Anne-Sophie Herbert
email: anne-sophie.herbert@edf.fr

Catherine Azzaro-Pantel
email: catherine.azzaropantel@ensiacet.fr

Denis Le Boulch
email: denis.le-boulch@edf.fr

A typology for world electricity mix: Application for inventories in Consequential LCA (CLCA)

Keywords: Electricity production mix, Life Cycle Assessment, Consequential Life Cycle Inventory, Greenhouse gas emissions, Energy transition ALCA, Attributional Life Cycle Assessment, CLCA, Consequential Life Cycle Assessment, CLCI, Consequential Life Cycle Inventory, FU, Functional Unit, GHG, Greenhouse gas, GR, Group, GWP, Global Warming Potential, LCA, Life Cycle Assessment, LCIA, Life Cycle Impact Assessment

Over the past two decades, the integration of environmental concerns into decision making has been gaining prominence both at national and global levels. Sustainable development now factors into policy design as well as industrial technological choices. For this purpose, Life Cycle Assessment (LCA) -which evaluates environmental impacts of products, processes and services through their complete life cycle -is considered a crucial tool to support the integration of environmental sustainability into decision making. In particular, Consequential LCA (CLCA) has emerged as an approach to assess consequences of change, considering both direct and indirect impacts of changes.

Currently, no long-term datasets of Consequential Life Cycle Inventories (CLCI) are available, particularly in the case of electricity production mixes. A first and fundamental step to begin filling this gap is to make available data on national level greenhouse gas emissions from electricity and create a typology of electricity production mixes to support policy making. The proposed typology is based on the analysis of the composition of electricity production mixes of 91 countries producing more than 10 TWh in 2012, on the one hand, and of their calculated greenhouse gas (GHG) emissions (in gCO 2 eq/kWh) from LCA using IPCC 2013 data, on the other hand. All types of primary energy resources are considered, and some are grouped according to similarities in their emissions intensities. Using graphical observations of these two characteristics and a boundary definition, we create a 4-group typology for GHG emissions per kWh, i.e., very low (0-37 gCO 2 eq/kWh), low (37-300 gCO 2 eq/kWh), mean (300-600 gCO 2 eq/kWh) and high (>600 gCO 2 eq/kWh). The typology is based on the general characteristics of the electric power generation fleet, corresponding respectively to power systems heavy on hydraulic and/or nuclear power with the remainder of the fleet dominated by renewables; hydraulic and/or nuclear power combined with a diversified mix; gas with a diversified mix; coal, oil and predominantly fossils. This typology describes the general tendencies of the electricity mix and, over time, it can help point to ways in which countries can transition between groups. Further steps should be devoted to the development of indicators taking into account grid interconnection, energy sector resilience in the quest for a mix optimum.

1.

Introduction

The growing concern regarding climate change from greenhouse gas (GHG) emissions, 60% of which are generated by the energy sector (OECD/IEA, 2014), is receiving a lot of attention. More than ever, the strong relation between the development of the energy sector and our planet's environment and climate requires a fuller understanding of the relations between energy and environmental and climate policies. Recent world events, such as the Conference Of Parties 21 in Paris, brought lots of expectations of institutional and governmental agreements [START_REF] Hopwood | The world waits for COP to deliver[END_REF]. Decisions have then been made by all world countries concerning actions about climate change, especially those related to energy production (United Nations, 2016), and countries have pledged commitment to achieve their energy transition. An energy transition is viewed here as a fundamental structural change in the energy sector of a certain country. Several items can be highlighted such as the increasing contribution of renewable energies and the promotion of energy efficiency. Those transitions could thus take different pathways [START_REF] Geels | Typology of sociotechnical transition pathways[END_REF] and should help to change paradigm from emitting energy production mixes to more virtuous ones. Careful attention needs to be paid to the specific area of electricity production in energy transition. In fact, electricity production worldwide is diverse and complex, and specific literature has been reported about this concern in different countries, such as Germany or France [START_REF] Strunz | The German energy transition as a regime shift[END_REF]Verbong andGeels, 2007, 2010;[START_REF] Percebois | Rapport Energie 2050[END_REF][START_REF] Alazard-Toux | Scénarios de l'ANCRE pour la transition énergétique Rapport 2013[END_REF]. This concept of diversity in the energy portfolio as applied to electricity generation is attractive for diverse reasons: having a range of energy options increases grid stability, reduces consumers exposure to price spikes in any energy source, and creates the choosing policy options for energy and environmental and climate policies. In that context, electricity production has to be seen not as juxtaposed production means, but as a single mix for each country (or area) which revolves around static drivers [START_REF] Herbert | Key drivers of a common dynamic vision of electricity production mix using IPCC 2007 GWP 100a indicator[END_REF]. This transition towards decarbonized energy systems involves mix disruptions that can occur through major changes (for example energy and environmental policies, new types of power plants).

Several methods and tools are available to assess environmental impacts and can help for decision support. [START_REF] Finnveden | Environmental systems analysis tools -an overview[END_REF] listed an overview of those numerous tools, such as Ecological Footprint (EF), Environmental Impact Assessment (EIA), Material Flow Analysis (MFA), Life Cycle Assessment (LCA). It must be yet emphasized that the choice of the tool largely depends on the decision level. For example, at policy level, methods such as EIA are particularly adequate for assessing environmental impacts of projects and use of natural resources. LCA is viewed as a mature, systemsoriented and analytical tool assessing potential impacts of products or services using a life cycle perspective. This study is focused on the impacts of electricity generation and, in that context, the LCA methodology is particularly relevant [START_REF] Finnveden | Environmental systems analysis tools -an overview[END_REF]. In LCA, the assessment of environment impacts is normalized by ISO 14040-44 (Comité Technique, 2006a;Comité technique, 2006b) following a four-step iterative process: goal and scope definition, Life Cycle Inventory (LCI), impact assessment (LCIA) and interpretation. By definition, LCA is a multicriteria-oriented analysis and gives the opportunity to assess a wide range of indicators, such as Global Warming Potential (GWP), acidification, eutrophication Fig. 1 -Boundaries of Attributional and Consequential LCA. Rectangles in light and dark grey represent the system boundaries respectively in Attributional and Consequential LCA. The boundaries of system expansion are represented by the white arrow. The Functional Unit (FU) is represented by white triangles. FU is defined according to ISO 14040 standards (Comité Technique, 2006a) as the quantified performance of a product system for use as a reference unit. In Attributional LCA, FU represents a portion of inventory and only direct changes, while either direct or indirect consequences due to FU are taken into account in Consequential LCA.

and land-use [START_REF] Hauschild | Identifying best existing practice for characterization modeling in life cycle impact assessment[END_REF]. A large amount of LCA works have been conducted concerning electricity production [START_REF] Curran | Report on the International Workshop on Electricity Data for Life Cycle Inventories[END_REF][START_REF] Curran | The international workshop on electricity data for life cycle inventories[END_REF][START_REF] Davidsson | A review of life cycle assessments on wind energy systems[END_REF][START_REF] Gagnon | Life-cycle assessment of electricity generation options: The status of research in year 2001[END_REF][START_REF] Hawkes | Estimating marginal CO2 emissions rates for national electricity systems[END_REF][START_REF] Mallia | Life cycle greenhouse gas emissions of electricity generation in the province of Ontario, Canada[END_REF][START_REF] May | Application of data quality assessment methods to an LCA of electricity generation[END_REF]Treyer andBauer, 2013, 2014;[START_REF] Turconi | Life cycle assessment (LCA) of electricity generation technologies: Overview, comparability and limitations[END_REF].

Furthermore, LCA is in constant methodological development. Over the past two decades, Consequential LCA (CLCA) [START_REF] Weidema | Market aspects in product life cycle inventory methodology[END_REF][START_REF] Earles | Consequential life cycle assessment: a review[END_REF][START_REF] Guiton | Analyse du Cycle de Vie Conséquentielle?: Identification des conditions de mise en oeuvre et des bonnes pratiques[END_REF] has emerged as a modelling approach to assess consequences of changes [START_REF] Ekvall | Limitations of Consequential LCA[END_REF]. CLCA as a macro-systemic approach differs from classical Attributional LCA (ALCA) which is generally applied at a micro-system level [START_REF] Guiton | Analyse du Cycle de Vie Conséquentielle?: Identification des conditions de mise en oeuvre et des bonnes pratiques[END_REF]. The main differences in both LCA approaches refer to goal and scope as well as inventory steps. [START_REF] Weidema | Marginal production technologies for life cycle inventories[END_REF] showed that Consequential modelling implies changes from Attributional in unitary processes interactions to expand the system, so that both direct and indirect impacts have to be considered, which is not the case in ALCA. CLCA has been discussed since the nineties [START_REF] Weidema | Market aspects in product life cycle inventory methodology[END_REF][START_REF] Weidema | Marginal production technologies for life cycle inventories[END_REF] but its development is more recent. Indeed, [START_REF] Zamagni | Lights and shadows in consequential LCA[END_REF] emphasized the evolution of this method with an increasing number of publications devoted to "Consequential" and "LCA" as keywords, highlighting the growing interest of LCA practitioners for assessing the consequences of change in addition to product Attributional assessments.

Inventory in CLCA yet requires specific inventory data, especially to assess indirect changes [START_REF] Ekvall | Limitations of Consequential LCA[END_REF][START_REF] Weidema | Marginal production technologies for life cycle inventories[END_REF]. The quality of inventory data is crucial for a reliable assessment: variability in Consequential Life Cycle Inventory (CLCI) may lead to uncertain LCIA results and may hamper the development of CLCA. Several methodologies using economic models to evaluate those data are available in the reported literature [START_REF] Weidema | Marginal production technologies for life cycle inventories[END_REF]. As CLCA includes all processes (direct and indirect) affected by change, some processes or energy fluxes remain in most studies [START_REF] Guiton | Analyse du Cycle de Vie Conséquentielle?: Identification des conditions de mise en oeuvre et des bonnes pratiques[END_REF][START_REF] Weidema | Guidelines for application of deepened and broadened LCA, Deliverable D18 of work package 5 of the CALCAS project[END_REF]. Fig. 1 illustrates the main differences between Attributional and Consequential assessment mainly affecting system boundaries and direct/indirect changes.

Electricity, as a major energy provider for processes [START_REF] Fernandez Astudillo | Exploring challenges and opportunities of life cycle management in the electricity sector[END_REF], is intrinsically often taken into account in system expansion with indirectly affected processes. But, in some cases, the lack of data concerning electricity makes practitioners exclude electricity change [START_REF] Ekvall | Attributional and consequential environmental assessment of the shift to lead-free solders (10 pp)[END_REF], or take too general data in databases [START_REF] Fernandez Astudillo | Exploring challenges and opportunities of life cycle management in the electricity sector[END_REF]. Only short-term country-level data have recently become available in the literature [START_REF] Amor | Implications of integrating electricity supply dynamics into life cycle assessment: A case study of renewable distributed generation[END_REF].

A consistent approach concerning electricity production for CLCA has not been established till now to our knowledge and the development of more generalized electricity production CLCI data represents a major challenge for CLCA application [START_REF] Zamagni | Lights and shadows in consequential LCA[END_REF][START_REF] Ekvall | Attributional and consequential environmental assessment of the shift to lead-free solders (10 pp)[END_REF]. If short-term country-level data start to be available in literature, reliable data are still lacking in a long-term perspective.

A first step to address this issue is to better understand electricity production mix worldwide. The aim of this work is to set a typology of electricity production mixes, based on potential greenhouse gas (GHG) emissions for electricity production and mix composition. Even if only GHG emissions are considered, the conceptual framework of LCA is used for several reasons: (i) LCA is particularly interesting as a systemoriented environmental assessment method; (ii) the typology that will be proposed could be further used for Consequential LCA which is the core of the work and (iii) could be finally extended to other criteria. Specific attention will thus be given to the relation between GHG for electricity production and mix composition factors in order to determine a mix typology.

2.

Material and methods

Scope definition

The first step of the proposed methodology is to determine an appropriated time scale for the study. The year 2012 is used a starting reference date to observe the effects of energy transition, following the growing concerns about GHG emissions and global warming (OECD/IEA, 2014;[START_REF] Den Elzen | Enhancing ambition in the major emitting countries[END_REF].

The typology has to be representative of most of mixes in the world. To avoid bias in the analysis, a well-established database and a set of representative countries must be taken into account. For this purpose, a same database for typology design is used. A freely accessible online world database that is TSP Database from The Shift Project (2015) has thus been selected. TSP data portal is an information platform that provides a free access to a wide range of global energy and climate statistics and combines data from IEA (OECD/IEA, 2015) and The World Bank (2015) in downloadable excel files.

The typology will have to be also representative of global mix dynamics. For this purpose, only countries with significant annual electricity production capacity will be considered. A preliminary screening of the potential of the electricity production of a more exhaustive list of countries shows that several production levels (5, 10, 20 TWh) can be highlighted. In that context, a 10 TWh level can be viewed as an average value for representing the minimal production level of the countries that will contribute to the typology. This finally leads to a set of 91 countries from every continent to be considered in the analysis.

Data collection and calculation of GHG emissions

The typology is based on two kinds of data, first, mix composition, expressed in percentage of the total production in 2012, and secondly GHG emissions of an energy amount of 1 kWh.

Mix composition

Using TSP Database (The Shift Project, 2015), the electricity generation data for the 91 countries satisfying a minimal production of 10 TWh used in this study are listed in Table 1. The primary resources and their related power plants taken into account in the typology are biomass and waste, coal, gas, geothermal, hydroelectric, hydroelectric pumped storage, nuclear, oil, solar/tide/wave and wind.

GHG emissions

GHG emissions are defined here as the potential greenhouse gas (GHG) emissions per kWh calculated using LCA methods. Well-established and average values for GHG emissions are required for the typology design. As shown by [START_REF] Hauschild | Identifying best existing practice for characterization modeling in life cycle impact assessment[END_REF], the best way to evaluate climate change is to use the IPCC (The Intergovernmental Panel on Climate Change) baseline model of 100-year model and radiative forcing based Table 2 -GHG emissions vs. type of primary resource, from SREEN report [START_REF] Moonmaw | IPCC Special Report on Renewable Energy Sources and Climate Change Mitigation[END_REF]. GHG emissions represent potential emissions in gCO 2 eq/kWh, from LCA results.

Type of primary resource GHG emissions in gCO 2 eq/kWh et al. (2015). From the available data, the computation of the 50 th percentile has been considered as a good compromise between all the technological specificities. The technologies represented in Table A.II.4 of the SREEN report [START_REF] Moonmaw | IPCC Special Report on Renewable Energy Sources and Climate Change Mitigation[END_REF] are arranged in the same manner as in the definition of the mix composition adopted in this study.

For example, biomass and waste are merged in the proposed terminology and considered separately in the SREEN report. So, in order to use single LCA data for that case, we calculate the mean between non-aggregated of SREEN global warming potentials, that is, the mean between global warming potential of biomass and of that of waste thus obtaining an aggregated value.

The LCA results that will be used in our calculations are presented in Table 2: Considering each country from i = 1 to 91 and using data from mix composition, the GHG emission for each primary resource m (for a total of 9), of each country is computed as follows:

GHG i,m = (Q m /Q Tot ) × GHG m for m = 1 to 9 (1)
GHG i,m : GHG emission of the primary resource for the country i in gCO 2 eq/kWh Q m : quantity of electricity produced by primary resource in TWh Q Tot : total production in TWh GHG m : GHG emission of the primary resource in gCO 2 eq/kWh.

The cumulative calculation for all primary resources then gives:

CF i = m (Q m /Q Tot × GHG m )
(2) m: primary resource CF i : GHG emissions for country i in gCO 2 eq/kWh.

This method is implemented for the 91 countries considered in the study. First, the GHG emissions are analysed to identify the occurrence of a change in the curve (for example plateau or increase) that may constitute the boundary of a potential group. Second, the mix composition histogram is analysed concurrently to establish if the observed change in GHG emissions is correlated with a mix change. In case of agreement, the boundaries of a group are identified as boundary candidates that will be further considered for typology development.

Typology development

Ranking and boundary definition

Building theoretical typology boundaries

The typology must represent every possible mix. As mentioned in Section 2.2.2, the first step of our work only gives discrete values corresponding to the GHG emissions of the studied countries. The objective is here to determine the theoretical mixes that can represent general compositions as shown in Fig. 2, so that the evolution of GHG emissions can be viewed as a continuous function of the mix composition. Even if numerous combinations of mixes can correspond to identical values of GHG emissions, the theoretical mix has to correspond to the really observed ones that define potential boundaries.

For this purpose, the following methodology is proposed to determine theoretical mixes. For the sake of illustration, an arbitrary example supports the methodology: the numerical values do not represent the results that have been actually observed.

For each potential boundary defined in Section 2.2.2, the GHG emissions and mix composition can be obtained. For example, let us consider two consecutive values of potential boundaries for a same group, i.e., 450 gCO 2 eq/kWh and 590 gCO 2 eq/kWh respectively. The first emission value (respectively the second one) corresponds to a mix composed of 90% gas (which contributes to 422.1 gCO 2 eq/kWh) and of a 10% contribution of diverse renewables (which contribute to 27.9 gCO 2 eq/kWh). The mix corresponding to 590 gCO 2 eq/kWh is also composed of 90% of gas (which contributes to 422.1 gCO 2 eq/kWh as abovementioned) and of Fig. 3 -GHG emissions for the selected countries, GHG emissions are ranked in increasing order, the changes in curves identified. Grey arrows correspond to intervals delimiting boundaries before their evaluation regarding mix composition and leading to six potential groups: black arrows correspond to the finally selected intervals for boundary determination. The term GR is the abbreviation of the final group.

10% of a really diverse mix of fossil and renewables (which contributes to 167.9 gCO 2 eq/kWh).

Similarities and differences between both mix compositions can thus be observed and the level of gCO 2 eq/kWh that each contributor (i.e. energy primary source) gives can thus be highlighted. It must be also emphasized that the determination of a continuous function between two bounds is also motivated by the identification of a bound expressed as an integer rounded to hundred (here 500 gCO 2 eq/kWh). In the example, both mixes have in common the gas contribution that will be kept for theoretical mix. The remaining energy sources then contribute to 77.9 gCO 2 eq/kWh, which is consistent with the observed mixes, that are largely composed of renewables, so that low emissions are involved, yet with some percents of fossil fuels (for example to back up demand change). It can be thus deduced that the theoretical boundary of 500 gCO 2 eq/kWh is reached with a mix composed of 90% of gas and 10% of a diverse mix, majorly composed of renewables, but also of some fossil fuels.

By definition, a lower bound of a group will be the upper bound of the previous one.

Typology group description

A qualitative analysis is performed for each country in order to identify the characteristics that can globally represent every mix belonging to a group. These general characteristics about mix composition must be applicable to every possible mix and will then define a typology group. A maximum number of three qualitative features is considered.

This qualitative assessment will be performed by histogram analysis (see Section 2.3.1) for each mix in order to detect which types of primary resources are significant. A primary resource is viewed as significant in mix composition for the typology if it represents at least 25% of the total mix composition. These observations will form the basis for the determination of major global characteristics of a group.

In order to avoid an exhaustive classification, i.e., limiting the number of features to 3, the primary resources that have similar values for GHG emissions and so a similar influence on mix are merged. This is typically the case for nuclear energy and hydropower, corresponding respectively to 4 and 16 gCO 2 eq/kWh.

Final typology

The typology involves two items for group definition as defined in Sections 2.3.2 and 2.3.3:

-GHG emission range, defined by continuous quantitative data of GHG emission results delimited by group boundaries, -Mix composition global characteristics, encompassing the identification of primary resources used to produce electricity and their quantitative contribution.

In order to represent all the cases taken into account in the typology, a spatial representation using a Geographic Information System (GIS) [START_REF] Heywood | An introduction to Geographical Information Systems[END_REF] that is well adapted for a multifaceted description of complex systems and their dynamics is used. For this purpose, QGIS 2.6.1 (QGIS, 0000), a widely used open source software tool for GIS representation and modelling is selected.

For each country, two kinds of information are visualized, i.e., first total electricity production for 2012 (see Section 2.2.1 The Shift Project, 2015), and second, typology group.

The same tool will be further used to represent the temporal evolution of the mix dynamics that will also be studied in a perspective of energy transition.

3.

Results and discussion It can be seen from the coloured patterns in Fig. 4 that the mix composition histograms exhibit 3 major composition types, i.e., hydroelectric, gas and coal. Three characteristics can thus be highlighted for group definition and description.

Typology: results and map projection

Representation of GHG emissions and mixes

Fig. 4 -Mix composition for the selected countries, GHG emissions are ranked in increasing order. Each grey border corresponds to potential groups represented by grey arrows in Fig. 3. Black brackets on left side represent the selected groups (black arrows in Fig. 3). The term GR is the abbreviation of the final group.

Group boundaries and mix composition

Three of the observed changes in the GHG emission curve can be found consistent with the typology, i.e., the existence of a potential boundary matches with a significant change in mix composition. The first occurrence corresponds to a GHG emission of 16 gCO 2 eq/kWh representing mixes that are largely composed of hydraulic and nuclear production (grey arrow in Fig. 3). A break into mix composition can be observed, affecting no major production, that is nuclear and/or hydropower, but the other modes that are composed largely of fossil fuels and renewables to a less extent. So the range [4-16] gCO 2 eq/kWh can be kept as a group (Gr 1). Let us consider now the range [100-230] gCO 2 eq/kWh, corresponding to the third grey arrow in Fig. 3. Even if a change in the curve can be observed at the upper bound, it does not correspond to a major change in mix composition. Indeed, hydropower plays an important role in mix. This explains why the two domains [30-100] and [100-230] gCO 2 eq/kWh can be merged together to form group Gr 2. Indeed, the mixes in that group have a major production mode composed mainly of nuclear and/or hydropower, and of other production modes composed of various primary resources, from renewables to fossils fuels. The range [317,570] gCO 2 eq/kWh, corresponding to the fourth grey arrow in Fig. 1, is well representative of mixes composed of gas as major production, and of various other production mixes, so that the range [317-570] can be considered as group Gr3. Finally, the break occurring at 800 gCO 2 eq/kWh, corresponding to the last grey arrow in Fig. 1, involves mixes with a majority of high emitting fossil fuel, i.e., coal and fuel so that it does seem necessary to distinguish between them. The identification of potential boundaries deduced from a graphical analysis is then followed by the analysis of theoretical mixes, so that the typology initiated from discrete values of carbon emissions of a set of countries could be extended to continuous values. Concerning the first group [4-16] (gCO 2 eq/kWh), the mixes are mainly composed of hydraulic and nuclear production, as observed in Fig. 4. The lower bound has been taken equal to zero, so that the potential of new technologies that will be less emitting than hydropower (which is at 4 gCO 2 eq/kWh) can be further considered. Clearly, a mix exclusively composed of either nuclear or hydraulic can be viewed as difficult to manage in a majority of cases, due to short-term demand management, for countries that do not have a strong grid connection with their neighbours or back-up generating capacity provided by others. So, to be consistent with this assumption, a bound with a theoretical mix composed of 75% of hydraulic or nuclear and 25% of a low emitting mean mix (composed of gas and renewables) has been selected, leading to an upper bound set at 37 gCO 2 eq/kWh. For the second group ranging from [30-230] gCO 2 eq/kWh from the graphical interpretation, the upper bound of the range has to represent mixes with high diversity in electricity generation, either from fossil or renewable sources, as it can be seen in Fig. 3. However, the value of carbon emissions of 230 gCO 2 eq/kWh seems to be low compared to the following break observed at 317 gCO 2 eq/kWh. A compromise solution involving a theoretical mix distributed between gas (50%) and a low emitting average mix (50%), including renewables, gas and only coal or fuel, leads to a bound of 300 gCO 2 eq/kWh. For the third group, the observed upper bound (570 gCO 2 eq/kWh) is determined by a theoretical mix mostly composed of gas and an average mix based on a variety of production means, (i.e., renewables, fossil fuels, hydropower etc.). A rounded value of 600 gCO 2 eq/kWh is finally adopted and is consistent with the graphical observation and the principle of "continuity "for the carbon emission evolution vs. mix composition. No upper bound has been fixed for Gr 4.

Final typology

From the results mentioned above, the final topology can be proposed as follows (Table 3).

As suggested in 3.1.1, it can be first highlighted that the groups differ from each other by three major production types: hydraulic and/or nuclear, gas and coal that have been merged in the typology into fossil fuels (oil being scarcely represented).

At first look and without considering the typology, it can be said that only major production conditions group affiliation. So the other production modes, i.e., those representing less than half of total production are also key components in the affiliation to a group or another. For instance, the difference between Gr 1 and 2 is due to the diversity of those other production modes. The same comment is valid for Gr 3 and 4, but in that case, the diversity benefit to GHG emissions that can be observed is lower.

Then two supergroups corresponding to groups, which have the same characteristics about their major productions, i.e. Gr1, 2 and Gr 3,4, respectively can be observable in the typology. This assumption could imply different efforts for instance to move from the emitting supergroup 3-4 to the supergroup 1-2 or to move through a supergroup in a dynamic vision of mixes involved in an energy transition.

The country-level data are available in Appendix A, Table A.

World representation of the typology

The typology can also be visualized through a map representation to give more information about countries and their groups, as shown in Fig. 5. First, it can be observed that the main producers are also those belonging to Gr 4 with the highest GHG emissions, i.e., China and India, United States, Russia and some European countries such as Spain, Germany or United Kingdom. An energy shifting towards a more virtuous group can be achieved through a drastic change in their electrical mix. The Japan case requires special attention as 2012 corresponds to one year after Fukushima events: Japan had to replace its nuclear production by other modes, such as coal and oil, which are highly emitting production means. But this change is temporary, since the reactivation of some nuclear power plants in 2016 (Nuclear Energy Institute, 0000). So Japan could be a good candidate to observe a quick evolution from a highest emitting group to lower ones.

Latin America is the lowest emitting continent, with five among seven countries being part of low emitting supergroups 1-2, with significant electricity production in Brazil.

Africa is mostly not represented in typology, due to too low electricity production under 10 TWh for 2012.

The European case can also give some guidelines to analyse the United States dynamics. As Europe, the United States is composed of states with strongly different mixes. Indeed, even if the energy transition in the United States has to be considered globally, the way that each of the 50 states can achieve such a transition by 2050 has also to be taken into account.

Understanding long-term dynamics of mixes

Uncertainty on group boundaries

The analysis on the determination of group boundaries is based on median results (Section 3.1.2). The objective of this section is to show how uncertainty may affect results. For this purpose, a sensitivity analysis is carried out using the same methodology as the one presented in Section 2.3.2, Fig. 5 -Typology and total production, per country. The circle size is proportional to total production in 2012 for countries, which produced more than 10 TWh as presented in Table 1. with the 25 th and 75 th percentiles as respective lower and upper bounds for GHG emission. As before, the data from IPCC [START_REF] Moonmaw | IPCC Special Report on Renewable Energy Sources and Climate Change Mitigation[END_REF] are used (see Table 4).

The obtained results are visualized in Fig. 6 and highlight the difference between the variation in major production and the one in other production mode. Transition zone 1 (corresponding to interval [11; 71]), with an amplitude of 60 gCO 2 eq/kWh, (belonging to supergroup 1) exhibits the same major production but presents some variations in other production mode. The mix diversity leads in most cases to more emitting results. For example even if a variation of 1% or 2% in gas contribution seems insignificant, it has a significant influence on the total GHG emissions of the mix. The amplitude of 60 gCO 2 eq/kWh may thus be viewed as the uncertainty embedded in the mass contribution of the mix, which may contribute significantly to total GHG emissions.

For transition zone 2 (corresponding to interval [235; 311]), major production changes from hydroelectric and/or nuclear to gas. So the results between those two groups are generally marked, and uncertainty appears to be low considering the relative values of GHG emissions. Moreover, it emphasizes the transition from supergroup 1-2 to supergroup 3-4.

Table 4 -Percentiles of GHG emissions vs. type of primary resources, from SREEN report [START_REF] Moonmaw | IPCC Special Report on Renewable Energy Sources and Climate Change Mitigation[END_REF]. GHG emissions represent the potential emissions in gCO 2 eq/kWh, from LCA results. Transition zone 3 (corresponding to interval [536; 693]) has an amplitude of 157 gCO 2 eq/kWh (corresponding to the interval [536; 693]). The observed uncertainty is significant and comes from the uncertainty in GHG emissions of the dominant production means in Gr 3 and 4. Indeed, when technologies such as gas, coal or fuel exhibit a high uncertainty in their GHG emissions, the uncertainty calculated for a global mix which is majorly composed of those kinds of technologies will be also high.

These transition zones could be useful to be observed for implementing energy policies and for leading to a more comprehensive set of recommendations for future research and policy. Indeed, they allow to identify a state where mixes of a group start to have characteristics from another one. Then, those transition zones could lead to a better understanding of the implications of transitions. This can already be observed in two examples, Denmark and France.

Denmark has recently achieved a major change in its mix composition by introducing renewables [START_REF] Mathiesen | Uncertainties related to the identification of the marginal energy technology in consequential life cycle assessments[END_REF], especially wind power, moving from Gr 4 to Gr 3 in the 2000s. This transition is a result of energy policies established since the seventies to move from a mix majorly based on coal or oil to a more virtuous one composed of renewables. The transition zones constitute good tool to identify how long the transition has taken to move permanently from Gr 4 to Gr 3. In the case of France, since the first oil crisis in 1973, the French government has decided to introduce nuclear power into the electricity mix to decrease the national energy dependency [START_REF] Percebois | Rapport Energie 2050[END_REF]. This leads to a change in the proposed typology from Gr 3 to Gr 2, and the analysis of mix composition in this transition zone could give us insight to better understand how this change has been conducted. Then, from historical mix dynamics, the typology can thus serve as a tool to identify how much time countries will take to achieve major changes and how such a transition can be characterized.

Influence of network

It must be emphasized that the interconnection of electricity networks between countries is not taken into account in the typology. However, the grid interconnection across countries can be viewed as a key feature in mix evolution. Indeed, some countries have a small production in their territory and benefit from their neighbours' production. This situation can offer the opportunity to some countries to have a more virtuous mix: this corresponds typically to some countries from Gr 1 or 2 with a lot of intermittent renewables, for which most of their demand is produced by their neighbours and transported through existing networks. This can be in some cases explained by a lack in resources (either natural or technological) so that a real dependency on network supply is observed.

The ratio of net imports (US Department of Energy, 2016) compared to total production (The Shift Project, 2015) expressed in percentage is represented in Fig. 7. A majority of countries use grid connection with imports and exports: a value of 10% either for exports or imports is considered to be significant.

The exchanges that have been considered are available in Appendix A, Table B.

As it can be seen in Fig. 7, the most virtuous countries from Gr 1, are most of the time net exporters. So, clearly, the quality of their mix is due to their own electricity generation. Exporting such power can be beneficial to the grid-connected countries. Besides, major importers, i.e. countries importing more than 10% of their energy belong mostly to Gr 3 and 4. So, even if a switch from a group to a more eco-friendly one for these countries is not impossible, it can be viewed as difficult to steer their energetic policy onto a markedly different energy path. A closer look at Group 2 shows that importers and exporters, (respectively major and small ones) are equally involved. So the network dependency may not be significant for these countries.

Of course we are aware that Fig. 7 only gives the general trend about network influence. A more thorough analysis could be yet carried out in order to include over time small power producers, for example African countries, which could be highly network-dependent. In addition, indicators and performance measures of the interdependence of a country from the grid must be properly defined and included in the typology. This highlights to consider the energy infrastructure with a systems approach.

Inertia in electricity power generation and resilience to energy change

As emphasized in Section 3.1.3, major power generation systems play a key role in the typology by defining the supergroup in which mixes are, i.e., less emitting with major production of hydraulic and/or nuclear and higher emitting with major production based on gas and fossil fuels.

If those elements are now considered in a dynamic perspective, three types of changes could affect the existing mixes. In an energy transition point of view, every country will try to move to a less emitting group. So a shifting towards a higher-emission group could be viewed as a temporary situation due to an increase in power demand that cannot be provided by low-carbon emitting technologies.

The first evolution type that can be considered involves changes within either "major" or "other production" groups, but these changes are not enough significant to create a definitive change of group. In these conditions, mixes evolve in the same group, or move to another one, but not permanently, thus corresponding to an incremental dynamics.

The second evolution type is relative to changes in "other production" modes so that Gr 4 (respectively 2) moves to Gr 3 (respectively to 1).

Finally, the evolution type that can be considered leads to drastic changes in major production, so that groups 4 or 3 can move to group 2 or 1, so that a breakthrough change occurs. 1. 10% is the limit between a high/low exporter or importer.

Of course, such evolutions are subject to system inertia. For example, a predominantly fossil-oriented mix e.g. from Gr 4 will have to diversify gradually its "other production" mode to move to another group, and changes will require some years to finally reach Gr 3.

Furthermore, due to country specificities, inertia may be different for each group, depending on the strategy of each country concerning major electricity power generation. This aspect can be considered as a key feature for mix evolution. This criterion of inertia has to be further taken into account in the typology.

It is also important to consider flexibility and robustness. The guarantee of good resistance to shocks, sometimes referred to as resilience, is another parameter that must be considered in energy policies. It can lead to particular choices, not only in terms of diversification, or supply structures and energy system technologies, but also in terms of R&D, so that, a wide range of technologies and skills is available.

These elements are particularly important to be examined in a typology definition in order to determine the horizon time, which will be necessary for a country to shift to another group. This suggests that an indicator based on resilience of a country's energy system can be useful to measure the effectiveness of adaptation policies addressing such issues as energy generation.

A step towards a better understanding of global energy transition

As demonstrated in a previous work [START_REF] Herbert | Key drivers of a common dynamic vision of electricity production mix using IPCC 2007 GWP 100a indicator[END_REF], only major evolutions could lead to a lasting mix change. This dynamics can be initiated by modifying one or a range of static drivers, such as existing power plants, resources, technological developments (such as energy storage, carbon capture and storage), energy policy and public opinion. Some of these drivers can be considered formally in a dynamic modelling of the energy system to determine energy policies. Other criteria such as public opinion are yet more subjective but their influence may be significant on the other factors. For example, public opinion will strongly influence energy policy and can make it change, as observed in Italy concerning nuclear power (OECD/IEA, 2009).

Moreover, all countries are unequal face to those elements: there are among them differences in the way the possible mixes are assessed and thus in how an ideal or more practically an optimum mix (if it does exist) can be reached in terms of kWh GHG emissions (either both direct and indirect) and mix composition. Preferences vary widely from country to country, for example between levels of development (Northern and Southern countries) or domestic resources. As aforementioned, an indicator measuring the capacity to implement energy adaptation projects and how successful the proposed implementation measures will be in increasing energy system resilience will be particularly useful [START_REF] Michaelowa | Use of indicators to improve communication on energy systems vulnerability, Resilience and adaptation to climate change[END_REF].

This approach could also help to better understand energy transition communication with a clearer vision of the potential evolution of energy systems.

The use of generalized data in the proposed typology could thus strongly benefit in decision-making processes, especially for the development of new climate policy and related research. Indeed, for illustration purposes, the electricity production mix could be envisioned not as a compilation of production means, which would evolve with the addition or withdrawal of production means as "pieces", but rather as a single malleable entity, which would evolve by stretching. The typology could help identify which kind of mix is observed and which transition is possible from a group to another one and when the "malleable entity" will move to another type of mix with different main characteristics. As highlighted in Section 3.2.1, the transition zones could give an easy way to identify potential energy transition dynamics.

Finally whereas the typology is helpful, the importance of country-level results should not be downplayed. LCA results that are commonly used are likely to vary significantly by country depending on factors such as the vintage of the generation fleet as well as that of the supply chain infrastructure. Yet, the typology gives results and key tendencies at a high level of granularity and so does not take into account those specific country-level data. The typology can be viewed as a tool to identify the process dynamics and key evolutions tendencies, about an energy transition, with no specific insight of the country considered. The analysis of the mix dynamic evolution of some selected countries could thus give more insight to establish the limit between the use of typology and the one of specific country-level data at a lower level of granularity.

Typology use in Consequential inventories

Literature review highlights that CLCA studies have been carried out with data on case by case basis [START_REF] Earles | Consequential life cycle assessment: a review[END_REF]. Such an approach is both time and expertise consuming. Moreover, available techniques from [START_REF] Weidema | Marginal production technologies for life cycle inventories[END_REF][START_REF] Weidema | Guidelines for application of deepened and broadened LCA, Deliverable D18 of work package 5 of the CALCAS project[END_REF] and Ecoinvent (Treyer andBauer, 2013, 2014) do not make consensus for LCA practitioners [START_REF] Fernandez Astudillo | Exploring challenges and opportunities of life cycle management in the electricity sector[END_REF], especially concerning uncertainty management. In most studies, CLCA involves general equilibrium and partial equilibrium models to estimate economy-wide indirect emissions, which are subject to many types of uncertainty. In that context, all consequences that follow are not generally well taken into account.

The literature review has shown that Consequential LCA suffers from a lack of knowledge of potential consequences from a policy examined, data gaps and large uncertainties, as well as from a lack of models that can capture the dynamic changes in land use patterns in different countries under specific economic drivers. Other indicators have been identified in the reported literature as relevant for electricity production impacts [START_REF] Hauschild | Identifying best existing practice for characterization modeling in life cycle impact assessment[END_REF]PEFCR, 2015), such as resource depletion, water footprint, acidification and eutrophication. The Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR5) [START_REF] Edenhofer | IPCC (Intergovernmental Panel on Climate Change): Climate change 2014: Mitigation of climate change. Working Group III contribution to the IPCC Fifth Assessment Report[END_REF] states, with very high confidence, that the observed changes in global climate are very likely due to the increase in anthropogenic greenhouse gas (GHG) concentrations. This is also emphasized in a recent SCORE LCA study [START_REF] Alexandre | Environmental impact indicators in LCA: state of the art, feedback and recommendations[END_REF] showing that according to practitioners responding to climate change involves to mitigate the emissions of greenhouse gases in the atmosphere.

The proposed typology can be considered as a first step to bridge this gap by considering typical groups of electricity power generation, in a worldwide and environmental vision. It must yet be strengthened by the development of indicators reflecting resilience to energy change and grid interconnection.

The development of the typology is interesting to represent the behaviour of some countries that share the same characteristics in a group. Then, data calculated from a mean mix representing each group could give easy to set data. With the selection of some countries in each group as a test bench, we could evaluate if global dynamics and data could be set by analysing the evolution of mixes through time in the typology, and otherwise what could be carried out to estimate them. This work will explore prospective data from public prospective studies available for each country selected. Conceptually, prospective studies estimate different future evolutions of current mixes [START_REF] Pottier | L'économie dans l'impasse climatique (Thèse d'Economie)[END_REF]. Furthermore, in Consequential LCA thinking, mixes strongly evolve through time leading to allocation problems [START_REF] Guiton | Analyse du Cycle de Vie Conséquentielle?: Identification des conditions de mise en oeuvre et des bonnes pratiques[END_REF]. Then, those results could constitute first methodological steps to establish Consequential LCI datasets.

Conclusions

Generalizing world electricity mixes production is a first step to evaluate the feasibility evaluation of Consequential specific inventory data. That is why a typology for mix assessment based on two criteria evaluated for 91 selected countries, i.e., mix composition (both qualitatively and quantitatively) and GHG emissions. Four groups have thus been established according to GHG emission range Gr1, very low (0-37 gCO 2 eq/kWh); Gr2 low (37-300 gCO 2 eq/kWh); Gr3 mean (300-600 gCO 2 eq/kWh) and Gr4 high (>600 gCO 2 eq/kWh) emitting countries. The GHG emissions have been associated with main characteristics of energy portfolio based on major and "other" modes for electricity power generation. Following this analysis, two supergroups {Gr 1; Gr 2} and {Gr 3; Gr 4} have been established based on major production mode. A map representation has shown the distribution of a set of countries (91) among the identified groups and subgroups and the possible path these countries are likely to carry out for achieving their energy transition. Moreover, this typology allows qualifying the change degree needed to achieve energy transition. Indeed, a transition is represented by a group change, involving either major or small productions, and the zone of uncertainty between groups could help identifying transition dynamics. The proposed typology can thus also be seen as an energy transition evaluation tool.

However, in order to use this typology in a Consequential LCI perspective, some criteria have to be developed in more depth. Firstly, national and cross-borders electrical grid networks are fundamental in electricity mix consumption, and this issue has to be considered in the further development of the typology by a so-called network indicator. Secondly, all the countries are not equal in terms of mix evolution inertia, so that an indicator based on energy sector resilience can be added. Lastly, linked to changing effort and inertia, tools to evaluate how mix can reach a so-called optimal group are required. These energy mix assessment criteria will be useful to evaluate Consequential inventory mix in order to reach a given target.

The proposed typology gives thus a macro-level vision of electricity production mixes that is more general than the country-level vision. Of course, to set reliable countrylevel dataset, additional factors have been completed to ensure reliable country-level datasets such as efficiency of country-level generation fleets, transmission and distribution losses, capacity factors, and higher-resolution generation detail (e.g. are natural gas plants peak or base-load for instance). The integration of grid modelling could constitute a further extension of this work. To support temporal dataset development, short-term (hourly) data from grid should be needed. The conciliation of short-term and long-term data will be probably required since in a long-term perspective. The influence that grid development and network may have on mix dynamics has to be investigated. The next step towards the evaluation of typology to design generalized Consequential inventory data concerning electricity production is to study the dynamic evolution of some countries selected from each group of the proposed typology using historical data.

Fig. 3

 3 Fig.3presents the GHG emissions ranked from the less to the most emitting country and Fig.4displays the mix composition histogram per country.First, the GHG emissions are analysed to identify the occurrence of a change in the curve (for example plateau or increase) that may constitute the boundary of a potential

Fig. 2 -

 2 Fig. 2 -Determination of theoretical mixes from typology boundaries-B inf and B sup correspond respectively to the lower and upper bounds, that have been identified by the 2-tuple (GHG emissions CF; mix composition MC). The different steps allow determining (CF s ; MC s ) corresponding to the theoretical bound B s .

Fig. 3

 3 Fig.3shows the GHG emissions of the different countries that exhibit a nonlinear behaviour.Six ranges can be observed in the GHG emission curve corresponding to a change in the curve: [16-30], [30-100], [100-230],, and [570-800] gCO 2 eq/kWh, leading to six potential groups.It can be seen from the coloured patterns in Fig.4that the mix composition histograms exhibit 3 major composition types, i.e., hydroelectric, gas and coal. Three characteristics can thus be highlighted for group definition and description.

Fig. 6 -

 6 Fig. 6 -GHG emissions for the selected countries, ranked in increasing order. The dotted lines correspond to the previous bounds for the intervals determined from the median value. The black arrows correspond to the finally selected intervals for boundary determination. The grey zones correspond to the intervals calculated using the 25 th and 75 th percentiles for GHG emissions: transition zone 1 from Gr 1 to 2, [11; 71] covering 6 countries, transition zone 2 from Gr 2 to 3, [235; 311] covering 1 country and transition zone 3 from Gr 3 to 4 [536; 693] covering 15 countries.

Fig. 7 -

 7 Fig.7-Comparison of imports and export by typology group. Countries considered are the ones selected for designing the typology presented in Table1. 10% is the limit between a high/low exporter or importer.

Table 1 -

 1 Selected countries for typology design. Each country is selected from its total production in 2012 superior to 10 TWh(The Shift Project, 2015).

	Africa		Asia	Middle East	Europe	America	Oceania
	Algeria	Azerbaijan	Kyrgyzstan	Bahrain	Austria	Iceland	Argentina	Australia
	Egypt	Bangladesh Lao	Iran	Belarus	Ireland	Brazil	New Zealand
	Ghana	India	Malaysia	Iraq	Belgium	Italy	Canada
	Morocco	Indonesia	Sri Lanka	Israel	Bulgaria	Netherlands	Chile
	Mozambique	Pakistan	Taiwan	Oman	Croatia	Norway	Colombia
	Nigeria	Philippines	Tajikistan	Qatar	Denmark	Poland	Cuba
	South Africa	Singapore	Thailand	Saudi Arabia	Estonia	Portugal	Ecuador
	Zambia	Japan	Uzbekistan	Jordan	Finland	Romania	Mexico
	Tunisia	Kazakhstan Viet Nam	Kuwait	France	Russia	Paraguay
		Hong Kong	China	Lebanon	Georgia	Serbia	Peru
				Lybia	Germany	Slovakia	Uruguay
				Syria	Greece	Slovenia	USA
				United Arab Emirates	Hungary	Spain	Venezuela
					Bosnia and	Sweden	Dominican
					Herzegovina		Republic
						Switzerland
					Czech Republic	Turkey
						UK
						Ukraine

Table 3 -

 3 World mix typology. Each group is characterized by boundaries defined by GHG emissions of kWh, major production, which represent predominant primary resources and other production mode that match total mix composition.

	Group	GHG	Bounds (gCO 2 eq/kWh)	Main characteristics Major production Other production
	1	Very low	0-37	Hydraulic and/or nuclear	Predominantly renewables
	2	Low	37-300	Hydraulic and/or nuclear	Diversification
	3	Average	300-600	Gas	Diversification
	4	High	>600	Coal, oil	Predominantly fossils

Acknowledgements

Funding: This work was supported by EDF R&D (SIRET 552 08131778287).

The authors would like to thank Miguel Lopez Botet Zulueta (EDF R&D OSIRIS), Sandrine Leclercq, Vincent Morisset, Yann Le Tinier (EDF R&D EPI), and Pr. Stephan Astier (INP Toulouse) for their fruitful review and suggestions about this work.

Appendix

See Tables A andB.


Rúbia Young
email: rubiayoungsun@gmail.com

Sun Zampiva

Claudir Gabriel Kaufmann Junior

Juliano Schorne Pinto

Priscila Chaves Panta

Annelise Kopp Alves
email: anelise.alves@ufrgs.br

Carlos Pérez Bergmann
email: bergmann@ufrgs.br

4 nanoparticles-A study of surface area and spinel inversion influence

Keywords:

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

Introduction

The magnesium ferrite (MgFe 2 O 4 ) spinel is a ceramic oxide with a face-centered cubic crystalline system. The general structure of spinels consists of divalent ions surrounded by oxygens forming tetrahedral sites, and trivalent ions surrounded by oxygen forming octahedral sites [START_REF] Ichiyanagi | Magnetic properties of Mg-ferrite nanoparticles[END_REF]. However, depending on the synthesis route or conditions, MgFe 2 O 4 does not strictly follow this structural pattern. Its structural configuration becomes a partially inverse spinel. The tetrahedral sites are partially filled by trivalent metal ions, and the octahedral sites are partially filled by divalent ions. Spinel position inversion directly affects the physicochemical properties of the ferrite [START_REF] Wang | High pressure Raman spectroscopy of ferrite MgFe2O4[END_REF][START_REF] Zhang | Temperature dependence of cation distribution and oxidation state in magnetic Mn-Fe ferrite nanocrystals[END_REF]. Since magnesium has a covalent character and oxygen has negligible mobility, iron presents the higher mobility in the magnesium ferrite structure [START_REF] Xavier | Effect of modofier ion the properties of MgFe2O4 and ZnFe2O4 pigments[END_REF][START_REF] Antão | Cation ordering in magnesioferrite, MgFe2O4, to 982 • C using in situ synchrotron X-ray powder diffraction[END_REF]. Synthetic MgFe 2 O 4 nanoparticles are produced by a variety of techniques such as coprecipitation [START_REF] Peng | Fe-based soft magnetic composites coated with NiZn ferrite prepared by a co-precipitation method[END_REF], sol-gel [START_REF] Hcini | Structural, dielectric and complex impedance properties of T0.6Co0.4Fe2O4 (TNi Mg) ferrite nanoparticles prepared by sol gel method[END_REF], hydrothermal route [START_REF] Kurian | A facile approach to the elucidation of magnetic parameters of CuFe2O4 nanoparticles synthesized by hydrothermal route[END_REF], microemulsion [START_REF] Junaid | Structural,spectral,dielectric and magneticproperties of Tb-Dy doped Li-Ni nano-ferrites synthesized via micro-emulsion route[END_REF], and solution combustion synthesis (SCS). SCS is a low cost, fast technique that produces high amounts of nanoparticles with high purity and structural homogeneity [START_REF] Pourgolmohammad | Effect of starting solution acidity on the characteristics of CoFe2O4 powders prepared by solution combustion synthesis[END_REF][START_REF] Zhang | Solution combustion synthesis of CaFe2O4 nanocrystal as a magnetically separable photocatalyst[END_REF][START_REF] Yeh | Effects of metal carbides as additives on combustion synthesis of Ti3(Al, Sn)C2 solid solutions[END_REF]. The SCS consists of precursors dissolved in aqueous medium (usually metallic nitrates) [START_REF] Patil | Chemistry of Combustion Synthesis: Properties and Applications Nanocrystalline Oxide Materials[END_REF] and a complexing agent (fuel) such as citric acid [START_REF] Huang | Synthesis of MgFe2O4 nanocrystallites under mild conditions[END_REF]; oxalic acid, glycine [START_REF] Xue | Facile synthesis of nanocristalline zinc ferrite via a self-propagating combustion method[END_REF], and urea [START_REF] Satyanarayana | Nanosized spinel NiFe2O4 A novel material for the detection of liquefied petroleoum gas in air[END_REF]. The solution is heated to 400 • C to initiate the fuel self-ignition and produce nanoparticles from the solution constituents. The selfsustaining ignition temperature is between 150 • C and 500 • C. With combustion, it can reach more than 1700 • C [START_REF] Dinka | In situ preparation of oxide based supported catalysts by solution combustion synthesis[END_REF]. A highly crystalline and deagglomerated solid product is formed at the end of the process [START_REF] Dubey | High-quality thin-multiwalled carbon nanotubes synthesized by Fe-Mo/MgO catalyst based on a solgel technique: synthesis, characterization, and field emission[END_REF].

Many studies on ferrites nanoparticles including MgFe 2 O 4 spinel have been recently described due to their distinct magnetic properties, which open up numerous application possibilities including cancer treatment [START_REF] Das | Impact of acidic catalyst to coat superparamagnetic magnesium ferrite nanoparticles with silica shell via sol-gel approach[END_REF][START_REF] Kobayashi | Cancer hyperthermia using magnetic nanoparticles[END_REF], biosensors [START_REF] Nonkumwong | Synthesis and cytotoxicity study of magnesium ferrite-gold core-shell nanoparticles[END_REF] and water purification [START_REF] Reddy | Spinel ferrite magnetic adsorbents: alternative future materials forwater purification?[END_REF]. MgFe 2 O 4 has also been used as a catalyst in the production of carbon structures such as carbon nanotubes (CNT). In CNT studies, spinels are usually based on metal catalysts (Ni, Co, Mo, Fe . . .) and are supported by oxides (MgO, Al 2 O 3 , SiO 2 and CaO) [START_REF] Gruneis | High quality double wall carbon nanotubes with a defined diameter distribution by chemical vapor deposition from alcohol[END_REF][START_REF] Wang | The large-scale production of carbon nanotubes in a nano-agglomerate fluidized-bed reactor[END_REF][START_REF] Ning | Improvement of Fe/MgO catalysts by calcination for the growth of single-and double-Walled carbon nanotubes[END_REF]. Magnesium ferrite spinel is an excellent catalyst for multiwall CNT (MWCNT) production. FeMg 2 O 4 when applied as a catalyst is often represented in the literature by Fe/MgO system [START_REF] Li | A scalable CVD synthesis of high-purity single-walled carbono nanotubes with porous MgO as support material[END_REF][START_REF] Zhang | Selective synthesis of single/Double/Multi-walled carbon nanotubes on mgO-Supported Fe catalyst[END_REF][START_REF] Wang | The large-scale production of carbon nanotubes in a nano-agglomerate fluidized-bed reactor[END_REF]. This is because metallic Fe 3+ ions are the active catalyst in the synthesis process, while MgO acts as the dispersant of Fe 3+ ions (support) [START_REF] Wang | The large-scale production of carbon nanotubes in a nano-agglomerate fluidized-bed reactor[END_REF].

The concentration of metallic ions (stoichiometry) and the catalyst powder morphology (crystallite size, agglomeration and surface area) directly influence the properties (density, quality and morphology) of the resulting carbon structures [START_REF] Tran | Carbon nanotubes synthesis by the ethylene chemical catalytic vapour deposition (CCVD) process on Fe, Co, and Fe-Co/Al2O3 sol-gel catalysts[END_REF][START_REF] Flahaut | Catalytic CVD synthesis of double and triple-walled carbon nanotubes by the control of the catalyst preparation[END_REF][START_REF] Ozden | 3D macroporous solids from chemically crosslinked carbon nanotubes[END_REF]. An interesting possibility is the production of 3D macroscopic carbon structures based on solid catalyst nanoparticles. The creation of such a nanoengineered 3D architecture remains one of the most important challenges in nanotechnology. The production of nanoengineered 3D carbon structures with controlled density and architecture is one of the most desirable steps for building next-generation carbon-based functional materials [START_REF] Iqbal | Highly flexible NiCo2O4/CNTs doped carbon nanofibers for CO2 adsorption and supercapacitor electrodes[END_REF]. These structures have potential application in areas such as supercapacitors [START_REF] Fan | Synthesis of ordered mesoporous TiO2-Carbon-CNTs nanocomposite and its efficient photoelectrocatalytic methanol oxidation performance[END_REF], catalytic electrodes, dry adhesion [START_REF] Serrano | Role of polymers in the design of 3D carbon nanotube-based scaffolds for biomedical applications[END_REF], artificial muscles [START_REF] Penga | An optimized process for in situ formation of multi-walled carbon nanotubes in templated pores of polymer-derived silicon oxycarbide[END_REF], gas absorbers [START_REF] Gui | Recyclable carbon nanotube sponges for oil absorption[END_REF] and environmental applications [START_REF] Hashim | Covalently bonded three-dimensional carbon nanotube solids via boron induced nanojunctions[END_REF][START_REF] Siddiqa | Silica decorated CNTs sponge for selective removal of toxic contaminants and oil spills from water[END_REF][START_REF] Ozden | Ballistic fracturing of carbon nanotubes[END_REF].

Aiming the obtention of 3D CNT structures, different techniques are reported in the literature, but basically, these involve CVD or CNT functionalization processes [START_REF] Ozden | 3D macroporous solids from chemically crosslinked carbon nanotubes[END_REF][START_REF] Gui | Recyclable carbon nanotube sponges for oil absorption[END_REF][START_REF] Hashim | Covalently bonded three-dimensional carbon nanotube solids via boron induced nanojunctions[END_REF][START_REF] Siddiqa | Silica decorated CNTs sponge for selective removal of toxic contaminants and oil spills from water[END_REF][START_REF] Ozden | Ballistic fracturing of carbon nanotubes[END_REF][START_REF] Ozden | Density variant carbon nanotube interconnected solids[END_REF][START_REF] Koizumi | Mechano-chemical stabilization of three-dimensional carbon nanotube aggregates[END_REF]. Most part of the studies based on CVD works with catalysts in solution, commonly ferrocene [START_REF] Gui | Recyclable carbon nanotube sponges for oil absorption[END_REF][START_REF] Hashim | Covalently bonded three-dimensional carbon nanotube solids via boron induced nanojunctions[END_REF][START_REF] Ozden | Density variant carbon nanotube interconnected solids[END_REF]. However, it is known that the CVD technique allows the use of different types of liquid and solid catalysts. There is no universal consensus regarding the nucleation and growth model of CNTs. However, the most accepted model so far has been proposed by Baker et al. [START_REF] Baker | Nafion membranes reinforced with magnetically controlled Fe3O4-MWCNTs for PEMFCs[END_REF]. This explains the growth of CNTs by the catalytic dissociation of a carbon source on the particles of a specific catalyst.

3D carbon macrostructures are formed with an elevated density of CNT [START_REF] Hashim | Covalently bonded three-dimensional carbon nanotube solids via boron induced nanojunctions[END_REF][START_REF] Siddiqa | Silica decorated CNTs sponge for selective removal of toxic contaminants and oil spills from water[END_REF]. The yield of CNT is strongly related to the catalyst surface area. Larger surface areas offer more metallic ions for CNT production [START_REF] Tran | Carbon nanotubes synthesis by the ethylene chemical catalytic vapour deposition (CCVD) process on Fe, Co, and Fe-Co/Al2O3 sol-gel catalysts[END_REF][START_REF] Flahaut | Catalytic CVD synthesis of double and triple-walled carbon nanotubes by the control of the catalyst preparation[END_REF]. In this work, the heat treatment temperature of MgFe 2 O 4 catalysts produced by SCS was varied to obtain the largest surface area. Factors such as crystallite size, degree of agglomeration and their correlation with van der Waals forces were studied. The influence of spinel inversion was also presented. The MWC-NTs were synthesized by chemical vapor deposition (CVD) using MgFe 2 O 4 as catalyst to produce 3D carbon macrostructures. C, the fuel (glycine previously dissolved in water) was added. The precursors/fuel stoichiometry was established via the propellants method [START_REF] Jain | A new approach to thermochemical calculations of condensed fuel-Oxidizer mixtures[END_REF]. The balanced equation ( 1) is presented below:

Materials and methods

Synthesis of

2Fe(NO 3 ) 3 • 9H 2 O + 1Mg(NO 2 ) 3 • 6H 2 O + 0, 20C 2 H 2 NO 2 (1)
The temperature and stirring were maintained for few minutes to promote perfect homogenization. The solution was inserted into an electric furnace (muffle) preheated to 400 • C until complete combustion (around 15 min). No grinding process was used.

Method

To study the catalyst influence, we established a standard methodology that varied only one, the catalyst. The samples were produced employing a CVD system. An alumina boat with MgFe 2 O 4 (0,100 g) was inserted in a furnace (Jung, model LT3-9013) with a silicon glass tubular chamber. The furnace was heated to 850 • C (50 • C/min) with continuous Ar flow (300 cm 3 /min) and then held for 45 min. At 850 • C, the H 2 valve was opened (30 cm 3 /min). After 15 min, the Ar valve was closed, ethylene valve was opened (30 cm 3 /min), and the H 2 valve was kept opened until the final of the plateau. When the system started to cool down, the ethylene valve was closed, the Ar valve was opened and the H 2 remained open until 500 • C. At this temperature, the H 2 and the ethylene were closed, and the Ar flow was kept opened until 100 • C. The alumina boat was removed from the furnace at 100 • C. The catalyst samples calcined at different temperatures were employed to produce the MWCNT. The resulting samples are described in Table 2.

Chemical and structural characterization

The crystallinity of the sample was evaluated by X-ray diffraction (XRD) using a PHILIPS diffractometer (model X'Pert MPD), 40 kV, 40 mA and Cu anode. The powders crystallite size were calculated by Scherrer's equation (eq. ( 1)):

D p = K ˇ1⁄2 COS (1)
In this equation, D represents the crystallite size, K is a constant whose value depends on the particle shape (we assume the particles to be spherical, so K=0.94), is the wavelength of the used electromagnetic radiation (1.5406 Å, value related to the characteristic main radiation emitted by copper), is the Bragg's angle and ␤ is the contribution of the crystallite size to the full width at half maximum (FWHM) of the diffraction peak correspondent in radians. The surface areas of the catalysts were obtained by nitrogen absorption method (BET-Quanta Chrome, Nova-1000 model). Nitrogen adsorption/desorption isotherms are used to investigate the Brunauer, Emmett, and Teller (BET) surface area and Barrett-Joyner-Halenda (BJH) pore size. These surface structure parameters were obtained using an automated surface area analyzer (Autosorb Quantachrome -NOVA 1000e-Gas Adsorption Surface Area and Pore Size Analyzer). BET was performed to the nanopowder catalyst and BET-BJH was carried out to the NTC sponge. The NTC bulk density and porosity percentage were analyzed by Archimedes method [START_REF] Heath | The Method of Archimedes[END_REF].

Results and discussion

MgFe 2 O 4 nanoparticles

The diffractogram (Fig. 1) shows characteristic crystalline peaks only for magnesium ferrite phase at all temperatures of heat treatment according to JCPDS 73-1960. This indicates the purity and homogeneity of the samples produced by SCS. As the temperature increases, the peaks becomes more intense and sharper, which is related to crystallinity and increasing crystallite size. The crystallite sizes calculated by the Scherrer's equation are shown in Fig. 2a. Even at the highest temperature of heat treatment, the crystallite size remained at the scale of dozen nanometers. This phenomenon is related to the covalent character of magnesium, which makes crystallization more difficult and leads to smaller crystallites [START_REF] Effenberg | Ternary Alloy Systems Phase Diagrams, Crystallographic and Thermodynamic Data[END_REF].

Interestingly, peak 111 appears for samples CT3, CT4 and CT5 treated above 900 • C, as better shown in the diffractogram ampli- fied box. This peak is directly related to the structure disorder degree [START_REF] Antão | Cation ordering in magnesioferrite, MgFe2O4, to 982 • C using in situ synchrotron X-ray powder diffraction[END_REF]. In the literature, the disorder that occurs at 957 • C is described as the change from magnesium ferrite ␣ to ␥ [START_REF] Bonnickson | High temperature heat contents of calcium and magnesium ferrites[END_REF]. As temperature increases, more energy is available for the elements to diffuse in the structure leading to order-disorder phase or to normal-inverse spinel transitions [START_REF] Antão | Cation ordering in magnesioferrite, MgFe2O4, to 982 • C using in situ synchrotron X-ray powder diffraction[END_REF][START_REF] Deraz | Effects of magnesia content on spinel magnesium ferrite formation[END_REF].

Surface area is one of the most important parameters in catalytic nanoparticles. Large surface areas provide more active ions for catalysis and increase reaction rates [START_REF] Ries | Surface area of catalysts[END_REF][START_REF] Rioux | High-Surface-Area catalyst design: synthesis, characterization, and reaction studies of platinum nanoparticles in mesoporous SBA-15 silica[END_REF]. Particles are composed of 2 or more single crystals (crystallites). The total surface area of a particulate sample and the size of the crystallites in the particles are usually inversely proportional. Smaller crystallites form smaller particles with higher surface area. However when the behavior is studied at the nanoscale (< 1 m), both can grow in the same direction [START_REF] Müller | The effect of particle agglomeration on the formation of a surface connected compartment induced by hydroxyapatite nanoparticles in human monocyte-derived macrophages[END_REF][START_REF] To | Deagglomeration of nanoparticle aggregates via rapid expansion of supercritical or high-Pressure suspensions[END_REF][START_REF] Kozulin | Ultrasonic deagglomeration of aluminum nanopowders with multi-walled carbon nanotube mixtures[END_REF]. As crystallite size decreases, the cohesiveness of the powder (the strength of the forces that cause agglomeration) increases [START_REF] Hartley | The role of the van der Waals force in the agglomeration of powders containing submicron particles[END_REF]. Thus nanoparticles tend to agglomerate and form larger particles with less surface area. This phenomenon is observed in Fig. 2a where the increasing crystallite size from sample CT0 to CT4 increased the particle surface area.

The increasing of the surface area and crystallite size to the same direction occurs due to the influence of forces like van der Waals that are predominant in interactions between the surfaces of nanoscale particles: Smaller particles have higher surface energy. These particles tend to agglomerate to balance the entropy forces [START_REF] Hartley | The role of the van der Waals force in the agglomeration of powders containing submicron particles[END_REF][START_REF] Lee-Desautels | Theory of van der waals forces as applied to particlate materials[END_REF][START_REF] Hotze | Nanoparticle aggregation: challenges to understanding transport and reactivity in the environment[END_REF]. A representative scheme of the relation between particle surface area, crystallite size and van der Waals forces is shown in Fig. 2b. The smaller the crystallites in the particle, the higher particle surface area influence. As the crystallite size increases, the surface energy becomes less representative compared to the influence of the particle volume. Diminishing the van der Waals forces interactions, the tendency of agglomeration is reduced. This increases the total sample surface area, however in a high volume particle, much of the sample becomes unavailable at the particle surface [START_REF] Yun | The effect of loading on sintering and catalytic activity of Pt/SiO2 hybrid catalyst powders synthesized via spray pyrolysis[END_REF][START_REF] Sharma | Size-dependent catalytic behavior of platinum nanoparticles on the hexacyanoferrate(III)/thiosulfate redox reaction[END_REF]. This reduces the total sample surface area available for catalysis, as shown to sample CT5 (Fig. 2a).

At a scale of a few dozen of nanometers, the reduction of the particle size did not increase the sample surface area, due the particles agglomeration. At higher scale sizes, the sample lost surface area due the particle volume. The ideal catalyst particle size is an intermediary value between the smallest possible volume and the smallest agglomeration degree. The micrographs (Fig. 3a, b, c andd) sequentially show the variation of the porous structures formed by the nanoparticle agglomerates with increasing porosity as the heat treating temperature increases (CT0-CT3).

The samples CT4 and CT5 (Fig. 3c andf) presented a distinct morphology compared to the rest of the samples. This morphologic alteration is explained by the increasing of diffusion at high temperatures. Above 957 • C, the diffusion energy is extremely high [START_REF] Torii | Low Temperature Sinterable Oxide Magnetic Material[END_REF][START_REF] Xavier | Effect of modofier ion the properties of MgFe2O4 and ZnFe2O4 pigments[END_REF][START_REF] Antão | Cation ordering in magnesioferrite, MgFe2O4, to 982 • C using in situ synchrotron X-ray powder diffraction[END_REF]. With increasing ionic movement, the ions tend to migrate to lower total free energy states through the free surface energy reduction. This causes the nanoparticles to form nanospheres [START_REF] Navarro | The Mechanics and Physics of Modern Grain Aeration Management[END_REF][START_REF] Reddy | Synthesis and characterization of ultrafine and porous structure of magnesium ferrite nanospheres[END_REF]. This change is clearly observed in the transmission micrograph shown in Fig. 4a andb.

The nanospheres at CT4 have higher crystallite and particle size compared to the CT3 nanoparticles, according to XRD and TEM, however they have a higher degree of deagglomeration, which kept higher surface area value to CT4 (Fig. 2a CT3 and CT4). CT5 is also formed by nanospheres, however at 1200 • C the diffusion is sufficient to promote particles coalescence and the formation of large sintered structures, which decreased the available surface area (Fig. 2a CT5). Thermogravimetric analysis (Fig. 5a andb) shows a mass loss around 100 • C due the sample dehydration. There are endothermic and exothermic peaks related to the magnetic reversible and irreversible structural changes such as Curie point between 200 and 800 • C (Fig. 5 orange area) [START_REF] Antão | Cation ordering in magnesioferrite, MgFe2O4, to 982 • C using in situ synchrotron X-ray powder diffraction[END_REF]. The small exothermic peak around 450 • C may be related to decomposition of a minimum organic residue [START_REF] Aono | Surface study of fine MgFe2O4 ferrite powder prepared by chemical methods[END_REF]. The endothermic heat flow between 800 • C and 1250 • C has no mass loss (Fig. 5 purple area), and this is related to the increasing ionic mobility in the spinel structure [START_REF] Xavier | Effect of modofier ion the properties of MgFe2O4 and ZnFe2O4 pigments[END_REF]. Above 1050 • C, literature indicates the sintering process of ferrite magnesium, due to particle coalescence, reaching a maximum value around 1250 • C [START_REF] Torii | Low Temperature Sinterable Oxide Magnetic Material[END_REF]. Above this temperature (Fig. 5 green area), the exothermic peaks are related to initial MgFe 2 O 4 decomposition into ␣-Fe 2 O 3 and MgO phases [START_REF] Kubota | Effect of treatment on magnetic Mgfe2O4 nanoparticles[END_REF].

MWCNT and 3D macrostructures synthesis

Raman spectra of the CVD produced samples (Fig. 6) show MWCNT characteristics D, G and G' bands at 1342, 1572 cm -1 and 2738 cm -1 , respectively [START_REF] Dileo | Purity assessment of multiwalled carbon nanotubes by Raman Spectroscopy[END_REF][START_REF] Saito | Double resonance Raman spectroscopy of single-wall carbono nanotubes[END_REF]. The G' band specifies the MWCNT purity degree [START_REF] Dileo | Purity assessment of multiwalled carbon nanotubes by Raman Spectroscopy[END_REF]. The MWCNT have a higher G' band intensity than D and G bands. Higher MWCNT purity implies fewer amorphous carbons and defects. MWCNT quality and quantity is also measured by the (I D /I G ) ratio. This ratio is related to the carbon graphitization degree (conversion of amorphous, semi-ordered or free carbon into three-dimensionally ordered graphitic structure) [START_REF] Yasuda | Graphitization mechanism during the carbon-Nanotube formation based on the In-Situ HRTEM observation[END_REF]. An elevated graphitization degree means high quality and quantity of CNT. [START_REF] Saito | Double resonance Raman spectroscopy of single-wall carbono nanotubes[END_REF]. The lower the (I D /I G ) ratio, the higher the graphitization degree.

CNT0 showed a higher (I D /I G ) ratio than the other samples. This result is justified by the tendency of catalysts powders with high agglomeration degree and low surface area to produce amorphous carbon and low density of MWCNT [START_REF] Tran | Carbon nanotubes synthesis by the ethylene chemical catalytic vapour deposition (CCVD) process on Fe, Co, and Fe-Co/Al2O3 sol-gel catalysts[END_REF][START_REF] Flahaut | Catalytic CVD synthesis of double and triple-walled carbon nanotubes by the control of the catalyst preparation[END_REF][START_REF] Ozden | 3D macroporous solids from chemically crosslinked carbon nanotubes[END_REF]. With the increasing of heat treating temperature, the catalyst surface area and crystallinity increased too. This directly influenced the MWCNT sample production as shown in the Raman spectra. The (I D /I G ) ratio decrease with increasing catalyst surface area.

This phenomenon is demonstrated in the MWCNT TEM micrographs Fig. 7a to Fig. 7f. A gradual increasing of MWCNT quality and homogeneity with the increasing of carbon graphitization degree is observed. The CNT0, CNT1 and CNT2 micrographs (Fig. 7a, b andc) presented the formation of a few MWCNT clusters and others carbon based structures. It is observed high particulates in the samples.

The elevated particle agglomeration led to a reduced number of catalytic ions available during the MWCNT synthesis. The MWCNT production was reduced. CNT3 (Fig. 7d) showed specific areas with high density of MWCNT and no presence of high particulates. CNT4 (Fig. 7e) has an elevated density of homogenous MWCNT in all sample, with no high particulates observed. Larger catalyst surface area implies more catalytic ions available for carbon graphitization. This leads to higher density of MWCNT production [START_REF] Ozden | 3D macroporous solids from chemically crosslinked carbon nanotubes[END_REF][START_REF] Yasuda | Graphitization mechanism during the carbon-Nanotube formation based on the In-Situ HRTEM observation[END_REF]. Sample CNT5 (Fig. 7f) presented a reduced MWCNT production. Few clusters of MWCNTs were observed. Although all the samples showed the formation of MWCNT, only the CNT4 (with the highest graphitization degree) had sufficient production to generate a well-defined 3D macrostructure. Fig. 8a shows the SEM micrograph of the CNT4. It is observed the formation of a dense entanglement of MWCNT and also the presence of some particles distributed in the sample. Fig. 8b shows a SEM image of the area mapped by EDS; Fig. 8c, d, e andf, present the EDS mapping of C, Fe, Mg and O respectively. Since Mg and Fe have similar distribution in the sample, the particles in the SEM image might be related to the MgFe 2 O 4 catalyst and its distribution throughout the sample indicates the multidirectional formation of CNT generated from the catalyst particles.

After the synthesis, part of the nanoparticles remains in the nanotubes structure, as observed in Fig. 8i. An EDS analysis (Fig. 8k) was performed for the area presented in Fig. 8i MWCNT with an elevated difference in the diameter size, Fig. 8 h have an external diameter of 17.5 nm while the MWCNT in Fig. 8 j have 74.2 nm. Despite this difference, both have a similar tubular structure, presenting in average more than 20 tubular walls. From the solid catalyst, the MWCNT grown in aleatory directions and forms a variety of junctions and curves, as presented in the SEM image (Fig. 8a) as well in the TEM image (Fig. 7e) allowing the formation of high volume macrostructures. Fig. 9 shows a diameter distribution histogram based on the CNT4 TEM image; The MWCNT in the CNT4 structure have mean diameter between 10 and 100 nm and a few tens of micrometers of a length. The diameters of MWCNT are mostly in the range of 20-40 nm.

Through the BET-BJH analysis, the macrostructure presented a small surface area (1136 m 2 /g), much of the structure volume is occupied by pores [START_REF] Sarawade | High specific surface area TEOS-based aerogels with large pore volume prepared at an ambient pressure[END_REF]. It presents a medium pore volume of 0.076 cm 3 /g and a medium diameter of 268 nm. These pores are classified as macropores [START_REF] Su | Hierarchically Structured Porous Materials[END_REF]. According to the Archimedes method, 54.41% of the 3D structure volume is related to pores. The density is very reduced, 0.2272 g/cm 3 . Elevated quantities of large pores allow the absorption of high volumes of substances in the structure.

The thermogram in Fig. 10 shows only one stage of decomposition for the CNT4 sample at temperatures between 450 and 650 • C, which is a typical result for MWCNT [START_REF] Silva | Surface properties of oxidized and aminated multi-Walled carbon nanotubes[END_REF][START_REF] Scheibe | Oxidation and reduction of multiwalled carbon nanotubes -preparation and characterization[END_REF]. DTG showed only one exothermic transition peak at approximately 650 • C. It is known that amorphous carbon burns at temperatures lower than 580 • C to 600 • C; defect-free single wall CNT, at 600 • C to 620 • C; and pure MWCNTs with 10 layers and more, at 750 • C to 790 • C [START_REF] Labunov | Growth of few-wall carbon nanotubes with narrow diameter distribution over Fe-Mo-MgO catalyst by methane/acetylene catalytic decomposition[END_REF]. The reduction of exothermic transition of the produced MWCNTs is likely due the presence of defects on the MWCNT walls as shown in the HRTEM image (Fig. 8 h) [START_REF] Scheibe | Oxidation and reduction of multiwalled carbon nanotubes -preparation and characterization[END_REF]. It can be inferred that the difference between these final weight loss values, 73 wt%, is equal to the MWCNT content of the sample. Therefore, the remaining 27 wt% corresponds to MgFe 2 O 4 . The magnesium ferrite structure decompose at temperatures higher than 1200 • C as presented in the thermogram for MgFe 2 O 4 (Fig. 5). No peaks related to amorphous phase burning were observed.

CNT4 presented a distinct higher MWCNT production compared to the other samples. The high density of MWCNT allowed the production of a MWCNT 3D macrostructure (Fig. 11b). 0,100 g of MgFe 2 O 4 produced a 3D macrostructure of 0.770 g and 8.78 × 14 × 9.9 mm. The carbon volume and shape are related to the alumina boat (Fig. 11a). The CT4 catalyst can be used to produce a variety of MWCNT macrostructures. The carbon structures grow accordingly with the crucible boat volume and shape specifications.

Besides the surface area, another important parameter in spinel based powder catalysts is the spinel inversion degree. As presented in the XRD analysis (Fig. 1), the increasing of heat treatment temperature leads to higher degrees of structural order-disorder transitions [START_REF] Antão | Cation ordering in magnesioferrite, MgFe2O4, to 982 • C using in situ synchrotron X-ray powder diffraction[END_REF]. These transitions cause inversions in the spinel. Thereby, the CNT4 elevated MWCNT production may not be related just to the CT4 high surface area. The CT3 and CT4 presented a small difference between surface areas (Fig. 2a). However, the MWCNT samples produced using these catalysts (CNT3 and CNT4) had a significant difference between the graphitization degrees (Fig. 6). This is explained by the elevated spinel inversion that occurs for samples treated at temperatures above 957 • C [START_REF] Bonnickson | High temperature heat contents of calcium and magnesium ferrites[END_REF]. In the magnesium ferrite structure, magnesium has a covalent character and oxygen occupies positions of low mobility. This leads to high mobility of Fe 3+ ions [START_REF] Antão | Cation ordering in magnesioferrite, MgFe2O4, to 982 • C using in situ synchrotron X-ray powder diffraction[END_REF]. With increasing temperature, these ions receive enough energy to move around in the structure switching or occupying new positions in the crystal lattice. In an inverse spinel structure, the A II ions occupy the octahedral sites, and part of the B III ions occupy the tetrahedral sites. This can be represented as (B III ) tet (A II B III ) oct O 4 [START_REF] Tilley | Understanding Solids[END_REF].

In the MgFe 2 O 4 lattice, with the increasing of heat treating temperature, gradually a higher number of Fe 3+ ions moves to the tetrahedral sites. As shown at Fig. 12, these sites are located closer to the crystallite surface than the octahedral ones [START_REF] Gateshki | Structure of nanocrystalline MgFe2O4 from X-ray diffraction, Rietveld and atomic pair distribution function analysis[END_REF]. The spinel inversion allows more Fe 3+ ions to be located on the crystallites surfaces; consequently, a higher quantity of Fe 3+ is available on the particles surfaces. It increased the MWNTC production. In sample CNT5, despite the favorable positions of Fe 3+ ions for catalytic activity, the MWCNT production was reduced. This result evidenced the strong influence of surface area on MWCNT production.

Conclusion

High purity and crystallinity MgFe 2 O 4 nanoparticles were produced by SCS. The heat treatment study showed that the van der Waals forces strongly influenced particles at the nanoscale. The increase in crystallite size resulted reduced particle agglomeration, which increased the catalyst surface area. However, large particles lost surface area due their higher volume. The optimum crystallite size was settled. Samples treated at temperatures above 957 • C presented high ionic diffusion rates, which promotes elevated structural disorder and spinel inversions. As diffusion increases, the nanoparticles morphological changes are clearly observed. Nanospheres are seen at temperatures above 1100 • C. The CT4 sample showed the highest surface area, besides a high spinel inversion degree that favors the presence of Fe 3+ ions on the nanoparticles surface. The CT4 catalyst produced a very dense MWCNT sample. This was successfully used to produce 3D MWCNT macrostructures. The volume and shape of these structures can be controlled by the crucible boat specifications. Nanoengineered 3D carbon structures with controlled density and architecture have great potential application in many areas including catalytic electrodes and environmental field.

Fig. 1 .

 1 Fig. 1. Heat treated nanopowders diffractogram. The box in the upper left side shows the 17.5 • -19.5 • area magnified. Changes on chemical and structural composition during the heat treatment were studied via thermogravimetry on a Netzsch 449 DSC/TG thermal analysis system. The MgFe 2 O 4 morphology was characterized by scanning electron microscopy (SEM) in a JEOL microscope (JSM 6060) with a maximum operational tension of 30 kV and nominal resolution of 3.5 nm. The applied tension was 10-20 kV. The MgFe 2 O 4 and CNT high resolution transmission electron microscopy (HRTEM) used a JEOL microscope (JEM 2010) operating between 80 kV and 100 kV with punctual resolution of 0.45 nm and line resolution of 0.20 nm. The quality and density of CNTs production were analyzed by Raman spectroscopy using a Renishaw inVia microRaman System with a laser wavelength of 532 nm focused onto the sample with a 50 × objective lens.Nitrogen adsorption/desorption isotherms are used to investigate the Brunauer, Emmett, and Teller (BET) surface area and Barrett-Joyner-Halenda (BJH) pore size. These surface structure parameters were obtained using an automated surface area analyzer (Autosorb Quantachrome -NOVA 1000e-Gas Adsorption Surface Area and Pore Size Analyzer). BET was performed to the nanopowder catalyst and BET-BJH was carried out to the NTC sponge. The NTC bulk density and porosity percentage were analyzed by Archimedes method[START_REF] Heath | The Method of Archimedes[END_REF].

Fig. 2 .

 2 Fig. 2. a) Crystallite size (in black) and particle surface area (in blue) variation with the temperature of heat treatment. The samples were analyzed straight after synthesis. No grinding process was used. CT0 to CT4 presented the increasing of particle surface area with the crystallite size. Sample CT5 presented the decreasing of particle surface area with crystallite size increasing. b) Representative scheme of the relation between particle surface area, crystallite size and van der Waals forces. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Fig. 3 .

 3 Fig. 3. SEM images of the samples. The nanoparticles formed structures with large porous at a) CT0, b) CT1, c) CT2, d) CT3. Samples e) CT4 and f) CT5 presented distinct morphology.

Fig. 4 .

 4 Fig. 4. TEM images a) CT3 nanoparticles; crystallites around 30 nm are observed in the particles and b) CT4 nanospheres; the spheres presented a higher particle size compared to the CT3 particles.

Fig. 5 .

 5 Fig. 5. CT0 Thermogravimetric analysis of a) DSC and b) TG (black line) and DTG (blue line). The white area represents the dehydration temperature, orange area presents the magnetic structural variation temperature, the purple area indicates the temperature of gradual structural disorder increasing and coalescence, and the green area is the temperature of phase decomposition. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Fig. 6 .

 6 Fig. 6. Raman spectroscopy of the MWCNT samples. The (ID/IG) ratio is presented in the box.

Fig. 7 .

 7 Fig. 7. TEM images of the produced MWCNT. a) CNT0, b) CNT1, c) CNT2 d) CNT3, e) CNT4 and f) CNT5.

Fig. 8 .

 8 Fig. 8. a) NTC4 SEM micrograph showing the entangled nanotubes, b) SEM of the area analyzed by EDS; EDS mapping of c) carbon d) iron e) magnesium and f) oxygen; NTC4 HRTEM presenting g) MWCNT growing agglomerated to the same direction; h) Single MWCNT with 17.5 nm of external diameter and 6.3 nm of internal diameter, the walls structure are well observed; j) single MWCNT with 74.2 nm of external diameter and 16.4 nm of internal diameter and i) TEM image showing the catalyst particles in the tubes structure.

  ; peaks of Mg, Fe, O, C and Cu are observed in the spectra. The ions Mg, Fe, O are related to the MgFe 2 O 4 nanoparticles in the MWCNT, C to the MWCNT and Cu is due the sample holder composition. Fig. 8 h and 8 j present two

Fig. 9 .

 9 Fig. 9. Histogram of the diameter distribution of MWCNT in the sample CNT4, at the left side, the CNT4 TEM image.

Fig. 10 .

 10 Fig. 10. CNT4 thermogravimetric analysis of TG (black line) and DTG (red dotted line). The curves behavior around 650 • C are related to MWCNT decomposition. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Fig. 11 .

 11 Fig. 11. a) 8.78 × 13.26 × 9.9 mm crucible boat used in the 3D macrostructure synthesis. b) Resulting 3D NTC macrostructure.

Fig. 12 .

 12 Fig. 12. Spinel ferrite Fd-3 m cubic crystalline structure. Representation of the ions occupying tetrahedral sites (blue), octahedral sites (yellow) and oxygen (red). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Table 1

 1 MgFe2O4 heat treatment temperatures.

	Sample	Calcination temperature ( • C)
	Ct0	-
	Ct1	500
	Ct2	700
	Ct3	900
	Ct4	1100
	Ct5	1200
	Table 2
	MgFe2O4 catalysts and their respective CNTs samples.
	MgFe2O4 catalyst	CVD produced samples
	CT0	CNT0
	CT1	CNT1
	CT2	CNT2
	CT3	CNT3
	CT4	CNT4
	CT5	CNT5
	The sample was treated at different temperatures as described in
	Table 1.
	2.2. Synthesis of the CNT 3D structures
	2.2.1. Materials
	MgFe 2 O 4 , Ar 99.999% (Sigma Aldrich), H 2 99.95% and ethylene
	99.99% (Sigma Aldrich).

Acknowledgments

The authors would like to thank the Coordination for the Improvement of Higher Education Personnel-CAPES for the financial support and the Microscopy and Microanalysis Center of Federal University of Rio Grande do Sul (CMM/UFRGS) for the technical support.