Abdel Karim

Virginie Degardin
email: virginie.degardin@univ-lille.fr

Vincent Cocquempot
email: vincent.cocquempot@univ-lille.fr

M Amine Atoui
email: matoui@univ-lille.fr

Soft Fault Detection and Localization in an Unshielded Twisted Pair Network using Power Line Communication

Keywords: Vehicular Embedded Networks, Monitoring, Fault Detection, Fault Localization, Soft Fault, Transmission System

Vehicular electrical wires and communication systems can be affected by faults. Depending on their severity, faults can be divided into two families: hard and soft faults. Hard faults indicate open/short circuits that can lead to serious breakdowns because they prevent the flow of energy or information. Even though soft faults do not have such severe impacts on the system, they may develop into hard faults in the long term, hence the need to detect them. In this paper, an unshielded twisted pair cable that undergoes a water-tree degradation is considered. A soft fault, which may correspond to the effect of a mechanical constraint on the cable, is introduced as a series resistor. The studied network is a tree-shape network composed of one source and multiple receivers, one at each end of a branch. Assuming that these receivers operate in a healthy state, to detect the fault, the transfer function from each receiver are monitored and a detection index is used. Another index, based on the comparison of the effect of the fault at each endpoint, is proposed to locate the affected branch. To summarize the detection and localization algorithm, a signature matrix is generated. Simulation results are presented to illustrate our approach.

INTRODUCTION

As part of autonomous/electric vehicles, Advanced Driver Assistance Systems (ADAS) have been integrated into vehicles to ensure road safety and driving comfort [START_REF] Lindgren | State of the art analysis: An overview of advanced driver assistance systems (adas) and possible human factors issues[END_REF]). These systems rely on electronic equipments that exchange information via dedicated communication networks. Since the One-Pair Ether-Net Alliance has encouraged the adoption of the automotive Ethernet for modern vehicular networks, the studied network is based on Unshielded Twisted Pair (UTP) cables (Zweck and Communication, 2019). This network is subject to failures that can have fatal consequences. Hence the need of a monitoring system that can distinguish between healthy and faulty states. Reflectometry and its derivatives are the main used monitoring methods for fault detection in networks [START_REF] Furse | Fault diagnosis for electrical systems and power networks: A review[END_REF]. Time Domain Reflectometry (TDR) and Frequency Domain Reflectometry (FDR) form the two main classes of reflectometry systems [START_REF] Furse | Frequency-domain reflectometry for on-board testing of aging aircraft wiring. Electromagnetic Compatibility[END_REF]. The experiments using reflectometry realized by [START_REF] Kafal | Baselining: A critical approach used for soft fault detection in wire networks[END_REF], on a two-wire transmission line, demonstrate that soft faults can have signatures which are much weaker than changes in impedance which can affect a cable during actual installation conditions. Furthermore, the measurement and simulations in [START_REF] Griffiths | The invisible fray: A critical analysis of the use of reflectometry for fray location[END_REF], on a two-wire conductor General Cable SKU 02301.R5.02 lamp cord [START_REF] Corporation | Cord & Cordset Products for industrial, commercial and specialty applications[END_REF], have also shown that these signatures are invisible using TDR, FDR and Spread-spectrum TDR in the frequency range of 100 MHz -200 MHz due to the normal impedance variation in the realistic environment of the cable. Moreover, even if the latter was detected, reflectometry based methods have a major drawback due to the use of special, expensive and bulky sensors and instruments [START_REF] Huo | Cable diagnostics with power line modems for smart grid monitoring[END_REF]. Finally, according to [START_REF] Kafal | Baselining: A critical approach used for soft fault detection in wire networks[END_REF], the effect of nodes on a reflectogram can be much stronger than the effect of a soft fault. A Power Line Communication (PLC) system can also be used to monitor the network. In [START_REF] Lallbeeharry | Plc applied to fault detection on in-vehicle power line[END_REF], a PLC system implemented a real time fault detection between two modems. The time evolution of the transfer function H( f ) between the two ends of a UTP cable is monitored, and statistical coefficients (i.e. Correlation coefficient ρ and Euclidean distance Eu) are used to detect soft faults.

In this paper, a soft fault detection and localization method is proposed and analyzed in a tree-shape network composed of one source and multiple receivers, one at each end of a branch. The soft fault -which can appear, for example, after an electric discharge or may be due to a bad contact between a wire and a connector -is represented by the insertion of a series resistance such as in [START_REF] Lallbeeharry | Plc applied to fault detection on in-vehicle power line[END_REF], while the network is being degraded by a Water Tree (WT) phenomenon as described in [START_REF] Huo | Cable diagnostics with power line modems for smart grid monitoring[END_REF]. This phenomenon represents the change in the dielectric permittivity of a cable insulation caused by humidity. The transfer function from each point of view of the receivers, between the source and each receiver is measured by the receivers at the end of each branch of the network, through Orthogonal Frequency Division Multiplexing (OFDM) pilot symbols sending by the source. Afterwards, two indices extracted from H( f ) are used to detect the presence of a soft fault then to locate the affected branch. The detection index is calculated between a reference H( f ) and the successive ones which are measured. Once the presence of a fault is confirmed, a localization index is then computed to determine the branch where the fault is located. The fault detection and localization methodology proposed in this paper is general, but for presentation reason, a network comprising one source, three receivers and two nodes is studied. In section 2, the detection and localization indices is presented along with the proposed method. To validate this work, data are generated using cable, degradation and soft fault models that are detailed in section 3.1. Simulations and the results are carried on in section 3.2. Conclusions and future work to be conducted close this paper.

FAULT DETECTION AND LOCALIZATION 2.1 Fault Diagnostic Indices

The indices presented in this section are based only on the transfer function H( f ) of the network.

Definition 1. CORRELATION COEFFICIENT. The correlation coefficient ρ i expresses the distance be-tween the reference transfer function and the successive acquisitions (measurements) of the transfer function at each receiver R i .

ρ i = | ∑ k H Re f erence R i ( f k ) • H * Test R i ( f k )| ∑ k |H Re f erence R i ( f k )| 2 (1) H Re f erence R i
is the first acquisition that is supposed to be the transfer function for the healthy network at the receiver R i . H Test R i is the measured transfer function that needs to be tested at a given time at the receiver R i . f k denotes the frequency in the range of 1 MHz to 100 MHz with a step of 10 kHz. X * denotes the conjugate of X, and |X| is the module of X. Definition 2. DETECTION INDEX. The detection index I D i is defined as :

I D i = |1 -ρ i | (2)
In a healthy case, I D i computed at each receiver i, is equal or close to zero and it deviates from zero when a fault occurs or in presence of network degradation. Once the fault is detected by one of the receiver, the network is considered as faulty.

Definition 3. LOCALIZATION INDEX. The localization index I L i is defined as the ratio between transfer function measured after fault detection at each receiver R i and the transfer function measured before fault detection at each receiver R i .

I L i = H Faulty R i ( f ) H Healthy R i ( f ) (3)
where I L i denotes the localization index at the receiver R i . H

Healthy R i ( f ) and H Faulty R i
( f ) are respectively the transfer function before and after the fault detection.

Illustration on a Study Case

To show the usefulness of the localization index, the three cases in Fig. 1 are explored. Let consider the multipath model found in [START_REF] Zimmermann | A multi-path signal propagation model for the power line channel in the high frequency range[END_REF], [START_REF] Zimmermann | A multipath model for the powerline channel[END_REF]) that describes the propagation of the waves in a network. According to the multipath signal propagation model, there are several possible paths from the source S to the receiver R i . Furthermore, the signal is reflected at each impedance discontinuity (localized fault and node). The received signal is thus a superposition of the first M significant paths considered. Hence, the transfer function H( f ) can be represented by the following equation : where M represents the number of the most significant paths, g i denotes the weighting factor of the path i representing the product of the reflection and transmission factors along the path, γ is the complex propagation constant and d i is the length of the path i. γ and g i are both depending on the frequency f . To simplify and to limit the number of paths M, the source and the receivers are considered matched to the network and the first two paths are taken into consideration.

H( f ) = M ∑ i=1 g i • e -γ•d i (4)
1. Healthy Network.

In a healthy network, only the direct path between the source and each receiver exists. Thus, the transfer function from the point of view of each receiver is :

H Healthy R 1 ( f ) = t 1N • e -γ•(l 0 +l 1 ) (5) H Healthy R 2 ( f ) = t 1N • e -γ•(l 0 +l 2 ) (6)
where t 1N is the transmission factor of the node N, l 0 , l 1 and l 2 are the lengths of each branch presented in Fig. 1.

2. Appearance of a Soft Abrupt Fault Before the Node.

If a fault occurs at the branch situated before the node N, several paths are possible.

From the point of view of R 1 , the two most significant paths are :

-Path no.1 : S -F -N -R 1 . -Path no.2 : S -F -N -F -N -R 1 .
Therefore, the frequency response from S to R 1 can be expressed as:

H R 1 ( f ) = t F • t 2N • e -γ•(l 0 +l 1 ) + (7) t F • r 2N • r F • e -γ•2•(l 0 -x) • t 2N • e -γ•(l 0 +l 1 ) = t F • (1 + r 2N • r F • e -γ•2•(l 0 -x) ) • t 2N • e -γ•(l 0 +l 1 )
By analogy for the second receiver R 2 :

H R 2 ( f ) = t F • t 2N • e -γ•(l 0 +l 2 ) + (8) t F • r 2N • r F • e -γ•2•(l 0 -x) • t 2N • e -γ•(l 0 +l 2 ) = t F • (1 + r 2N • r F • e -γ•2•(l 0 -x) ) • t 2N • e -γ•(l 0 +l 2 )
where t F and r F represent respectively the transmission and reflection factors of the fault. t 2N is the transmission factor of the node N. The attenuation due to the signal propagation from the source to each receiver i, e -γ•(l 0 +l i ) , are multiplied by the same value in ( 7) and ( 8).

Hence, I L 1 and I L 2 are equal :

I L 1 = I L 2 = t F • (1 + r 2N • r F • e -γ•2•(l 0 -x) ) • t 2N t 1N (9)
3. Appearance of a Soft Abrupt Fault after the Node.

If a fault occurs after the node N in the branch directly linked to the receiver R 1 , several paths are also possible. From the point of view of R 1 , the transfer function is expressed as:

H R 1 ( f ) = t F • t 3N • e -γ•(l 0 +l 1 ) + (10) t F • r 3N • r F • e -γ•2•x • t 3N • e -γ•(l 0 +l 1 ) = t F • (1 + r 3N • r F • e -γ•2•x )t 3N • e -γ•(l 0 +l 1 )
From the point of view of R 2 , the transfer function is expressed as :

H R 2 ( f ) = t 3N • e -γ•(l 0 +l 2 ) + (11) t 3N • r F • e -γ•2•x • t 3N • e -γ•(l 0 +l 2 ) = (1 + t 3N • r F • e -γ•2•x )t 3N • e -γ•(l 0 +l 2 )
where r F denotes the reflection coefficient of the fault. t 3N and r 3N are respectively the transmission and reflection coefficient of the node N. In this case, the weighting factors from ( 10) and ( 11) are different. Therefore, I L 1 is different than I L 2 :

I L 1 = t F • (1 + r 3N • r F • e -γ•2•x ) • t 3N t 1N ( 12
)
I L 2 = (1 + t 3N • r F • e -γ•2•x ) • t 3N t 1N (13)
This index allows us to eliminate the effect of the attenuation due to the signal propagation from the source to the i th receiver : e -γ(l 0 +l i ) . This index depends on the attenuation due to the propagation between the node and the fault positions : e -γ•2•x , the severity of the fault represented by r F and/or t F and the impact of the fault on the node represented by the change of the transmission and the reflection factors of the node.

Fault Detection and Localization Method

The proposed fault detection and localization method (see Fig. 2) is based on the two aforementioned indices.

Compute It is divided into two phases : "Detection" then "Localization". These phases are discussed in the following.

Detection Phase:

The network is considered as faulty, if, at least, one of the indices, I D i , computed at each receiver is higher than its predefined threshold ψ i .

Otherwise the network is considered as healthy.

The threshold ψ i is empirically deduced to detect abrupt soft faults while taking into account the slow variation due to WT. The localization phase is triggered after the presence of a fault is confirmed.

Localization Phase:

When a fault is detected, the indices I L i are computed. To determine the branch where the present fault is located, they are evaluated and compared to each branch's fault reference signature. This comparison would suggest a set of candidates.

A candidate stands for the presence of fault in a branch j, denoted F B j . Multiple faults are out of the scope of this paper. Only single faults are assumed.

Table 1: Signature matrix.

F B 1 . . . F B N I L 1 α 1,1 • • • α 1,N . . . . . . . . . . . . I L m α m,1 . . . α m,N min R i (I L i ) β 1 . . . β N
The proposed signature matrix is derived and depends on the considered network's topology. The ma-trix rows correspond to the behaviour of m + 1 indicators, the m receiver's localization indices plus the set of receivers at which the localisation indices values are minimum, towards the presence of a fault F B j . Its columns describe the N branch's fault reference signatures.

A general signature matrix is provided in table 1. It is characterized by two types of parameters: α and β. A parameters α i, j represents a Boolean variable which takes its values in the set {0, 1}. Equal localization indices relative to a fault in a branch j, admit a value 1 for their Boolean variables in the column j of the signature matrix. The Boolean variables of those with different values take 0. A parameter β j is a subset, in respect to F B j , of receivers at which the localisation indices' values are the lowest, such as

β j ⊂ {R 1 , . . . , R m }.
The signature matrix is build in such a way that faults are decoupled in a set of tests such as the m + 1 indicators reacts to a particular subset of faults and each branch's fault j is characterised by a certain configuration of the indicators values. Different configurations arise depending on where the fault is located on the network. These configurations can be generalised by four cases.

• If a fault F B j occurs prior to all the network's nodes, the closest branch to the source: all the m indices computed at the receivers situated after it lead to a same value. The localization indices are equal, ∀i, α i, j = 1.

• If a fault F B j occurs behind all the network's nodes, at the end of the network: all the m indices computed at the receivers situated before it take different values, ∀i, k, α i, j = α k, j . The localization index i directly linked to the affected branch has the lowest value among all the indices, β j = I L i .

• If a fault F B j is located at the branch directly linked to a receiver i: only the indices computed at the receivers situated after the nearest node to the receiver i share the same values, for each k, k > i, α k, j = 1. The index of localization related to the receiver i has the lowest value among all the indices, β j = I L i .

• If a fault F B j occurs between two nodes, a branch not linked to any receiver: only the indices computed at the receivers situated before the farthest node, among the two nodes, to the source take different values, for each k, k < i, α i, j = α k, j . The receivers sharing the same values have the lowest values compared to the remaining indices' values.

for each k, k > i, I L k ∈ β j .

Summary

The step-by-step procedure of the proposed fault detection and localization scheme based on PLC is summarized as follows:

1. Construct the signature matrix with respect to the network topology.

2. Define the reference H( f ) from each receiver's point of view.

3. Acquire successive H( f ) from each receiver i, compute and monitor

I D i .
4. If no change is detected, return to step (3), otherwise go to the next step.

5. Compute the localization indices I L i and evaluate their equality and minimum inequality.

6. Compare the resulting evaluation vector of step 5 to the fault signature in the signature matrix.

7. From step ( 6), deduce the faulty branch.

APPLICATION CASE STUDY

Data Generation

In this work, the studied network is the automotive Ethernet, which is based on Unshielded Twisted Pairs (UTP). This type of network is chosen for its ability to meet the needs of ADAS [START_REF] Hank | Automotive ethernet: In-vehicle networking and smart mobility[END_REF]. Simulations of such a network are used to validate the proposed method, before applying it in a real network. In this paper, a deterministic chain matrices -based model [START_REF] Galli | A novel approach to the modeling of the indoor power line channel-part ii: Transfer function and its properties[END_REF] of the network is used to compute H( f ). The model is based on the physical characteristics of the network (topology, type of cable, terminal devices i.e. source and receivers) and on the transmission line theory. Each quadripole element of the network is represented by its corresponding ABCD chain matrix and the global ABCD chain matrix is obtained by the product of each individual element matrix [START_REF] Peres | Abcd matrix: A unique tool for linear two-wire transmission line modelling[END_REF]. For UTP networks, chain matrices can be computed using the equations found in [START_REF] Al-Asadi | Twisted pair cable design analysis and simulation[END_REF], [START_REF] El Sahmarany | Méthodes d'amélioration pour le diagnostic de câble par réflectométrie[END_REF], [START_REF] Truong | Twisted-pair transmission-line distributed parameters[END_REF] and [START_REF] Ramo | Fields and waves in communication electronics[END_REF]. To our knowledge, the work by [START_REF] El Sahmarany | Méthodes d'amélioration pour le diagnostic de câble par réflectométrie[END_REF] is the only one between them that took the twisting phenomenon in consideration. Thus, the equations found in the latter one are used to compute those parameters in the marge of frequencies going from 1 MHz to 100 MHz. These parameters are used in the deterministic model to deduce H( f ).

Degradation and Soft Fault Model

Degradation: The degradation is defined by an irreversible process in one or more of the primary parameters of the cable with either time, duration of use or an external cause (e.g. humidity, heat, etc . . . ). The WT degradation caused by humidity affects the dielectric permittivity of the wire ε. Since the capacitance of the cable is directly related to ε, then the WT degradation is represented as two capacitors as shown in Fig. 3 [START_REF] Mugala | High frequency characteristics of medium voltage XLPE power cables[END_REF]. A model of the WT degradation seen in [START_REF] Förstel | Grid diagnostics: Monitoring cable aging using power line transmission[END_REF] and [START_REF] Sanniyati | Water tree in polymeric cables: A review[END_REF] is used to reproduce a degradation going from 0% to 5% with a step of 0.1% (y = 0 to y = 0.05). The total permittivity of an aged sample given by [START_REF] Huo | Cable diagnostics with power line modems for smart grid monitoring[END_REF] is computed using ( 14) :

ε total = ε insulator ε W T y ε insulator + (1 -y)ε W T (14)
where ε W T is the permittivity of the affected zone shown in blue in Fig. 3. ε insulator is the permittivity of the pure insulation and y is the percentage of affected zone.

In the following, the WT degradation is considered as a normal evolution of the cable characteristics.

Soft Fault: This type of faults only produces very small impedance changes [START_REF] Auzanneau | Progress In Electromagnetics Research B[END_REF]. Therefore, a soft fault is usually represented by a small localized modification of the characteristic impedance of the line. In this paper, the soft fault is represented by a sudden insertion of series resistor.

Simulations and Results

As an example, several simulations are conducted on the network shown in Fig. 4. This network has one source, three receivers, two nodes and five branches. Note that the source and all the receivers are matched to the line (Same impedance of the characteristic impedance of the network Z c = 120 Ω).

The three transfer functions of the network are computed and their corresponding Bode diagram are shown in Fig. 5 and6. 1. Detection: We assume that the level of degradation increases by 1% every 50 successive acquisi- tions of H R i ( f ). In this case, where the cable undergoes a WT degradation with the absence of a soft fault, the computed

I D i between H re f erence R i ( f ) and H test R i ( f )
increases at a slow pace as shown in Fig. 7 for each receiver. H test R i ( f ) denotes the successive test data that needs to be tested at a given time. When an abrupt soft fault occurs, its effect differs from one receiver to another. A 5 Ω resistor is inserted in series in the branch B 3 at t = t 0 , a rupture in the variation of the detection index appears as shown in Fig. 8. Therefore, to detect the abrupt soft fault, it is sufficient to detect the rupture by one of the receiver.

Localization:

Once the fault is detected, to locate the affected branch, the localization index I L i is computed at each receiver. Since I L i is a complex index, its module |I L i | and phase φ(I L i ) are presented in Fig. 9. The results show that I L 1 is equal to I L 2 and both of them are different from I L 3 as it has been proven in section 2. By changing the position of the fault from one branch to another and observing the behavior of the localization indices a topology dependent signature matrix can be obtained.

Table 2: Signature matrix of the network in Fig. 4.

F B 1 F B 2 F B 3 F B 4 F B 5 I L 1 1 1 1 0 0 I L 2 1 1 1 0 0 I L 3 1 0 0 0 0 min R i (I L i ) R 3 R 1 ∩ R 2 R 2 R 1
Note that a 1 in the table represents equality and a 0 represents a difference. All the indices holding the number 1 are equal and the index holding the number 0 is different from all the other indices. For instance, let consider that the localized soft fault occurs at the branch B 2 or the branch B 3 . The fault is then located before the node connected to the receivers R 1 and R 2 , therefore I L 1 is equal to I L 2 . To differentiate between these two cases (fault at B 2 or at B 3 ), the minimum index should then be computed. The branch B 2 is directly linked to R 3 , thus if the fault is in this branch, the minimum index is I L 3 . If there was no exclusive minimum min(I L i ) = I L 1 = I L 2 , then the fault is in the common branch of the two receivers as in the case of fault in B 3 .

CONCLUSIONS AND FUTURE WORK

In this paper, a fault detection and localization methodology based on the transfer function H( f ) is proposed to detect a soft fault. This methodology is based on two indices. At first, the detection index is computed. Once the fault is detected by one of the receivers, the localization index is computed at each endpoint of the network. The localization indices are checked against one another to verify whether they are equal or not. The results of these tests are compared to each branch fault's signature in the proposed signature matrix. The proposed matrix is constructed with respect to the network topology. To illustrate our proposal, two bus networks are considered: a degrading two nodes network and an Y-shaped (one node) network. Further work will be realized to expand the validity and study the sensitivity of the detection and localization indices, in more complex networks. Also, it would be interesting to introduce statistical equality tests for better and accurate comparison between localization indices when noise is considered. This approach will be also validated on a test bench that is under construction.

Figure 1 :

 1 Figure 1: Y-Shaped networks.

Figure 2 :

 2 Figure 2: Fault detection and localization algorithm.

Figure 3 :

 3 Figure 3: Representation of an aged sample.

Figure 4 :Figure 5 :

 45 Figure 4: Unshielded twisted pair network.

Figure 6 :

 6 Figure 6: Phase of the transfer function of each receiver φ(H R i ( f )).

Figure 7 :Figure 8 :

 78 Figure 7: Variation of the detection indices (Degraded network without abrupt soft fault).

ACKNOWLEDGEMENTS

This research work is done in the framework of the ELSAT2020 project which is co-financed by the European Union with the European Regional Development Fund, the French state and the Hauts-de-France Region Council.


Paul-Aymé Toulemonde

Julie Diani

Pierre Gilormini

Nancy Desgardin

Robert Nevière

Propellant cohesive fracture during the peel test of a propellant/liner structure

Keywords: Aerospace, Composites, rubbers

The integrity of propellant/liner structures in rocket motors is critical to ensure controlled combustion of the engine. In an effort to improve the bonding between the liner and the propellant, it is necessary to characterize it well. Therefore, a propellant-liner structure, bounded thanks to co-curing, has been submitted to a peel test while recording the macroscopic fracture energy and the local displacement field on the propellant free surface.

The experimental setup includes two cameras in order to record the displacement field on the propellant free surface. Upon loading, the peel force stabilizes quickly due to a cohesive fracture in the propellant, providing access to the fracture energy. While the crack propagates through the propellant, it is observed that only a small localized area is submitted to strain and most of the structure remains unstrained.

Introduction

The design of rocket motors includes a rubber layer in contact with the metal or composite motor case that acts as an insulation interface preventing excessive heating of the case during propellant combustion. This rubber layer is adhered to the propellant thanks to a flexible liner. appearance of voids or cracks within the propellant, which could appear during storage or transport for instance and could cause the failure of the engine. Therefore, the propellant/liner bond is a critical feature for the design of rocket motors. From a material point of view, strategies have been found to strengthen this joint [1,2]. In order to characterize the propellant/liner structure mechanically, several experimental tests including classic mode I fracture tests, poker-chip test, etc [3], double cantilever sandwich [4], sandwich single-lap joint [5] were performed to assess the adhesive and cohesive fracture energies. These studies provide macroscopic quantitative measurements of the energy needed to detach the liner from the propellant and in some cases, qualitative microscopic observations of the propellant cohesive fracture or propellant/liner debonding. In the present contribution, the propellant/liner bond is tested via a peel test. Such a test has been used for testing the adhesion of bonded composite-to-aluminium joints for instance [6]. Cohesive fracture within the propellant is observed when the liner and the propellant are well bonded. Propellants are highly filled rubbers (fillers volume ratio can exceed 80%), which can sustain large strains up to 60% before breaking when submitted to uniaxial tension. Their mechanical behavior is complex showing nonlinear viscoelasticity with damage [7,8]. Consequently, it is legitimate to question the occurrence of large strains that could dissipate some energy during propellant/liner debonding and, as a consequence increase the cohesive fracture energy. To access the local strain field, digital image correlation (DIC) is used. The non-contact method is based on the mapping of an image in a loaded state to an image in a reference state and has been successfully applied at the microscale and nanoscale (for instance [9,10]) and for finite deformations [11,12]. Despite an apparent simplicity in implementation, DIC results are sensitive to various parameters and must be studied with care. Numerous DIC codes can provide estimates of the strain field [13], its accuracy depending on the speckle, lights, cameras and optics [10].

During the peel test, the fracture energy is deduced from the peel force while the local state of strain on the propellant free surface is monitored with two cameras. On the one hand, macroscale observations verify the 90° peel-angle and provide estimates of the strain field over the whole thickness of the propellant slab, while on the other hand, observations at the microscale allow following the crack propagation and measuring the strain field in front of it.

Material and experimental setup

The tested propellant is a plasticized glycidyl azide polymer (GAP)-based binder highly filled with energetic (ammonium perchlorate and octogen) particles and the liner is a silica filled Hydroxyl-terminated polybutadiene (HTPB). A woven cloth has been added within the liner in order to use it as peel arm. The cloth does not affect the liner high flexibility but prevents its stretchability that would add undesired energy dissipation when pulled during the peel test.

The liner is partially cured during 24 hours at 65 °C. Then the propergol is added and both materials are co-cured during three weeks at 40 °C to promote adhesion at the interface. At the end, the liner/propellant structure is cut with a saw into slabs 100 mm long, 25 mm wide and 6 mm thick (5 mm propellant and 1 mm liner).

The largest propellant free surface in the propellant/liner structure is bonded with a polyurethane glue to a steel plate in order to fix it easily to a friction free rolling bench mounted onto a Zwick/Roell (Ulm, Germany) Z020 tension machine to perform 90° angle peel test. The position of the bench is controlled with a manual hydraulic displacement actuator to ensure that the peel arm remains vertical. The peel rate is 25 mm/min. The peel force is recorded with a 2 kN Zwick/Roell load cell. Note that one could be tempted to choose to glue the liner on the steel plate and use the propellant as the peel arm in order to mimic the rocket motor structure. Actually, the poor flexibility of the propellant creates troubles to maintain the peel angle constant, rendering the fracture analysis much more complex.

Two cameras have been installed, one on each side of the structure. A scale representation of the recorded image frames is shown in Fig. 1. At the macroscale, a texture was created with spray painting while at the smaller scale the presence of microscopic fillers ranging from 0 to 150 micrometers creates a natural speckle. Descriptions of the cameras and optics are given in Table 1.

Image mapping is performed with elastix v4.8 which is distributed as open source [14,15]. Element sizes of 16x16 pixels were used. The non-rigid transformations are defined by Bsplines and a gradient descent optimization method is applied. From the estimated transformation 𝑢(𝑋) between two images, one estimates the in-plane deformation gradient

tensor 𝐹 = 1 + )* )+
. Finally, the Green-Lagrange strain tensor,

𝐸 = - . 𝐹 / 𝐹 -1 (1)
is calculated as finite strain tensor. The three in-plane components of 𝐸 and its magnitude will be used to present the strain field in the deformed state.

In order to examine the performance of the textures and optics, reference images were submitted to artificial unidirectional sinusoidal displacements along the vertical direction with various amplitudes and frequencies. A random noise defined by the difference between two reference images was also added to the sinusoidal transformations. Image correlation was applied between the reference image and the artificially modified images. Such a test allows testing a wider variety of displacement fields [13] that apply rigid body motions prior to the experiment [16]. As one could expect, the speckles performed well when the sinusoidal period was large (half of the image height) and so for a wide range of strain (Fig. 2). The maximum positive strain applied (130%) was successfully reached while compression values up to 40%

were also satisfactorily attained. The main difficulty with high compressions is the 'disappearance' of pixels. As one could expect, the performance of the speckles degrades significantly as the sinusoidal period decreases (Fig. 2). Therefore, it will be possible to capture large strain but qualitative results only are anticipated for high strain gradient.

Results

Cohesive fracture energy

During the peel test, the peel force is recorded over time. Fig. 3 shows that after the initial increasing tension of in the liner, the force reaches a plateau at 11 +/-1 N. This plateau exhibits the stable propagation of the peel process. Due to the low stretchability and high flexibility of the liner/cloth structure mentioned in section 2, the fracture energy is easily evaluated by [17,18]:

𝑊 = 𝐹/𝑏 (2)
with 𝑏 the width of the peel arm. Therefore, a fracture energy of 𝑊 = 440 +/-40 J.m -2 is obtained that is of the same order of magnitude as values reported for HTPB based propellants [1]. The measured discrepancy can be due to the material inhomogeneity that may induce local variation of the fracture energy [19]. Finally, after propellant/liner separation, a layer of propellant is witnessed on the liner, prooving of a cohesive fracture in the propellant.

Strain field

In order to evaluate the strain field during the peel test, a first analysis is carried out on the large frame images. A large part of the images being irrelevant, the DIC is carried out on reduced images which are the part of the images appearing in the red boxes in Figure 4. The digital image correlation is performed between the reference image (Fig. 4a) and the deformed image obtained 1.5 second later (Fig. 4b). The difference between the reference image and the deformed image inversely transformed with the strain field calculated by DIC, defines a residual map (Fig. 4c). Here, such a map evaluates the DIC performance, and a good correlation is obtained where the picture is green while poor results were obtained in the spots of the yellow pixels. Therefore, the strain field obtained where the correlation is not satisfactory (indicated by yellow pixels in Fig. 4c) is irrelevant. These areas of unsatisfactory correlation correspond to the orange and red areas displayed in Fig. 4d. Analyzing the rest of the image, the propellant slab seems to remain undeformed. Nonetheless, at this scale, the image definition is coarse and observation of the cohesive fracture of the propellant requires to zoom in.

Thanks to the microscale images, a closer look is taken at the propellant/liner separation to follow the crack while it propagates in the propellant. In order to better assess the strain field in front of the crack, DIC is applied to two small frame images (Fig. 5). One may note in Fig. 5 the blurry part at the top of the image helping recognizing the limit between the propellant and the liner/cloth structure, the propellant appearing below this limit. As for the large frame images, a reduced part of the images, contained in the red boxes (Fig. 5), is used for DIC. The residual map comparing the reference image and the inversely transformed deformed image allows identifying the crack path before the eye could recognize it, that is when the crack opens. The crack path is read on the residual map (Fig. 5c). It is recognized by an unsatisfactory image correlation displayed by the yellow pixels. Apart from the crack area, DIC performed well since the residual map is mostly green. Consequently, the strain field in front of the crack tip is now discussed. The poor results obtained when the images were deformed artificially with a high frequency sinusoidal signal (Fig. 2) demand to be cautious with the estimates of the strain field in the close vicinity of the crack, where high strain gradients are obtained.

Figure 6 shows the estimated strain field when comparing the reference image and the deformed image presented in Fig. 5. One reads that in front of the crack and in other areas of the image, the strain is small and very often null. This result confirms what was observed in large frame pictures: the propellant barely deforms during the peel test. As a consequence, for the propellant/binder structure tested, the propellant deformability and viscoelasticity do not help increasing its resistance to cohesive fracture. Finally, it appears that the distance between the crack and the liner is of the order of magnitude of 300 µm, which is consistent with the characteristic distance of chemical species migrations at the liner/propellant interface [20]. As shown in [21], cohesive fracture within the propellant is achieved when the right concentration of bond promoter is added due to reaction at the surface of ammonium perchlorate.

Conclusion

In order to characterize the bonding of a propellant/liner bond structure, an instrumented peel test of a propellant/liner structure was proposed. The main feature was to implement two image acquisition chains recording the evolution of the propellant/liner separation at both the macro and micro scales while monitoring the peel force. It was observed that the propellant/liner separation was the result of a cohesive fracture within the propellant. At the macroscopic scale, the peel force reaches rapidly a plateau providing access to the propellant cohesive fracture energy. At a lower scale, the strain field observations proved that only a very localized area of propellant is submitted to strain. The fracture appears at a distance of the liner that is consistent with the chemical species migrations at the liner/propellant interface.

Fig. 1 .Fig. 2 .Fig. 3 . 14 Fig. 4 .

 123144 Fig. 1. Experimental setup.

Fig. 5 .

 5 Fig. 5. Example of digital image correlation on small frame images: (a) reference image (the

Fig. 6 .

 6 Fig.6. Strain field measured by Fig. 5 image correlation.

Table 1

 1 Image acquisition systems

		Small frame images	Large frame images
	Camera	SVcam-eco267MVGE	SVcam-eco655MVGE
	Frame size	1392x1040 pixels	2448x2050 pixels
	Pixel size	1.7 microns	11 microns
	Acquisition	12.5 Hz	2 Hz
	Optics	Telecentric Moritex	mm Computar
		MLZ07545HRD	M6Z1212-3S

Acknowledgement

The authors acknowledge the financial support of DGA.


Antonin Gajan

Constance Lecourt

Blanca Torres Bautista

Laure Fillaud

Julien Demeaux

Ivan T Lucas

Solid Electrolyte Interphase instability in operating Lithium-ion batteries unraveled by Enhanced-Raman Spectroscopy

The fundamental understanding of the electrode/electrolyte interfacial processes in lithium-sodium ion batteries (LIBs) and of their dynamics upon cycling is of prime importance for the development of new generation electrode materials. Operando analyses using the utmost sensitive techniques are required to produce an accurate depiction of the underlying processes at the origin of the battery performance decay. If enhanced Raman Spectroscopy through the use of signal nano-amplifiers (SHINs) shows the required sensitivity, its implementation in operando conditions and particularly on functional materials in contact with organic electrolytes remains challenging. This work through extensive optimization of SHINERS conditions for operando diagnosis of LIB materials, including the design of near-infrared active amplifiers and the control of the photon dose, demonstrates the possibility to track the dynamics of composition of the electrode/electrolyte interface upon cycling of LIB coin-cells and uncovers the origin of the irreversible capacity of tin electrodes proposed as alternative to graphite anodes.

New high-energy electrode materials proposed as alternative to graphite anode (i.e. tin, silicon) and layered cathode materials hold great promise for the development of new generation of lithium-ion batteries 1 (LIBs), but the ineluctable degradation of their performance during the repeated charge and discharge cycles still limits their wide scale development. One key problem lays at the electrode-electrolyte interface, where non-aqueous electrolyte reacts at the electrode surface upon charging of the battery. Reactions products either diffuse away or accumulate at the electrode surface to form semi-passive layers, the Solid Electrolyte Interphase SEI 2 on the anode side and the Cathode Electrolyte Interphase CEI. [START_REF] Edström | The Cathode-Electrolyte Interface in the Li-Ion Battery[END_REF] The properties of such surface layers (stability/build-up, ionic diffusivity, passivating ability) are mainly dictated by the composition of the electrolyte (reduction and oxidation potential) and by the nature of the electrode material (intrinsic reactivity, lithiation mechanism). [START_REF] Shi | Identification of Diethyl 2,5-Dioxahexane Dicarboxylate and Polyethylene Carbonate as Decomposition Products of Ethylene Carbonate Based Electrolytes by Fourier Transform Infrared Spectroscopy[END_REF] To minimize the performance decay of newly developed LIB electrode materials, both the electrolyte and the electrode formulation should be therefore revisited. [START_REF] Tu | Designing Artificial Solid-Electrolyte Interphases for Single-Ion and High-Efficiency Transport in Batteries[END_REF] Much attention has been paid these last four decades to the processes taking place at the anodes with a focus on the SEI properties 6 7 8 and more recently on the CEI. [START_REF] Zhang | Dynamic Evolution of Cathode Electrolyte Interphase (CEI) on High Voltage LiCoO2 Cathode and Its Interaction with Li Anode[END_REF] One main challenge associated to the characterization of surface films developed on electrode materials remains the composition evaluation, especially in operando conditions. The characterization of thin passive films (tens of nanometer thick) is not that straightforward as very few techniques possess the sufficient sensitivity, or provide clear/meaningful chemical information with limited interference with the probe (photon-or electron-beam damage associated to x-ray or electron spectroscopies). Extensive efforts to access in situ / operando composition characterization of electrode material surface in contact with liquid electrolyte have been deployed, with techniques like FTIR 10 11 , differential electrochemical mass spectrometry DEMS [START_REF] Buqa | SEI Film Formation on Highly Crystalline Graphitic Materials in Lithium-Ion Batteries[END_REF] , nonlinear-spectroscopy (Sum Frequency Generation: SFG) [START_REF] Horowitz | In Situ Potentiodynamic Analysis of the Electrolyte/Silicon Electrodes Interface Reactions -A Sum Frequency Generation Vibrational Spectroscopy Study[END_REF] and more recently Raman spectroscopy. Raman spectroscopy, although powerful to characterize electrode materials [START_REF] Baddour-Hadjean | Raman Microspectrometry Applied to the Study of Electrode Materials for Lithium Batteries[END_REF] , shows blind when it comes to characterize thin surface layers like SEI made of (in)organic compounds with low cross-section for Raman scattering. To circumvent this limitation, Raman signal enhancement techniques using the plasmon resonance properties of metal nanostructures [START_REF] Willets | Localized Surface Plasmon Resonance Spectroscopy and Sensing[END_REF] were successfully implemented to extract compositions of surface films developed on various electrode materials upon contact with organic carbonate electrolytes. Surface Enhanced Raman spectroscopy (SERS) was implemented operando on SERS-active silver and gold nanostructured electrodes starting from 2000. 16 17 18 More recently Tipenhanced Raman Spectroscopy (TERS) using silver or gold scanning nanoprobes demonstrated the ex situ mapping of the SEI composition distribution with nanoscale resolution on a non-SERS active electrode material (silicon anode [START_REF] Nanda | Unraveling the Nanoscale Heterogeneity of Solid Electrolyte Interphase Using Tip-Enhanced Raman Spectroscopy[END_REF] ) in a similar way as nano-infrared mapping was achieved on tin anodes a few years before. [START_REF] Ayache | Near-Field IR Nanoscale Imaging of the Solid Electrolyte Interphase on a HOPG Electrode[END_REF] TERS, despite recent significant advances since its demonstration under operando conditions 21 22 , has not been yet implemented in LIB electrolyte, due to the difficulty to control the atmosphere and prevent the electrolyte evaporation in open cells. Alternatively, Shell-Isolated Nanoparticle-Enhanced Raman Spectroscopy (SHINERS) [START_REF] Li | Shell-Isolated Nanoparticle-Enhanced Raman Spectroscopy[END_REF] using metal@SiO2 core-shell nanoparticles as plasmonic signal amplifiers deposited on pristine electrode materials has been carried out to study chemical changes at various electrode/electrolyte interfaces upon cycling in LIB electrolyte, including silicon [START_REF] Hy | In Situ Surface Enhanced Raman Spectroscopic Studies of Solid Electrolyte Interphase Formation in sodiu Ion Battery Electrodes[END_REF] and carboncoated Zn0.9Fe0.1O anodes [START_REF] Cabo-Fernandez | In-Situ Electrochemical SHINERS Investigation of SEI Composition on Carbon-Coated Zn 0[END_REF] and LiNixMnyCo1-x-yO2 (NMC) cathodes. [START_REF] Li | Surface Changes of LiNi x Mn y Co 1-xy O 2 in Li-Ion Batteries Using in Situ Surface-Enhanced Raman Spectroscopy[END_REF] In this study, we have addressed three major gaps in analytical studies of interfacial processes in LIB electrolyte using enhanced Raman techniques: i) the usual misconception of in situ (spectro-electrochemical) cells, ii) the possible interference of the Raman laser and light nano-amplifiers with beam-sensitive organic electrolytes ("knock-out" damages), iii) the inaccurate interpretation of SEI/CEI chemical signatures based on infrared spectra or computed Raman spectra (DFT). By revisiting the SHINERS implementation under operando conditions, we were able to track the dynamics of interfacial composition changes on tin anodes upon cycling in LIB electrolyte. This work provides important insights in the degradation mechanism of organic carbonate electrolytes and on the origin of the interfacial instability specific to tin, 27 28 proposed as an alternative to graphite given its high theoretical lithium retention capability (> 900 mAh.g -1 ).

SHINERS optimization in LIBs -SHINs decorated tin electrodes, polypropylene thin separator and lithium foil counterelectrode were assembled in a coin-cell like operando Raman cell (Figure 1a) filled with minimum amounts of electrolyte (60µL, preventing from further re-dissolution of the SEI, as discussed in Supplementary Information (SI, Fig. S4). Au@SiO2 nano-bipyramid SHINs were synthesized by adapting two multistep protocols described by Liz-Marzan [START_REF] Sánchez-Iglesias | High-Yield Seeded Growth of Monodisperse Pentatwinned Gold Nanoparticles through Thermally Induced Seed Twinning[END_REF] and Guyot-Sionnest [START_REF] Liu | Mechanism of Silver(I)-Assisted Growth of Gold Nanorods and Bipyramids[END_REF] and then drop-casted on the pristine tin electrode. The association of such anisotropic SHINs with near-infrared laser sources (785 nm), provides the highest Raman signal enhancement while minimizing the strong fluorescence signal of battery electrolytes and cycled electrodes, allowing the extraction of the Raman signal of a molecular monolayer in contact with LIB electrolytes (SI, Fig. S5). We introduced here also for the first time the use of scanning Raman probe (50 µm diameter circular pattern) for SHINERS to prevent electrolyte degradation inherent to photothermal effect or possible light-induced electron transfer (plasmon electrochemistry [START_REF] Ou | Thermal and Nonthermal Effects in Plasmon-Mediated Electrochemistry at Nanostructured Ag Electrodes[END_REF] ) at the electrode/electrolyte interface. In the scanning mode, the photon dose per surface unit can be considerably reduced while maintaining high level of Raman signal, as demonstrated by the untouched electrolyte signature obtained even after 200 s at 4.5 mW (see Figure 1b). The full description of the revisited SHINERS implementation can be found in SI (Fig. S2,4,5).

Dynamics of the interfacial composition on tin -Operando SHINERS measurements were then performed every 100mV (chronoamperometry step, 300 s) starting from the open circuit potential 2.8 V (OCP) down to 100mV (two forward scans separated by 20' at the OCP to let the electrode to relax). EC-DEC LiPF6 electrolyte mixture was chosen for its poor filming ability on tin anodes described in previous works. [START_REF] Lucas | Interfacial Processes at Single-Crystal β-Sn Electrodes in Organic Carbonate Electrolytes[END_REF] A typical cyclic voltammetry (CV, 1 mV.s -1 ) of the tin electrode in such electrolyte (Figure 1a) shows a strong intensity reduction peak centered on 1.0V in addition to the lithiation/delithiation current peaks at potential lower than 0.4V and higher than 0.7V respectively. In contrast with EC-EMC LiPF6 electrolyte mixture (further discussed in SI, Fig. S4), the passivation of tin is not achieved after the first cycle as the irreversible peak at 1.0V is still of strong intensity during the second CV scan.

Selected individual spectra and 2D color map (spectra vs potential) corresponding to the first (forward) potential scan are presented in Figures 2a &b respectively. For the sake of clarity, only spectra associated to the 2V -0.1V potential range are presented (the full set of data can be found in SI, Fig. S8). Spectra were baseline subtracted and normalized to the intensity of the electrolyte band at 900cm -1 . The first potential forward scan extracted from the CV cycling of tin (Figure 1a) in such electrolyte at 1 mV.s -1 is given to ease the correlation between the electrochemical phenomena and the chemical changes at a given applied potential. While between the OCP and 2V no Raman bands other than the ones of the electrolyte are detected (current intensity close to zero), a few noticeable spectral changes are visible at potential as low as 1.6 V, which corresponds to the onset of the reduction current peak centered at 1 V on tin electrode:

• starting from 1.6V, new features at 501, 1065/1074, 1139, 1266/1285, 1486 & 1573 cm -1 highlighted by black and red dotted lines emerge,

• while the intensity of some bands remains constant throughout the whole remaining potential range, i.e. 600, 1139, 1486 • upon lithiation, a new band at 513 cm -1 appears, the band at 1065 cm -1 remains constant and the bands at 501, 1074 and 1285 cm -1 increase in intensity. Similar behavior is observed during the second forward scan as depicted in Figure 3, with the bands at 501 and 513 cm -1 which increase concomitantly with the irreversible current peak and momentary disappears in the 1-0.4V potential range. Also, the 1065/1074 and 1285 cm -1 band intensities increase and then fade while the band at 1266 cm -1 increases. Note that no changes in the Raman signatures were observed upon relaxation of the electrode potential to the OCP in between the two forward scans.

The successful tracking of the SEI composition upon cycling, despite the fact that the SEI thickness can reach on tin electrodes tens of nanometers [START_REF] Aurbach | A Comparative Study of Synthetic Graphite and Li Electrodes in Electrolyte Solutions Based on Ethylene Carbonate-Dimethyl Carbonate Mixtures[END_REF] (Raman signal from gold bipyramids through such a thick dielectric layer could be compromised), can be explained by the fact that the SEI builds up only at the electrode surface with the SHINs appearing as floating on the surface film developed on cycled rinsed tin electrodes (Fig- ure 4b). If operando SHINERS tests run on tin anodes in polypropylene carbonate (PC)-based electrolyte were also successful (SI, Fig. S17, full results to be published), loss of Raman signal on tin was systematically observed at potential lower than 1V vs Li/Li + in EC-EMC LiPF6 (SI, Fig. S10) and could not be recovered upon scanning at higher potential. Further work is necessary to understand the relation between the filming properties of the electrode depending on the electrode material and on the electrolyte, the potential applied and the behavior of the plasmon signal amplifiers.

SEI SHINERS signatures assignment -The recurrent problem of spectral assignment was addressed here by establishing a databank of Raman signatures of a series of organic compounds reported in the literature 4 7 8 as possible degradation products of carbonate based electrolytes. These latter were either purchased (DEDOHC, Li2CO3, LiF, PEO, sodium propionate) or synthesized (LEC, LMC, LEMC, LEDC, LPDC) through much effort and precautions by adapting different published protocols detailed in SI. A series of complementary operando experiments (tin in PC-LiPF6, gold in EC-DEC LiPF6) or ex situ tests were also carried out to emphasize the strong dependence of the SEI composition with the nature of the electrode material and of the electrolyte and the pertinence/robustness of the present analysis. By comparison of spectra presented in Figure 4a and in SI (Fig. S11,S13,S14,S15,S16) we propose the following band assignment:

accumulations) every 100mV steps (after 5min chronoamperometry) from the OCP (2,8V) down to 0.1V vs Li/Li+ (first forward scan). Dotted lines correspond to new Raman bands appearing during polarization (only bands marked with red dotted lines were attributed). (b) A linear sweep voltammogram obtained on this same system between 2.0 V and 0.1 V (versus lithium) at 1 mV/s is given to guide the reader eyes. The 2D color map representing the Raman band intensity as a function of the applied potential allows to appreciate the dynamics of composition of the tin electrode /electrolyte interface.

• the double 501-513 cm -1 band which is of strong intensity on tin in situ, almost absent ex situ on rinsed electrode, could be assigned to Diethyl 2,5-Dioxahexane Dicarboxylate (DEDOHC), as this compounds is liquid at room temperature and soluble in the electrolyte. Note that this band is absent on gold (SI, Fig. S7).

• the bands at 1074 and 1266 cm -1 (which is the most pronounced feature on rinsed tin electrode and which also appears on gold) could be assigned to lithium ethylene monocarbonate (LEMC).

LEMC vs LEDC -Lithium Ethylene dicarbonate (LEDC) has been proposed as early as 1996 by Aurbach's group as a possible product of reduction of ethylene carbonate (EC) on noble metal electrodes (Ni, Au) via a single electron pathway 33 34 and since has been identified in situ by ATR on gold electrode 4 10 by comparison with chemically synthetized LEDC. However a recent work by Wang et al [START_REF] Wang | Identifying the Components of the Solid-Electrolyte Interphase in Li-Ion Batteries[END_REF] , rediscussed by Freunberger [START_REF] Freunberger | Interphase Identity Crisis[END_REF] , evidenced that the synthetic route followed by many authors to produced LEDC via the intermediate di-lithium alkoxide (LiO-CH2-CH2-OLi) actually lead to LEMC given that the reaction intermediate is not as believed the di-but the monolithiated counterpart. The insulating character of LEDC which contrast with the high Li + conductivity of LEMC additionally supports their claim. In the present study, LEMC is detected (1074 and 1266 cm -1 ) at potential below 1V concomitantly with the disappearance of the bands at 1065 and 1285 cm -1 , which could support the formation of LEMC following the (electro)chemical evolution of a non-stable compound within the SEI (Figure 4c summarizes the different mechanisms proposed by Wang et al [START_REF] Wang | Identifying the Components of the Solid-Electrolyte Interphase in Li-Ion Batteries[END_REF] which can explain the formation of LEMC, LEC, LMC in EC-DEC LiPF6). As the Raman spectrum of synthetic LEDC shows a strong intensity band at 1090 cm -1 which the position does not seem to be influenced by solvation effect (see Fig. S16), the possible formation of LEMC from the LEDC hydrolysis/protonation [START_REF] Wang | Identifying the Components of the Solid-Electrolyte Interphase in Li-Ion Batteries[END_REF] is not evidenced here. It is also important to mention the absence of peaks associated with LEC and LMC (see in SI, Fig. S12b andS15), since the formation of LMC and LEC could be facilitated in presence of EMC of DMC in the electrolyte as reported by Xu et al [START_REF] Xu | Syntheses and Characterization of Lithium Alkyl Mono-and Dicarbonates as Components of Surface Films in Li-Ion Batteries[END_REF] and Wang et al. [START_REF] Wang | Identifying the Components of the Solid-Electrolyte Interphase in Li-Ion Batteries[END_REF] Finally, note that in PC based electrolyte, the bands at 1074 and 1266 cm -1 associated with LEMC, are absent and instead two bands are present at 924 and 1088 cm -1 which we assigned to Lithium Propylene di-Carbonate (LPDC) (see SI Fig. S17), in agreement with the conclusions of Dedryvère et al. [START_REF] Dedryvère | Characterization of Lithium Alkyl Carbonates by X-Ray Photoelectron Spectroscopy: Experimental and Theoretical Study[END_REF] Formation of electrolyte soluble DEDOHC -The double band at 501-513 cm -1 which appears in situ only on tin anode and not on gold and which almost disappears ex situ after thorough rinsing of the tin electrode with DEC, suggests that it can be attributed to the DEDOHC compound. The dynamics of the 501 cm -1 band appearance and disappearance supports this claim as DEDOHC may be produced upon reduction of the electrolyte between 1.6 and 1V till the current drops to zero, then diffuses away. Similar dynamical behavior has been observed in previous studies 27 32 by operando AFM and ellipsometry. DEDOHC has been reported in a series of papers by the groups of Somorjai and Komvopoulos 4 10 11 using ex situ and in situ IR spectroscopy on different electrode materials (Ni 4 , Au 4 10 , Si 11 ) in EC-DEC LiPF6, including tin anode. 4 10 Its formation on tin would result from a catalytic process involving surface tin oxides, the formation of alkyl or alkoxide radicals and the reaction with EC and DEC 4 (Figure 4c), explaining why it is not observed on gold. The specific properties of tin electrode surface promoting the formation of soluble DEDOHC in the electrolyte could account for the large irreversible currents and the delayed passivation of the electrode. Slight change in the linear carbonate structure EMC instead of DEC may limit the formation of DEDOHC leading to drastic improvement of the passivating ability of tin anodes as shown earlier. Finally SHINERS results, which will be detailed in a following publication, were collected on tin in PC based electrolyte (Fig. S17). In such electrolyte, which do not requires the use of linear carbonate (PC is liquid at room temperature), the 501 cm -1 band is not observed supporting the DEDOHC formation mechanism proposed in Figure 4c involving both DEC and EC.

In summary, SHINERS using Au@SiO2 bipyramid nanoparticles (anisotropic SHINs) with plasmon absorption in the nearinfrared spectral domain prove to successfully extract within LIB electrolyte the chemical signature of Raman blind molecular monolayer and of passive films (SEI) developed on tin and gold electrodes. Confocal Raman microscopy associated with scanning near-infrared laser allows minimizing the Raman and fluorescence signal of the surrounding LIB organic electrolyte and cancelling out of its photothermal breaking on SHINs upon light exposure. The dynamic tracking of the SEI formation and composition evolution on tin anode in coin-cell filled with EC-DEC LiPF6 is consistent with the recently developed hypothesis that upon reduction of the EC-DEC solvent mixture, LEMC is predominantly formed over the often reported LEDC which is prone to hydrolysis and not clearly identified here. We further back up this idea by evidencing the formation of LPDC (and the absence of LEMC), upon cycling of tin in cells filled with PC LiPF6. Other electrolyte reduction products often reported, e.g. LEC, LMC, LiF, PEO were not clearly identified during the early stage of the SEI formation, Li2CO3 proved absent. A Raman signature which shows an interesting dynamics of appearance and disappearance along the reductive potential scan was clearly assigned to the electrolyte soluble DEDOHC compound which forms on tin but not on gold anodes through a catalytic process involving tin oxide, DEC and EC. The cyclic formation of DEDOHC, concomitant with the repeated emergence upon cycling of the cathodic peak around 1.2V, accounts for the poor passivating properties of tin in EC-DEC LiPF6 electrolyte and for its large irreversible capacity. Some Raman bands were left unattributed, emphasizing the necessity to explore, depending on the electrode and electrolyte composition, further electrolyte reduction and oxidation paths involving possible SEI or CEI components respectively. The accurate description of the composition of electrolyte interphases the key element behind all Li, Na or multivalent-ion battery operation and failure, is the prerequisite to the design of new electrolyte and electrode formulations. Similarly to this study, the experimental (and theoretical) chemical signatures of SEI (and CEI) compounds obtained through chemical synthesis could be gathered within an openaccess online database available to the battery community. Although the SHIN activity is still not fully understood and needs further work (e.g. the origin of the potential dependent signal extinction in EC-EM LiPF6 electrolyte), the major optimizations around SHINERS implementation for LIB applications proposed in this study could be used as benchmarks for the characterization of any systems beyond LIBs which operate under harsh conditions.

ASSOCIATED CONTENT

The Supporting information includes the experimental details and seventeen figures illustrating: -The optimization of the SHINERS measurements for LIB applications (S1 to S5) -The full set of data of operando SHINERS measurements in LIB electrolyte (S6 to S10) -The Raman signature assignment covering synthesis protocols and Raman signature of synthetic SEI compounds (S11 to S17).

Figure 1 :

 1 Figure 1: (a) Electrochemical response (cyclic voltammetry) of a tin electrode in the spectro-electrochemical coin-cell filled with EC-DEC LiPF6 electrolyte (counter electrode: lithium foil). (b)Electrolyte Raman signature depending on the time of exposure to the laser probe, on the laser power and on the signal excitation mode ("still" laser or "scanning" laser).

Figure 2 :

 2 Figure 2: Dynamics of the SEI composition on tin in EC-DEC LiPF6 -(a) Operando SHINERS on tin electrode polarized in EC/DEC=50/50 (v/v), 1.0 M LiPF6 electrolyte. Raman spectra were collected at 785 nm (laser power 0.9 mW, tacq = 3 seconds, 8 accumulations) every 100mV steps (after 5min chronoamperometry) from the OCP (2,8V) down to 0.1V vs Li/Li+ (first forward scan). Dotted lines correspond to new Raman bands appearing during polarization (only bands marked with red dotted lines were attributed). (b) A linear sweep voltammogram obtained on this same system between 2.0 V and 0.1 V (versus lithium) at 1 mV/s is given to guide the reader eyes. The 2D color map representing the Raman band intensity as a function of the applied potential allows to appreciate the dynamics of composition of the tin electrode /electrolyte interface.

Figure 3 :

 3 Figure 3: Comparison of SHINERS spectra collected on tin in EC/DEC LiPF6 electrolyte during the second (blue) and first (black) forward potential scans, the bands of interest are enlightened in red.

Figure 4 :

 4 Figure 4: SEI composition and formation mechanisms (a) Comparison of reference Raman spectra of LEMC and DEDOHC with the Raman spectra obtained on gold electrode in situ at 100mV, on tin electrode at 100mV and ex situ on tin electrode after a 100mV polarization. The bands highlighted in red and blue correspond respectively to the one of the electrolyte and to the most distinguishable one of DEDOHC. All three experiments were performed in similar conditions in EC/DEC=50/50 (v/v), 1.0 M LiPF6 electrolyte. (b) SEM micrograph of pristine (left) and cycled (right) tin electrodes (CV in EC-DEC LiPF6) decorated with SHIN agglomerates, showing the growth of a rough surface film (SEI) below the particles. (c) Summary of possible electrolyte degradation mechanism to explain the composition of the SEI on tin anodes in contact with EC-DEC LiPF6.

ACKNOWLEDGMENT

We would like to thanks J.-M. Siaugue and A. Michel from PHENIX lab for fruitful discussion on core-shell particle synthesis, L. Boudinet and C. Bignaud for their bachelor and master internship work respectively preceding this study, F. Pillier in LISE lab for the electron microscopy analyses.

Funding Sources

This project was funded by the battery manufacturer Saft (a company of Total), the ANRT (CIFRE n°2018/1579) and by the ANR-CarLIB (grant numbers: ANR-15-CE05-0002).

AUTHOR INFORMATION Corresponding Author

Ivan T. Lucas -Sorbonne Université, CNRS, Laboratoire LISE, 75000 Paris, France -Email: ivan.lucas@sorbonne-universite.fr

Author Contributions

A.G. produced all the Raman and electrochemical data presented in this work. C.L. and L.F. synthesized the LEMC, LEDC, LPDC, LMC, LEC organic compounds and analyzed their NMR spectra. L.F., B.T.B., A.G. and I.L. developed the protocol for the synthesis of 90 and 120 nm isotrope SHIN objects (Au@SiO2 spherical nanoparticles). L.F., A.G. and I.L. developed the protocol for the synthesis of 70 and 140 nm anisotrope SHIN objects (Au@SiO2 bipyramid nanoparticles) for red and near-infrared Raman probes. I.T.L. and A.G. developed the spectro-electrochemical coin-cells and SHINERS analyses using scanning Raman probes. I.T.L. and J.D. led this study. A.G., C.L., B.T.B., L.F., J.D. and I.T.L. wrote this manuscript and contributed to discussion.