M Corral Valero

P Raybaud

Computational chemistry approaches for the preparation of supported catalysts: progress and challenges

Keywords: density functional theory, catalyst preparation, catalyst activation, alumina, additives, metals, sulfides

Introduction

Michel Che pioneered the fundamental concepts of Interfacial Coordination Chemistry (ICC) applied to the "craft" of heterogeneous catalysis preparation, which remains a strategic stage for the catalyst's life. [1] Since these earlier concepts, the atomic scale's understanding of the genesis of a heterogeneous catalyst remains highly challenging, despite numerous relevant experimental works in this area. [2][START_REF] Marceau | Ion Exchange and Impregnation[END_REF][START_REF] Munnik | [END_REF][5][START_REF] Regalbuto | Catalyst Preparation: Science and Engineering[END_REF][START_REF] Toulhoat | Catalysis by Transition Metal Sulfides. From molecular theory to industrial applications[END_REF] Indeed, shortcomings in this scientific field come from the fact the catalyst's genesis involves chemical reactions that take place in interfacial liquidsolid or gas-solid media, where characterization as well as molecular simulation techniques are challenged. Thus, most current preparation protocols are mainly optimized by trial and error approaches based on the experimentalist's chemical intuition or by high throughput experimental mapping. One of the directions proposed by Michel Che in his paper published in the Proceedings of the 10 th International Conference in Catalysis encouraged "to perform theoretical calculations to provide a more solid foundation to model the elementary steps describing the catalyst preparation at a molecular scale." [1] Almost thirty years later, one must recognize that the atomic scale's insights of computational chemistry into catalyst preparation remains scarce.

Thanks to the progress of theoretical chemistry and the historical development of density functional theory (DFT), [START_REF] Hohenberg | [END_REF]9] many research works have been devoted either to the fine description of the active sites and mechanisms of the "working catalyst" [10] or to the identification of structure-activity relationships such as "volcano curves", [11][12][13] both contributing to the rational design of heterogeneous catalysts. However, computational chemistry (including DFT based approaches) may have an equally important role to play in the rational understanding of the genesis of heterogeneous catalysts (particularly when supported) in order to provide relevant "theoretical" guides and descriptors on the optimal pathways to prepare a targeted catalyst.

In this perspective, we propose an overview on how computational chemistry recently began to pave the way in this direction and which methods may contribute even more to this challenging field in the near future. For that purpose, we will consider the relevant and generic case study of alumina-supported catalyst. Firstly, we briefly recall the main chemical steps and materials involved in heterogeneous catalysts' preparation. Then, we show and discuss the computational chemistry methods and their applications to the various steps of catalysts' preparation. We finally highlight some possible future emerging research topics.

The preparation of a supported catalyst follows a rather complex stepwise procedure involving several key steps shown in Figure 1: synthesis and thermal treatment of the alumina support starting from the boehmite precursor, impregnation of the support with metallic salts and (in)organic additives, drying/calcination and activation. Figure 1 also illustrates some examples of quantum molecular models that will be discussed in the course of this perspective. Depending on the catalyst formulation, the (in)organic additives may be added at the different stages of the preparation in order to tune the interaction of the metal with the support and to optimize its further activation. [START_REF] Toulhoat | Catalysis by Transition Metal Sulfides. From molecular theory to industrial applications[END_REF]14] It is well documented that each one of these steps may have an impact on the final properties of the supported active phase (chemical state, electronic structure, size and morphology), although it is often difficult to identify the precise origin in the preparation steps on the resulting catalytic properties.

As for the impregnation procedure, incipient wet impregnation (IWI) is the most widespread method in both industry and academia. It consists on the impregnation of the catalyst support by an aqueous mother solution containing metallic salt complexes with or without (in)organic additives. The impregnated material is then dried and/or calcined and the supported metal phase is activated by either hydrogen reduction and/or sulfidation depending on the type of the targeted active phase (metals, sulfides...).

Methods

For addressing these questions, the robust machinery of molecular simulation can be used by combining various theoretical levels. State of the art DFT approaches [START_REF] Hohenberg | [END_REF]9] provide relevant atomic scale insights on the structures and energies of the adsorption modes and transition states [16]. On top of these calculations, thermal and entropic corrections can be calculated within the formalism of ab initio thermodynamics to determine the relative stability of key intermediates as a function of (T, p) reaction conditions. Simultaneously diverse spectroscopy simulations (vibrational frequencies, core level shift, chemical shift, …) are very useful to make the link with in situ characterizations (infrared, X-ray photoelectron spectroscopy, nuclear magnetic reasonance, X-ray absorption spectroscopy…). Here, we recall that some aspects challenging the expected "chemical accuracy" of state of the art DFT [START_REF] Perdew | AIP Conf. Proc[END_REF] must be carefully checked before simulating the genesis of a supported catalyst in the same way as it is achieved for the simulation of the reactivity of catalysts' surface models. As the various steps of heterogeneous catalysts (before activation) often involve transition metal (hydr)oxide precursors at interfacial systems, some questions on the accuracy of results may arise from the difficult treatment of electron correlation (either static or dynamic) in DFT, such as selfinteraction errors, multi-reference systems, and long ranged electron interaction. [START_REF] Yu | [END_REF]19] To overcome some of these problems, one has to choose properly the exchange-correlation functional (including in some cases dispersion corrections or Hartree-Fock exchange).

Moreover, some of the most complex open-shell systems may even require to go beyond the Kohn-Sham DFT formalism or at least to check DFT reliability by using multiconfigurational methods, which are very computationally demanding and cannot be yet systematically applied to the systems of this perspective. [19] As we will illustrate it for the impregnation step, simulating solid-liquid interface for catalysis is rather challenging [20] and it is often required to go beyond static approaches and explore the potential energy surface by free or biased ab initio molecular dynamics (AIMD) [21,22] which is rather computationally demanding when the solvent is treated explicitly by quantum method. For that purpose, the solvent can in some cases be treated implicitly by a continuum model parametrized by the value of its dielectric constant. [23] Alternatively, empirical force field based molecular dynamics and Monte-Carlo simulations are also very useful to address larger scale effects by generating an ensemble of configurations and determining the dynamics and equilibrium properties for complex binary systems. Nevertheless, the utilization of the latter approaches is scarce in catalysis since most catalytic reactions involve chemical bond formation and cleavage phenomena which need the development of non-standard reactive force-fields by fitting the parameters on ab initio calculations for benchmark systems.

[24]

The catalyst support under thermal treatment

We will consider here the gamma polymorph of aluminum oxide (γ-alumina or γ-Al 2 O 3 ), which is the support of many active phases used in numerous industrial catalytic processes such as hydrotreatment, [START_REF] Toulhoat | Catalysis by Transition Metal Sulfides. From molecular theory to industrial applications[END_REF] Fischer-Tropsch synthesis, [25] hydrocracking, [START_REF] Marcilly | Acido-basic catalysis[END_REF] naphtha reforming [START_REF] Sinfelt | Handbook of Heterogeneous Catalysis[END_REF], alkane dehydrogenation, [START_REF] Sattler | [END_REF] or biomass conversion, [29][30][31]. The reasons for this choice in so many applications are multiple: alumina exhibit versatile textural properties (such as tunable pore distribution, and surface areas of about 250 m 2 g -1 ) as well as local surface structures with tunable acid-basic properties useful for impregnation and reactivity. γ- calculations with a thermodynamic model of the dehydration process. [START_REF] Digne | [END_REF] However, in order to go beyond in the field of catalyst synthesis, we need to understand the behavior of nanocrystallites of such hydroxydes and γ-alumina itself. During the thermal treatment, the synthesis of γ-alumina involves the topotatic transformation of boehmite, [34] that is, the morphology of γ-alumina nano-crystallites is inherited from the one of the boehmite precursor. This transformation impacts the textural properties of alumina supports, namely surface area and pore volume, as they can be either the outcome of complex agglomeration phenomena either of alumina nano-crystallites during calcination or of boehmite nanocrystallites in solution prior to its calcination into alumina.

Interestingly, it was possible to capture by DFT simulation the essence of the boehmite to alumina bulk dehydration process. [35] In particular, AIMD simulations reveals first the inter-layer mobility of protons leading to water formation inside the structure of boehmite activated at rising temperature. Then, geometry optimization shows how the water release induces a collapse of the boehmite structure through a cross linking of adjacent layers and shearing movement. Last but not least, the Al atoms migrate from their initial positions, in an octahedral coordination environment, to other positions in which they bind to the oxygen sublattice in tetrahedral coordination centers following a pairwise diffusion. One should bear in mind that this atomistic model is not based on the spinel MgAl 2 O 4 structure though some local features around Al centers are recovered. Firstly, the plot of the thermodynamic profile along this complex process leads to the identification of relevant bulk structures for alumina where the distribution of Al atoms is more complex than the defective spinel like structure of MgAl 2 O 4 usually evoked for this solid [34], since Al occupies non spinel sites in octahedral and tetrahedral positions with vacancies in both octahedral and tetrahedral spinel sites (Figure 1c left). The occupation of non-spinel sites was also confirmed by other DFT calculations [36] combined to extensive computational screening of ~1.5 billion structural candidates and force-field energy minimization of 122000 structures. [37] Beyond this bulk model, slab models allowed to solve the nature and concentration of the surface OH groups on the most abundant facets (110), ( 100) and (111) exposed by alumina crystallites as a function of thermal treatment and water vapor pressure within ab initio thermodynamic formalism. [38][39][40] This model enabled to revisit the experimental IR spectra of the hydroxyl groups present at the surface in gas phase (Figure 1c right), [41] but also Al NMR experiments. [42] Besides, surface energies of the support were evaluated and compared to micro-calorimetry measurements: [43] these are key descriptors to predict equilibrium morphologies of alumina crystallites [44], morphology effects on materials acid-basic properties, and how sintering effects might occur under thermal treatment. Following an earlier proposal made by Busca, [45] the most recent extension of these molecular models including the edges of the alumina crystallites enables to rationalize the 1 H NMR spectra of the hydroxyls groups of the support. [46] This analysis has proven to be one important step forward in the rationalization of the chemical properties of this support as, during the preparation of a catalyst, the precursors anchor predominantly with the hydroxo groups at the surface, [1] as we will illustrate in what follows.

The catalyst support in water solution

The boehmite precursor

Due to particularities of the calcination process of boehmite into alumina, that is, the effects driven by the so-called topotactical transformation, it is critical for the support's synthesis to understand how the boehmite precursor may influence the resulting alumina agglomeration and its textural properties. This can be controlled by selective modification of the surface energies of the different exposed boehmite facets in aqueous solution. However, simulating solid-aqueous solution at variable pH is not feasible nowadays by the sole use of DFT simulation. One reasonable compromise is to evaluate DFT surface energies and structures at the pH corresponding to the Zero Point of Charge (ZPC) and to compute the surface energy change as the surface charge evolves with the protonation and deprotonation of the various OH modeled at the DFT level. The latter requires knowledge of the hydroxo groups' protonation constants (pKa) which can be obtained with reasonable accuracy from the empirical multi-site complexation model (MUSIC) [47][48][49] or more complex AIMD simulations such as those in ref. [50]. In the latter example, AIMD approach remains strongly time consuming: several millions of CPU hours on high performance computers for several tens of picoseconds trajectories to reach the required convergence level. Once pKa constants are known, they can be used to compute surface charge as a function of pH, temperature and ionic force with software packages solving speciation in solution such as PHREEQC. [START_REF] Parkhurst | User's guide to PHREEQC (version 2)-A computer program for speciation, batch-reaction, one-dimensional transport, and inverse geochemical calculations[END_REF] This approach was applied for boehmite by AIMD simulation in water [START_REF] Raybaud | [END_REF] in order to compute the interfacial energies of this solid (001), ( 010) and (101) facets and determine the various OH sites present at ZPC. According to the afore-mentioned topotactic transformation, these facets will give rise to the (100), ( 110), (111) facets of alumina which explains why controlling the boehmite morphology is crucial for the final support. Then, by applying the MUSIC model, the interfacial energies and Gibbs-Curie-Wulff morphology of boehmite nanocristallites have been deduced as a function of the surrounding water pH. [53] We will illustrate this approach further in the case of alumina.

Other ways to control particle morphology involve the addition of organic additives in solution or the use of different solvents acting as selective surfactants. The impact of xylitol on boehmite morphology was investigated by a combined experimental and theoretical study. [54] In that case, it was rather efficient to use implicit solvent approach, such as the "Conductor-like Screening Model" (COSMO) [55,56], to compute the adsorption energies of xylitol on different boehmite facets and deduce the morphology of boehmite crystallites in the presence of xylitol. [54] This organic molecule preferentially adsorbs in the hydroxyl nests of the (001) and ( 101) surfaces (Figure 1b right), which are thus stabilized as observed experimentally. However, the final form of the observed boehmite particles is not always driven by thermodynamics: kinetic limitations of nucleation and growth phenomena need also to be considered. In a recent work, some groups have been trying to deduce the extent at which these are affecting particle morphology (see Kerisit et al. [57], where they used a similar approach to the one of Ref. [53] in deriving, for different pH, the morphology at both thermodynamic equilibrium and kinetic limited conditions). For a full description of the dynamics of this process, one needs to take into account the interactions between facets of finite size particles and with solution, which is beyond the possibilities of the standard periodic DFT calculations quoted above. Thus, the development of coarse-grained models to study the dynamics of nucleation and growth in solution could be a most interesting perspective in this area.

Impregnation of alumina

Coming back to γ-Al 2 O 3 surfaces, in order to address questions related to the impregnation step, it is mandatory to simulate phenomena taking place at the solid liquid interface (SLI), to make the bridge with interfacial coordination concepts [1] and rationalize the impregnation steps. As illustrated in the case of boehmite, this remains a rather challenging question for molecular simulations in general as one needs to handle the various intricate factors involved during the impregnation step (pH, concentration of the precursors with couter-ions…). As underlined in introduction, it is recommended to proceed by a deconvolution of each chemical factor.

Some of these factors relies on the atomic scale's description of the water-alumina interface.

As mentioned for boehmite, one way is to undertake AIMD of interfacial systems which requires quite large simulation cells and time to recover relevant insights such as the structure of the alumina-water interface (Figure 1d) and spectroscopic features of interfacial OH groups in water. Interestingly, the analysis of radial distribution functions show that monocoordinated  1 -OH of the (110) surface are H-bond donors to water molecules (leading also to proton transfer through Grotthuss-like mechanism), whereas mono-coordinated  1 -OH of the (100) surface interact preferentially between themselves via an intra-surface hydrogen bond network. [58] The distinct behavior of the two surfaces in water may impact their respective reactivity with metallic precursors or additives during impregnation, which will certainly be the topic of future investigations. The structural differences between the two interfaces can be evidenced by the simulation of their vibrational spectra (Figure 2a) showing that water induces a more pronounced red-shift of the vibrational frequencies of OH groups on the alumina (110) surface than on the (100). Such simulation should be in principle compared to experimental Attenuated Total Reflectance Infrared (ATR-IR) spectroscopy, which can be conducted on alumina powders. [59,60] However, one must stress that a direct experimental interpretation is challenging to achieve since the alumina crystallites present in such powder samples do not exhibit usually one or two well-defined surfaces only but a much broader distribution of facets and edges. Nevertheless, the use of DFT simulation helps to identify precisely which site on which facet is at the origin of the observed phenomena.

Alternatively, it is also proposed to work on alumina single crystals exposing one unique well-defined surface [61] in order to identify the nature of interfacial OH groups by combining Sum Frequency Generation (SFG) spectroscopy [62] and DFT simulations [63]. Other AIMD simulations also revealed the long range effect (up to 10 Å) of the (110) surface on the water structuration and proton redistribution at the interface. [64] These effects are suspected to impact the reactivity of the interface with respect to the sorption of metallic precursors or (in)organic additives.

As for boehmite, following an AIMD analysis of the surface OH groups, the protonation constants of these hydroxo groups were computed by using the MUSIC empirical model, [48] and the surface charge of alumina powders in solution modeled with PHREEQC [START_REF] Parkhurst | User's guide to PHREEQC (version 2)-A computer program for speciation, batch-reaction, one-dimensional transport, and inverse geochemical calculations[END_REF] in reasonable good agreement with experimental titration curves. This work showed that the extent at which alumina powders accumulate charge in solution depends mostly on the reactivity of mono-coordinated µ 1 -OH groups. Future works in this area should aim at computing the complexation constants between these surface groups and molecules from the impregnating solutions. This will undoubtedly make catalyst impregnation more predictable, though it is a very ambitious goal from the computational point of view.

Hence, to explore the reactivity of various species at the interface where the aqueous solvent is treated explicitly, it is mandatory to go beyond standard AIMD approaches by using biased AIMD simulations within the formalism of metadynamics [21] or blue moon sampling [22,65] which enables the scan of the energy landscape of a given reaction. They belong to the socalled biased or constrained AIMD simulation family because the searched reaction pathway is more efficiently explored, within the limits of CPU limitations, by constraining the system along relevant collective variables (bond distances, angles, coordination number…). In addition, metadynamics simulations use a set of mathematical tools that allow the system to overcome more easily activation barriers and hence a more efficient use of computational resources in reactivity focused studies. The latter methods have recently shown the selective interaction of polyols with specific Al surface sites of alumina in solution, which may play a role in the stabilization of alumina in hot water. [66] An alternative approach can be provided by reactive force fields which reduce significantly the computational cost of molecular dynamics -and thus increase the number of explored configurations-for larger systems. [24] The main peculiarity of the latter approach is its ability to model bond breaking and forming mechanishms. Still, as in conventional force fields, they require, as a preliminary step, a robust parametrization of the force field against quantum calculations on benchmark systems to ensure the transferability of the force fields to a wider range of possible reactions. To further accelerate the molecular dynamics simulation, it is envisaged that machine learning approaches could also be used in close conjunction with DFT approaches to develop force fields in the course of the AIMD simulation itself.

[67]

The dried/calcined catalyst

As described in Figure 1, the next important stage of the catalyst preparation is the drying or calcination of the impregnated support. This stage features the strength and nature of interactions of the metal oxide precursor and/or (in)organic additives with the support. This will directly impact how the metal oxide precursor will be subsequently activated in reducing or sulfiding environment. This stage can be studied by state of the art ab initio thermodynamics and spectroscopic simulations in gas phase. The surface state of the support must be considered with great care, according to the (T, P) conditions applied during this thermal treatment (drying ~ 120°C, calcination ~ 450-500°C) which strongly influences the hydroxylation state of alumina (or any other oxides) and thus, the nature of the interaction between the precursor and the support.

The effect of residual alkaline counter-ions used in the mother impregnation solutions or in the alumina synthesis method was studied by DFT approaches and revealed how sodium cations exchange preferentially the proton of tri-coordinated  3 -OH leading to a Na + species solvated by the neighboring surface OH groups and thus decreasing the surface Brønsted acidity of the support as highlighted by IR spectra and frequency calculations of the OH stretching bands. [68] Conversely, exchanging chloride anions with mono-coordinated  1 -OH groups was shown to enhance the surface Brønsted acidity which can be profitable for some industrial processes such as naphtha reforming. [46,69] Interestingly, the most recent results

combining 1 H NMR experiments and DFT calculations indicate that chlorides preferentially exchange  1 -OH located at edges of the nano-crystallites. [46] Moreover, the effect of chlorides is not restricted to the support, as it may also coordinate to the metal directly as found for Cu 2+ in an oxychlorination catalyst in reference [70], where it was reported by DFT calculations that the number of Cl -anions bound to Cu 2+ depends on the alumina surface and on the thermal treatment. Such a "ligand effect" of Cl atoms was also reported to stabilize Pt clusters in a highly dispersed state on alumina, [71] and is reminiscent of the earlier Che's ICC concept. [1] Regarding Co containing catalysts (Fischer-Tropsch and hydrotreatment), DFT calculations highlighted the most favourable anchoring sites and modes for Co 2+ hydrated ions on the γ-Al 2 O 3 surfaces following a molecular recognition mechanism comparable to an epitaxy relationship (Figure 3), [72] in the spirit of earlier proposals. [73] Such a strong metal-support interaction (involving O of the alumina network) enhances the formation of interfacial Co(OH) 2 phase as suggested by earlier experiments [74] which may be detrimental to the subsequent activation of Co. Similar precipitation phenomenon of Ni(OH) 2 has been observed by XAS also in the case Ni 2+ on alumina surface. [75] To avoid this, the alumina surface must be modified by silicic acid impregnation followed by a thermal treatment in order to generate an amorphous silica-alumina layer. In this case, DFT calculations showed that the molecular recognition mechanism is strongly diminished. Alternatively, organic additives are widely used nowadays in catalysis preparation to control the interaction of precursors of the metal active phase with the support as well as to optimize its subsequent activation. [START_REF] Toulhoat | Catalysis by Transition Metal Sulfides. From molecular theory to industrial applications[END_REF]14] DFT calculations may identify the adsorption modes and energies of these various species under drying or calcined conditions. For acetic acid and ethylene glycols, it was shown that their interaction involve a hydrogen bond network formed between the hydroxylated alumina surface and the molecules (Figure 3). [76] As already discussed for boehmite, surface hydroxyls nests stabilizes these organic additives in some specific sites of the alumina surface, which is consistent with FT-IR characterization. [60] DFT results showed that those sites may be the same as the ones identified for cobalt for some molecules and alumina orientations, which reveals that a competition for adsorption may take place. However, since the calculated adsorption free energies are favored for cobalt(II) due to entropic gain resulting from nitrate decomposition, it also open the question for the future works to explore the nature of the cobalt precursor used and the sequence in which cobalt and additive are impregnated.

Activation step

This last step is a very critical one for the formation of the wished active phase and it is still the subject of numerous challenging questions, particularly when the optimal active phase must be composed of several metallic elements such as alloyed nanoparticles or mixed transition metal sulfides (TMS) nanoparticles. [77] The complexity of this step is due to the numerous chemical events involved during the reduction or sulfo-reduction steps of the oxidic precursors into the metallic or sulfided phase, respectively. Here, DFT calculations will certainly provide more relevant insights in the future in order to improve the understanding of (sulfo)reduction mechanisms in presence of H 2 and/or H 2 S leading to the removal of O atoms from the coordination sphere of the metals in oxide precursors. Many fundamental key questions can be addressed regarding the rate limiting steps and intermediates of the reduction, the nature and location of the most refractory O sites (on the metal or at the metalsupport interface ?) In the case of the activation of MoS 2 catalysts, experimental works proposed that its formation goes through a series of oxysulfides and also MoS 3 intermediates, whose structures are poorly known due to their amorphous features. [78,79] Interestingly, reverse Monte-Carlo simulations have been used to establish the first atomistic models of MoS 3 exhibiting a distorted Mo chain. [80] Figure 4 illustrates ongoing DFT simulations addressing this challenging problem of the nature of supported oxysulfides and various MoS 3 polymorphs before their transformation into the MoS 2 active phase. [81] Significant progress can be expected in this area in the coming years which will certainly allow to identify more systematically thermodynamic and kinetic parameters of phase transformation as a function of activation conditions. If so, this will provide the basis for in situ/operando modelling of the activation step as it has been initiated so far for catalytic reactivity. [82] At this stage, ab initio thermodynamics showed how the sulfo-reductive conditions, T and p(H 2 S)/(H 2 ), used during the activation step may impact key features of the MoS 2 or WS 2 based active phases. It was shown that the equilibrium 2D-morphology of the Mo(W)S 2 nanoparticles may be tuned by the H 2 S/H 2 partial pressures (Figure 4c) and several experiments have confirmed these predictions, [83][84][85][86][87] although we cannot exclude that 2D-morphologies can also be determined by kinetic effects induced by the precursors, the preparation methods [86] and/or support effects. [88] Moreover, ab initio thermodynamics revealed how sulfo-reductive conditions (including those used in activation conditions) impact the edge energies and thus the stability of Co and Ni promoters [89] and other first row transition b)

Al O S Mo a) c) H 2 /H 2 S
metals [90] located at the edges of Mo(W)S 2 phases. This shows how ab initio thermodynamics is a necessary piece of the puzzle of the so-called "in situ/operando model".

Many key questions concern the nucleation and growth mechanisms of metallic particles from isolated atoms to clusters and to bigger particles and how they might impact the dispersion of the active phase. This requires to quantify the stability of such entities (in their oxidized and reduced state) but also their mobility on a given support which is also possible through DFT calculations. For Pd, it was shown by DFT calculations that the stability of isolated metallic atoms and diffusion rates depend strongly on the hydroxylation state of alumina: [91,92] the stronger adsorption energies and lower diffusion rates of the isolated Pd found on the hydroxylated (110) surface than on the dehydrated (100) is interpreted to be a strong indication that the dispersion is greater on the (110) surface. For Pt 1 O x species (as found in some Pt oxide precursors), DFT calculations combined to HR-TEM experiments showed that the transformation of Pt 1 O x into large Pt oxide particles is thermodynamically unfavored with respect to their transformation into hydride Pt 13 cluster (Figure 1f left) in the presence of H 2 . [93] This underlines the role of the reduction step on the metallic particle growth mechanism. Further ab initio thermodynamics studies showed how the reduction conditions, T and p(H 2 ), impact the resulting morphologies of Pt 13 cluster on alumina, [94] which has been successfully compared to in situ XANES characterization. [95] Here again, the role of the "ligand effect" of the support on the metal can be crucial for the stabilization of highly dispersed particles with various shapes. [96] A DFT study quantified how chlorine atoms present on alumina stabilize small Pt 3 clusters: this effect of chlorine on particle size is actually used to maintain a high dispersion of Pt particle during the regeneration of naphtha reforming catalysts. [71] For MoS 2 based catalyst, an epitaxy relationship was highlighted by ab initio thermodynamics between the edges of MoS 2 clusters (containing 6 Mo atoms) and anatase-TiO 2 surfaces which is at the origin of the higher active phase dispersion reported on the TiO 2 support. [88] Once sufficient DFT data on the reaction barriers and energies of the various transformation and diffusion processes involved will be determined, kinetic Monte-Carlo approaches may be implemented to quantify the full genesis pathways as it was proposed for zeolite synthesis.

[97]

Conclusions

In this perspective, we have highlighted one of the most challenging topics for computational chemistry, related to the molecular scale description of the preparation and activation steps of heterogeneous catalysts. Although the goal to simulate the entire complexity of these steps might appear too ambitious for such theoretical approaches, we have highlighted several possible methodologies that could address some individual parts of the problem within a reductionist formalism allowing to describe correctly the chemical phenomena we want to address. We recall them here: state-of-the-art DFT, ab initio molecular dynamics (including biased AIMD), kinetic Monte-Carlo, reactive force-field molecular dynamics. We have also illustrated an example of multi-scale approach combining empirical MUSIC model and DFT descriptor to include pH effect during impregnation. Once more progress will be achieved allowing the determination of an extended database of chemical descriptors for one of the preparation steps described before, it will be possible to develop a more complete multi-scale approach taking into account preparation conditions with multi-parameters (T, pH, concentration, pressure) as it starts to be accessible nowadays for gas phase catalytic reactions. [98] For sake of clarity, we mainly focused on alumina supported catalysts and illustrate how it is important to use a well-defined models of the support that also take into account the system hydroxylation state at the relevant thermal and water pressure conditions of each preparation step: this plays a key role for simulating properly the phenomena involved in the so-called interfacial coordination chemistry (ICC) concepts. The computational approaches presented here can certainly be extended to other metal oxide supports and catalytic materials (as mentioned for TiO 2 and zeolithe synthesis above), provided that a reliable DFT model of the exposed surfaces is available.

In practice, these simulations will need larger and larger computational resources (particularly AIMD) as well as a lot of human effort in order to explore the numerous configurations and reaction coordinates involved (combination of bond distances, angles, …) not necessarily known a priori. Advances in this field will go hand in hand with the novel application of mathematical techniques, some of them based in artificial intelligence methods, [99] to accelerate the sampling of multi-dimensional potential energy surfaces but also by the incentive from an ever more rational understanding of the complex chemical phenomena involved in the preparation steps of heterogeneous catalysts.

Before closing this perspective, we would like to underline that in spite of the expected continuous progress of computational methods presented before, it would be too optimistic to expect that they would fully replace experimental approaches. This is even more true than in the case of the prediction of catalytic reactivity mentioned in introduction. First, the availability of well-defined set of experimental data is highly recommended to improve and

validate not yet mature simulation methodologies. Once validated, these methodologies will accelerate the research in a subsequent step, by reducing the number of "trials and errors" attempts. Finally, decoding the complex molecular phenomena involved in catalyst's preparation should work hand in hand with experimental approaches, in order to reinforce the synergy between "in vivo" and "in silico" designed catalyst, as suggested by Michel Che three decades ago. [1]

Figure 1 :

 1 Figure 1: Usual synthesis pathways of heterogeneous catalysts (a) and molecular models

Al 2 O

 2 [START_REF] Marceau | Ion Exchange and Impregnation[END_REF] and other polymorphs are usually prepared from the calcination of aluminum mono-(AlOOH) or tri-hydroxides (AlO(OH) 3 ) such as boehmite (Figure1bleft), gibbsite, tohdite, diaspore or bayerite.[START_REF] Euzen | Handbook of Porous Solids[END_REF] Calcination of any of these hydroxides at temperatures above 1000 °C leads to the formation of α-Al 2 O 3 (corundum). Metastable forms of Al 2 O 3 can be obtained by quenching the calcination procedure at lower temperatures. The γ-alumina form is the outcome of boehmite calcination at 450 °C. Since the bulk structures of these hydroxides are well defined, it was possible to show how the relative thermodynamic stability and the transition temperatures of bulk mono-and tri-hydroxides can be recovered by combining DFT

Figure 2 :

 2 Figure 2: a) Calculated IR spectra for alumina OH groups of both faces in vacuum (dots) and

[ 72 ]Figure 3 .

 723 Figure 3. DFT optimized structures for adsorbed organic molecules (ethylene glycol and acetic acid) and Co 2+ on alumina after drying. In the Co 2+ containing structure, three types of

Figure 4 .

 4 Figure 4. DFT structures of a) MoO x S y intermediates supported on alumina surface, and b)

Acknowledgements P.R. and M.C.V are indebted to Professor Michel Che, who was an active member of IFP New Energy Scientific Council, and shared with us his deep knowledge of catalyst preparation.

Fundings

This work is part of the "RatiOnAl Design for CATalysis" (ROAD4CAT) industrial chair, project IDEXLYON funded by the French National Research Agency (ANR-16-IDEX-0005) and the Commissariat-General for Investment (CGI) within the framework of Investissements d'Avenir program ("Investment for the future"). Part of this work was also supported by the French National Research Agency within the framework of the ANR-14-CE08-0019 SLIMCAT project.


Justine Touja

Vincent Gabaudan

Filippo Farina

Sara Cavaliere

Laure Caracciolo

Dr Lénaïc Madec
email: lenaic.madec@univ-pau.fr

Hervé Martinez

Athmane Boulaoued

Joachim Wallenstein

Patrik Johansson

Sara Cavaliere Ac

Laure Caracciolo Bd

Lénaïc Madec Bd

Hervé Martinez Bd

Lorenzo Stievano Abf

Laure Monconduit
email: laure.monconduit@umontpellier.fr

Self-supported carbon nanofibers as negative electrodes

Keywords: K-ion batteries, Self-supported electrode, Carbon nanofibers, K + storage mechanism, Solid Electrolyte Interphase

Introduction

Lithium-ion batteries (LIB) have been widely employed for portable devices and electrical vehicles, but the limited resources and the uneven distribution of lithium cause great concerns for large scale application such as stationary energy storage. In the last decade, the research based on batteries working with Earth's crust abundant elements such as sodium or potassium has been widely developed. [1] Beyond the interest of using abundant elements that can lead to lower prices of precursors and electrolyte salts, potassium-ion batteries (KIB) exhibit in particular other significant advantages. For instance, the low standard potential of the K + /K couple (-2.95 V vs. SHE) in organic EC:DEC solvents, even compared to Li + /Li (-2.80 V vs. SHE) is promising for the development of high energy density batteries. Moreover, the high ionic conductivity of potassium-based electrolytes, deriving from the weak Lewis acidity of K + , is favorable for the development of high power density batteries.

Regarding negative electrodes, as for LIB, most studies focus on carbonaceous materials such as graphite, hard carbon, or carbon nanotubes. [2,3] The electrochemical intercalation of potassium into graphite was studied by different groups using both ex situ and in situ characterization techniques, which showed a staging mechanism up to the formation of KC 8 with a theoretical capacity of 279 mAh.g -1 . [4][5][6][7][8] Very recently, outstanding performance (constant reversible capacity of around 255 mAh.g -1 for over 2000 cycles) was obtained for KIB with graphite in high loading. [9] However, all such electrodes need to be formulated with a binder and supported on a current collector, which led to a dead volume/weight inside the battery and higher costs. Self-supported electrodes are thus of considerable interest in order to increase the overall energy density by removing the weight of binder, conductive additive, and current collector. [10] Self-standing electrodes made of carbon nanofibers (CNF) of different natures were tested in LIB, presenting good capacities ranging between 250 and 350 mAh.g -1 at 30 mA.g -1 , which is close to the specific capacity of graphite (370 mAh.g -1 ). [11][12][13][14] Similar performance (250 mAh.g -1 at 50 mAh.g -1 ) can be obtained for sodium-ion batteries. [15][16][17][18] More recently, CNF were applied as anodes for KIB (Table S1) delivering capacities near that of graphite. [19] The potassium storage mechanism for CNF has both capacitive and faradaic contributions whose proportions can be altered by the carbonization temperature. Higher temperatures lead to the formation of graphitic domains for potassium intercalation. [20] In contrast, oxygen functionalization increases the capacitive behavior. [21] Other modifications such as doping with heteroatoms, [22] or encapsulating CNTs in the fibers, [23] have been proposed to enhance the electronic conductivity and increase the active sites for potassium storage. In most cases using CNF-based anodes, however, the first cycle coulombic efficiency (CE) is lower than 50 %, due to the electrolyte decomposition and the concomitant formation of a solid electrolyte interphase (SEI) at the electrode surface. Recently, it was shown that the use of potassium bis(fluorosulfonyl)imide (KFSI) as the electrolyte salt produces a much more efficient SEI than that formed in the more conventional potassium hexafluorophosphate (KPF 6 ) salt. [9,24,25] In this work, we carefully investigate the electrochemical performance of self-supported CNF prepared by electrospinning as negative electrodes in KIB. As the performance is strongly influenced by the electrolyte, two different carbonate-based electrolytes were tested, using either KFSI or KPF 6 salts. To rationalize their performance in more detail, X-ray photoelectron spectroscopy (XPS) was performed to determine the nature and the evolution of the SEI upon cycling. Finally, a specific focus was put on understanding the electrochemical storage mechanism of the CNF by using a combination of cyclic voltammetry and operando Raman spectroscopy.

Results and discussion

Characterizations of CNF

The morphology of the CNF mat was examined using scanning electron microscopy (SEM) showing a uniform network with an average fiber diameter of 250 nm (Fig. 1a inset).

The X-ray diffraction (XRD) pattern (Fig. 1a) revealed a broad peak at 2θ = 25° corresponding to the (002) reflection of graphite and a small large contribution centered at 43° corresponding to the (101) reflections of a turbostratic carbon structure. [26] The Raman spectrum (Fig 1b) displays two bands at 1350 and 1580 cm -1 , corresponding to the disordered (D-band) and graphitized (G-band) sp 2 carbon, respectively. The I D /I G ratio of 1.7 calculated by fitting the spectrum with three Lorentzian and two Gaussian components indicates a disordered structure with local graphite inclusions (turbostratic domains) typical of electrospun PAN-derived CNF (Fig. S1). [26][27][28][29] The presence of these turbostratic domains is confirmed with the transmission electron microscopy (TEM) image (Fig. 1c). The nitrogen adsorption-desorption curve of CNF, which is a type IV isotherm with type H3 hysteresis loop, corresponds to a meso/macro porous structure with a Brunauer-Emmet-Teller (BET) surface area of 30 m 2 .g -1 and an average pore width of 5 nm (Fig. 1d). The electronic conductivity of the self-standing mat was ca. 30 S.cm -1 . [30] The carbon 1s and oxygen 1s XPS core spectra (Fig. 1e,1f), from which a sp 2 /sp 3 ratio of 23 was calculated, indicate that the carbon is mostly graphitic, but that a consistent amount of non-graphitic carbon is also present. Altogether, the bulk (XRD), the close-to-surface (Raman), and the surface (XPS) assessments of the degree of graphitization show that the fibers contain ordered graphitized domains of relatively small sizes (< 2.5 nm, according to Raman spectroscopy).

Half-cell cycling performance

The electrochemical performance of the CNF mats as KIB negative electrodes was evaluated in half-cells vs. K metal using 0.8 M KFSI in EC:DEC and 0.8 M KPF 6 in EC:DEC electrolytes (Fig. 2). Overall, whatever the electrolyte, the galvanostatic profiles (Fig. 2a,d) exhibit three distinct pseudo-plateaus at 0.7, 0.5 and 0.2 V during the first discharge. The first irreversible plateau at 0.7 V can be attributed to the formation of the SEI, occurring mainly during the first cycle. A significant difference in reversibility is observed as a function of the salt: the KFSI-based electrolyte shows a much better CE (63%) than the KPF 6 -based one (45%). The latter performance is better than most data in the literature, all of which were cycled with a KPF 6 -based electrolyte (Table S1). [19][20][21] Moreover, the stabilization of the CE takes longer to reach for the KPF 6 -based electrolyte: 99.5% is reached first after 50 cycles, whereas only 10 cycles are needed for the KFSI-based electrolyte. A capacity retention of ca. 200 mAh.g -1 over 50 cycles is obtained for both electrolytes (Fig. 2b,e). Thus the SEI seems to be less efficient for the first cycles with KPF6, but after thirty cycles it stabilizes and the batteries performance are very close.

The rate capability test, using current densities from 25 mA.g -1 to 1000 mA.g -1 , (Fig. 2c,f), shows both lower capacities at low rate and a more rapid capacity fading for the KFSI-based electrolyte (only 60 mAh.g -1 at 500 mA.g -1 ), while the KPF 6 -based electrolyte can sustain a capacity of 150 mAh.g -1 at 1000 mA.g -1 . The better performance of CNF with KPF6 electrolyte when the current is increased may be due to the fact that the electrolyte decomposition is reduced at high current rates. It is worth noticing that both salts show close ionic conductivities at room temperature, 7.9 and 10.6 mS.cm -1 for KPF 6 (0.8M) and KFSI (0.8M) in EC:DEC respectively. On the other hand it is now well known that the high reactivity of K metal results in specific reaction of electrolytes on its surface, which can influence the other electrode/electrolyte interface via a cross-talk mechanism. [31] Thus we have simply measured the polarization of the K plating/stripping of K//Al cells as function of the rate (corresponding to the C-rate used in Figure 2) for 0.8 M KFSI and 0.8 M KPF 6 in EC:DEC. The latter shows a lower polarization (Fig. S2), thus highlighting that full (with CNF electrode) cell may lead to better rate capability.

In both electrolytes, the CNF electrodes withstand these high current densities without being damaged, since they retain capacities close to initial ones when back to a lower current density, i.e., 130 mAh.g -1 (KFSI) and 200 mAh.g -1 (KPF 6 ) at 200 mA.g -1 , which is higher than graphite, known to suffer at high current densities. [8]

SEI passivation/ stability

As the high electrolyte reactivity at the K metal surface can produce electrolyte degradation species able to migrate to the CNF electrodes, the SEI at the CNF surface may arise from electrolyte degradation generated at both the K metal and CNF electrodes, as previously been demonstrated. [31] As a consequence, as function of electrolyte the electrode/electrolyte interface can hereby be heavily modified and impact the battery performance. To rationalize the different electrochemical behavior observed, especially the coulombic efficiency (CE), XPS was measured to identify the SEI layer. After the first cycle, both the C 1s and the O 1s core spectra have completely changed compared to the pristine electrode, by coverage by electrolyte degradation species, resulting in a large decrease of the C=C peak from the CNF (Fig. S3, Tables S2,S3). From the XPS quantification data, the content of each species (in at.%) detected at the CNF electrodes surface after 1 cycle and 50 cycles was determined (Fig. 3, Table S2 andS3). For the KPF 6 -based electrolyte, 70 at.% consists of organic species (ROCOOK, PEO and other -CO containing species), the remainder being inorganic species (K 2 CO 3 and KOH). In contrast, the KFSI-based electrolyte produces a much lower content of organic species; 35 at.% (mainly ROCOOK, and few PEO and other -CO containing species), while much more salt degradation products including KF, that was not observed for KPF 6 , are formed (65 at.%). After 50 cycles, the amount of ROCOOK increases for both KPF 6 (46 to 62 at.%) and KFSI (34 to 56 at.%) -based electrolytes, suggesting a relatively higher rate of accumulation of carbonate-based solvent degradation species. [9,24,25] At first sight, this might appear surprising in the light of the much lower CE for the KPF 6 -based electrolyte (Fig. 2) and the recent literature reporting the formation of thinner SEI with KFSI. This can, however, be explained by the formation of a more stable SEI at the CNF surface with KFSI, so that new organic species can accumulate over cycling, whereas a less stable SEI is formed with KPF 6 , so that organic species accumulation is hindered. This likely produces the higher ratio of inorganic/organic species after 50 cycles for the KFSIbased cell (0.6) as compared to the KPF 6 -based cell (0.3). Using different CV scan rates results in a cathodic peak at 0.02 V and an anodic peak shifting from 0.4 V to 1.1 V as a function of scan rate (Fig. 4b). This can be analyzed using the following equation: [32] i = aν b , where i is the peak current, ν the scan rate, and a and b are constants that can be derived by performing CV at different scan rates. b indicates whether the K + storage mechanism is faradaic, b = 0.5, or capacitive, b = 1. Here, b values of 0.64 and 0.86 are obtained for the KFSI-and the KPF 6 -based electrolyte, respectively, suggesting both faradaic and capacitive contributions in both cases, even though with a stronger faradic contribution for the KFSIbased electrolyte. Similar measurements done by Lin et al. for CNF derived from PAN carbonized at 1250 °C using a KPF 6 -based electrolyte gave b = 0.66. [20] These results are rather surprising, since the choice of salt anion in principle is not supposed to influence the potassiation mechanism of CNF. Nevertheless, these results confirm the different rate capability tests for the two systems, since a better performance is observed when using a KPF 6 -based electrolyte, which also shows the strongest pseudocapacitive contribution. Indeed, the pseudocapacitive mechanism is expected to be faster and less resistive than the intercalation-induced faradaic reaction. [20] The operando Raman spectroscopy study (Fig. 5) focusing on the two main CNF bands, D and G (typical of carbonaceous materials with sp 2 carbon hybridization) shows two large and partially overlapping bands, characteristic of disordered graphitic structures at OCP (ca.

2.9 V). The D band centered at 1350 cm -1 is due to the ring breathing mode of A 1g symmetry, while the G band at 1580 cm -1 is attributed to the in-plane bond-stretching with E 2g symmetry. [33][34][35][36][37] The capacity of the first discharge (600 mAh.g -1 ) in the operando set-up is noticeably higher than the ca. 300 mAh.g -1 obtained in coin cells. This could be due to the large volume of electrolyte used in the in situ cell, inducing additional electrolyte degradation, and is in line with the lower capacity of 300 mAh.g -1 obtained in the second discharge, where the parasitic reactions can arguably be inhibited by the electrode passivation. Upon discharge, the K + insertion induces a significant red shift of 45 cm -1 of the G band (Fig. 5). This lowering is due to bond weakening in the distorted rings and concomitant softening of the vibrational density of states. [38] A deeper analysis of the band modification shows a relatively fast shift of the G band as function of discharge during the first slope region, until 0.7 V, then the shift is slower during the second slope (0.7-0.25 V), with the shift roughly proportional to the voltage decrease rate. Finally, a steeper shift step follows, starting from 0.25 V until the end of discharge. Relying on already existing models for disordered carbons, [39,40] we ascribe these three stages to the following K + -ion storage mechanisms: first adsorption on defects and edges of graphitic sheets, which are favorable sites for adsorption, [41,42] then intercalation within interplanar space, and finally insertion in the pores. Some potassium plating is also possible at the very end of the discharge, corresponding to the small flat part in the G band position curve.

For graphite intercalated with potassium, the G band splits into two components as stage VI proceeds, [43] and as this is not observed here, which might indicate some more random insertion of K + within the graphitic domains of the CNF electrode. [20] For the D band, known to stem from defects and disorder in graphite nanocrystals, [34,44] the position remains virtually unchanged. However, its intensity decreases and its width increases upon K + insertion, which can be attributed to structural disorder induced by the adsorption of K + at sites where the graphitic structure is not fully developed, such as defects and edges. Moreover, just as an expanded graphite structure is well-known to help Na + intercalation, [45][46][47] we believe that the K + intercalation phase (the second long slope), is assisted by the prior adsorption on defect and edges of graphene (first slope), providing an expansion of the inter-layer spacing.

Conclusions

Self-standing mats of CNF were prepared by carbonization of electrospun polyacrylonitrile.

They were directly used without any additional binder and conductive additive as negative electrodes vs K in KFSI and KPF 6 in EC:DEC electrolytes. In both cases, a stable capacity around 200 mAh.g -1 was obtained over 100 cycles with promising rate capability, especially with KPF 6 . Although similar capacity retention was obtained for both electrolytes, the coulombic efficiency takes longer time to stabilize near to 99 % with KPF 6 compared to KFSI. This distinct evolution prompted us to investigate the SEI composition and its growth during cycling for both electrolytes. The K + storage mechanism was deduced from CV and operando spectroscopy analysis as being a three stages process: adsorption on defect and edges of graphitic sheets, intercalation within the inter-plane space and insertion in the porosity. This storage mechanism is in good agreement with the conclusion of the CV analysis, which suggests a mixed faradic/capacitive mechanism.

The XPS analysis of the SEI demonstrates that the low CE observed for first cycles with KPF 6 is due to the formation of poorly efficient (i.e., unstable and/or not passivating) SEI at the surface of both K metal and CNF electrodes. On the contrary, the use of 0.8 M KFSI in EC:DEC triggers the formation of a more efficient (i.e., more stable and passivating) SEI at the surface not only of the CNF electrode but also of the K metal leading to higher CE from first cycles. Overall, these results highlight the crucial role of the K metal reactivity/passivation in half cell studies compared to most of the literature that neglects the K metal impact on the electrochemical performance. A more precise determination of the reactivity of K metal against the commonly used KPF 6 and KFSI EC:DEC electrolytes is currently under investigation and will be the object of a forthcoming article, but preliminary results showed that a higher polarization is measured when KFSI EC:DEC is used, resulting in a lower rate capability. The better rate capability of the CNF electrode in KPF 6 based electrolyte is likely due to SEI layers formed at both CNF and K metal electrode.

The understanding of the complex SEI formation, its dependence upon the electrolyte composition and its evolution upon cycling is mandatory for the development of KIB. This work underlines the significant impact of the salt anion on the performance of negative electrode materials and open the way to the development of optimized electrolytes for KIB.

Experimental section Synthesis

Self-standing CNF mats were prepared by carbonization of an electrospun polyacrylonitrile (PAN) mat with a process also described elsewhere. [30] Briefly, PAN precursor solutions were prepared dissolving the desired amount in N,N-dimethylformamide (DMF) and stirred overnight. The typical solution 8.5 % PAN/DMF was prepared dissolving 0.85 g of polymer (Sigma Aldrich, M W = 150,000) in 9.15 g of solvent (Sigma Aldrich, 98% purity). The obtained solution was then electrospun for 8 h using a Spraybase ® rotating drum system (100 rpm) with an applied potential of 15 kV, a flow rate of 1.0 mL.h -1 and a needle tip-target distance of 10 cm. The resulting PAN nanofibrous mats were stabilized in oven in air: a first step at 150 °C allows to dehydrate the sample, while a second step at 270 °C allows the cyclisation of the polymer; the dwelling times were set at 2 and 3 hours respectively, while both heating ramps were set at 2.5 °C min -1 . The obtained mats were carbonized in a tubular oven under inert gas flow (argon) and heated at 4 °C.min -1 up to 1500 °C, leading to a selfstanding mat of CNF. The carbonization temperature was held for 2 hours and then the oven cooled down naturally.

Characterization

Scanning electron microscopy (SEM) images were acquired with a Hitachi S-4800 electron microscope. XRD data were recorded with a PANalytical X'Pert Pro MPD diffractometer with a Cu Kα radiation source (λ=1.5418 Å) and a step size of 0.033° between

Operando Raman spectroscopy

After the voltage value at OCP is stabilized (usually it takes few hours), a slow C/25

(assuming a theoretical capacity of 300 mAh.g -1 ) discharge-charge galvanostatic cycling was performed using a GAMRY Series G300 potentiostat, in the range of 1.5 -0 V versus K + /K.

The Raman spectra were collected at room temperature by a nitrogen-cooled CCD detector connected to a Dilor XY 800 spectrometer (Horiba GmbH), equipped with a 514 nm Ar/Kr laser operated with a power of 50 mW at the excitation source (< 2 mW on the sample surface), 1800 grooves/mm grating, 300 mm focal length and pinhole size of 400 µm. The spectra were collected in the backscattering geometry with 50X objective (confocal micro-Raman mode). Each spectrum was the average of 10 accumulations of 2 min each at a resolution of 2 cm -1 . The focal plane was adjusted to be at the electrode surface and very close to the edge of the aperture in the current collector, so that to lessen in-homogeneity of the electric field lines distribution on the probed spot. Before carrying out the operando experiments, we have checked first the stability of the electrode with the same Raman set-up described above. During more than 12 hours of continuous illumination of the electrode, the I D /I G ratio, the peak positions and general shape of the spectra remained unchanged.

Therefore, there is no significant effect of the laser on the graphitization degree and the structure of the CNF. Spectral processing consisted essentially of performing spikes removal, smoothing of noisy spectra, baseline correction, normalization and fitting. These steps were achieved using Origin-lab (V.8) and MATLAB (R2017b) software. To fit the D and G band of disordered carbons, many models have been used in literature. The most common one consists of five functions around 1100, 1350, 1500, 1580, 1620 cm -1 . This model however lacks physical significance. [37,48] Moreover, this model for the case of carbons where the G and D bands are exposed to deep modifications -like of the kind encountered in electrochemical cycling, it makes it hard to follow the changes of so many peaks. In this work, we have explored systematically several routines to deconvolve the spectra using models with different combinations of Lorentzian (L), Gaussian (G), as well as Breit-Wigner-Fano (BWF) profile bands (See r 2 values compared in Fig. S5,S6). The best fit (Fig.

S5, S6

) was achieved with a model consisting of 2 co-centered Lorentzians for D band, one Lorentzian for G band and one Gaussian situated between D and G accounting for sp 3 carbon of amorphous carbon. [35,36,49] X-ray Photoelectron Spectroscopy XPS was performed using an Escalab 250 Xi spectrometer using a monochromatized Al Kα radiation (hν=1486.6 eV). Electrode samples were put on a sample holder using an insulating uPVC tape (reference 655 from 3M). Then the samples transfer was performed through an argon-filled glovebox directly connected to the spectrometer, in order to avoid moisture/air exposure of the samples. Analysis was performed using the standard charge compensation mode and an elliptic 325 x 650 µm X-ray beam spot. Core spectra were recorded using a 20

eV constant pass energy with a 0.15 eV step size and iterative scans at a dwell time of 500 ms.

Using CasaXPS software, the binding energy scale was calibrated from the 285.0 eV peak (C-C/C-H). A non-linear Shirley-type background was used for core peaks analysis while 70%

Gaussian -30% Lorentzian Voigt peak shapes and full width at half-maximum constraint ranges were selected to optimize areas and peak positions.

Electrochemical measurements

CNF discs with a diameter of 12 mm and a mass loading of 1 mg.cm -² were cut in the mat, dried overnight at 80°C under dynamic vacuum and used as self-supported electrodes in CR2032 type coin cells (stainless steel 304L) assembled in an argon-filled glovebox.

Potassium metal (Sigma Aldrich) was used as counter and reference electrodes and a glass fiber (Whatman, GF/D) as the separator soaked with 0.8 M KFSI (purity 99.9 %, Solvionic)

or KPF 6 (purity 99%, Solvionic) dissolved in a 1:1 (v:v) mixture of ethylene carbonate (EC, anhydrous, purity 99 %, Alfa Aesar)/diethylene carbonate (DEC, anhydrous, purity 99 %, Sigma Aldrich). The electrochemical measurements were performed with a MTI battery tester between 0 and 2 V. Cyclic voltammetry curves were conducted on BCS cycler (Biologic).

Figure 1 .

 1 Figure 1. Characterization of the pristine self-supported mat of CNF. (a) XRD pattern with SEM image (inset), (b) Raman spectrum, (c) TEM image, (d) nitrogen adsorption/desorption isotherm, and XPS spectra (e) of C 1S and (f) O 1s regions.

Figure 2 .

 2 Figure 2. Electrochemical performance of CNF vs K. Galvanostatic charge/discharge at 25 mA.g -1 , capacity retention and C-rate with 0.8 M KFSI (a-c) and KPF 6 (d-f) in EC:DEC.

Figure 3 .

 3 Figure 3. SEI composition as derived from XPS quantification of CNF electrodes recovered from CNF/K cells after 1 and 50 cycles), using 0.8 M of either KFSI or KPF 6 in EC:DEC as electrolyte. Striped/plain areas highlight inorganic/organic species.

Figure 4 .

 4 Figure 4. Cyclic voltammetry of CNF using 0.8 M KFSI in EC:DEC at 0.05 mV/s (a) and cyclic voltammetry at different scan rates from 0.05 to 2.5 mV/s (b).

Figure 5 .

 5 Figure 5. Operando Raman spectra (a) along with the corresponding electrochemical cycling charge-discharge curves and the G band position (b) for the CNF electrodes using the KFSIbased electrolyte.

10° and 80° .

 80° Ex situ Raman spectrum was obtained with a Horiba Jobin-Yvon LabRAM ARAMIS spectrometer with an excitation wavelength of 473 nm. The CNF morphology was characterized with a JEOL 2200FS transmission electron microscope (TEM) operating at 200 kV, equipped with a CCD camera GATAN USC (17 million pixels). For the TEM analysis, the samples were suspended in ethanol and deposited after sonication (VWR Ultrasonic Cleaner) onto copper grids coated with carbon. Nitrogen adsorption/desorption isotherms were determined at -196 °C by means of a TriStar II, Micromeritics apparatus after outgassing overnight at 200 °C under vacuum (10 -3 Pa). The specific surface area was calculated by using the Brunauer-Emmett-Teller (BET) equation and taking 0.162 nm 2 as the cross-sectional area of one N 2 molecule. The pore-size distribution was calculated from the adsorption branch of the adsorption-desorption isotherms by the Barrett-Joyner-Halenda equation. Electrical conductivity was measured using a 2400 Keithley in a 4-wire configuration on a 5 x 40 x 0.05 mm carbon electrode strip in a Fumatech MK3-L cell operated in the current range 0-100 mA.

Acknowledgements:

The authors thank the analysis and characterization platform from the "Pôle Chimie Balard". The research leading to these results has received funding from the European Research Council under the European Union's Seventh Framework Program (FP/2007-2013)/ERC Grant Agreement SPINAM no. 306682. SC acknowledges IUF for financial support. We warmly thank the Alistore-European Research Institute (ERI) network for the active collaboration between the two members, the Chalmers University and ICGM. The authors acknowledge also the French RS2E Network (RS2E) (STORE-EX Labex ProjectANR-10-LABX-76-01).

Declaration of interests

☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.