Carlos A Castilla-Martinez

Dominique Granier

Christophe Charmette

Guillaume Maurin

Pascal G Yot

Umit B Demirci
email: umit.demirci@umontpellier.fr

Rubidium hydrazinidoborane: Synthesis, characterization and hydrogen release properties

Keywords: Hydrazine borane, Hydrazinidoborane, Hydrogen storage, Rubidium

2

Introduction

Energy is one of the most critical issues for humanity, and it is expected that the energy demand will rise by up to 30% by 2040 [1]. The primary source of energy nowadays is based on fossil fuels, but their consumption in large scale has led to the release of pollutants and greenhouse gases directly into the atmosphere, causing phenomena such as global warming, with all the negative consequences that these imply [START_REF] Parry | Fossil-fuel subsidies assessed[END_REF]. Besides, fossil fuels are limited resources that are being depleted rapidly.

One of the alternatives to circumvent this issue is the use of hydrogen as an energy carrier because this fuel is clean and nontoxic. As an element, hydrogen is the most abundant in the universe and its molecular counterpart H2 can be produced by a variety of methods [START_REF] Mazloomi | Hydrogen as an energy carrier: prospects and challenges[END_REF][START_REF] Zhang | The survey of key technologies in hydrogen energy storage[END_REF][START_REF] Møller | Hydrogen -A sustainable energy carrier[END_REF][START_REF] Abdalla | Hydrogen production, storage, transportation and key challenges with applications: a review[END_REF]. One of the major challenges for the use of hydrogen as a fuel is its storage. Currently, various approaches are under investigation: hydrogen can be stored by compression in high-pressure gas cylinder, as a liquid in cryogenic tank, or stored in a host material. There are some requirements that a given technology must have to be possibly considered for hydrogen storage, such as high gravimetric/volumetric hydrogen storage capacity, reversibility of storage, stability in use, and cycle life [START_REF] Abdalla | Hydrogen production, storage, transportation and key challenges with applications: a review[END_REF][START_REF] Sinigaglia | Production, storage, fuel stations of hydrogen and its utilization in automotive applications -a review[END_REF][START_REF] Durbin | Review of hydrogen storage techniques for on board vehicle applications[END_REF]. On this basis, a series of materials (e.g. metal hydrides, carbonaceous porous hosts, metal organic frameworks, and inorganic compounds) have emerged owing to high hydrogen storage capacities [START_REF] Lai | Hydrogen storage materials for mobile and stationary applications: current state of the art[END_REF][START_REF] He | Hydrogen carriers[END_REF][START_REF] Ren | Current research trends and perspectives on materials-based hydrogen storage solutions: a critical review[END_REF][START_REF] Berenguer-Murcia | Hydrogen storage in porous materials: status, milestones, and challenges[END_REF].

Boron and nitrogen-based compounds have received particular attention as chemical hydrogen storage materials [START_REF] Hamilton | B-N compounds for chemical hydrogen storage[END_REF][START_REF] Staubitz | Ammonia borane and related as dihydrogen sources[END_REF][START_REF] Chua | Development of amidoboranes for hydrogen storage[END_REF][START_REF] Moussa | Boron-based hydrides for chemical hydrogen storage[END_REF]. Ammonia borane NH3BH3 (AB) is a typical example. It is a crystalline solid at ambient conditions that was reported for the first time in 1955 [START_REF] Shore | The crystalline compound ammonia-borane, H3NBH3[END_REF], and since then it has been widely investigated for hydrogen storage [START_REF] Li | Ammonia borane, past as prolog[END_REF][START_REF] Akbayrak | Ammonia borane as hydrogen storage materials[END_REF]. This material shows high gravimetric hydrogen density with 19.6 wt % H, and is able to release, exothermically and thus irreversibly, 1 equiv H2 between 80-105 °C, then an additional 1.2-1.4 equiv H2 between 120-200 °C, and finally the residual amount of hydrogen above 500 °C. However; there are some problems with this process: reaction rates are slow; undesired by-products like borazine B3N3H6, ammonia NH3 and diborane B2H6 can be formed; and the solid polymeric residue is of complex composition [START_REF] Demirci | Ammonia borane, a material with exceptional properties for chemical hydrogen storage[END_REF]. In other words, AB in pristine state is not suitable for hydrogen storage.

Hydrazine borane N2H4BH3 (HB) is another boron and nitrogen-based compound that has been studied as hydrogen storage material [START_REF] Hügle | Hydrazine borane: a promising hydrogen storage material[END_REF]. HB is a derivative of AB: i.e., one protic hydrogen H δ+ of AB is substituted by an NH2 group [START_REF] Yot | Unraveling the mechanical behaviour of hydrazine borane (NH2-NH2-BH3)[END_REF]. HB possesses a gravimetric hydrogen density of 15.4 wt % H, and it has four protic hydrogens (H δ+ ) and just three hydridic hydrogens (H δ-). The dehydrogenation of HB starts at about 60 °C, and the borane loses 2 equiv H2 up to 250 °C, together with hazardous and toxic by-products (like N2H4 and NH3). Worse, a shock-sensitive residue forms when HB is heated above 300 °C [START_REF] Goubeau | Borinhydrazin und seine pyrolyseprodukte[END_REF][START_REF] Moury | Hydrazine borane: synthesis, characterization, and application prospects in chemical hydrogen storage[END_REF]. Accordingly, derivatives of it obtained by chemical modification have been developed.

Alkali metal hydrazinidoboranes MN2H3BH3 (MHB) are obtained by substitution of one of the protic H δ+ of the middle NH2 of HB by an alkali cation M + , which results in an equivalent number of H δ+ and H δ-in the as-formed ionic compound. Usually, MHB is synthesized by reaction of HB with a metal hydride MH. The first hydrazinidoborane that has been synthesized is LiN2H3BH3 (LiHB; 11.7 wt % H). Wu et al. [START_REF] Wu | Metal hydrazinoborane LiN2H3BH3 and LiN2H3BH3•2N2H4BH3: crystal structures and high-extent dehydrogenation[END_REF] and Moury et al. [START_REF] Moury | Lithium hydrazinidoborane: a polymorphic material with potential for chemical Hydrogen storage[END_REF] reported two different phases of LiHB by ball milling, in different conditions, an equimolar mixture of HB and LiH.

The former group obtained the high-temperature phase α-LiHB (monoclinic, s.g. P21/c), while the latter group reported the low-temperature phase β-LiH (orthorhombic, s.g. Pbca). The phase transition takes place at around 90 °C and then α-LiHB dehydrogenates while liberating 2.6 equiv H2 up to 150 °C. Another derivative is NaN2H3BH3 (NaHB; 8.9 wt % H; monoclinic, s.g. P21/n). It has to be prepared at -30 °C [START_REF] Moury | Sodium hydrazinidoborane: a chemical hydrogen-storage material[END_REF], because of the high reactivity of NaH with HB at ambient conditions (-27.7 kJ mol -1 ) [START_REF] Pylypko | Metal hydride-hydrazine borane: towards hydrazinidoboranes or composites as hydrogen carriers[END_REF]. NaHB starts to release H2 below 60 °C, and is capable of liberating 2.6 equiv H2 in the range 60-150 °C (together with traces of both N2 and NH3) [START_REF] Moury | Sodium hydrazinidoborane: a chemical hydrogen-storage material[END_REF].

A last derivative of HB is KN2H3BH3 (KHB; 7.2 wt % H; monoclinic, s.g. P21). It has to be synthesized in stainless steel vessel and in THF for safety reasons [START_REF] Chua | Alkali metal hydride modification on hydrazine borane for improved dehydrogenation[END_REF], since the reaction between KH and HB is highly exothermic (70.3 kJ mol -1 ) [START_REF] Pylypko | Metal hydride-hydrazine borane: towards hydrazinidoboranes or composites as hydrogen carriers[END_REF]. KHB shows slightly better dehydrogenation properties than NaHB and LiHB do, with a first dehydrogenation step peaking at 52 °C [START_REF] Chua | Alkali metal hydride modification on hydrazine borane for improved dehydrogenation[END_REF]. With the help of computational calculations, the role of the M + cation was predicted to be crucial as it is believed to drive the formation of the complex intermediate M-H⋅⋅⋅H3N2BH2 [START_REF] Banu | Dehydrogenation of lithium hydrazinidoborane: insight from computational analysis[END_REF][START_REF] Li | Theoretical sudy of the metal-controlled dehydrogenation mechanism of MN2H3BH3 (M = Li, Na, K): a family of hydrogen storage material[END_REF]. This is known as the "metal cation driven hydride-transfer" mechanism that was first reported for alkali amidoboranes MNH2BH3, namely the alkali derivatives of AB [START_REF] Kim | Hydrogen-release mechanisms in lithium amidoboranes[END_REF][START_REF] Shevlin | Dehydrogenation mechanisms and thermodynamics of MNH2BH3 (M = Li, Na) metal amidoboranes as predicted from first principles[END_REF]. However the dehydrogenation mechanism is more complex, as with amidoboranes [34].

Studying the isotopomer LiN2H3BD3, Tan et al. brought to light occurrence of counterintuitive homopolar H δ+ ⋅⋅⋅H δ+ and D δ-⋅⋅⋅D δ-reactions resulting in the formation of H2 and D2 (together with heteropolar H δ+ ⋅⋅⋅H δ-reactions leading to HD) [START_REF] Tan | The decomposition of α-LiN2H3BH3: an unexpected hydrogen release from a homopolar proton-proton pathway[END_REF].

No other HB derivatives have been reported so far. There have been efforts to synthesize Mg(N2H3BH3)2 (11.6 wt % H) or Ca(N2H3BH3)2 (9.3 wt% H) by ball milling MgH2 or CaH2 with 2 equiv HB, but all attempts failed [START_REF] Pylypko | Metal hydride-hydrazine borane: towards hydrazinidoboranes or composites as hydrogen carriers[END_REF]. We are continuing to target these alkaline-earth derivatives while exploring alternative synthesis protocols. Chemical modification is an efficient strategy in getting derivatives of HB (as well as of AB) with improved dehydrogenation properties in terms of temperature, kinetics, and H2 purity. We therefore regard as being crucial to obtain and study new derivatives. There are three reasons for this. First, it is important to increase our knowledge and fundamental understanding of boron and nitrogen-based materials through new compounds. Second, they may widen the prospects for hydrogen storage applications. Third, they might open up new opportunities. Accordingly, in this work, we present a new derivative, namely rubidium hydrazinidoborane RbN2H3BH3 (RbHB; 4.65 wt %), which is isostructural (monoclinic, s.g. P21) to KHB.

Experimental

Materials

Hydrazine hemisulfate N2H4⋅½H2SO4 (≥98%), sodium borohydride NaBH4 (≥98%), and anhydrous 1,4-dioxane (≥99.8%) were purchased from Sigma-Aldrich and used for the synthesis of HB according to a procedure we reported elsewhere [START_REF] Moury | Hydrazine borane: synthesis, characterization, and application prospects in chemical hydrogen storage[END_REF]. All the manipulations were done in an argon-filled glovebox (MBraun M200B; O2 < 0.1 ppm and H2O < 0.1 ppm) and a Schlenk line under argon atmosphere. Rubidium (ingot, 99.6%, Sigma-Aldrich) and extra dry THF (99.5%, Acros Organics) were used for synthesis of RbHB in the glovebox at ambient temperature. It is worth mentioning that we preferred Rb to RbH for three reasons. (i) RbH has not been found to be available commercially. (ii) Using RbH supposes a lab-preparation of it and a subsequent reaction with HB; this is a two-step process, whereas the approach proposed herein is a one-step process. (iii) As reported elsewhere [36], the use of RbH is not convenient, for safety reasons, because of its high reactivity (leading to highly exothermic processes).

Synthesis

HB readily reacts with Rb, as AB does [36]. Accordingly, for evident safety reasons, our syntheses were integrally performed in the glovebox where the O2 and H2O concentrations are below 0.1 ppm; in our conditions, we are not able to fix/change the reaction temperature and the reactions were performed at ambient temperature. Typically (Figure S1), HB (246 mg, 5.36 mmol) was dissolved in 20 mL of THF and poured into one flask that contained Rb (400 mg, 4.68 mmol). Release of H2 started immediately. The reaction was left under stirring for 30 h.

After, the remaining rubidium was collected (90 mg, 1.05 mmol). The solid precipitate was washed two times with THF and two times with 1,4-dioxane, dried under vacuum and recovered.

At best it was possible to recover 393 mg of product (corresponding to a yield of 83% with respect to the reacted alkali metal).

Methods

The as-obtained RbHB was analyzed by solid-state 11 B magic angle spinning nuclear magnetic resonance (MAS NMR; Varian VNMR400, 128.31 MHz) and Fourier Transformed Infrared (FTIR) spectroscopy (NEXUS instrument, ThermoFisher Scientific; equipped with an attenuated total reflection accessory from 600 to 4000 cm -1 wavelength). The dehydrogenated RbHB samples were analyzed in the same way.

The crystalline structure of RbHB was determined by powder X-ray diffraction (PXRD) analyses.

A PANalytical X'Pert Multipurpose diffractometer equipped with an X'Celerator detector (Cu-Kα1/α2 radiation λ = 1.5418 Å, 45 kV, 30 mA) equipped with fixed divergence slits was used.

The pattern was collected using Bragg-Brentano geometry on a spinning zero background sample holder loaded in the glovebox. The powder was protected using a Kapton foil stuck onto the sample holder with Apiezon grease (H quality) to prevent any O2 and H2O contamination. The diffraction pattern was recorded in the 15-80° angle range in 2θ using a step size of 0.017, and a scan step time of about 450 seconds. The crystalline state of the dehydrogenated solids was controlled using a Debye-Scherrer configuration (Cu-Kα1 radiation λ = 1.54059 Å). The samples were filled in glass capillary of 0.5 mm diameter in the glovebox, sealed and then analyzed in the equipment.

The thermal behavior of RbHB was analyzed by thermogravimetric (TG) analysis (Netzsch STA 449 F1 Jupiter) that was coupled to a mass spectrometer (MS; Netzsch QMS 403 D Aëolos Quadro). It was also analyzed by simultaneous differential scanning calorimetry (DSC; Q600, TA Instruments).

The thermal behavior of RbHB was also analyzed at isothermal conditions (80, 100, 120 and 140 °C) in a stainless steel reactor. Inside the glovebox, 100 mg of sample was weighed, placed into the reactor and then closed tightly. Outside the glovebox, the reactor was connected to a pressure controller and immersed in an oil bath kept at the designated temperature. The dehydrogenation of the sample was followed by measuring the pressure change. The collected data were converted into mol H2 per mol of RbHB (i.e. equiv H2) and were plotted as a function of time.

Caution

Handling of HB-based materials must be done in the safest conditions. The solid residue of HB after heat treatment above 300°C produces a shock-sensitive material. Though such shocksensitive solid has not been obtained with hydrazinidoboranes yet, care must be taken in any case; there is always a risk to form an instable by-product.

Otherwise, RbHB as well as the solids recovered after heat treatment at any temperature have to be "neutralized" by reaction (alcoholysis) with a small amount of heavy alcohol (e.g. isopropanol) under hood (release of some H2). The as-formed borates are trashed in the container of alkaline aqueous wastes. (ii) Handling of Rb must be done under inert atmosphere, due to its high reactivity and the violent reaction (flash fire) that occurs when in contact with water/moisture. A fast, quite "explosive", reaction takes place between Rb and HB if they are put directly into contact in solid state. It is important to mention here that, at the beginning of this work, we tried to synthesize RbHB at 0°C, outside of the glovebox, using Schlenk glassware connected to a vacuum-argon line. However, we faced some security issues (likely because of traces of moisture and/or oxygen) and we decided to continue our experiments directly in our glovebox (where the O2 and H2O concentrations are below 0.1 ppm). In doing so, we found the safest conditions to prepare RbHB.

Results and discussion

Preliminary comments

Our first attempt to synthesize RbHB (under argon atmosphere) was to put Rb and HB in a round-bottom flask and then add extra-dry THF. We observed a violent reaction between both reactants. After different attempts, we found that it is of extreme importance to dissolve HB in THF before putting it onto Rb. In doing so (Figure S1), RbHB was synthesized in safer conditions a number of times. The same protocol could be used to target other derivative. For example, the cesium derivative should be readily obtained by reaction of Cs with HB in the same conditions (i.e. O2 and H2O-free atmosphere and extra-dry solvent).

RbHB is a white powder. We looked for suitable (anhydrous) organic solvents. It is insoluble in THF, dioxane, and toluene. We also checked its behavior in the presence of a protic solvent.

RbHB reacts with water and ethanol (by solvolysis) and the reaction is accompanied by H2 generation. These observations are consistent with those made for the other alkali hydrazinidoboranes [START_REF] Wu | Metal hydrazinoborane LiN2H3BH3 and LiN2H3BH3•2N2H4BH3: crystal structures and high-extent dehydrogenation[END_REF][START_REF] Moury | Lithium hydrazinidoborane: a polymorphic material with potential for chemical Hydrogen storage[END_REF][START_REF] Moury | Sodium hydrazinidoborane: a chemical hydrogen-storage material[END_REF] and amidoboranes [START_REF] Chua | Development of amidoboranes for hydrogen storage[END_REF][START_REF] Weng | Hydrogen generation from hydrolysis of MNH2BH3 and NH3BH3/MH (M = Li, Na) for fuel cells based unmanned submarine vehicles application[END_REF].

Molecular structure

The sample was analyzed by solid state 11 B MAS NMR spectroscopy and the spectrum compared to that of HB (Figure 1). The spectrum of HB shows a single signal (centered at -24 ppm) which split shape is due to quadrupolar coupling [START_REF] Eaton | NMR of boron compounds[END_REF]. The spectrum of RbHB shows a comparable split signal at lower chemical shift (centered at -17.5 ppm), which is consistent with the substitution of one H δ+ by Rb + . This indicates a different electronic arrangement in the NBH3 environment of RbHB where -NH(Rb)is a stronger donor complex with BH3 [START_REF] Xiong | Highcapacity hydrogen storage in lithium and sodium amidoboranes[END_REF]. This might also suggest shortening of the B-N bond of the anion N2H3BH3 - [START_REF] Chua | Development of amidoboranes for hydrogen storage[END_REF]. Interestingly, the NBH3 signal of RbHB environment could also be interpreted as the occurrence of the complex Rb-H⋅⋅⋅H3N2BH2 [START_REF] Banu | Dehydrogenation of lithium hydrazinidoborane: insight from computational analysis[END_REF][START_REF] Li | Theoretical sudy of the metal-controlled dehydrogenation mechanism of MN2H3BH3 (M = Li, Na, K): a family of hydrogen storage material[END_REF] or the dimer N2H3(Rb)BH2N2H2(Rb)BH3 [START_REF] Chua | Alkali metal hydride modification on hydrazine borane for improved dehydrogenation[END_REF]. The signal at -1.4 ppm is attributed to an N3BH

environment and that at +3.7 ppm to an BN4 environment [START_REF] Kobayashi | Mechanism of solid-state thermolysis of ammonia borane: a 15 N NMR study using fast magic-angle spinning and dynamic nuclear polarization[END_REF]. The presence of such environments implies the formation of highly dehydrogenated polymeric residue of RbHB.

Dehydrocoupling is likely to happen because of a slow evolution of RbHB at room temperature, which besides could be aggravated during analysis (rotor rotation). Furthermore, we cannot rule out the hypothesis that such species may form during synthesis itself.

RbHB was analyzed by FTIR spectroscopy and the spectrum was compared to that of HB (Figure 2). The N-H stretching region is much different. For RbHB, there are fewer bands of lower intensity. Less intense bands are also observed at the N-H bending region. This observation indicates differences between N2H4-of HB and N2H3(Rb)-of RbHB in terms of electronic structure and binding strength. This is typical of a derivative where there are weaker interactions of H δ+ and H δ-between molecules (because of longer distance due to the big Rb + cation) [START_REF] Kim | Rules and trends of metal cation driven hydride-transfer mechanisms in metal amidoboranes[END_REF][START_REF] Zhang | First-principles of intermediate products in the decomposition of metal amidoboranes[END_REF].

Regarding the B-H stretching region, RbHB presents bands at lower wavenumbers than HB does, also suggesting a different chemical environment for the B-H bonds and the BH3 groups.

The N-N stretching band shows a slight change; it is less intense for RbHB. In the B-N region, the signal peaking at 906 cm -1 for HB has red-shifted to 838 cm -1 for RbHB, indicating a stronger B-N bond, which is in good agreement with the downfield shift of the boron resonance (Figure 1). Shorter B-N bonds are typical of hydrazinidoboranes [START_REF] Wu | Metal hydrazinoborane LiN2H3BH3 and LiN2H3BH3•2N2H4BH3: crystal structures and high-extent dehydrogenation[END_REF][START_REF] Moury | Lithium hydrazinidoborane: a polymorphic material with potential for chemical Hydrogen storage[END_REF][START_REF] Moury | Sodium hydrazinidoborane: a chemical hydrogen-storage material[END_REF][START_REF] Pylypko | Metal hydride-hydrazine borane: towards hydrazinidoboranes or composites as hydrogen carriers[END_REF][START_REF] Chua | Alkali metal hydride modification on hydrazine borane for improved dehydrogenation[END_REF] and amidoboranes [START_REF] Chua | Development of amidoboranes for hydrogen storage[END_REF]36,[START_REF] Xiong | Highcapacity hydrogen storage in lithium and sodium amidoboranes[END_REF]. It is also to mention that the spectrum of RbHB is rather comparable to that of LiHB and NaHB (Figure S2). All of these spectroscopic observations are consistent with the formation of RbHB.

Stability

The stability of RbHB in solid state was investigated by 11 B MAS NMR and FTIR spectroscopy.

Two RbHB samples were stored in the argon-filled glovebox for one and seven months after synthesis. The 11 B MAS NMR spectra (Figure 1) show several changes. The most prominent one is the change of the signal at -17.5 ± 1 ppm, attributed to a NBH3 environment which loses its initial asymmetric shape. It is symmetric, suggesting much higher isotropy around the boron atom [START_REF] Eaton | NMR of boron compounds[END_REF].

The intensity of the signal at around -35 ppm and that of the hump at around -15 ppm are higher.

The former signal is characteristic of a BH4 environment. The latter one may be ascribed to an N2BH2 environment [START_REF] Stowe | In situ solid state 11 B MAS-NMR studies of the thermal decomposition of ammonia borane: mechanistic studies of the hydrogen release pathways from a solid state hydrogen storage material[END_REF] . A broad signal of low intensity can be seen between 0 and -12 ppm The FTIR analysis (Figure 2) revealed that the N-H stretching bands broaden with time. The broadening extends beyond 3400 cm -1 , which indicates the presence of polymeric compounds.

Consistently, the B-H stretching bands become weaker in intensity.

In

Crystal structure

The PXRD pattern of RbHB was first compared to that of HB (Figure 3). It does not exhibit any diffraction peaks of HB and can be considered as a pure phase. The broad peaks were identified from Kapton foil used to protect the solid. However, as suggested by the 11 B MAS NMR results, the presence of some amorphous dehydrogenated products cannot be discarded.

The PXRD pattern of RbHB was indexed as a single phase (cf. supplementary material (SM), Tables S1 andS2, and Figures S3 and4 respectively) [START_REF] Wu | Metal hydrazinoborane LiN2H3BH3 and LiN2H3BH3•2N2H4BH3: crystal structures and high-extent dehydrogenation[END_REF][START_REF] Moury | Sodium hydrazinidoborane: a chemical hydrogen-storage material[END_REF]. As a derivative of AB, RbNH2BH3 shows a different monoclinic cell (P21/c) and a volume per formula unit of 94.24 Å 3 [36] which is slightly smaller than the volume per formula unit of 107.22 Å 3 of RbHB; this is consistent with the bigger size of the anion N2H3BH3 -. The volume per formula unit of HB is also smaller with 80.154 Å 3 [START_REF] Wu | Metal hydrazinoborane LiN2H3BH3 and LiN2H3BH3•2N2H4BH3: crystal structures and high-extent dehydrogenation[END_REF], which is consistent with the presence of the bulkier cation Rb + in RbHB.

In RbHB, the cation Rb + is octahedrally coordinated with 6 [N2H3BH3] -anions (Figure S4). In RbNH2BH3 also, it was found a pseudo-octahedron around each Rb + cation [36]. The coordination number for Li + , Na + , and K + in the corresponding hydrazinidoboranes were lower, being 4, 5 and 4, respectively [START_REF] Moury | Lithium hydrazinidoborane: a polymorphic material with potential for chemical Hydrogen storage[END_REF][START_REF] Moury | Sodium hydrazinidoborane: a chemical hydrogen-storage material[END_REF][START_REF] Pylypko | Metal hydride-hydrazine borane: towards hydrazinidoboranes or composites as hydrogen carriers[END_REF][START_REF] Chua | Alkali metal hydride modification on hydrazine borane for improved dehydrogenation[END_REF]. A higher coordination number is consistent with the bigger size of Rb + , i.e. a bigger surface for coordination with the anions [N2H3BH3] -that may interact with Rb + through their two N and six H atoms. For RbHB, the locations of the H atoms linked to the N and B atoms were computationally determined (cf. SM, and Figure S5), allowing to bring out interatomic distances. The shortest Rb⋅⋅⋅Rb distance was found to be 4.246 Å. This is slightly higher than the distance 4.025 Å calculated for RbNH2BH3 [36], which is explained by the bigger size of the anion [N2H3BH3] -.

The shortest Rb⋅⋅⋅N distance was determined as 3.132 Å (vs 3.06 Å for RbNH2BH3). It is longer than the shortest distance in KHB (2.959 Å) [START_REF] Chua | Alkali metal hydride modification on hydrazine borane for improved dehydrogenation[END_REF], because of the bigger size of Rb + vs K + . The Rb⋅⋅⋅H-B and Rb⋅⋅⋅H-N distances resulting from the geometry optimization are between 2.56 and 3.10 Å. The N-N distance is 1.43 Å as obtained from the Rietveld refinement, and it is similar to that observed for HB (1.41 Å) [START_REF] Moury | Hydrazine borane: synthesis, characterization, and application prospects in chemical hydrogen storage[END_REF] and slightly shorter compared to the bond determined in the 27,29]. The B-N bond was determined as being of 1.55 Å, which is consistent with the values 1.54-1.55 Å found for LiHB, NaHB and KHB [START_REF] Moury | Lithium hydrazinidoborane: a polymorphic material with potential for chemical Hydrogen storage[END_REF][START_REF] Moury | Sodium hydrazinidoborane: a chemical hydrogen-storage material[END_REF][START_REF] Pylypko | Metal hydride-hydrazine borane: towards hydrazinidoboranes or composites as hydrogen carriers[END_REF][START_REF] Chua | Alkali metal hydride modification on hydrazine borane for improved dehydrogenation[END_REF]. The bond is besides shorter than the B-N bond of HB (1.59 Å) [START_REF] Moury | Hydrazine borane: synthesis, characterization, and application prospects in chemical hydrogen storage[END_REF][START_REF] Wu | Metal hydrazinoborane LiN2H3BH3 and LiN2H3BH3•2N2H4BH3: crystal structures and high-extent dehydrogenation[END_REF]. This is in good agreement with the aforementioned shortening of the B-N bond by Rb + insertion as suggested by 11 B MAS NMR and FTIR spectroscopy. All of the data are in favor of the formation of an ionic salt where the anion [N2H3BH3] -underwent strong electronic rearrangement due to the presence of Rb + .

In RbHB, the average intermolecular H δ+ ⋅⋅⋅H δ-distances were found to be about 2.84 Å. This is slightly longer than the corresponding data reported in the parent HB (2.246 Å) [START_REF] Moury | Hydrazine borane: synthesis, characterization, and application prospects in chemical hydrogen storage[END_REF], and in the other alkali derivatives: α-LiHB (2.01 Å) [START_REF] Wu | Metal hydrazinoborane LiN2H3BH3 and LiN2H3BH3•2N2H4BH3: crystal structures and high-extent dehydrogenation[END_REF], β-LiHB (2.25 Å) [START_REF] Moury | Lithium hydrazinidoborane: a polymorphic material with potential for chemical Hydrogen storage[END_REF], NaHB (2.41 Å) [START_REF] Moury | Sodium hydrazinidoborane: a chemical hydrogen-storage material[END_REF], and KHB (2.357 Å) [START_REF] Chua | Alkali metal hydride modification on hydrazine borane for improved dehydrogenation[END_REF].

Thermal analysis

Thermolytic decomposition of RbHB was studied by TG analysis (heating rate of 5 °C min -1 ) and the generated gas was analyzed by MS. The material is stable up to ca. 60°C (Figure 5). Then, it starts to decompose. Over the range 60-100 °C, RbHB loses ca. 5 wt % of its initial weight, due to the release of H2 and NH3. Based on calculations made for LiHB, the formation of NH3 can be explained by H-shift across the N-N bond (leading to BH3MN⋅⋅⋅NH3) followed by breaking of the N⋅⋅⋅N link, the reaction being exothermic in nature [START_REF] Banu | Dehydrogenation of lithium hydrazinidoborane: insight from computational analysis[END_REF]. This may explain the NH3 release from RbHB in our conditions, and the bigger size of Rb + (allowing higher coordination with the N-N-B backbone) would be favorable to the aforementioned H-shift. The second decomposition step of our TG analysis takes place in the range 115-145 °C where the mass loss is low with 0.5 wt %. There is a third decomposition step between 150-200 °C, with a mass loss of 1.3 wt %. For both of these last two steps, only H2 was detected by MS. The overall weight loss from 60 to 200 °C is thus 6.8 wt %. This is higher than the 4.65 wt% H carried by RbHB and such a difference may be explained by the loss of a significant amount of NH3. In our conditions, there was no detection of any other volatile by-products (e.g. B2H6, N2H4, or B3N3H6).

In terms of onset temperature of dehydrogenation, RbHB shows better dehydrogenation properties in comparison to HB, and is more or less comparable to LiHB. In contrast, NaHB and RbHB was also analyzed by DSC (Figure 5). The DSC profile clearly suggests a complex decomposition path. A first event, which is endothermic, peaks at 80 °C. As for HB, NaHB, and KHB [START_REF] Moury | Hydrazine borane: synthesis, characterization, and application prospects in chemical hydrogen storage[END_REF][START_REF] Moury | Sodium hydrazinidoborane: a chemical hydrogen-storage material[END_REF][START_REF] Chua | Alkali metal hydride modification on hydrazine borane for improved dehydrogenation[END_REF], melting takes place and precedes the dehydrogenation of RbHB (Figure S6).

Then follow several exothermic events. The DSC and MS analyses suggest five successive steps.

According to MS, these dehydrogenation steps peak at 97, 112, 135, 161, and 188 °C. The first dehydrogenation step is concomitant with a deammoniation step peaking at 95 °C. These results suggest a complex dehydrogenation mechanism. Due to the exothermicity of the dehydrogenation process (cf. SM, and Figures S7 andS8), rehydrogenation of the dehydrogenated samples is considered not possible under normal conditions [START_REF] Hamilton | B-N compounds for chemical hydrogen storage[END_REF][START_REF] Staubitz | Ammonia borane and related as dihydrogen sources[END_REF][START_REF] Chua | Development of amidoboranes for hydrogen storage[END_REF][START_REF] Moussa | Boron-based hydrides for chemical hydrogen storage[END_REF], and if regeneration is considered, it should be done by other means [START_REF] Summerscales | Regeneration of ammonia borane from spent fuel materials[END_REF].

Looking back to the TG analysis, we can see a slight gain of mass (hump) at around 110 °C. A similar feature was reported for HB, NaHB and KHB. This is explained by a "buoyancy effect" that takes place when the melted borane solidifies while releasing H2 [START_REF] Moury | Hydrazine borane: synthesis, characterization, and application prospects in chemical hydrogen storage[END_REF][START_REF] Chua | Alkali metal hydride modification on hydrazine borane for improved dehydrogenation[END_REF]. Foaming accompanies this effect which generally intensifies the aforementioned hump (Figure S6).

Dehydrogenation experiments

Experiments under isothermal heating were carried out in a stainless steel reactor at 80, 100, 120 and 140 °C. The dehydrogenation of RbHB is a two-step process (Figure 6). Similar profiles were reported for LiHB and NaHB for example [START_REF] Moury | Lithium hydrazinidoborane: a polymorphic material with potential for chemical Hydrogen storage[END_REF][START_REF] Moury | Sodium hydrazinidoborane: a chemical hydrogen-storage material[END_REF], even though for RbHB the break in the slope is more prominent. The first step takes place in the first 23, 20, 17 and 13 minutes for the sample heated at 80, 100, 120 and 140 °C, respectively. Then the second step follows. The initial H2 release rates, i.e. those determined for the first step, have been used to determine the apparent activation energy (cf. SM, and Figure S9). To do that it was assumed that RbHB releases H2 only (the evolution of NH3 as by-product was neglected for the calculation). An apparent activation energy of 30.1 kJ mol -1 was calculated. This value is lower compared to other boron and nitrogen-based materials. Pure ammonia borane and LiH-doped ammonia borane have respective apparent activation energies of 183 and 75 kJ mol -1 , calculated by the same method [START_REF] Kang | Ammonia borane destabilized by lithium hydride: an advanced on-board hydrogen storage material[END_REF]. Another example is lithium amidoborane and LiBH4-doped lithium amidoborane, whose apparent activation energies are of 71 and 61 kJ mol -1 , respectively [START_REF] Chen | Synthesis and hydrogen storage properties of lithium borohydride amidoborane complex[END_REF]. In the case of hydrazinidoboranes, the calculated apparent activation energy for LiHB is 58 kJ mol -1 [START_REF] Moury | Lithium hydrazinidoborane: a polymorphic material with potential for chemical Hydrogen storage[END_REF]; however, NaH-doped NaHB possesses lower activation energy of 14.4 kJ mol -1 [START_REF] Moury | Pure hydrogen-generating "doped" sodium hydrazinidoborane[END_REF]. This lower value for NaHB can be related to its better dehydrogenation properties.

The dehydrogenation curves (Figure 6) were further exploited to compare RbHB to HB [START_REF] Moury | Hydrazine borane: synthesis, characterization, and application prospects in chemical hydrogen storage[END_REF],

LiHB [START_REF] Moury | Lithium hydrazinidoborane: a polymorphic material with potential for chemical Hydrogen storage[END_REF], and NaHB [START_REF] Moury | Sodium hydrazinidoborane: a chemical hydrogen-storage material[END_REF], all having been studied in identical experimental conditions. Heated at 100 °C, RbHB, HB and LiHB have comparable H2 release results: they all release 0.60 [START_REF] Møller | Hydrogen -A sustainable energy carrier[END_REF] equiv H2 in 1 h. This is less than the 2.6 equiv H2 liberated by NaHB. Heated at 140 °C for 1 h, RbHB releases 2 equiv H2 vs 1.1 for HB. Slightly better performance was reported for LiHB with 2.2 equiv H2. To sum up, RbHB has improved dehydrogenation properties in comparison to those of pristine HB, is comparable to LiHB, and is less attractive than NaHB. The destabilization effect of Rb + is confirmed. At this point, it would appear that there is no relation between the cation size and the thermal stability of the MHB compounds.

Dehydrogenated samples

The solid residues recovered upon the completion of the isothermal experiments were analyzed by FTIR (Figure 7). The intensity of the N-H and B-H stretching bands decays as the heating temperature increases. A similar behavior can be observed for the signal assigned to the N-H asymmetric bending. All of these bands almost disappeared for the solid heated at 140°C. This is consistent with the formation and release of H2 from RbHB.

The solid residues were also analyzed by PXRD (Figure S10). There is no diffraction peak. The solids are amorphous, suggesting polymeric materials. BN4 [START_REF] Kobayashi | Mechanism of solid-state thermolysis of ammonia borane: a 15 N NMR study using fast magic-angle spinning and dynamic nuclear polarization[END_REF]. The broad signal between +10 and +40 ppm is composed of mainly three signals centered at +14, +20, and +24 ppm. They are due to the presence of borazine-linked polymeric species. The broadening of the signals is due to the quadrupolar effect of the trivalent boron, usually assigned to BN2H and BN3. It seems that like in the case of AB, dehydrocoupling of RbHB results in the formation of polyborazylene-like compounds. Further investigations may appear necessary to go further in the identification of the dehydrogenated residues, but as for AB and its derivatives, this is a task that has proven to be difficult [START_REF] Summerscales | Regeneration of ammonia borane from spent fuel materials[END_REF]. This remains a challenge in our field.

Conclusions

We have synthesized rubidium hydrazinidoborane RbN2H3BH3 (RbHB; 4.65 wt % H) for the first time. Spectroscopic and structural investigations on RbHB were carried out. The substitution of one H δ+ of N2H4BH3 by Rb + has been successful as evidenced by 11 B MAS NMR and FTIR spectroscopy. The presence of Rb + has a destabilization effect on the anion N2H3BH3 -. The destabilization is so important that the solid slightly evolves with time towards dehydrogenated polymeric compounds. The crystal structure of RbHB is monoclinic, with a space group P21 (No.

Figure 1 .

 1 Figure 1. 11 B MAS NMR spectra of HB, RbHB, RbHB after 1 month (stored at room temperature and under argon atmosphere), and RbHB after 7 months. The signals for the samples stored 1 and 7 months were deconvoluted. The chemical shifts (in ppm) are indicated.

Figure 2 .

 2 Figure 2. FTIR spectra of HB, RbHB, RbHB after 1 month (stored at room temperature and under argon atmosphere), and RbHB after 7 months. The bands have been assigned.

  and it can be attributed to N2BH and N3BH environments. Another broad signal deconvoluted into 4-5 signals appeared at positive chemical shifts. The signals at <+10 ppm are typical of tetravalent BN4 environments; the other ones are due trivalent boron environments generally observed for borazine-linked polymeric species[START_REF] Xiong | Highcapacity hydrogen storage in lithium and sodium amidoboranes[END_REF][START_REF] Kobayashi | Mechanism of solid-state thermolysis of ammonia borane: a 15 N NMR study using fast magic-angle spinning and dynamic nuclear polarization[END_REF][START_REF] Kim | Synthesis and characterization of poly-(aminoborane) as a new boron nitride precursor[END_REF][START_REF] Gervais | Chemically derived BN ceramics: extensive 11 B and 15 N solid-state NMR study of a preceramic polyborazilene[END_REF][START_REF] Gervais | 11 B and 15 N solidstate NMR investigation of a boron nitride preceramic polymer prepared by ammonolysis of borazine[END_REF][START_REF] Li | Two novel derivatives of ammonia borane for hydrogen storage: synthesis, structure, and hydrogen desorption investigation[END_REF][START_REF] Roy | Supported ammonia borane decomposition through enhanced homopolar B-B coupling[END_REF].

  ): monoclinic P21 (No. 4) unit cell; Z = 2; a = 5.8128(2) Å, b = 6.7010(2) Å, c = 5.8143(2) Å and β = 108.915(2)°. RbHB is isostructural to KHB [29], whereas α-LiHB and NaHB crystallize in different monoclinic cells (P21/c and P21/n

Figure 3 .

 3 Figure 3. Comparison of the X-ray powder diffraction patterns of pure HB and RbHB. The stars indicate the peaks due to the Kapton foil used to prevent the samples from air and moisture.

  case of the other MHBs (α-LiHB 1.469 Å; β-LiHB 1.495 Å; NaHB 1.46 Å; KHB 1.47 Å)[25-

Figure 4 .

 4 Figure 4. Crystal structure of RbN2H3BH3 along the [100], [010], and [001] directions. H, B, and N atoms are represented by pink, green, and blue spheres. Rb atoms are represented by fuchsia spheres.

Figure 5 .

 5 Figure 5. TGA-DSC (5°C min -1 ) and MS results (m/z = 2, 17, 28, 32 for H2, NH3, B2H6 or N2, N2H4 respectively) of RbHB. The peak temperatures (°C) observed by DSC and MS are given.

Figure 6 .

 6 Figure 6. Time evolution of H2 release from RbHB under heating at a constant temperature (80, 100, 120, and 140 °C).

Figure 7 .

 7 Figure 7. FTIR spectra of RbHB, and of the solid residues recovered upon heating a sample of RbHB at 80, 100, 120, or 140 °C for 24 h. The bands have been assigned.

Figure 8 .

 8 Figure 8. 11 B NMR spectra of RbHB, and of the solid residues recovered upon heating a sample of RbHB at 80, 100, 120, or 140 °C for 24 h. The signals for the solid residues were deconvoluted. The chemical shifts (in ppm) are indicated.

4 )

 4 ; it is isostructural to KHB[START_REF] Chua | Alkali metal hydride modification on hydrazine borane for improved dehydrogenation[END_REF]. The cell parameters are such as a = 5.8128(2) Å, b = 6.7010(2) Å, c = 5.8143(2) Å, and β = 108.915°. Investigations on thermal properties of RbHB were also carried out, under heating at a constant rate (5 °C min -1 ) and in isothermal conditions. RbHB has improved dehydrogenation properties in comparison to the parent HB. The destabilization effect of Rb + has been confirmed. The performance of RbHB is comparable to that of LiN2H3BH3, but is less attractive than NaN2H3BH3, in terms of onset temperature of dehydrogenation and H2 release kinetics. Upon the evolution of H2, an amorphous polymeric solid, mainly

  other words, RbHB is not stable at room temperature. It evolves by dehydrocoupling. Dimeric species as those discussed above form. With time, highly dehydrogenated polymeric species (e.g. polyiminoborane-and polyborazylene-like) form. It is worth mentioning that the AB derivative, RbNH2BH3, also undergoes slow decomposition at room temperature[36]. The reason is the size of the Rb + cation that can destabilize the molecule[START_REF] Luedtke | Hydrogen release studies of alkali amidoboranes[END_REF].

Acknowledgements

The authors acknowledge the Mexican National Council for Science and Technology CONACyT for the scholarship to CACM.

polyborazylene-like, forms. Further investigation would be helpful in going further in the identification of this dehydrogenated solid, the reaction mechanisms, and the exact role that Rb + plays.

Synthesizing and analyzing a new compound like RbHB have provided a number of results that firstly add knowledge about boron and nitrogen-based materials. Secondly, the presented results have shown that the dehydrogenation properties of RbHB are interesting, but for an application in the field of chemical hydrogen storage, RbHB is not the best candidate. It is comparable to LiHB in terms of dehydrogenation properties whereas it carries less hydrogen (theoretical amount of 4.65 wt% H vs 11.7 wt % H). Thirdly, we now have one more HB derivative and future works could be to explore its potential as precursor of boron nitride-based ceramics; the presence of the alkali cation, as possible crystallization promoter [START_REF] Yuan | How to increase the h-BN crystallinity of microfilms and self-standing nanosheets: a eeview of the different strategies using the PDCs route[END_REF], might allow getting highly crystalline boron nitride at low temperature or a crystalline structure different from the hexagonal one.


Xin Huang

Cecilia Zanni-Merk
email: cecilia.zanni-merk@insa-rouen.fr

Bruno Crémilleux

23rd International Conference on Knowledge-Based and Intelligent Information & Engineering Systems

Keywords: Industry 4.0, Semantics, Deep Learning, Time series

Manufacturing enterprises are engaged in implementing new technologies to enhance their manufacturing lines in a smart way. These new technologies give manufacturing enterprises the knowledge, understanding, insight and foresight to improve products, processes and decisions, thereby creating a competitive advantage. In this paper, we explore the use of semantics to enhance deep learning models. We propose an ontology-based LSTM neural network, in which the deep architecture is designed with an ontology to extract high-level cognitive features and stacked LSTM layers for learning temporal dependencies. Our model is applied to a real manufacturing data set with multivariate time series for classification problems. The experiments show that our model can improve performance compared with conventional methods.

Introduction

Industry 4.0 is a vision of current industrial development based on the Cyber-Physical Systems (CPSs) and includes a set of other techniques such as the Internet of things, cloud computing, and artificial intelligence. A significant characteristic of Industry 4.0 is the fusion of information technologies and manufacturing, which creates smart factories. CPSs are central to the smart factory and comprise intelligent machines, storage systems and production facilities capable of automatically exchanging information, trigging actions and controlling each other independently [START_REF] Toro | A perspective on knowledge based and intelligent systems implementation in industrie 4.0[END_REF]. The tools, machines, workstations, and operators are interconnected, facilitating process traceability, adaptive and flexible control of the production equipment and real-time response to uncertain situations. Manufacturing enterprises are engaged in implementing these new technologies to enhance their manufacturing lines in a smart way. Briefly, these manufacturing enterprises need to analyze the flow of data measured by the sensors in the CPSs and to understand their meaning, locally and collectively, for intelligent decision making.

Deep learning based methods often ignore the meaning and interrelationships among data and the internal activities of the manufacturing process cannot be well explained. The data collected during a manufacturing process, past successful experiences and industry standards need to be combined and capitalized as knowledge. This knowledge needs to be formally represented for exploitation, i.e. reusing and sharing knowledge of the manufacturing process. Semantic technologies [START_REF] Efthymiou | On knowledge reuse for manufacturing systems design and planning: A semantic technology approach[END_REF] give the necessary tools to do so.

In this paper, we make the hypothesis that the joint use of semantics and deep learning will improve the quality of the interpretation of huge amounts of data and allow us to gain new insights. This hypothesis includes two aspects:

• Deep learning for semantics: Ontologies are widely used for knowledge representation and reasoning about semantic content in a structured way. However, manual ontology development is a hard and expensive task that usually requires knowledge of domain experts and skills of ontology engineers.

Handcrafted ontologies are often inflexible and complex, which limits their usefulness and makes crossdomain alignment difficult. Recent advances in deep learning [START_REF] Argüello Casteleiro | Ontology learning with deep learning: a case study on patient safety using pubmed[END_REF] have the potential to help mitigate these issues with automatic ontology development and alignment. Hohenecker and Lukasiewicz [START_REF] Hohenecker | Deep learning for ontology reasoning[END_REF], for example, present a novel deep learning based reasoning approach rather than logic-based formal reasoning to ontology reasoning. • Semantics for deep learning: Conversely, there are quite a few works on semantics for deep learning.

Research in semantic data mining [START_REF] Dou | Semantic data mining: A survey of ontology-based approaches[END_REF] has shown that the integration of semantic and data often presents better results on the tasks of data mining and deep learning. However, in most cases of application, formal knowledge representation is usually not well explored. Knowledge is generally encoded in a highly formal and abstract way in description logic. In most machine learning and deep learning algorithms, the input is always in the form of a numerical vector. It is often impractical to apply semantics directly to raw data. Previous works have shown that semantics is mostly applied to data pre-processing by enriching input vectors with ontological concepts and properties [START_REF] Hotho | Ontology-based text document clustering[END_REF][START_REF] Hotho | Ontologies improve text document clustering[END_REF], and post-processing by using distance measures with ontology semantics [START_REF] Jing | Ontology-based distance measure for text clustering[END_REF] rather than in the key stages of machine learning approaches which include model design and training. However, knowledge representation approaches such as ontologies have a similar "geometrical" structure to the ones of deep neural networks. Exploiting this analysis, we expect that a deep architecture designed with the assistance of an ontology can provide high-level cognitive features. Exploring semantics both in the learning and the interpretation process may enhance effectiveness and flexibility of the machine learning approach. However, few advances have been made from this perspective [START_REF] Wang | Ontology-based deep restricted boltzmann machine[END_REF][START_REF] Phan | Ontology-based deep learning for human behavior prediction with explanations in health social networks[END_REF].

In this paper, we explore a novel approach to enhance deep learning models by introducing semantics into a deep learning process. Specifically, we propose an ontology-based model OntoLSTL, in which a core manufacturing process ontology is used to design the deep neural networks that will be used for learning. Based on the instantiation of this core ontology (that includes generic concepts such as lines, machines, sensors ...) with information about a specific production line, our method infers the structure of the deep architecture that will be used to learn the features we are interested in. Moreover, by the use of this generic ontology, our method is comprehensive and can be applied to any manufacturing process.

The rest of this paper is organized as follows: Section 2 presents the concepts and technologies used in this paper. Section 3 and 4 deal with the design of deep architectures with semantics. Section 5 presents the experiments, evaluation metrics and results. Section 6 concludes the paper and discusses future work.

Related works and background

Semantic technologies for manufacturing process modeling

A manufacturing process is often very complex with multiple relations within its internal activities. Therefore, we should choose a formal framework such as ontologies for the representation of the manufacturing process. The term 'ontology' originated in a branch of philosophy which was subsequently included in the field of artificial intelligence for knowledge-based systems and adapted to the information retrieval problems. An ontology offers the concepts and their relations in a domain with a commonly agreed and formal expression such that it is possible to exchange semantic information among computers, and it has the reasoning capability for getting implicit information. An ontology of the manufacturing process refers to a universal model of the structure of manufacturing as an ordered wholeness. Several ontologies, such as PRONTO [START_REF] Vegetti | Product ontology: definition of an ontology for the complex product modelling domain[END_REF] or MASON [START_REF] Severin Lemaignan | Mason: A proposal for an ontology of manufacturing domain[END_REF], have been developed for general knowledge representation of whole product's life-cycle in industry. Some other ontologies, such as the work of Oltramari [START_REF] Ferrario | A first-order cutting process ontology for sheet metal parts[END_REF], have been developed for a specific manufacturing process that can be used in the production of sheet metal parts. Akmal and Batres [START_REF] Akmal | A methodology for developing manufacturing process ontologies[END_REF] present a methodology for developing manufacturing process ontologies, incorporating a group of standards for categorizing classes of processes with formal analysis of the concept.

Deep Learning

The concept of deep learning stems from the study of artificial neural networks. Deep learning architectures such as deep neural networks, deep belief networks and recurrent neural networks have been applied to many fields including computer vision or natural language processing. Deep learning combines low-level features to form more abstract high-level representation.

Recurrent Neural Networks (RNNs)

RNNs are applied to make use of sequential information. In traditional neural networks we assume that all inputs (and outputs) are independent of each other. But for many tasks, this assumption is not true. For example, if the idea is to predict the next word in a sentence, it is useful to know which words come before it. RNNs are called recurrent because they perform the same task for every element of a sequence, with the output depending on the previous computations. Another way to think about RNNs is that they have a "memory" which captures information about what has been calculated so far. Theoretically, RNNs can make use of information in arbitrarily long sequences, but in practice, they are limited to looking back only for a few steps. In addition, RNNs suffer of the so called vanishing gradient problem [START_REF] Hochreiter | Gradient flow in recurrent nets: the difficulty of learning long-term dependencies[END_REF], meaning that new approaches are needed to learn long-term dependencies.

Long short-term memory networks (LSTMs)

Long Short-Term Memory networks (LSTMs) are a special kind of RNNs, capable of learning longterm dependencies. They were introduced by Hochreiter and Schmidhuber [START_REF] Hochreiter | Long short-term memory[END_REF], and were optimized and popularized by many works [START_REF] Dyer | Transition-based dependency parsing with stack long short-term memory[END_REF][START_REF] Sheng | Improved semantic representations from tree-structured long short-term memory networks[END_REF]. LSTMs can prevent the vanishing and exploding gradient problems experienced in RNNs by employing multiple gates (input (I G ), output (O G ), and forget (F G ) gates) that enforce constant error flow through the internal states of special units called 'memory cells'.

Deep learning for time series analysis

Traditional work often depends on hand-crafted feature engineering which is expensive to create and requires specific domain knowledge. The process of feature learning is costly and useless, because it generates many features without significance. Feature selection should be applied to determine the most relevant variables in traditional machine learning methods. However, feature selection is usually computationally very expensive that might also cause loss of valuable training information.

In recent years, deep learning based methods have been demonstrated to be particularly useful for learning time series. Purely feed-forward networks ignore the temporal structure of time series. Recurrent neural networks (RNNs) have shown their capabilities for automatic temporal dependency learning from raw time series data [START_REF] Hermans | Training and analysing deep recurrent neural networks[END_REF]. For this reason, previous research attempted to use deeper architectures and more data in order to get better results compared with shallow neural networks. Pankaj et al. [START_REF] Malhotra | Long short term memory networks for anomaly detection in time series[END_REF] have shown that stacking LSTMs as hidden layers and a sigmoid activation unit enables the learning of higher level temporal features and better results for anomaly detection. Niek et al. [START_REF] Tax | Predictive business process monitoring with lstm neural networks[END_REF] use the same idea of stacking LSTMs and have found that one model with multitask learning yield higher accuracy than predicting them by separate models. Deeper neural networks with stacking recurrent layers can get good results for univariate time series analysis such as manufacturing events analysis. However, for multivariate time series, empirical experiments [START_REF] Ba | Do deep nets really need to be deep?[END_REF] showed that deeper neural networks have found similar or even worse results compared with shallow networks with only one or two hidden layers.

Additionally, the deep networks which are too big and have too many layers makes training difficult and often inefficient due to a lot of meaningless features. For example, a manufacturing time series may contain thousands of redundant features with only a small part of essential features. These redundant or uncorrelated features will degrade the performance of neural networks. In order to solve this problem, some unsupervised learning technologies such as Autoencoder and CNN have been introduced for automating features extraction from raw sensor inputs. Autoencoder [START_REF] Hinton | Reducing the dimensionality of data with neural networks[END_REF] is used to learn the encoding of features with lower dimension from high dimensional multivariate time series, and the learned features representation will be used directly as the input of LSTMs. Nijat et al. [START_REF] Mehdiyev | Time series classification using deep learning for process planning: A case from the process industry[END_REF] present a multi-stage deep learning method by stacking LSTMs Autoencoder and Deep Belief Networks to address the problem of multivariate time series classification for monitoring the product quality in the process industry. CNNs [START_REF] Lee | Unsupervised feature learning for audio classification using convolutional deep belief networks[END_REF] is often used for image processing tasks to learn high-quality features for the classification task. Zheng et al. [START_REF] Zheng | Time series classification using multi-channels deep convolutional neural networks[END_REF] have used CNNs to learn the features of multivariate time series automatically, and combine these learnt features at a final classification layer. Francisco et al. [START_REF] Francisco | Deep convolutional and lstm recurrent neural networks for multimodal wearable activity recognition[END_REF] present a deep learning framework DeepConvLSTM composed of convolutional layers and LSTM recurrent layers that are capable of automatically learning feature representations and temporal dependencies respectively.

Measured values coming from sensors in a manufacturing process usually have relationships among each other. We have already known that there are some relations among the sensor variables obtained with a manufacturing process. But the learning process of Autoencoder or other convolutional based methods is random and uncontrollable. Therefore, we believe that an approach integrated with process information, taking into account the relationships among the measured values by the sensors, will make the learning process more efficient and accurate.

Process Ontology modeling

In this section, we present a methodology for building a process ontology for manufacturing, such that its structure can assist in the design of deep neural networks. It is evident that the data from sensors are always collected with timestamps, constituting then, time series. It it the case of the sensors in the CPSs in an industrial context, that will continuously collect production-related data. In general, time series are a series of data points indexed in a time order [START_REF] Zheng | Time series classification using multi-channels deep convolutional neural networks[END_REF][START_REF] Malhotra | Lstm-based encoder-decoder for multi-sensor anomaly detection[END_REF]. Definition 3.1. A time series X = {x (1) , x (2) , ..., x (n) } is a time ordered sequence of arrays, where each array x (t) ∈ R m contains m values such as x t 1 , x t 2 , ..., x t m and is associated with a timestamp. This array of m values correspond to the input variables measured at a certain time. Definition 3.1 was adopted by most works on time series analysis. But this definition can not represent the real manufacturing process. This definition only considers the order of the final products and their properties, but the relations between the manufacturing lines and the machines cannot be reflected.. The values in the same time point may come from different production lines with dependencies. These relations can be formulated with an ontology as a directed acyclic graph. So we can have a new definition for manufacturing time series as follow: Definition 3.2. A manufacturing time series X = {x (1) , x (2) , ..., x (n) } is a time ordered sequence, where each point x (t) ∈ G = (V, E) is a directed hypergraph which contains v vertices to represent the sensor values where s i precedes s j in a production line. We can have a formal definition of hyperedges E = {(s i , s j )|(s i precedes s j ), s i , s j ⊆ V, s i s j = ∅)}.

In the example in Figure 1, we can clearly see the difference between traditional time series and manufacturing time series. Both examples in Figure 1 show the evolution of time series in time t and t + 1, the properties are independent in traditional time series, but each property in a manufacturing time series is dependent on other properties.

Abstract ontology for manufacturing time series

In general, a manufacturing process aims to achieve given requirements by transforming raw materials into physical objects with specific functions, shapes, structures, and other properties. The works in the previous subsection can provide ontologies for describing the manufacturing activities and their relationships which can be used in many software applications for monitoring a manufacturing process. These ontologies can only provide representations of manufacturing domain knowledge from a general point of view. The challenge here is how to represent the manufacturing domain knowledge properly to facilitate the integration with machine learning applications. For using a deep leaning approach to learn a model of the line from the time series coming from the sensors, we only need to know the hierarchical relationships among the different manufacturing activities and do not need to know the specific meaning of each activity. Therefore, we propose a novel methodology for developing the abstract ontology of the manufacturing process by using only a small number of classes and properties.

• We have production lines (represented by concept L). A standard manufacturing process may include several production lines (meaning that each part is sequentially assembled into the final product through several lines), each line has its own geographic information and depends on the result of a previous line.

• Each production line can consist of multiple machines (represented by concept M).

• The machines are equipped with sensors (represented by concept S) to monitor their operation.

• The data collected from these sensors represented by datatype properties (property measures).

• The dependency relation about lines or machines can be expressed by an object property precedes.

• The inclusion relation between lines and machines can be expressed by an object property hasM achine.

• Similarly, the inclusion relation between machines and sensors can be expressed by an object property hasSensor.

Therefore, even a complicated production process can be simplified as an abstract ontology (Figure 2). The constructed abstract ontology can be aligned with any existing manufacturing ontology like PRONTO [START_REF] Vegetti | Product ontology: definition of an ontology for the complex product modelling domain[END_REF] or MASON [START_REF] Severin Lemaignan | Mason: A proposal for an ontology of manufacturing domain[END_REF] to get semantic information about the manufacturing process. In this paper, we do not consider the order among the machines for simplifying the description.

Instantiation of the abstract ontology

Each factory has a specific production process that can be considered as an instantiation of this abstract ontology. In the example in Figure 3, the part begins at line 0 and then is processed by line 1, and another part begins at line 2. The processing parts from line 1 and line 2 can be assembled into a final product in the assembly line 3. All the information of manufacturing process can be expressed with the assistance of our abstract ontology in Figure 2. We propose a methodology that aims at instantiating the abstract ontology for a specific manufacturing process which is associated with a manufacturing time series.

1. Identify the production lines of a manufacturing process and their hierarchical relationships precedes(line0, line1). 2. Identify the machines of each production line hasM achine(line0, machine1). 3. Identify the sensors of each machine hasSensor(machine1, s1).

Identify the values measured by the sensors measures(s1, val1)

Applying these steps to the manufacturing facility in Figure 3 yields to the instantiation presented in Figure 4 that will be used as a foundation for the design of the deep architecture.

Ontology based deep architecture

In this section, we present our algorithm to design the ontology-based deep architecture for learning the representation of products and its extension OntoLSTM for learning the temporal dependencies. An ontology-based deep architecture is a deep neural network model with stacking of several fully connected layers called dense layers deriving from an ontology. A flat representation of a product's properties as an input cannot reflect the domain knowledge. The key idea of our algorithm is that the representation of a product will be learnt from its own manufacturing lines, the representation of a manufacturing line can be learnt from its own machines and the representation of a machine will be learnt from its sensors and the collected data. Therefore, a product can be represented by these hierarchical relations.

In this way, the presented ontology-based deep architecture can learn a set of manufacturing component representations from both the data distribution and the manufacturing process ontology. In this architecture, each production line and each machine correspond to a dense layer in the neural network. The model construction (see algorithm 1) starts from adding a series of dense layers for learning the representation of production lines iteratively. Similarly, a series of dense layers can be added also for learning the the representation of machines. Then the object property precedes and hasM achine can help us to find the topological structure of our production lines. Therefore, we can connect the added dense layers with the topological structure. we can already get a representation of the manufacturing process. However, since industrial production data usually come in the form of time series, we need to stack an LSTM to capture the temporal dependencies of the data. If a production line does not have a precedent one (that is, it is the last assembly line), its associated dense layer is connected to the LSTM layer.

The proposed model is called OntoLSTM. The neural network includes two parts: an ontology-based neural network which is a stack of several dense layers for learning the representation of lines and machines of a manufacturing process, and a LSTM neural network for learning the temporal dependencies. Based on the manufacturing process ontology in Figure 4, we can construct our ontology-based LSTM neural network as in Figure 5 with Algorithm 1. The production assembly process is composed of 4 manufacturing lines with precedence dependencies and 9 machines. Therefore, our OntoLSTM model will have 13 dense layers and 1 LSTM layer. Each dense layer dense( )(x)) 1 represents a machine or a production line x.

Experiments

To show the feasibility of our approach, we present here a series of experiments by using a dataset proposed by a real manufacturing company. The goal is to predict which products will fail quality control.

Data sets

Bosch has supplied an open huge dataset2 (14.3 Gb) from Kaggle. Each row of this data set represents a product, described by mixed features (numerical, categorical, date stamps), as it progresses through each production line and labels indicating the parts as good or bad. Measurements are done on each part along the assembly line to predict internal failures. Each part has a unique Id. The dataset contains an extremely large number of anonymized features. The training data has 1184687 samples with 968 numerical features, 2140 categorical features and 1156 date features. Features are named according to a convention that tells the production line, the machine on the line, and a feature number. For example, L3 S36 F 3939 is a feature measured on line3, machine36, and the feature number is 3939. The date features provide a timestamp for each measurement. Each date column ends in a number that corresponds to the previous numerical or categorical feature number e.g. the value of L0 S0 D1 is the time at which L0 S0 F 0 was taken.

Preprocessing and Model Construction

The dataset contains 1184687 observations taken from production lines. Each observation is a vector of thousands of sensor measurements plus a label for indicating pass/fail quality control. There are only a small amount of cases of failure (encoded as 1), whereas a much bigger amount of cases pass the quality control and are encoded as 0. This is a 1:171 proportion. To overcome the problems related to the learning of imbalanced data, several techniques [START_REF] He | Learning from imbalanced data[END_REF] have been proposed, such as oversampling, undersampling, and cost-sensitive method. In this experiment, we use the techniques of undersampling by discarding data of the majority class. There are a number of methods available to undersample a dataset used in a typical classification problem. For undersampling the dataset, we use the toolkit Imblearn [START_REF] Lemaître | Imbalanced-learn: A python toolbox to tackle the curse of imbalanced datasets in machine learning[END_REF] which has already integrated principal oversampling and undersampling methods.

Although the features in this data set are still anonymous, the names of the features and their corresponding timestamps can give us the information about the production lines. We can see which feature comes from which line and which machine by considering its name, and also the order among the lines and Table 1. Classification performances of the different studied methods stations with simple data mining on date features. All this information helps us to build an ontology of the manufacturing process from the Bosch dataset.

Results evaluation

Our task is a typical classification problem, we can use the typical evaluation metrics (Accuracy, AUC, F1...) for classification problems and compare with other methods. Standard cross-validation cannot be used in this experiment, because data have time stamps. Therefore, the first 80% of the data set is used for training, and the remaining 20% is used as a validation set. We made three simulations for each method to avoid sampling bias. Table 1 shows the performances of our model OntoLSTM compared to other two methods. DenseLSTM is a deep learning method which stack several dense layers and a LSTM layer. Au-toencoderLSTM stacks Autoencoder and LSTM networks encoding the high dimensional input data to the hidden layers by using relevant activation functions and trying to reconstruct the original inputs through the decoder layer to minimize the reconstruction loss. Our model OntoLSTM outperforms the other two methods. Increasing the depth of the neural network can improve its performance, however it also increass the difficulty of training and optimization (gradient vanishing/exploding problem). DenseLSTM simply increases the number of layers in the neural network, however it almost failed in our experiments. For a huge data set with a large number of features, optimization becomes difficult. Unsupervised learning technologies like Autoencoder is usually used to solve the issue caused by a lot of features. Indeed, Autoencoder is usually used to get a better feature representation and reduce the dimension of features, but the process of encoding and decoding will make the algorithm inefficient. Our model OntolSTM builds the neural networks by integrating manufacturing process ontology for learning a better representation of line/machine. Therefore, OntolSTM can get better results while balancing the efficiency of the algorithm.

Conclusions

In this paper, we present a methodology to design an abstract ontology for representing manufacturing time series with the goal of facilitating the integration of machine learning applications. We introduce a novel ontology-based neural network model OntoLSTM for manufacturing time series classification. We have demonstrated with experiments that ontologies can assist the design of a deep architecture that is capable of extracting high-level cognitive features for representing the lines and machines of a manufacturing process. Stacking the ontology-based neural network with an LSTM network for learning the temporal dependencies yields a better performance when compared with conventional methods. In addition, we can easily build models for any new production lines by stacking simple neural network with our algorithm.

In the future, we are interested in manufacturing process mining with ontology-based neural networks. We expect the new model can predict not only the quality of the final products but also provide smart maintenance plans for the machines during the monitoring of the manufacturing process, to avoid the failures that could occur in the future.

Finally, we acknowledge the Normandy Region (France) for having funded these works in the framework of the NormanDeep project.

Fig. 1 .

 1 Fig. 1. Time series vs Manufacturing time series

Fig. 2 .

 2 Fig. 2. Abstract ontology for manufacturing time series

Algorithm 1 1

 1 Ontology based deep architecture design OntoLSTM(Instances of Ontology O) Let T be an empty model of neural networks Let L be the set of individuals of concept Line in O Let M = be the set of individuals of concept M achine in O for each l in L do T .add(dense( )(l)) end for for each m in M do T .add(dense( )(m)) end for for each x in L do Let s l = {p is a set instantiation of the concept Line in O | precedes(p, x)} for each y in s l do dense( )(y) → dense( )(x) // → indicate the two layers are connected end for Let s m = {m is a set instantiation of the concept M achine in O | hasM achine(x, m)} for each z in s m do dense( )(z) → dense( )(x) end for if (s r = {h is a set instantiation of the concept Line in O | precedes(x, h)} = ∅) then dense( )(x) → LST M end if end for return T In dense( )(x), represents the number of output neurons for the concerned layer.

Fig. 5 .

 5 Fig. 5. The structure of OntoLSTM for our example of manufacturing process

https://www.kaggle.com/c/bosch-production-line-performance