Tarik Benaddi
email: tarik.benaddi@imt-atlantique.fr

Charly Poulliat
email: charly.poulliat@enseeiht.fr

Romain Tajan
email: romain.tajan@ims-bordeaux.fr

A General Framework and Optimization for Spatially-Coupled Serially Concatenated Systems

Keywords: Turbo codes, Spatially coupling, AWGN, EXIT charts, Saturation, bit-interleaved coded-modulation, Faster-than-Nyquist

In this paper, we provide a general framework for spatially-coupled concatenated systems. We explicit the analogy with spatially-coupled protographs and provide an adapted EXIT chart analysis. By proposing a continuousvalued coupling matrix, we propose a code design procedure for faster convergence. When considering general bitinterleaved coded-modulation scheme, we also conjecture that the spatially-coupled scheme of general detectors saturates to a value very close (lower bound) to the threshold given by the Area theorem.

I. INTRODUCTION

The convolutional counterparts of LDPC codes are called LDPC convolutional (LDPC-C) codes [START_REF] Jimenez Felstrom | Time-varying periodic convolutional codes with low-density parity-check matrix[END_REF]. They are obtained by spatial coupling of LDPC codes and can also be described by a sparse parity check matrix and consequently being decoded with message passing decoding algorithms. Because of the coupling, this family of codes show very good thresholds thanks to the so-called saturation phenomenon: it was proven for the Binary Erasure Channel (BEC) that the threshold under belief propagation decoding converges to the optimal Maximum a posteriori (MAP) threshold [START_REF] Kudekar | Threshold saturation via spatial coupling: Why convolutional ldpc ensembles perform so well over the bec[END_REF]. Later on, an other proof using the potential function extended these results to other channels [START_REF] Kumar | Threshold saturation for spatially coupled ldpc and ldgm codes on bms channels[END_REF], [START_REF] Nguyen | Spatially-coupled codes and threshold saturation on intersymbolinterference channels[END_REF]. Different methods can be used to construct LDPC-C such as unwrapped LDPC codes [START_REF] Pusane | Deriving good ldpc convolutional codes from ldpc block codes[END_REF] or spatially-coupled protograph codes [START_REF] Mitchell | Spatially coupled ldpc codes constructed from protographs[END_REF].

Recently, different papers extended the spatial coupling to serial and parallel concatenated turbo-codes [START_REF] Moloudi | Spatially coupled turbo codes[END_REF]- [START_REF] Zhang | Braided convolutional codes: a new class of turbo-like codes[END_REF] and derived the saturation proof for the BEC channel. In [START_REF] Moloudi | Spatially coupled turbo codes[END_REF], [START_REF] Moloudi | Spatially coupled turbo-like codes[END_REF], authors proposed the spatial coupling of systematic parallel and serial turbo-codes and concluded that for the BEC, coupled serially concatenated schemes have better threshold than their parallel counterparts. In order to terminate the spatially coupled ensemble, they proposed to force the last information symbols to zeros. Finally, they studied rate compatible ensembles using puncturing.

In [START_REF] Moloudi | Finite length weight enumerator analysis of braided convolutional codes[END_REF], same authors studied the finite length performance of braided convolutional codes (BCC) and showed that the minimum distance of the coupled ensemble is lower bounded by the the minimum distance of the uncoupled ensemble. In [START_REF] Costello | New perspectives on braided convolutional codes[END_REF], the authors reviewed the literature results on BCC and present an unified description on BCC with respect to other turbo-like codes.

In this paper, we intend to generalize previous works to the framework of the spatial coupling of general serially concatenated systems. First, we will describe the proposed coupling procedure, then we show that it is actually the Fig. 1: The transmitter spatial coupling of a generalized multi-edge type (MET) or protograph LDPC code [START_REF] Ryan | Channel codes: classical and modern[END_REF]. Secondly, inspired by Protograph EXIT (P-EXIT) charts [START_REF] Liva | Protograph ldpc codes design based on exit analysis[END_REF], we describe the EXIT chart equations to study the asymptotic behaviour of such systems. We also explain where the coupling gain comes from and describe the termination and the tail-biting procedures. Thirdly, thanks to the proposed framework, we explore the effect of the syndrome former memory and, by redefining the proposed coupling matrix into a continuous-valued matrix, we introduce an optimization procedure that optimizes the coupling in order to reduce the number of iterations before convergence. Finally, we discuss some results and we conjecture that the threshold of spatially-coupled serially concatenated systems is lower bounded by the threshold given by the area theorem [START_REF] Hagenauer | The exit chart-introduction to extrinsic information transfer in iterative processing[END_REF].

II. SYSTEM DESCRIPTION

Without loss of generality, as an example of serially concatenated system, we consider in this paper general bit-interleaved coded-modulation (BICM) schemes where the outer component is an error correcting code and the inner component is a general modulation scheme (coded modulation or modulation with memory as in the case of faster-than-Nyquist scheme).

A. General spatially coupled Bit-interleaved coded modulation schemes

Let us consider the serially concatenated scheme composed of an error correcting code and a modulator as depicted in Fig. 1. For ease of exposition, we consider the simple case of a convolutional code. First, a k-bits information block s ∈ {0, 1} k is encoded with the outer code, denoted O, into a n-bits coded block u ∈ {0, 1} n (the code rate is R = k/n). Then, the sequence u is interleaved by the an interleaver π to obtain v and mapped into x = {x i } i with x i ∈ {0, . . . , M -1} where M is the modulation order. Finally, the vector x is modulated into the analog signal x(t) using a shaping filter. For ease of notations, the mapping and the shaping are considered jointly and constitute the modulator M.

In this paper, we assume that the complex signal x(t) is sent over an additive white Gaussian noise (AWGN) channel with a one-sided power spectral density N 0 .

At the receiver side, a classical iterative turbo receiver is considered [START_REF] Berrou | Near optimum error correcting coding and decoding: Turbo-codes[END_REF], [START_REF] Fàbregas | Bit-interleaved coded modulation[END_REF]: the received signal y(t) is first processed by a soft-input soft-output (SISO) demodulator M -1 . The obtained extrinsic log-likelihood ratios (LLRs) of the demodulated bits, L e (I), are deinterleaved and used as an a priori LLRs, L a (O), by the outer decoder O -1 . This latter runs a BCJR algorithm [START_REF] Bahl | Optimal decoding of linear codes for minimizing symbol error rate (corresp.)[END_REF] and provides the extrinsic LLRs corresponding to the coded bits, L e (O). When interleaved, these latter constitute the a priori LLRs of the demodulated bits, denoted L a (I) of the SISO M -1 . After a fixed number of turbo iterations, the estimated information bits are deduced from the sign of the a posteriori LLRs of the decoded bits L ap (O). A sketch of the turbo receiver architecture with the exchanged LLR messages is depicted in Fig. 2.

Inspired by the representation of multi-edge type (MET) LDPC codes and protograph codes [START_REF] Ryan | Channel codes: classical and modern[END_REF], we propose the vectorized view of Figs. 1 and 2 depicted in Fig. 3. The information and the transmitted bits sequences s and x are represented by circular nodes, the inner and the outer components are represented by rectangles and the interleavers are placed above the corresponding edges. By a slight abuse of notation, the estimated information bits are also denoted by s. A very similar representation was proposed by [START_REF] Moloudi | Spatially coupled turbo codes[END_REF], [START_REF] Costello | New perspectives on braided convolutional codes[END_REF] and was referred to as the compact graph representation. We will adopt the same terminology.

B. Spatially-Coupled BICM

In this section, we describe how one can spatially couple the concatenated scheme in Fig. 3. Our proposed framework is in spirit equivalent to [START_REF] Moloudi | Spatially coupled turbo codes[END_REF]- [START_REF] Zhang | Braided convolutional codes: a new class of turbo-like codes[END_REF], [START_REF] Amat | Spatially coupled turbo codes: Principles and finite length performance[END_REF], however, its formalism provides better control on design tools (component base matrix, coupling procedure, mapping optimisation, termination) and presents analogous properties to those of spatially-coupled protograph codes [START_REF] Mitchell | Spatially coupled ldpc codes constructed from protographs[END_REF] (rate loss, component base matrix, two coupling "waves", tailbiting . . . ).

Inspired from spatially-coupled protographs, spatiallycoupled turbo-codes (SC-TC) can be obtained by a variant of the edge spreading rule (ESR) [START_REF] Mitchell | Spatially coupled ldpc codes constructed from protographs[END_REF]: in the compact graph, the encoded bits v are demultiplexed into m s + 1 bundles, and the obtained graph is replicated a certain number of times, say L. Afterwards, the L graphs are interconnected by interchanging the ends of the bundles of the same type. In order to describe these interconnections, we introduce the integer-valued coupling matrix B defined as:

B = [b 0 , b 1 , . . . b ms ] ∈ {0, . . . n} ms+1
where b i represents the width (number of bits) of the bundle connecting any copy t with its adjacent neighbour

(t + i). B should verify ms i=0 b i = n.
Alike spatially-coupled LDPC codes, L is called the coupling length and m s the syndrome former memory. For code design and convergence analysis purposes, as we are going to see later, it is more convenient to consider a continuous-valued coupling matrix. To this end, we will rather divide B by n: now, b i ∈ [0, 1] is interpreted as the fraction of the bits u passed from the constituent code of the stage t to the stage (t + i). We then have:

ms i=0 b i = 1 (1)
An example of this construction is illustrated in Fig. 4.

C. Tail-biting

In the coupled compact graph, one can notice that we end up with some vacant bundles connections at the beginning and some additional unconnected bundles at the end. One way to solve this problem is by connecting these latter all the way back with the former. This is called tailbiting and one can easily show that the rate of the obtained SC-TC is exactly equal to R. However, this scheme does not show the desired coupling gain since, locally, each stage behaves exactly as the underlying TC scheme in Fig. 3.

D. Termination

A second solution is:

• adding m s modulators at the end in order to connect the last remaining bundles • adding known information bits at the m s first and the m s last mappers in order to fill the vacant bundles connections A simple example is depicted in Fig. 5. The m s black circles at the boundaries represent the added known bits.

We can show that, in this case, the final code rate of the coupled ensemble, when puncturing is not used, is equal to:

R L = L L + m s R = R - m s L + m s R (2)
which is analogous to the design rate of spatially-coupled protographs. Observe that the termination (finite value of L) results in a rate loss equal to ms L+ms R. This penalty vanishes to 0 as L → +∞.

The difference of Eq. ( 2) with the rate expression computed in [START_REF] Moloudi | Spatially coupled turbo-like codes[END_REF] comes from the following reasons:

• Their rate loss is induced by last imposed ′ 0 ′ s information bits used to terminate the encoder to the Fig. 5: Terminated spatially-coupled turbo-codes zero state 1 . In our case, the rate loss is caused by the additional ending mappers and by the inserted known bits at the boundaries (to keep a constant number of output bits x at all stages). • Eq. ( 2) stands for both systematic and non systematic codes As it is going to be showed later, the less connected mappers 2 (alike the less connected check nodes at the boundaries of spatially-coupled protographs) induce a wave effect phenomenon: the known bits at the boundaries will help generating more reliable LLRs, which, through iterations, will progressively propagate towards the center. The intermediate stages will behave the same way as the uncoupled system, as far as the coupling gain did not affect them yet.

III. ASYMPTOTIC CONVERGENCE ANALYSIS

In this section, we perform the asymptotic convergence analysis of the SC-TC. Density evolution (DE) was considered in [START_REF] Amat | Spatially coupled turbo codes: Principles and finite length performance[END_REF] to analyze the performance over the binary erasure channel (BEC). In the case of AWGN and 1 The "zero state" refers to the overall SC scheme (i.e. last transmitted symbol are 0's) and not the internal state of each constituent code 2 less connected because they have lesser real information bits at their inputs in comparison to their counterparts with general inner and outer components, evaluating the threshold by tracking the evolution of the density of the messages become a prohibitive task. Alternatively, EXIT charts [START_REF] Brink | Convergence behavior of iteratively decoded parallel concatenated codes[END_REF] are usually considered.

A. Exit Chart of the uncoupled system

For a fixed channel parameter, here the symbol (or bit) energy to noise ratio E s /N 0 (resp. E b /N 0 ), the EXIT charts track the evolution of the mutual information (MI) between the current extrinsic LLRs and the corresponding bits through iterations. To this end, it uses the input-output transfer function of the different SISO blocks (here the SISO of the inner and the SISO of the outer components). As depicted in Fig. 3, the demodulator transfer function T M (.)3 computes the extrinsic MI I e (I) (between the LLRs L e (I) and the corresponding bits) based on the channel observations and the a priori MI I a (I) (between L a (I) and the corresponding bits). Similarly, the outer decoder transfer function T O (.) computes both the extrinsic MI I e (O) (between L e (O) and the corresponding bits) and the a posteriori MI I ap (O)(between the LLRs L ap (O) of the corresponding bits) based on the a priori MI I a (O) (of L a (O) and the corresponding bits).

We perform the demodulator and the outer decoder updates until the maximum number of iterations is reached (no convergence) or I ap (O) = 1, ∀i (convergence). The analytic expressions of T O (.) and T M (.) are not available in the general case, consequently, we estimated them by Monte Carlo simulations. An example of EXIT charts and corresponding recursions steps are depicted in [14, Fig. 6].

B. Spatially-coupled EXIT (SC-EXIT) chart

For our analysis, we consider the i th stage of the spatially-coupled compact graph depicted in Fig. 6. The following notations are used:

• The subscript i differentiate between variables relative to each stage; i-k . For the SC-TC, we perform all demappers updates and all outer decoders updates of each iteration in parallel as for the flooding scheduling used in standard belief propagation algorithm [START_REF] Ryan | Channel codes: classical and modern[END_REF]. Using the fact that b k represent proportions according to Eq. ( 1), the differences here with the uncoupled TC are the mixtures and the boundary conditions as follows:

• I k e (i + ) (resp. I k a (i + )) is the extrinsic (resp. a priori) MI between the LLRs transmitted from M -1 i (resp. from O -1 i+k ) to O -1 i+k (resp. to M -1 i );
• I k e (i + ) = I e (I i ).b k and I a (I i ) = I k a (i + ).b k • I k e (i -) = I e (O i ).b k and I a (O i ) = I k a (i -).b k •
The a priori MIs coming from the added boundary nodes are equal to 1. The threshold of the SC-TC is then defined as the lowest

E b /N 0 such as I ap (O i ) → 1, ∀i.
Note that the obtained SC-EXIT chart can no more be interpreted graphically as in [START_REF] Hagenauer | The exit chart-introduction to extrinsic information transfer in iterative processing[END_REF]Fig. 6], but are rather thought as a kind of Protograph EXIT (P-EXIT) chart [START_REF] Liva | Protograph ldpc codes design based on exit analysis[END_REF], where the variable and check nodes are generalized and where the (m s + 1) incident edges are weighted by {b k } k .

C. Minimizing the number of iterations

The SC-EXIT chart analysis provides the asymptotic performance of an iterative scheme given a large number of iterations and a large codeword length. We can then apply classical non linear optimization methods to design coupling matrices such as [START_REF] Storn | Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces[END_REF]. When performing this optimization, we have observed that there exit several coupling matrices enabling to operate to the best achievable threshold (up to a given numerical precision). Among them, we have also observed that the convergence rate to this threshold was very different from one matrix to another. Therefore, an interesting additional criterion to consider when designing real systems is the convergence rate. In this section, we provide a framework that allows improving the convergence rate of SC-TC without altering the threshold or increasing the syndrome former memory m s . To this aim, we consider the following procedure:

1) Pick an adequate initial coupling matrix B 0 ; 2) Evaluate the threshold of the corresponding SC-TC;

3) Find the optimal coupling B opt such as:

• The SC-TC converges at same threshold.

• The number of iterations before convergence is minimal Several trials showed that coupling with the uniform matrix B 0 (ie. all uniformly distributed non null entries b k ) is a good representative of the SC-TC ensemble defined by a fixed m s , a fixed L and all possible Bs. Concerning the computation of the threshold in step 2, it can be estimated using the interval halving method. However, for step 3, two strategies can be adopted:

1) Search over a set of the form (with N ∈ N):

{b k } k | b k = 1 and b k ∈ { a N |a ∈ ❏0, N ❑} .
2) Since B belongs to a continuous space, we can perform the differential evolution algorithm [START_REF] Storn | Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces[END_REF]. The feasible set can be extremely reduced. Actually, due to the symmetry of the graph Fig. 5, both coupling matrices B 1 = {b k } k and B 1 = {b ms-k } k lead to the same performance. Furthermore, the dimension of the problem can be reduced by replacing the equality constraint in Eq. ( 1) by the polytope:

ms-1 i=0 b i ≤ 1 (3)
and deduce, at the end, b ms = 1 -ms-1 i=0 b i . NB: If B were an integer-valued vector, only the first strategy is possible and the optimization becomes infeasible especially for targeted values of n (asymptotic length) and m s . This is the rational behind the choice of Eq. (1).

IV. APPLICATIONS

A. Faster-Than-Nyquist based BICM

As a first application, we propose to illustrate an advanced BICM scheme based using a Faster-Than-Nyquist (FTN) waveform as a modulation device. This leads to a kind of bit interleaved coded modulation with memory that is efficiently decoded using iterative decoding. In this paper, we consider the circular FTN waveform as presented in [START_REF] Tajan | Circular faster than nyquist: Transmitter and iterative receiver design[END_REF]. Considering a root raised cosine (RRC) shaping filter with roll-off β and symbol period T . The Comparison to the Area under the EXIT is also depicted.

B = [ 1 2 , 1 2 ]
FTN symbol period is T s = τ T where τ ∈ ]0, 1] leads to an overall spectral efficiency of

η = R L log 2 (M ) τ (1 + β) (bits/s/Hz).
Each coded and interleaved block v is modulated into P = n log 2 (M ) symbols from the M -ary alphabet M. Let a = [x 0 , x 1 , . . . x P -1 ], the circular FTN waveform is obtained by circularly filtering these P symbols as follows:

x(t) = P -1 p=0 x p h T (t -pT s )w T (t) (4)
where h

T (t) = ∞ l=-∞ h(t -lT )
is the circular shaping filter and w T (t) is a window of length T = P T s . At the receiver, a low complexity SISO equalizer for mitigating the Inter-Symbol Interference (ISI) introduced by FTN is considered as given in [START_REF] Tajan | Circular faster than nyquist: Transmitter and iterative receiver design[END_REF]. This is mainly based on a frequency domain fractionally spaced Linear Minimum Mean Square Error (L-MMSE) soft interference canceler (see [START_REF] Tajan | Circular faster than nyquist: Transmitter and iterative receiver design[END_REF] for more details).

1) Coupling gain: Figure 7 depicts the evolution of the a posteriori MI through iterations of the spatially-coupled serially concatenated scheme CC+FTN at E s /N 0 = 5dB. The threshold of the uncoupled system is E s /N 0 = 5.32dB. As expected, the known bits at the boundaries help propagate a coupling gain (also referred in the literature as the wave effect). Therefore, even if the E s /N 0 is bellow the threshold of the uncoupled system, all stages converge after 47 iterations. In the meanwhile, the intermediate VNs behave as in the uncoupled system.

2) Threshold: In Fig. 8, we plot the threshold vs the rate 4 of the SC-TC FTN. For comparison, we included the uncoupled TC FTN and the threshold bound A * given by the Area theorem [START_REF] Hagenauer | The exit chart-introduction to extrinsic information transfer in iterative processing[END_REF]. As depicted, the coupling gain is of 1dB in comparison to the uncoupled system and is at just 0.45dB from A * . As L increases (here up to 145), we see that the spatially-coupled system saturates to a value very close to A * . This gap can be made smaller by considering larger L and a very big number of iterations.

3) Coupling optimization: With different syndrome former memories m s and the best coupling matrices B, we Comparison to the Area under the EXIT is also depicted.

B = [ 1 2 , 1 2 ]
observed that thresholds remain almost the same. Consequently and in order to simplify our analysis, we focus here only on the number of iterations before convergence. After running our optimization procedure, we observe that for our SC-CC FTN scheme, a coupling matrix of the form:

B opt = [ 1 2 , (m s -1)"0"s, 1 2 ] (5)
is the optimal form that minimizes the number of iterations without degrading the threshold. Figure 10 depicts the number of iterations for the uniform and the optimized B opt coupling matrices. By changing the coupling matrix, we are able to converge 1.7, 2.13, 2.1, 2.6 and 3 times faster when m s equals 2, 3, 4, 5 and 6 respectively at L = 145. For space issues, we choose to plot the results only for m s equals 2, 3 and 6. On the other hand, observe that, with larger m s , the convergence rate is faster. However, this comes at the expense of higher rate loss because a larger amount of known bits (black nodes) are introduced.

B. Linear modulation

Let us consider now the same systematic [START_REF] Pusane | Deriving good ldpc convolutional codes from ldpc block codes[END_REF][START_REF] Moloudi | Spatially coupled turbo codes[END_REF] outer code concatenated with a rate-1 accumulator of transfer function 1/(1 + D). We choose 16-QAM modulations. In order to avoid a doubly-iterative system, we consider Gray mapping since its Exit chart is quasi flat. The obtained results are plotted in Figs. 9 and 11. One can see that the spatial coupling allows to gain 1dB in comparison to the uncoupled family and that we are at only 0.29dB from the threshold given by the area theorem.

When we optimize the coupling, the coupling matrix has no more the form in Eq. ( 5). The obtained coupling matrices are given in Fig. 11. Thanks to the coupling optimization, we are able to reduce the number of iterations by 1%, 22.3% and 20.8% when m s equals 1, 2 and 3 respectively. On the other hand, when m s = 2, observe that the convergence needs more iterations in comparison to m s = 1: This is due to the fact that, by optimizing the coupling matrix, we are slightly improving the threshold in this case, but at the expense of more iterations.

V. CONCLUSION

In this paper, we presented a general framework of serially concatenated SC-TC. We showed the analogy with

Fig. 2 :Fig. 3 :

 23 Fig. 2: The receiver

Fig. 4 :

 4 Fig. 4: SC TC transmitter. The coupling is done according to B = [0.5, 0.5].

Fig. 6 :

 6 Fig. 6: Compact graph of the terminated SC TC receiver

Fig. 7 :

 7 Fig. 7: Convergence of SC FTN at Es/N 0 = 5dB.

Fig. 8 :

 8 Fig. 8: Threshold of coupled and uncoupled proposed FTN scheme.

Fig. 9 :

 9 Fig. 9: Threshold of coupled and uncoupled proposed 16-QAM scheme.

T M (.) depends implicitly on E b /N 0

In order to obtain the spectral efficiency in (bits/s/Hz) in Fig.8, multiply R by log 2 (M )/η where η is the occupied bandwidth.

L

] [0.37, 0.17, 0.17, 0.29] (c) ms = 3 Fig. 11: Iterations before convergence of different syndrome former memories at the threshold given in Fig. 9 spatially-coupled protographs and described the SC-EXIT chart analysis. We optimized the coupling matrix and designed couplings with faster convergence rates. From the obtained results, we conjectured that the SC TC scheme saturates to a value very close (lower bounded) by the Area theorem. Future work will investigate other channels and study the performance of window decoding.


Suzanne Anouti

Geert Haarlemmer

Maxime Deńiel

Anne Roubaud

Analysis of Physicochemical Properties of Bio-Oil from Hydrothermal Liquefaction of Blackcurrant Pomace

Bio-oils obtained from hydrothermal liquefaction of biomass are black viscous fuels with good heating values. This paper presents results of physical and chemical characterization of bio-oils produced by hydrothermal liquefaction of blackcurrant pomace. The oils are analyzed with standard normalized tests and compared to specifications required by commercialized biofuels and conventional fuels. Iodine value and total acid number are determined, showing relatively high values. GC/MS analysis demonstrates that bio-oil recovery by solvent extraction followed by subsequent evaporation of the solvent leads to the loss of some volatile compounds in the bio-oil. Thermogravimetric analysis are performed to study the volatility of HTL bio-oils, as well as to evaluate the carbon residue after evaporation. The viscosity of a bio-oil recovered by ethyl-acetate extraction was measured with a rotational viscometer at 25 °C, leading to a viscosity of 1.7 Pa•s. The results show furthermore that adding sodium hydroxide to the reaction medium has a limited influence on the properties of bio-oils. The choice of extraction solvent has conversely a significant influence on the quality of the produced oil. We demonstrate in this paper how standardized tests can be applied to hydrothermal bio-oils, to compare them with commercial fuels and evaluate the need for upgrading.

INTRODUCTION

Biomass is a generic term referring to materials derived from living, or recently living, organisms that include all organic matter of plant and animal origin. Typical examples of biomass are forestry and agricultural biomasses, food processing residues, algae, and sewage sludge. It is highly available worldwide and can be used as a source of renewable energy using biochemical or thermochemical processes. Studies on thermal conversion of biomass for biofuel production include works in gasification, [START_REF] Kirubakaran | A review on gasification of biomass[END_REF][START_REF] Heidenreich | New concepts in biomass gasification[END_REF] pyrolysis, [START_REF] Dupont | Biomass pyrolysis experiments in an analytical entrained flow reactor between 1073 and 1273[END_REF][START_REF] Mohan | Pyrolysis of Wood/ Biomass for Bio-oil: A Critical Review[END_REF][5] and hydrothermal processes. [START_REF] Kruse | Water -A magic solvent for biomass conversion[END_REF][7][START_REF] Peterson | Thermochemical biofuel production in hydrothermal media: A review of sub-and supercritical water technologies[END_REF][START_REF] Tekin | A review of hydrothermal biomass processing[END_REF] Gasification and pyrolysis require dry biomass and need a drying step in the process. [START_REF] Haarlemmer | Simulation study of improved biomass drying efficiency for biomass gasification plants by integration of the water gas shift section in the drying process[END_REF] Hydrothermal processes convert wet feedstocks into renewable fuels and are very promising to save energy by avoiding drying of the biomass, as they use water as the reaction medium. [START_REF] Deńiel | Energy valorisation of food processing residues and model compounds by hydrothermal liquefaction[END_REF] To convert wet biomass into bio-oil, hydrothermal liquefaction (HTL) uses the properties of subcritical water, that is very reactive at relatively high temperatures and pressures (250-370 °C and 10-30 MPa). [START_REF] Kruse | Water -A magic solvent for biomass conversion[END_REF] The resulting products are generally organic, aqueous, and gaseous phases. The organic phase is often referred to as HTL raw (organic) residue or biocrude. It contains a liquid oily product (bio-oil) consisting of a complex mixture of compounds, a solid residue commonly known as char (insoluble in organic solvents) and a variable amount of water. The aqueous phase contains water-soluble products (acids, ketones, and other light polar compounds). The gaseous phase is particularly rich in CO 2 , and contains light hydrocarbons in small amounts. At the laboratory scale, the separation of the raw organic residue into bio-oil and char is generally carried out by extraction with an organic solvent, having ideally a similar polarity to that of target compounds contained in the bio-oil.

HTL bio-oils are complex mixtures containing a vast variety of oxygenated compounds of different molecular weights. [START_REF] Deńiel | Energy valorisation of food processing residues and model compounds by hydrothermal liquefaction[END_REF][START_REF] Toor | Hydrothermal liquefaction of biomass: A review of subcritical water technologies[END_REF][START_REF] Vardon | Chemical properties of biocrude oil from the hydrothermal liquefaction of Spirulina algae, swine manure, and digested anaerobic sludge[END_REF] Nitrogen-containing compounds are also common in bio-oils. [START_REF] Huang | Recent progress in the direct liquefaction of typical biomass[END_REF][START_REF] Ramos-Tercero | Process Water Recycle in Hydrothermal Liquefaction of Microalgae To Enhance Bio-oil Yield[END_REF] The bio-oil is dark in appearance and has a strong odor as well as low volatility and high viscosity. Various additives (mostly pH regulators) and catalysts influence the liquefaction reaction pathways and distribution of products. [START_REF] Zhu | Hydrothermal liquefaction of barley straw to bio-crude oil: Effects of reaction temperature and aqueous phase recirculation[END_REF] Bio-oils presented in the literature are most of the time characterized by their heating value and chemical composition. [START_REF] Yin | Subcritical hydrothermal liquefaction of cattle manure to bio-oil: Effects of conversion parameters on bio-oil yield and characterization of bio-oil[END_REF] Yin [START_REF] Yin | Subcritical hydrothermal liquefaction of cattle manure to bio-oil: Effects of conversion parameters on bio-oil yield and characterization of bio-oil[END_REF] reports bio-oils with average higher heating value of 35.5 MJ•kg -1 Hoffman at al. [START_REF] Hoffmann | Co-processing potential of HTL bio-crude at petroleum refineries -Part 1: Fractional distillation and characterization[END_REF] reports a heating value of 40.4 MJ•kg -1 while Gan and Yuan [START_REF] Gan | Operating condition optimization of corncob hydrothermal conversion for bio-oil production[END_REF] report a heating value of 25.4 MJ•kg -1 . HTL bio-oils, even though extremely variable, are usually more calorific compared to other liquid fuels obtained from thermoconversion processes of biomass, like pyrolysis bio-oils. [START_REF] Mohan | Pyrolysis of Wood/ Biomass for Bio-oil: A Critical Review[END_REF] Typical oxygen content for HTL bio-oils is 15-20%. [START_REF] Minowa | Thermochemical liquefaction of indonesian biomass residues[END_REF][START_REF] Mørup | Construction and Commissioning of a Continuous Reactor for Hydrothermal Liquefaction[END_REF] Many analytical techniques can be used to characterize the chemical nature of bio-oils. Gas Chromatography coupled with Mass Spectrometry (GC/MS) is the most frequently used method to analyze the composition of HTL bio-oils. [START_REF] Vardon | Chemical properties of biocrude oil from the hydrothermal liquefaction of Spirulina algae, swine manure, and digested anaerobic sludge[END_REF][START_REF] Zhu | Hydrothermal liquefaction of barley straw to bio-crude oil: Effects of reaction temperature and aqueous phase recirculation[END_REF][START_REF] Villadsen | Development and Application of Chemical Analysis Methods for Investigation of Bio-Oils and Aqueous Phase from Hydrothermal Liquefaction of Biomass[END_REF][START_REF] Valdez | Characterization of Product Fractions from Hydrothermal Liquefaction of Nannochloropsis sp. and the Influence of Solvents[END_REF] Apart from the analyses cited above, few authors report functional fuel properties such as viscosity, density, volatility, and stability of HTL bio-oils. Some exceptions are Kumar and Pant [START_REF] Kumar | Production and characterization of biocrude and biochar obtained from non-edible de-oiled seed cakes hydrothermal conversion[END_REF] and Wang at al. [START_REF] Wang | Upgrading of hydrothermal liquefaction biocrude from algae grown in municipal wastewater[END_REF] Such information is however important to identify potential applications as fuels. Each application is typically designed for a particular fuel. Inversely, most fuels target a particular application, with some crossover possibilities. There is a very large panel of standardized tests applied to commercially available fuels to assess their handling, storage, and combustion properties.

In this paper, we focus on hydrothermal liquefaction experiments of blackcurrant pomace. This resource is a representative of fruit and vegetable processing residue rich in fibers, with some proteins and lipids. The hydrothermal conversion of this resource (and fruit residues in general) has received little attention in the literature. [START_REF] Deńiel | Energy valorisation of food processing residues and model compounds by hydrothermal liquefaction[END_REF][START_REF] Pavlovic | Hydrothermal Reactions of Agricultural and Food Processing Wastes in Sub-and Supercritical Water: A Review of Fundamentals, Mechanisms, and State of Research[END_REF] We report both experiments on the choice of solvents to recover bio-oil and on the use of sodium hydroxide as additive to modify the initial pH of the reaction mixture. Most studies looking at these factors in the liquefaction process concentrate on yields and sometimes in differences in chemical composition. [START_REF] Valdez | Characterization of Product Fractions from Hydrothermal Liquefaction of Nannochloropsis sp. and the Influence of Solvents[END_REF][START_REF] Yuan | Comparative studies of thermochemical liquefaction characteristics of microalgae using different organic solvents[END_REF][START_REF] Singh | Effect of solvent on the hydrothermal liquefaction of macro algae Ulva fasciata[END_REF] No real investigation was done comparing the effect of extraction solvent or additives on the physicochemical properties of HTL bio-oils. Mazaheri et al. [START_REF] Mazaheri | Sub/ supercritical liquefaction of oil palm fruit press fiber for the production of bio-oil: Effect of solvents[END_REF] compared different solvents like benzene, diethyl ether, and acetone used for recovering different oil fractions. Yang et al. [START_REF] Yang | Selective extraction of bio-oil from hydrothermal liquefaction of Salix psammophila by organic solvents with different polarities through multistep extraction separation[END_REF] investigated the extraction of chemical families from bio-oil using one or multistep extraction and comparing bio-oil yields of each solvent-extracted fraction. Valdez et al. [START_REF] Valdez | Characterization of Product Fractions from Hydrothermal Liquefaction of Nannochloropsis sp. and the Influence of Solvents[END_REF] present the extraction and analysis of bio-oils from microalgae with different solvents. These studies focus on yields and chemical compositions of the extracted bio-oils.

HTL bio-oil is not a commercially traded product and no clear applications have been identified yet. There are therefore no real standards for these oils to comply to. Most authors assume these oils are upgraded to diesel or fuel oil in an expensive catalytic upgrading process. [START_REF] Ou | Technoeconomic analysis of transportation fuels from defatted microalgae via hydrothermal liquefaction and hydroprocessing[END_REF] Adapting the existing standards, the properties of the bio-oils can be easily compared with commercial fuels, which could direct their use toward certain applications. The objective of this paper is to apply a variety of classic analysis techniques related to storage, volatility, and combustion qualities. The values found will be compared to commercial fuels.

We tested various analysis techniques on five different bio-oils produced by HTL of blackcurrant pomace. The objective is to determine some physicochemical properties of HTL bio-oils by testing two methods developed for biodiesel, as well as to investigate the use of thermogravimetric analysis as an alternative method to evaluate the distillation behavior and to determine Conradson Carbon Number (CCR) values. The results allow the comparison of HTL bio-oils with commercial fuels (diesel, gasoline, heavy marine fuel oil). The influence of extraction solvents and NaOH as additive on bio-oil quality can be identified. We also shortly present the GC/MS results where we show the loss of some volatile compounds during the solvent evaporation step following bio-oil extraction.

EXISTING STANDARDS FOR ANALYSIS OF FUELS

AND BIOFUELS All commercial fuels have criteria related to storage. The iodine value (standard EN 14111 standard) is used in biodiesel characterization to evaluate the degree of unsaturation, due to the presence of unsaturated long chain fatty acids or their derivative compounds. The higher the iodine value, the more CC bonds are present. The presence of unsaturated compounds can lead to undesirable polymerization reactions during storage, decreasing fuel quality. Iodine value is therefore an indicator that could also be used to determine unsaturation in HTL bio-oils and also helpful to evaluate the storage potential. To our knowledge, no iodine value measurements have been performed on HTL bio-oils.

Another important quality measurement of petroleum crude oil and biodiesel to avoid corrosion risk to machinery and storage tanks is the total acid number, known as TAN (standards EN 14104 and ASTM D664). This measurement is often determined specifically to analyze biodiesel and pyrolysis oils, [START_REF] Oasmaa | Norms, Standards, and Legislation for Fast Pyrolysis Bio-oils from Lignocellulosic Biomass[END_REF][START_REF] Wang | Hydrothermal liquefaction of Litsea cubeba seed to produce bio-oils[END_REF] and could be applied to determine the acidity of HTL bio-oils. Wang et al. [START_REF] Wang | Hydrothermal liquefaction of Litsea cubeba seed to produce bio-oils[END_REF] present the TAN of hydrothermal bio-oil from Litsea cubeba seeds, they found a value of 100 mg KOH per gram bio-oil.

Volatility and combustion properties are highly important when considering a liquid fuel quality. One of the main qualities is its heating value expressed in its Lower Heating Value (LHV) or Higher Heating Value (HHV) (standard NF EN 14918). Another major parameter is related to the volatility of the fuel. For storage purposes the product should be stable, and its vapor pressure expressed as the Reid Vapor Pressure (standard ASTM D323) should be inferior to a certain value (seasonal parameter). On the other hand, the boiling point distribution and the end of the distillation are also regulated (standard ASTM D86). Because HTL bio-oils produced in batch reactors are available in limited quantities, characterization by a full distillation curve is usually not possible. Other analysis techniques are then applied to understand the evaporation behavior of bio-oils. For instance, thermogravimetric analysis (TGA) can be applied to any type of sample that will undergo a mass change over time under the effect of temperature in a given atmosphere. The evaporation and thermal decomposition are part of the changes that are detected by thermogravimetry. [START_REF] Zhu | Hydrothermal liquefaction of barley straw to bio-crude oil: Effects of reaction temperature and aqueous phase recirculation[END_REF] TGA is often used to predict the thermal behavior of the major macromolecular components of biomass during their thermal conversion to biofuel. [START_REF] Collura | Thermal behaviour of Miscanthus grasses, an alternative biological fuel[END_REF] In other published works, TGA is used to simulate distillation or to show the volatility of products resulting from the biomass conversion. The distillation of different biodiesels produced from different vegetal oils are compared in term of percentage of weight loss at different temperatures. [START_REF] Gautam | Determination of important biodiesel properties based on fuel temperature correlations for application in a locomotive engine[END_REF] Comparison of volatilities of extracted fractions from HTL bio-oils obtained with different solvents are performed by TGA, showing the influence of solvent polarity on the extracted chemical groups from bio-oils. [START_REF] Yang | Selective extraction of bio-oil from hydrothermal liquefaction of Salix psammophila by organic solvents with different polarities through multistep extraction separation[END_REF] Fuels meant purely for combustion have more specifications related to combustion residues. These include soot-forming tendencies and Conradson carbon number (CCR, standards EN ISO 10370 and ASTM D4530). The standard CCR method is usually used to evaluate carbon residue from a fuel, which corresponds to the nondistillable residues that form after complete distillation. As well as for the evaporation behavior, TGA can also be used as an alternative method to the standard CCR method. [START_REF] Ghetti | A rapid heating TGA method for evaluating the carbon residue of fuel oil[END_REF] Transportation fuels such as petrol, jet fuel and diesel have more narrow specifications in terms of combustion properties, for example, Research Octane Number (RON, standard ASTM D2699) and cetane number (standards EN ISO 5165 and ASTM D613). These latter two are of little interest for nonupgraded hydrothermal oils.

Viscosity of the fuel is another important parameter (standards EN ISO 3104 and ASTM D445). Viscosities of HTL bio-oils are rarely mentioned in the literature compared to pyrolysis bio-oils, but they are presented as highly viscous and semisolid liquids. High values were found between 8 and 1000 Pa•s. [START_REF] Minowa | Thermochemical liquefaction of indonesian biomass residues[END_REF][START_REF] Elliott | Liquid Hydrocarbon fuels from biomass[END_REF] Furthermore, according to Mohan et al., [START_REF] Mohan | Pyrolysis of Wood/ Biomass for Bio-oil: A Critical Review[END_REF] pyrolysis oils behave as a non-Newtonian fluid at low temperatures and in addition the viscosity increases in time due to aging. It is well agreed that rheological properties of bio-oils change over time. High storage temperatures are particularly unfavorable. This is due to many factors like polymerization, oxidation, and others. Studies on rheological properties and aging can be performed to give an indication of how viscosity of bio-oils change over time depending on temperature and storage conditions. [START_REF] Oasmaa | Norms, Standards, and Legislation for Fast Pyrolysis Bio-oils from Lignocellulosic Biomass[END_REF][START_REF] Oasmaa | Acidity of Biomass Fast Pyrolysis Bio-oils[END_REF]

MATERIALS AND METHODS

Materials and Resource Characterization.

The experiments were performed using blackcurrant pomace supplied by a local manufacturer as the substrate. Table 1 gives the composition and Higher Heating Value (HHV) of the biomass used in this study. Blackcurrant pomace is a residue from berry pressing, mainly constituted by seeds and peels: it is a wet and fiber-rich biomass, also containing a non-negligible amount of proteins and a small amount of lipids.

For each HTL experiment, distilled water was used. Pellets of Sodium Hydroxide NaOH were purchased from Merck and used as received. Ethyl-acetate, isopropyl alcohol, acetone, and hexane were purchased from Sigma-Aldrich and used as received. In our experiments, all the reagents were analytical grade.

3.2. Production of Bio-Oil by HTL of Blackcurrant Pomace. Hydrothermal liquefaction experiments were performed in a 0.6 L stainless steel (SS316) stirred batch reactor (Parr Instruments Company). In a typical experiment, the reactor is filled with approximately 240 g of biomass slurry prepared from blackcurrant pomace and distilled water at a constant 14 wt % dry matter to water ratio. In some cases, sodium hydroxide is added as an additive. The autoclave is leak tested, purged and pressurized to 1 MPa with nitrogen gas, to ensure sufficient pressure for gas analysis after the reaction. The pressure inside the reactor is a function of the reaction temperature, the amount of water, and the amount of produced gas during the process. The reactor is stirred at 600 rpm and is heated to the reaction temperature in about 35 min. Once the reactor reaches the reaction temperature, it is held during a specified time within ±1 °C of the specified operating temperature (holding time). For these experiments, a 60 min holding time is applied. After the holding time, the reactor is rapidly cooled to room temperature in 20 min by an air quench.

After venting the reactor for gas analysis, the content of the reactor is first filtered on a Buchner filter to separate the aqueous phase from the raw organic residue. The raw organic residue is sticky, and removed from the reactor as best as possible. The reactor is then weighed, and the weight difference with the empty reactor is counted as raw organic residue. The raw organic residue is dried at room temperature under air circulation until a stable mass was obtained.

The raw residue is extracted with a solvent, using a 10-fold amount of solvent to separate the bio-oil from the char. Bio-oil is recovered after evaporation of the solvent at room temperature under air circulation, until a stable weight is obtained. It is assumed that no residual solvent is left in the bio-oil. The char is also dried at room temperature under air circulation, until a stable weight is obtained. Weight loss of the char after extraction and drying is used to determine the proportion of solventsoluble organics in the raw residue, and therefore the bio-oil yield. All yields reported in this study are expressed in weight percentage of the dry biomass (wt % DM).

3.3. Physicochemical Properties. A wide variety of tests are available, many subject to strict standards.

Total Acid Number (TAN) and Iodine Value. The total acid number (TAN) and the iodine value are determined according to European standards EN 14104 and EN 14111, respectively. These standards have been defined for the fatty acid methyl esters FAME of Biodiesel. The procedures followed in this paper are adapted to be able to use color indicators even when the oil is black. Both TAN and iodine value measurements were repeated three times for each sample.

Total acid number (TAN) is the number of milligrams of potassium hydroxide KOH required to neutralize the acidic functions present in one gram of oil. It is expressed in mg KOH per gram of the sample. To determine the TAN value, 50 mg of bio-oil sample was dissolved in 10 mL of isopropyl alcohol and then titrated by a 20 mM KOH solution, using Phenolphthalein as a color indicator. The concentrations of bio-oil in isopropyl alcohol must be kept low to guarantee a certain transparency and good solubility of nonpolar compounds.

Iodine value is the mass of halogen binding to double-bonds contained in 100 g of bio-oil. It is expressed in grams of iodine (I 2 ) per 100 g of the sample, according to the standard EN 14111. It is based on the reaction between a Wijs (ICl) solution and the double-bonds contained in compounds of the bio-oil. Excess of the Wijs reagent becomes I 2 by adding potassium iodide KI. The concentration of formed iodine I 2 is then determined by titration with sodium thiosulfate. Bio-oil samples of 90-100 mg were used for iodine value measurements.

GC/MS Analysis. GC/MS (gas chromatography/mass spectroscopy) analysis of bio-oil was performed in Clarus 500/600S PerkinElmer apparatus. The column used is a DB-1701 capillary column 60 m × 0.25 mm, 0.25 μm film thickness. The injection of 1 μL of bio-oil samples is performed on Split mode 10:1 with Helium as carrier gas. The injector temperature is maintained at 250 °C. The GC oven temperature is programmed from 45 °C (10 min) to 230 °C at a rate of 6 °C•min -1 , and held at 230 °C during 9.17 min. It is then raised to 250 °C at a rate of 10 °C•min -1 , held at 250 °C during 20 min. The transfer interface and the ion source in the mass spectrometer are maintained at 230 °C. Bio-oil samples dissolved in ethyl acetate are filtered with a 0.45 μm PTFE filter before analysis. The National Institute of Standards and Technology (NIST) mass spectral library is used to identify the compounds.

Thermogravimetric Analysis. (TGA) was conducted on a SETSYS SETARAM instrument, using platinum crucibles to carry bio-oil samples. Bio-oil samples of approximately 5 to 6 mg were used. The oven temperature was first risen from 35 to 900 °C at a heating rate of 10 °C•min -1 , under a 50 mL•min -1 nitrogen flow. Temperature was then held for 10 min at 900 °C under a 50 mL•min -1 air flow to perform the combustion stage.

Viscosity Measurements. The dynamic viscosity of bio-oil is measured using a rotational viscometer at different temperatures. The viscometer is supplied by Sheen, composed of a sample cell placed in a thermostat adapter and a range of different cylindrical spindles. A sample volume between 8 and 11 mL (depending on the viscosity of the liquid) is used. Viscosity is determined by measuring the torque required to rotate the spindle at the required frequency.

Other Analysis. The higher heating value HHV (MJ•kg -1 ) of bio-oils is measured using a Parr 6200 bomb calorimeter (Parr Instruments Company). The organic carbon content of the bio-oils is determined by a total organic carbon analyzer (Shimadzu SSM-5000A). Density is determined by letting a droplet of the bio-oil float in distilled water. By adding ethanol under constant stirring, the density of the aqueous phase is progressively lowered until the droplet begins to sink. The density of the water is then measured by weighing a determined volume in a graduated cylinder. [START_REF] Bowler | A simple titration method for determining the specific gravity on one drop of urine[END_REF] This method allows the density measure of very small volumes.

RESULTS

4.1. Bio-Oil Yields. In Table 2, we present the results obtained from the liquefaction experiments. The experiments were performed at 300 °C. Holding time was 60 min. The experiments deal with variations in the extraction solvent and the addition of sodium hydroxide in the initial mixture. From the results, it appears that increasing the initial pH is favorable to the bio-oil yield. The calorific value of the bio-oils are all comparable, except for the oil obtained with hexane. The oil obtained with hexane is of a higher quality: it has a higher heating value and carbon content but shows a much lower yield.

The water content of the extracted bio-oils is typically less than 1% and it is not further discussed in this paper. The specific gravity of the bio-oil varies from 0.96 (L5) to 0.99 (L4).

4.2. Viscosity. Due to the limited capacity of the autoclave reactor and the recovery procedure, most samples were available in small quantities. The viscosity measurement requires a large volume that was not available for most samples. The ethyl acetate extracted bio-oil without additives (sample L1-EA) was the only analyzed bio-oil. The evolution of the dynamic viscosity as a function of the temperature is presented in Figure 1. At low temperatures, the oil is free-flowing but highly viscous. With increasing temperatures, the viscosity decreases rapidly.

Total Acid Number (TAN) and Iodine

Value. Total acid number (TAN) and iodine value for the studied bio-oils are presented in Table 2. The TAN values are much higher than those specified in standards for commercial fuel and biodiesel (typically TAN ≤ 0.5 mg KOH g -1 ). The acidity of these bio-oils is probably related to the presence of long-chain fatty acids, carboxylate and phenolic compounds. Phenolic compounds usually have pK a values between 9 and 10. They can therefore be titrated by a 20 mM KOH solution (pH around 12) as validated in the text of the ASTM D664 standard. Note that the bio-oil extracted with hexane has the highest TAN value (159 mg KOH g -1 ).

The results of iodine value measurements show that the highest iodine values are obtained for ethyl-acetate extracted biooils. The fact that iodine values are generally very high suggests that this titration is neither selective nor dedicated toward double bonds in linear fatty acid chains. The double bonds in substituents of aromatic derivatives and other cyclic compounds may also be titrated. The proportions of the TAN and iodine values due to fatty acids, relative to other compounds, has not been determined.

4.4. GC/MS Analysis. Bio-oil is obtained by solvent extraction from a raw organic residue followed by subsequent evaporation of the solvent. This evaporation step has an influence on the chemical composition of the bio-oil, as illustrated in Figure 2. In fact, it compares GC-MS analysis of a bio-oil prior to solvent evaporation, and the same bio-oil after solvent evaporation and redissolution in the same solvent. In this case, ethyl-acetate was used to perform solvent extraction.

From these chromatograms, it can be observed that solvent evaporation causes the loss of many light components, corresponding to retention times between 12 and 24 min. Figure 3 gives a more precise overview of the volatile compounds that are lost: 20 volatile compounds were positively identified in this nonevaporated bio-oil when analyzed without any dilution in GC/MS. Those molecules are not recovered after evaporation. This is confirmed when zooming in on a part of the chromatogram up to 25 min. This zoom is presented in Figure 3. In the same Figure 2, we also show the chromatogram of the bio-oil produced in basic conditions (L5). It is clear that both biooils are relatively close in chemical composition of the more volatile components. Some peaks of mainly phenolic components are reduced, due to better solubilization in aqueous phase of phenol monomers and dimers at high pH values.

The compounds identified in Figure 3 are listed in Table 3. Heavier compounds are evaporated to a smaller extent and are relatively unaltered.

The nature of the solvents also has an influence on the chemical composition of the bio-oil. Figure 4 shows the chromatograms of bio-oils extracted from the same raw organic residue with four different solvents (L1 to L4). The main identified compounds are presented in Table 4, only positive identifications are presented. In practice, due to the abundancy of the compounds there are many overlaying peaks, making positive identification delicate. The chromatograms show that these four bio-oils are mostly alike except for bio-oil extracted with hexane (L2), which contains fewer identified compounds. The broad peak (10) detected at 58 min corresponds to fatty acids derivatives.

As for the bio-oil extracted with ethyl-acetate (L1), the identified peaks in GC/MS are similar to the acetone extracted bio-oil (L3). However, we note that ethyl-acetate extracted biooil (L1) contains other peaks (having a relative area percentage Table 3. Identified Volatile Compounds in Bio-Oil (using the NIST Spectra Database Match) ≥3%) nonidentified by the NIST database and absent in other bio-oils. The highest bio-oil yield was obtained by using acetone to perform the extraction. These abundant unknown peaks confirm that ethyl acetate is a powerful extraction solvent with high affinity to diverse compounds, more than the three others tested solvents. We mention also that low-boiling-point compounds with a phenol ring structure are characteristic of these bio-oils, in particular phenol and guaiacol (2-methoxyphenol). At higher retention times, heavier compounds corresponding to lipid derivativesamong them fatty acid methyl esters and long-chain amidesand potential oligomeric structures are detected.

4.5. Thermogravimetric Analysis. In this section, we present results of the thermogravimetric analysis (TGA) of the extracted bio-oils. The percentage of weight loss of the bio-oils at 250 °C on TG curves can be an indication to evaluate the proportion of the bio-oil that is not analyzed by GC/MS. TGA analysis also allows comparing the volatility of the studied biooils and of some well-known commercial fuels.

Figure 5 and Figure 6, respectively, present the TG (thermogravimetric) and the DTG (differential thermogravimetric) curves, showing the percentage and the rate of weight loss versus the temperature. In Figure 5, the heavy Iraqi crude, diesel, light cycle oil and heavy gas oil TG curves are reproduced from the literature. [START_REF] Hashimoto | Evaporation characteristics of a palm methyl ester droplet at high ambient temperatures[END_REF][START_REF] Goncalves | Investigation of petroleum medium fractions and distillation residues from Brazilian crude oils by thermogravimetry[END_REF][START_REF] García Barneto | Thermogravimetric Monitoring of Crude Oil and Its Cuts in an Oil Refinery[END_REF] These curves are produced at the same heating rate (10 °C•min -1 ) under nitrogen atmosphere. They are presented here as references for comparison purposes with the studied bio-oils (L1-L5). From Figure 5, it can be seen that hydrothermal oils are situated in the light end of the heavy refinery streams and are not dissimilar to heavy crude oil. They are, however, generally much heavier than diesel fuel. TG and DTG curves are very similar for most oils, except for the one recovered by extraction with hexane (L2). This was also observed by GC/MS analysis in section 4.4, underlining the importance of solvent polarity. Most polar solvents produce oil with similar evaporation behavior, even when very basic hydrothermal conditions are used (sample L5).

TG analysis shows also the presence of residual solvent left after evaporation in the recovered bio-oil, as in the case of isopropyl alcohol and acetone extracted bio-oils. This is confirmed by GC/MS analysis where total ion chromatograms present peaks of these two solvents in their corresponding biooils. These solvents are less volatile than ethyl-acetate, which evaporates more rapidly at ambient conditions.

From the DTG curves on Figure 6, several temperature zones can be identified. Each zone is more or less significant, depending on its content in volatiles. Peaks on DTG curves observed at 100 °C correspond to the evaporation of compounds having a boiling point near or less than 100 °C, as well as residual extraction solvent and traces of water. In Table 5, we compare the samples analyzed by TGA according to the identified temperature zones.

The weight loss at 250 °C can be used to estimate the weight percentage of bio-oil that is analyzed by GC/MS, because it is the maximal temperature of the GC oven. We consider that the area between 250 and 350 °C corresponds to less volatile compounds. Above 350 °C, pyrolysis phenomena thermally degrade the biooils, resulting in weight loss showed on DTG curves between 350 and 500 °C. Finally, we estimate that the end of the bio-oil distillation (or evaporation behavior) occurs around 500 °C. To give a good estimation of the carbon residue after distillation, the residue is further heated until 900 °C and then burned in air for 10 min.

The estimation of the carbon residue is given in Table 5 for the five studied bio-oils with a standard deviation obtained for three analysis repetitions. Carbon residue values confirm that the biooils are rich in heavy structures that have difficulties evaporating. Polar solvents like isopropyl alcohol, acetone, and ethyl acetate extract these complex polar heavy structures, which are not extracted by the use of hexane (nonpolar solvent).

DISCUSSION

Commercial fuels are subject to a large number of normalized quality criteria. Some of these criteria are presented in Table 6.

To date, no precise application is targeted for this type of oil. A general comparison with some common properties and fuels can direct the use of these oils. As can be observed in Table 6, HTL bio-oils are obviously far from commercial quality standards, even for heavy fuel oil. It is however interesting to compare the quality of hydrothermal bio-oils with commercial fuels to evaluate the need for upgrading. Xiu and Shabazi [START_REF] Xiu | Bio-oil production and upgrading research: A review[END_REF] present different upgrading techniques and the (dis)advantages. Properties such as TAN and iodine value can be used as indicators. Hydrothermal bio-oils are for instance far too acidic for most applications, even though the oil produced under very basic conditions is somewhat less acidic than oils produced under acidic conditions. It seems that the initial presence of a strong base is not sufficient to guarantee a low acidity of the bio-oil. The work of Hoffman et al., [START_REF] Hoffmann | Co-processing potential of HTL bio-crude at petroleum refineries -Part 1: Fractional distillation and characterization[END_REF] who present bio-oil from hardwood produced at supercritical conditions, suggests that increasing the temperature of the conversion lowers the acidity of the bio-oil. Wang at al. [START_REF] Wang | Upgrading of hydrothermal liquefaction biocrude from algae grown in municipal wastewater[END_REF] showed that the acidity of hydrothermal bio-oils from algae can be reduced significantly by hydrotreatment on a platinum catalyst.

The chemical composition and evaporation behavior appears globally little affected by the conditions under which the bio-oil is produced. This is true for the initial pH of the mixture, but also for the extraction solvent as long as it remains polar. Changing from a polar solvent to a nonpolar solvent improves the bio-oil quality at the detriment of a lower yield. The high-fiber content of blackcurrant pomace could partially explain these results. Hydrothermal degradation of fibers is responsible for the formation of many oxygenate compounds such as acids, ketones, aldehydes and esters produced by degradation of cellulosic polymers, [START_REF] Yin | Hydrothermal liquefaction of cellulose to bio-oil under acidic, neutral and alkaline conditions[END_REF] as well as phenol derivatives from lignin degrada- tion. [START_REF] Kang | Hydrothermal conversion of lignin: A review[END_REF] It is therefore understandable that polar solvents would be much more efficient to extract those types of molecules.

Figure 7 shows how hydrothermal oils compare to commercial fuels for two different criteria. The best quality oil, produced by hexane extraction (L2), may be applicable as heavy fuel oil for marine applications. Acidity and stability may be a problem and will need to be examined closer. This does not mean that the fuels are inadequate but it does mean that end users may need to adapt their installations and create norms and standards to ensure the compatibility with these fuels.

The comparison with heavy Iraqi crude [START_REF] García Barneto | Thermogravimetric Monitoring of Crude Oil and Its Cuts in an Oil Refinery[END_REF] presented in section 4.2 is interesting as the evaporation behavior is very similar. It suggests that hydrothermal oils can be considered replacement for crude oils that may interest refineries. In any case, removing the oxygen to create a drop in replacement transport fuel will require some effort.

CONCLUSION

Hydrothermal liquefaction is capable of producing high heating value bio-oils with interesting yields. These oils are free-flowing but still have an elevated viscosity. The bio-oil extracted with hexane seems to be the best-performing bio-oil compared to biooils extracted with the other three solvents. This bio-oil has the lowest carbon residue, a nearby diesel volatility and a heating value that allows it to replace certain fuels, such as those for marine applications. It should be interesting to test this oil in a low-frequency piston engine.

The main problems with all hydrothermal bio-oils are their acidity and viscosity. Producing bio-oils in basic medium seems to improve the bio-oil quality by lowering its acidity, but this value remains far too important for most existing installations. Some degree of upgrading is consequently necessary to deal with these problems.

Most normalized tests for fuel properties require dedicated equipment and large sample volumes. Some of these techniques can be adapted for characterization of hydrothermal bio-oils at the laboratory scale, which are available in small volumes. It was shown that the applied techniques can yield valuable information about hydrothermal bio-oils. This information can be used to evaluate the bio-oils from a process optimization perspective.

■ AUTHOR INFORMATION

Corresponding Author *E-mail: geert.haarlemmer@cea.fr. Tel.: 0033438782495. Fax: 0033438785251.

Figure 1 .

 1 Figure 1. Evolution of the viscosity of bio-oil L1-EA from hydrothermal liquefaction of blackcurrant pomace.

Figure 2 .

 2 Figure 2. Chromatograms of extracted and resolubilized bio-oil after evaporation of the solvent.

Figure 3 .

 3 Figure 3. Volatile compounds identified by GC/MS analysis in ethyl-acetate extracted bio-oil (before evaporation of the solvent).

Figure 4 .

 4 Figure 4. Chromatograms of oil extracted with different solvents.

Figure 5 .

 5 Figure 5. Weight loss curves of bio-oils obtained by different solvents and of some reference fuels.

Figure 6 .

 6 Figure 6. Differentiated weight loss curves for bio-oils obtained by different solvents.

Table 1 .

 1 Composition and HHV of Blackcurrant Pomace a

		blackcurrant pomace
	moisture content (wt %)	59.6
	fiber content (wt % dry matter)
	NDF	61.7
	ADF	52.8
	ADL	35.4
	proteins (wt % dry matter)	16.9
	lipids (wt % dry matter)	3.5
	ash content at 550 °C (wt % dry matter)	4.5
	elemental composition (wt % dry matter)
	C	50.3
	H	6.8
	O	36.8
	N	1.9
	S	0.2
	HHV (MJ•kg -1 )	18.51

a NDF: Neutral Detergent Fibers; ADF: Acid Detergent Fibers; ADL: Acid Detergent Lignin; HHV: Higher Heating Value.

Table 2 .

 2 Yields Obtained from Liquefaction and Extraction Experiments

	id oil	solvent used	initial pH	oil yield	carbon content oil HHV (MJ/kg) TAN (mg KOH/g oil) iodine value (g I 2 /100 g oil
	L1 -EA	ethyl acetate	3.2	28 (±3) %	75 (±1) %	33.4	134	150
	L2 -HEX	hexane	3.2	17 (±3) %	80 (±1) %	38.4	159	117
	L3 -ACET	acetone	3.2	32 (±3) %	74 (±1) %	35.0	134	131
	L4 -ISOP	isopropyl alcohol	3.2	29 (±3) %	73 (±1) %	34.6	137	118
	L5 NaOH -EA	ethyl acetate	13 (NaOH)	33 (±3) %	69 (±1) %	35.5	108	149

Table 4 .

 4 Identified Compounds in Extracted Bio-Oils with Four Different Solvents

	0	2-pentanone -4-hydroxy -4-methyl	6	2-hydroxypyridine-4-
				carboxaldehyde
	1	2-cyclopenten-1-one, 2,3-dimethyl-	7	2-hydroxypyridine-4-ethyl
				ketone
	2	phenol	8	hexadecanoic acid, methyl
				ester
	3	guaiacol	9	dodecanamide derivative
				with double bond
	4	phenol, 4-methyl-	10 octadecadieanamide
				derivatives (C18 double
				bond)
	5	phenol, 4-ethyl-

Table 5 .

 5 Volatility of the Bio-Oils and Residual Carbon in Weight Percentage

	% evaporated	L1	L2	L3	L4	L5
	100 °C	1	0	2	6	5
	100-250 °C	5 6	7 5	5 7	5 8	5 3
	250-350 °C	1 7	1 6	1 6	1 5	1 7
	350-500 °C	14	5	14	12	14
	250 °C	5 7	7 5	5 9	6 4	5 8
	carbon residue	7.3 ± 0.6	2.7 ± 0.6	9.25 ± 0.01	6.3 ± 0.6	8 ± 0.6

Table 6 .

 6 Comparison of the Properties of HTL Bio-Oils with Required Values for Commercial Fuels

		gasoline	diesel	biodiesel	heavy marine fuel oil	HTL bio-oil
	standard	NF EN 590	NF EN 228	NF EN 14214	NF ISO 8217
	density 15 °C (kg•m 3 )	720-775	820-845	860-900	920-1010	960-990
	CCR (wt %)	≤0.2	≤0.3	≤0.3	≤2.5-20	3-9
	TAN (mg KOH per g bio-oil)	≤0.5	≤0.5	≤0.5	≤2.5	108-159
	iodine value (gI 2 /100g bio-oil)	NA	NA	≤120	NA	117-149
	HHV (MJ•kg 1-)	4 8	4 5	≥35	NA	33-38
	water (wt %)	NA	≤0.02	≤0.05	<0.5	<1
	viscosity (mPa•s) @40 °C	<0.3	3-4	<4	8.5-690

Notes

The authors declare no competing financial interest.

■ ACKNOWLEDGMENTS

The authors would like to acknowledge financial support from the French Research National Agency ANR (LIQHYD project, grant ANR-12-BIME-0003). The authors are also grateful to Julien Roussely, Marine Blanchin, Heĺeǹe Miller and Sebastien Thiery for help on the analyses of the products.


Abhishek Pariyar

Laszlo S Toth

Satish V Kailas

Laurent Peltier

Imparting high-temperature grain stability to an Al-Mg alloy

Al alloys, despite their excellent strength-to-weight ratio, cannot be used at elevated temperatures because of microstructural instability owing to grain growth and precipitate coarsening, thus, leading to a drastic loss in their strength. In this work, we have attempted to address the issue of grain growth by introducing in-situ formed polymer derived ceramics in an Al-Mg alloy. A stable grain structure with minimal loss in hardness when exposed to 450 °C and 550 °C for 1 hour was obtained due to the particle pinning of the grain boundaries by the Zener mechanism. Table 1 Chemical composition of the as-received Al 5052 alloy. Element Al Mg Si Cr Mn Fe Cu Zn Wt.% 96.2 2.81 0.15 0.31 0.10 0.26 0.10 0.06

Al alloys have widespread applications in the aerospace, automotive and shipping industries because of their excellent strengthto-weight ratio compared to other alloys [ 1 , 2 ]. However, most Al alloys cannot sustain their strength at high temperatures, except for a few, like L1 2 phase forming alloys, which can be used till 343 °C [START_REF] Kearney | ASM Handbook, Properties and Selection: Nonferrous Alloys and Special-purpose Materials[END_REF][START_REF] Ceschini | Aluminum and Magnesium Metal Matrix Nanocomposites[END_REF][START_REF] Baradarani | [END_REF][START_REF] Pandey | High Strength L12 Aluminum Alloys[END_REF] . One of the reasons for this reduction in strength is due to the increase in grain size when exposed to such temperatures.

As explained by the Hall-Petch relationship, the strength of a material increases with the decrease in grain size [ 7 , 8 ]. However, there is also an increase in the grain boundary area per unit volume with a decrease in grain size. It has been shown by Humphreys and Hatherley that if a cube shape is assumed for a grain, reducing the thickness of a sheet in rolling by 50% leads to an increase in the surface area of the grain by ~16%, whereas, reducing it by 99% increases the surface area by ~3267% [ 9 , 10 ]. Decreasing the grain size leads to an increase in the free energy of the system, which makes the microstructure unstable. The grains tend to grow in order to minimize this energy by reducing the grain boundary area per unit volume [START_REF] Kumar | [END_REF] . Further, the rate of grain growth also increases with the increase in temperature [START_REF] Abbaschian | Physical Metallurgy Principles[END_REF] .

One way to solve the problem of grain stability in aluminum alloys at high temperatures is by dispersing second phase particles within the Al alloy matrix. Such particles, when present at the grain boundaries, apply a retarding/drag force on their movement when exposed to high temperatures. This inhibits further motion of the grain boundaries which prevents grain growth by the wellknown Zener pinning mechanism. It has been estimated that the Zener pinning effect becomes more pronounced with a decrease in the particle size and increase in their volume fraction [ 9 , 11 ]. However, this mechanism is not active for precipitation hardening Al alloys, because the precipitates coarsen or dissolve at high temperatures. This causes the strength to drop due to recrystallization, grain growth and by the reduction in the number of dislocation barriers. However, the Zener mechanism can also be activated in both precipitation hardening and work hardening Al alloys, by introducing second phase particles, such as ceramics, which are not part of the alloy system [ 12 , 13 ]. It is beneficial to use nanosized ceramic particles as reinforcements because Zener pinning is more effective with the decrease in the particle size. However, using such particles have issues related to their agglomeration within the matrix. In addition to that, they also raise the manufacturing costs and are potentially hazardous to health during handling due to their nano-size [14] . Further, the breaking of ceramic particles below a particular size is difficult as there is a reduction in the defect density with a decrease in the particle size, making the fracture of ceramic particles progressively more difficult.

Ceramics can also be obtained from special types of polymers that are readily available. These polymers can be pyrolyzed into ceramic particles. Such polymers contain inorganic elements in their main polymeric chain along with organic components on their branches. Most of the organic components are released as volatiles during pyrolysis and the remaining part forms the amorphous ceramics (called polymer derived ceramics (PDCs)) [15] . Such polymers can be easily broken into nanoscale size, due to their low fracture toughness, by severe plastic deformation methods, especially by high shear processes (e.g. friction stir processing), and can be dispersed within the alloy matrix. Then, by heating to their pyrolysis temperature, the nanosized polymer can be converted into nanosized ceramics in-situ within the alloy. PDCs have very high temperature stability as they are ceramics and can potentially pin the grain boundaries even at high temperatures. Hence, in this work, an attempt has been made to impart high temperature grain stability to an Al-Mg alloy by introducing second phase particles in the form of in-situ polymer derived ceramics using friction stir processing. The particles aided in pinning the grain boundaries and maintaining their stability even at 0.85 T m with minimal loss in hardness.

The matrix material chosen was Al 5052-H32 sheet (composition shown in Table 1 ) of 6 mm thickness which was annealed at 343 °C for 2 hrs prior to processing. The polymer used was polymethylhydrosiloxane (PMHS) (Sigma-Aldrich) in the liquid state. The liquid polymer was first cross-linked at room temperature for 8 hrs into a rigid solid structure by adding 5 wt.% 1,4-diazabicyclo [2.2.2] octane (Sigma-Aldrich). The cross-linked polymer was then subjected to ball milling for 60 minutes. The ball-milled polymer powder was filled into grooves of width 3 mm and depth of 4 mm in the Al 5052-O plates. Friction stir processing (FSP) [16] was performed on the plates in order to disperse the polymer particles within the matrix using a hot die steel tool in H13 hardened condition having a conical pin with double start threads. The tool pin length was 5 mm, the diameter at the base was 10 mm, the diameter at the tip was 8 mm, and the shoulder diameter was 25 mm with a chamfer angle of 20 °. Three back-to-back passes were performed initially at a tilt angle of 2 °using 500 RPM tool rotation speed and 12 mm/min traverse speed. The plunge depths were 5.3 mm, 5.4 mm and 5.5 mm for the first, second and third passes, respectively. After the third pass, the whole plate was put for pyrolyzing the polymer in a furnace at 500 °C for 10 hrs. Subsequently, a fourth pass with the same experimental conditions, but using a plunge depth of 5.6 mm, was performed in order to close the pores formed due to the emission of gases during the pyrolysis of the polymer. For thermal stability tests, different specimens of the same material state were subjected to heat treatments at 450 °C and 550 °C for 1 hour in Argon gas environment. A temperature of 550 °C was specially chosen to test the current material beyond conditions already reported in the literature [ 10 , 17 ]. Another specimen was prepared under identical conditions but without the PDC particles and tested at 550 °C for 1 hour in Argon gas environment for comparison. The microstructure of each specimen was analyzed before and after the heat treatment at the same location using electron backscattered diffraction (EBSD) on JEOL 6500F. However, the electropolishing prior to the scan for the heat-treated specimen removed a few micron thickness resulting in a new surface for the scan. Scanning electron microscope (Zeiss Supra 40) and transmission electron microscope (Philips CM 200) were also utilized to observe the microstructures. The hardness tests before and after the heat treatments were performed using a Zwick-Roell ZHV1 equipment.

Fig. 1 (a) shows the inverse pole figure (IPF) map of the parent material before performing FSP. It can be seen that the grains were large and nearly equiaxed, typical of a recrystallized microstructure. Fig. 1 (b) shows the distribution of the grain size in the parent material. It can be observed that the highest fraction of the grains was between 10 to 20 μm, and the maximum size with the lowest fraction was around 150 μm.

Fig. 2 (a) is the IPF map showing the microstructure of the FSP-obtained nugget zone before the heat treatment at 450 °C for 1 hour. As can be seen, FSP produced a fine-grained microstructure with equiaxed grains. There was drastic grain refinement during the processing. The histogram for the grain size distribution is shown in Fig. 2 (b); the average measured grain size was 1.0 ±0.74 μm (number fraction-weighted). The IPF map of the exactly same area after heat treatment is shown in Fig. 2 (c). The microstructure looked very similar even after heat treatment. It was not identical to the one before heating because of the removal of a thin layer before the second EBSD measurement. The grain size distribution histogram after heat treatment is plotted in Fig. 2 (d). The average grain size was 0.96 ±0.71 μm, which was similar to the non-heattreated condition. This shows that there was minimal change in the average grain size for this material, even after exposing it to such high temperature.

Experiments were conducted at an even higher heat treatment temperature. Fig. 2 (e) is the IPF map before the heat treatment at 550 °C for 1 hour. The grain size distribution in Fig. 2 (f) shows the same initial average grain size of 1.0 ±0.71 μm (same material state as for the lower temperature heat treatment). The microstructure of the specimen after heat treatment is shown in Fig. 2 (g). It is again observed that the microstructure looked very similar to the non-heat-treated condition. The average grain size after the heat treatment was 1.0 ±0.72 μm, which was the same as that before the heat treatment. The grain size distribution ( Fig. 2 (h)) was very similar to the specimen heat-treated at 450 °C. Also, a closer observation of Fig. 2 (g) shows that grains with similar orientation did not coalesce. This result demonstrates the large resistance to grain growth in this material even at such a high temperature (solidus temperature of Al 5052 is 607 °C).

Abnormal grain growth (AGG) is known to occur during the thermal treatments of friction stir processed Al alloys [ 18 , 19 ] (especially when grain boundary disorientations are small). Moreover, AGG has also been observed in other materials that have supposedly a stable grain structure due to particle pinning [ 20 , 21 ]. In such situations, some regions may exhibit no change in grain size, whereas others might show drastic grain growth. Hence, it was imperative to check all the regions in the nugget zone to ensure that such a scenario was not prevalent. Figs. 3 (a-i) show the backscattered electron (BSE) micrographs obtained using a scanning electron microscope (SEM) from different regions in the nugget zone of the specimen with PDC particles heat-treated at 550 °C for 1 hr. The reference for the location of each BSE micrograph is schematically represented in Fig. 3 (I). It can be observed from the SEM micrographs that AGG did not occur in any of the examined regions which proves that the microstructure was stable throughout the whole nugget zone, even at 550 °C. Figs. 3 (j-n) show the BSE micrographs after heat treatment at 550 °C for 1 hr, for a specimen prepared under identical conditions but without the PDC particles. Fig. 3 (II) is a schematic representation of its nugget zone that serves as a reference for the locations of BSE micrographs shown in Figs. 3 (j-l). It can be observed from these figures that AGG occurred in this specimen when tested under the same conditions. As mentioned earlier, dispersion of second phase particles in the matrix applies a retarding force/drag on low and high angle grain boundaries and this has effects on recovery, recrystallization, and grain growth. The drag pressure ( P z ) from a random distribution of particles in the Zener pinning mechanism by which the grain boundary motion is inhibited by particles pinning is expressed as [START_REF] Humphreys | Recrystallization and Related Annealing Phenomena[END_REF] :

P z = 3 f γ 2 r ( 1
)
where r and f are the radius and volume fraction of the reinforcing particles, respectively and γ is the boundary energy per unit area.

Zener pinning is active when the particle size is less than the grain size. The maximum grain size limit ( D ) until the Zener pinning can be operational is given by [22] :

D = 4 r 3 f (2)
Analyzing the SEM micrographs with the ImageJ software, r and f were 57 nm and 0.025, respectively, for which the limiting grain size D was calculated as 3 μm. As the measured grain sizes were well below this value, the operation of the Zener pinning mechanism is confirmed in the current specimen.

In Fig. 4 , the TEM bright-field images of the specimen before heat treatment (550 °C, 1 hr) are shown. It can be observed in Fig. 4 (a-g) that nanoparticles were distributed throughout the specimen. These nanoparticles did not show any change in the diffraction contrast with respect to different tilt positions. Such a characteristic is generally exhibited by amorphous domains. Since the pre-ceramic PMHS polymer is known to convert into amorphous SiOC (PDC) after pyrolysis [23] , and because PMHS is the only source for such amorphous particles in this system, it can be presumed that those nanoparticles were SiOC ceramic particles. The nanoscale size of the PDC can be obtained as the preceramic polymer can be easily fractured during FSP before the pyrolysis stage. Hence, the PDC particles can be expected to be in a size range comprising the micron to the nanoscale (as it is unlikely that fracturing during FSP will produce only nanoparticles). At higher magnifications ( Fig. 4 (h) and 4 (i)), PDC nanoparticles at the grain boundaries (marked by yellow boxes) can be clearly observed. The presence of such nanoparticles can impart large drag forces to the grain boundaries and hence, impede their motion when exposed to higher temperatures.

In the TEM images ( Fig. 4 (a-g)), sub-micron particles were also observed, albeit far lower in number compared to the nanoparticles. Such particles could have been contributed from the impurities present in the matrix, and they could also be PDC particles. As mentioned above the total particle volume fraction was 2.5%, the impurities ( Table 1 ) add up to ~0.5% after conversion to volume percentage so the PDC particles were present at about 2% volume fraction. The impurity-particles had no role in the grain boundary pinning, as it was observed in the SEM micrographs in Fig. 3 (j-n) that AGG occurred in the specimen that did not contain PDC particles despite having sub-micron particles distributed throughout the nugget zone. Hence, it is clear that the reason for the specimen to show grain stability at 450 °C and 550 °C was due to the presence of PDC particles. Part of these particles were located at the grain boundaries, while the remaining part was within the grains. Even if some boundaries did not contain PDC particles, their migration could be stopped by the particles situating within the grains. Additionally, it is also possible that the amorphous nature of the pyrolyzed PDC particles gave superior interfacial strength between the particles and the matrix. This would also increase the strength of pinning of grain boundaries by the particles. Note also that the possible β intermetallic phase (Mg 5 Al 8 ) had no contribution at all to Zener pinning as the Mg was fully in solid solution at the processing temperature (~435 °C to 500 °C) as well as during heat treatments (550 °C and 450 °C).

Grain stability up to 550 °C was successfully achieved, however, the stability in the mechanical properties must also be verified. In this regard, micro Vickers hardness tests were performed before and after the heat treatments. The hardness before the heat treatment was 76 ±0.764 HV and after the heat treatments at 450 °C and 550 °C were 74 ±0.471 HV and 72 ±0.373 HV, respectively. Hence, from the hardness values, it can be inferred that the strength of the material changed very slightly even after exposure to such high temperatures. Kumar et al. [START_REF] Kumar | [END_REF] have shown that for a friction stir processed 5086 Al alloy, the grains are stable up to 250 °C and the specimen exhibits AGG when exposed to 350 °C for 1 hr. However, in the same work, it was observed that a friction stir processed and aged twin roll cast Al-Mg-Sc alloy exhibits considerable grain growth only when exposed to 550 °C for 1 hr.

In summary, grain stability has been imparted to Al 5052 alloy till 550 °C by the presence of PDC particles using the FSP process. The loss in hardness was only 5.3% after heat treatment at 550 °C. Therefore, the material maintained stable microstructure and strength during the thermal cycle. The reason for such behavior was due to grain boundary pinning by the in-situ formed PDC particles. Since the material was fabricated using FSP, verification for AGG was also performed, and was found that there was no AGG throughout the nugget zone where the particles were dispersed. However, a specimen prepared under identical conditions, but without the PDC particles, exhibited AGG at 550 °C.

Fig. 1 .

 1 Fig. 1. (a) IPF map of the parent material. (b) The initial grain size distribution of the parent material.

Fig. 2 .

 2 Fig. 2. (a) IPF map of the specimen before heat treatment at 450 °C for 1 hr. HT stands for heat treatment. The black regions in the IPF map are non-indexed areas where some large particles were present. (b) The grain size distribution before the heat treatment. (c) IPF map of the specimen after the heat treatment. (d) The grain size distribution after the heat treatment. (e) IPF map of the specimen before heat treatment at 550 °C for 1 hr. (f) The grain size distribution before the heat treatment. (g) IPF map of the specimen after the heat treatment. (h) The grain size distribution after the heat treatment.

Fig. 3 .

 3 Fig. 3. (I) Schematic representation of the nugget zone for reference showing the location of each SEM-BSE micrograph of the specimen containing PDC particles heat-treated at 550 °C for 1 hr. The corresponding SEM-BSE micrographs for each region: (a) Top-left (b) Top-central (c) Top-right (d) Middle-left (e) Middle-central (f) Middle-right (g) Bottom-left (h) Bottom-central (i) Bottom-right. (II) Schematic representation of the nugget zone for reference showing the location of each SEM-BSE micrograph of the specimen without PDC particles heat-treated at 550 °C for 1 hr. The corresponding SEM-BSE micrographs for each region: (j) Top-left (k) Top-central (l) Top-right. (m) and (n) SEM-BSE micrographs at higher magnifications showing AGG in the specimen without PDC particles after heat treatment at 550 °C for 1 hr.

Fig. 4 .

 4 Fig. 4. (a-g) Low magnification bright-field images showing the nanoparticles and sub-micron particles. (h) and (i) Bright-field images at higher magnifications showing the nanoparticles at the grain boundary.

Acknowledgements

This work was supported by the French State through the program "Investment in the future" operated by the National Research Agency (ANR) and referenced by ANR-11-LABX-0 0 08-01 (LabEx DAMAS). Abhishek Pariyar acknowledges the Ph.D. scholarship awarded by the Ministry of Human Resource Development, Govt. of India. Also, special thanks to Dr. Yudong Zhang (LEM3, Labex-DAMAS, Metz, France) for the help provided during TEM operation and its data analysis. Thanks also to Dr. Julien Guyon (LEM3, Labex-DAMAS, Metz, France) for the help provided during SEM operation. A special appreciation and thanks to Dr. Chandra Shekhar Perugu (Department of Materials Engineering, IISc Bangalore, India) for discussions.

Declaration of Competing Interest

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