally in dissolved form), with an estimated diameter of 40 nm Absence of Au colloids 20 nm zoom C = 10 µg/L