Meriem Ghrib

Marc Rébillat
email: marc.rebillat@ensam.eu

Guillaume Vermot Des Roches

Nazih Mechbal

Automatic damage type classification and severity quantification using signal based and nonlinear model based damage sensitive features

Structural health monitoring (SHM) is an emerging technology designed to automate the inspection process undertaken to assess the health condition of structures. The SHM process is classically decomposed into four sequential steps: damage detection, localization, classification, and quantification. This paper addresses damage type classification and severity quantification issues as classification problems whereby each class corresponds to a given damage type or a certain damage extent. A Support Vector Machine (SVM) is used to perform multi-class classification task. Classically, Signal Based Features (SBF) are used to train SVMs when approaching SHM from a machine learning perspective. In this work, starting from the assumption that damage causes a structure to exhibit nonlinear response, it is investigated whether the use of Nonlinear Model Based Features (NMBF) increases classification performance. NMBF are computed based on parallel Hammerstein models which are identified with an Exponential Sine Sweep (ESS) signal. A study of the sensitivity of classification performance to the noise contained in output signals is also conducted. Dimension reduction of features vector using Principal Component Analysis (PCA) is carried out in order to find out if it allows robustifying the classification/quantification process suggested in this work. Simulated data on a cantilever beam with various damage types and severities as well as experimental data coming from a composite aeronautic plate with various damage severities generated with a unique and original laser process are considered for demonstration. For both application cases, results show that by introducing NMBF, classification performance is improved. Furthermore, PCA allows for high recognition rates while reducing features vector dimension.

Introduction

Structural Health Monitoring (SHM) [START_REF] Staszewski | Health Monitoring of Aerospace Structures: Smart Sensor Technologies and Signal Processing[END_REF][START_REF] Farrar | An introduction to structural health monitoring[END_REF][START_REF] Boller | Encyclopedia of Structural Health Monitoring[END_REF] combines advanced sensor technology with intelligent algorithms to interrogate the health condition of monitored structures. SHM is of interest in many applications, including civil, mechanical, and aerospace engineering. Generally, the SHM process entails establishing: (1) the existence of damage, (2) the damage location, (3) the type of damage, and (4) the damage severity [START_REF] Rytter | Vibrational Based Inspection of Civil Engineering Structures[END_REF]. The focus is more particularly put here on the monitoring process of composite structures. A major difference between fault diagnosis and SHM lies in the fact that in fault diagnosis the monitored systems are by nature "discrete" in the sense that faults can appear on a limited number of components that compose the whole system whereas monitored structures are "continuous" in the sense that damage can appear anywhere within the structure. This renders the task of SHM much more multidisciplinary and challenging than fault diagnosis. Furthermore, accessing to experimental data that covers all damaged cases may not always be possible. Indeed, structural damages are very difficult to reproduce in a realistic manner on actual samples. Moreover, within the case of composite structures, the diversity of damage types and the lack of knowledge on their evolution make the use of only experimental data very difficult. Currently, several works deal with this challenging problem of generating controlled real damages with dedicated NDE systems and postmortem test to validate them [START_REF] Ghrib | Laser shock a novel way to generate calibrated delamination in composites: concept and first results[END_REF]. As a consequence, a considerable amount of literature exists on damage detection and localization techniques [START_REF] Coverley | Impact damage location in composite structures using optimized sensor triangulation procedure[END_REF][START_REF] Vergé | Active damage detection and localization applied to composite structure using piezoceramic patches[END_REF][START_REF] Hajrya | Principal component analysis and perturbation theory based robust damage detection of multifunctional aircraft structure[END_REF][START_REF] Fendzi | A general Bayesian framework for ellipse-based and hyperbola-based damage localisation in anisotropic composite plates[END_REF]. However, very little research has been undertaken to respond to damage type classification and severity quantification issues.

In [START_REF] Kim | Damage classification using adaboost machine learning for structural health monitoring[END_REF] damage classification is performed using time-frequency representations and the Adaboost machine learning algorithm. In [START_REF] Mao | Structural damage classification comparison using support vector machine and Bayesian model selection[END_REF], damage type classification is transformed into a group classification process, under the influence of uncertainty. More recently, Vitola et al. [START_REF] Vitola | Structural damage detection and classification based on machine learning algorithms[END_REF] proposed a data-driven methodology for the detection and classification of damages by using multivariate data driven approaches and machine learning algorithms. In [START_REF] Mesnil | Instantaneous and local wavenumber estimations for damage quantification in composites[END_REF], the damage quantification problem is approached from a full wavefield perspective and through local wavenumber estimation. In a unsupervised learning framework, novel damage indices for damage quantification have been proposed recently in [START_REF] Entezami | An unsupervised learning approach by novel damage indices in structural health monitoring for damage localization and quantification[END_REF].

These approaches all have a common feature: they rely only on linear non model-based features as inputs to more or less evoluted machine learning algorithms. But in many cases, damages that appear on complex structures (such as cracks, impacts, or delaminations) can generate nonlinear dynamical responses that may be used for damage monitoring [START_REF] Farrar | Nonlinear system identification for damage detection[END_REF][START_REF] Worden | A review of nonlinear dynamics applications to structural health monitoring[END_REF][START_REF] Nichols | Nonlinear features for SHM applications[END_REF][START_REF] Kerschen | Past, present and future of nonlinear system identification in structural dynamics[END_REF]. Furthermore, complex structures often exhibit a nonlinear behavior even in their healthy states. A robust and reliable SHM system must then be able to deal with nonlinear damages, and to distinguish between their effects and inherent nonlinearities in healthy structures. There have been some works in that direction, but never for damage classification or quantification purposes. In a linear framework, some authors [START_REF] Johnson | Transmissibility as a differential indicator of structural damage[END_REF][START_REF] Haroon | Time and frequency domain nonlinear system characterization for mechanical fault identification[END_REF] have shown that a nonlinear damage will impact the transmissibility functions (i.e. the frequency domain ratio between two different outputs of the system) and they used such information to detect and locate the damage. Extending the notion of transmissibility functions to nonlinear systems that can be described by Volterra series, Lang et al. [START_REF] Lang | A novel approach for nonlinearity detection in vibrating systems[END_REF][START_REF] Lang | Transmissibility of non-linear output frequency response functions with application in detection and location of damage in MDOF structural systems[END_REF] were able to quantify the decrease of linearity generated by a nonlinear damage and thus to effectively detect and locate it. However, as such approaches are focusing on the loss of linearity, they do not seem to be able to deal with systems that are nonlinear in their healthy states, a fact that is quite common in real life. To overcome this drawback, several authors attempted to fit a nonlinear model to the nonlinear structure under study and to compare the actual and predicted outputs, or directly the model coefficients, under different damage conditions [START_REF] Adams | Application of frequency domain ARX features for linear and nonlinear structural damage identification[END_REF][START_REF] Adams | Classifying linear and nonlinear structural damage using frequency domain ARX models[END_REF][START_REF] Bornn | Structural health monitoring with autoregressive support vector machines[END_REF][START_REF] Bornn | Damage detection in initially nonlinear systems[END_REF]. By doing so, they were able to detect numerically and experimentally a nonlinear damage even in an initially nonlinear structure. In [START_REF] Rébillat | Nonlinear structural damage detection based on cascade of Hammerstein models[END_REF], a relatively general nonlinear model (parallel Hammerstein model, belonging to the class of Volterra models) has been used to extract successfully nonlinear damage and structure related information for damage detection purpose.

In this article, we aim at exploiting a richer nonlinear representation of our test structure and at investigating whether the use of nonlinear model based features allows for an enhanced damage classification/quantification approach. More specifically, damage type classification and severity quantification issues are transformed into classification problems whereby each class corresponds to a given damage type or severity. A support vector machine is used to perform multiclass classification. Two types of features are used as inputs to the SVM algorithm: Signal Based Features (SBF) and Nonlinear Model Based Features (NMBF). SBF are rooted in a direct use of response signals and do not consider any underlying model of the structure under study. To compute NMBF, parallel Hammerstein models are considered to model the damaged structure. The model is identified using an Exponential Sine Sweep (ESS) [START_REF] Antonin | Modeling of nonlinear audio systems using swept-sine signals: application to audio effect[END_REF][START_REF] Rébillat | Identification of cascade of Hammerstein models for the description of nonlinearities in vibrating devices[END_REF][START_REF] Novak | Nonparametric identification of nonlinear systems in series[END_REF][START_REF] Rébillat | Repeated exponential sine sweeps for the autonomous estimation of nonlinearities and bootstrap assessment of uncertainties[END_REF] excitation signal and NMBF are afterwards computed based on the identified Hammerstein kernels. PCA has generally been used in SHM field as a technique to establish damage sensitive features [START_REF] Hajrya | Principal component analysis and perturbation theory based robust damage detection of multifunctional aircraft structure[END_REF][START_REF] Tibaduiza | Damage classification in structural health monitoring using principal component analysis and self-organizing maps[END_REF]. In this work PCA is used to reduce the dimension of features vector, the aim being to find out if dimension reduction allows robustifying the suggested classification/quantification approach. Furthermore a study of the sensitivity of classification performance to the noise contained in output signals is performed. Simulation results on a realistic cantilever beam with various damage types and severities as well as experimental data coming from a composite plate representative of aeronautic applications with various damage severities generated with a unique and original laser process are considered as demonstration examples.

The remaining of the paper is organized as follows. Firstly, the test structure considered for demonstration is presented. Then, the main key ingredients of the classification/quantification approach suggested in this work are introduced. The application of the proposed approach to damage type classification is afterwards detailed. Simulation results of various damage types used to derive damage sensitive features are described and classification and quantification performances are presented for this application case. The application of the proposed approach to damage severity quantification is subsequently elaborated using experimental data. Conclusions and perspectives are finally drawn.

Test structures

Beam simulated model

The first test structure on which we conducted our study is a simulated beam model developed in a previous work for the investigation of a vibration-based structural health monitoring procedure on high voltage electrical switch insulators [START_REF] Rébillat | Structural health monitoring of high voltage electrical swich ceramic insulators in seismic areas[END_REF]. SDTools Matlab toolbox (SDT for Matlab) [START_REF] Balmès | SDTools, vibration software and consulting[END_REF] is used to simulate the dynamic response of our model. The base model is a cantilever beam of length 4 m and circular section of radius 0.17 m. The beam is in steel with properties E = 210 GPa, = 0.285 and = 7800 kg/m 3 . At a given nodal position a torsion spring is placed between the two rotational Degree Of Freedom (DOF) instead of the continuum coupling. The torsion spring stiffness (parameter k v ) is calibrated to be close to its saturation level, the threshold is defined as 95% of the maximum frequency of the first mode. Several non-linearities for the torsion spring stiffness are implemented to simulate various damage types. Four types of torsion spring stiffness nonlinearities are considered as shown through force-relative displacement curves (Fig. 2): bilinear stiffness, saturation, dead zone and Coulomb friction. The non-linearities are defined as relative to a healthy state for which the torsion spring behaves linearly. One defines a generic damage severity parameter ˛that should vary between 0 (healty) and 1 (totally damaged). A first simulation in the healthy state is also performed to define R max , the maximum relative displacement for the undamaged structure. The four nonlinearities have been chosen as idealized representations of four types of damage:

• The bilinear stiffness (Fig. 2(a)) physically corresponds to a crack that is opening and closing and thus applies a lower stiffness in traction. Compression stiffness is set to k v . Traction stiffness is set to (1 -˛)k v . • The saturation (Fig. 2(b)) can be seen as a dry friction phenomenon associated with a spring and may correspond to relative movements between parts of a system caused by an overdrive in force. The base stiffness is set to k v , the saturation occurs for a maximum relative displacement set to (p 0 + (1 -˛)(1p 0 ))R max . Thus, for ˛equal to 0, there is no saturation and for ˛equal to 1, a saturation occurs at a relative displacement of p 0 R max . Hence, p 0 can be seen as a percentage of saturation. • The dead zone models systems where damage causes some mechanical slack without any contact between two parts. The dead zone (Fig. 2(c)) applies to the torsion spring a linear stiffness k v only outside a relative displacement threshold, defined as ˛p0 R max . Thus, for ˛equal to 0, there is no dead zone and for ˛equal to 1, a dead zone extends over a relative displacement range of -p 0 R max to p 0 R max . Hence, p 0 can be seen as a percentage defining the extent of the dead zone. • The Coulomb friction models systems where damage manifests itself by the appearance of Coulomb friction due for example to the lack of lubrication between parts. It consists of a brutal force sign switch (Fig. 2(d)) and applies the linear torsion spring stiffness, with a force offset between traction and compression. The offset occurs for relative displacements under a tolerance of 10 -6 and it is generated by considering an offset of ˛p0 R max in the relative displacement observation. Thus, for ˛equal to 0, there is no force offset and for ˛equal to 1, the force goes from -k v p 0 R max to p 0 R max . Hence, p 0 can be seen as a percentage defining the Coulomb friction.

Aeronautic composite plates

The second test set of test structures considered for demonstration consists of CFRP composite plates equipped with piezoelectric elements and containing various delamination severities. Damage is introduced into samples (see Fig. 3) in a calibrated way using Laser Shock Wave Technique (LSWT) and more particularly symmetrical laser shock configuration. LSWT is chosen as an alternative to conventional damage generation techniques such as conventional impacts and Teflon inserts since it allows for a better calibration of damage in type, depth and size [START_REF] Gay | Study of the response of CFRP composite laminates to a laser-induced shock[END_REF][START_REF] Ghrib | Laser shock a novel way to generate calibrated delamination in composites: concept and first results[END_REF][START_REF] Ghrib | Vermot des Roches, Automatic damage quantification using signal based and nonlinear model based damage sensitive features[END_REF]. Four CFRP test coupons are considered. The first one is kept in a healthy state. The second one was subjected to a symmetrical laser impact at 0 ns time delay and at 100% of the maximum energy of the two laser beams [START_REF] Ghrib | Vermot des Roches, Automatic damage quantification using signal based and nonlinear model based damage sensitive features[END_REF]. This resulted in approximately 7 mm diameter delamination which occurred at 1.1 mm depth. The third coupon was subjected to two symmetrical and contiguous laser impacts which resulted in 14 mm diameter delamination while the fourth coupon was impacted with three contiguous impacts which in turn resulted in approximately 21 mm diameter delamination. A photography and a C-Scan analysis of the four coupons is provided in Fig. 4 highlighting the fact that generated damaged exists but are not visible.

Proposed damage classification/quantification approach

Fig. 5 illustrates the main key ingredients of the classification/quantification workflow proposed in this work. An input signal is firstly selected to excite a test structure containing a given damage type and severity. The structure response signal is then recorded and damage sensitive features are extracted. In this work, the first question which arises is whether NMBF allow for an enhanced damage classification/quantification strategy on the two application cases previously described. Two types of features are thus considered: SBF and NMBF.

Excitation signal

The input signals considered in this work are a punctual force in the z direction for the cantilever beam (red arrow in Fig. 1) and an electrical excitation of a piezoelectric element for the composite plate whose amplitudes are defined by an exponential sine sweep curve. Various parameters are specified to define the input sweep signals for each considered application cases:

• the minimum frequency f min (set to 0.7 Hz for the beam and to 1 kHz for the composite plate) • the maximum frequency f max (set to 25 Hz for the beam and to 100 kHz for the composite plate) • the sampling frequency f s (set to 110 Hz for the beam and to 1 MHz for the composite plate)

• the sweep duration t length (set to 180 s for the beam and to 0.45 s for the composite plate) • the sweep amplitude amp (set to 1000 N for the beam and to 10 V for the composite plate)

SBF

Signal based features are rooted in a direct use of response signals and do not consider any underlying model of the test structure. Four signal-based features are considered in this study and are computed as follows. Let s ref (t) and s d (t) be the structure output signal in reference and damaged state respectively, where t refers to time, one defines the SBF as in Table 1.

NMBF

NMBF are considered based on previous work presented in [START_REF] Bakir | Damage type classification based on structures nonlinear dynamical signature[END_REF][START_REF] Hajrya | Principal component analysis and perturbation theory based robust damage detection of multifunctional aircraft structure[END_REF]. Parallel Hammerstein models are selected to model the damaged structure (see Fig. 6). The reason behind this choice is that such model is sufficiently general and it allows to capture all or part of damage generated nonlinearities. Furthermore, Parallel Hammerstein models are easy to estimate and to interpret. The model is fully represented by its kernels h n (t) n ∈ {1,...,N} (N being the model order and can be automatically estimated [START_REF] Rébillat | Repeated exponential sine sweeps for the autonomous estimation of nonlinearities and bootstrap assessment of uncertainties[END_REF]) and is identified by means of Exponential Sine Sweeps excitation signal

Signal envelope

ENV = T 2 t=T 1 A 2 s ref,d (t) T 2 t=T 1 A 2 s ref (t) (4) • s ref,d (t) = s ref (t) -s d (t) • A s(t) = s 2 (t) -H {s} (t)

e(t).

Once the model is identified, the system output s(t) can be rewritten as follows:

s(t) = N n=1 (h n * e n )(t) (5)
= (h 1 * e)(t)

+ N n=2 (h n * e n )(t) (6) = s L (t) + s NL (t) (7) = s L (t) + n odd (h n * e n )(t) + n even (h n * e n )(t) (8) = s L (t) + s NL o (t) + s NL e (t) (9)
The output signal is decomposed into a linear part and a nonlinear part [START_REF] Coverley | Impact damage location in composite structures using optimized sensor triangulation procedure[END_REF]. The nonlinear part is in turn decomposed into odd harmonics contribution and even harmonics contribution [START_REF] Hajrya | Principal component analysis and perturbation theory based robust damage detection of multifunctional aircraft structure[END_REF]. Three Non Linear Model Based Features (NMBF) are defined and computed as detailed in Table 2.

SVM and PCA

SVMs Support vector machine learning technique is used, in this work, for classification. SVMs [START_REF] Cristianini | An Introduction to Support Vector Machines: And Other Kernel-based Learning Methods[END_REF] are originally introduced by Vapnik and co-workers [START_REF] Bernhard | A training algorithm for optimal margin classifiers[END_REF][START_REF] Vapnik | Statistical Learning Theory[END_REF] and successfully extended by a number of other researchers. SVM learning algorithm separates a given set of binary labeled training data with a hyper-plane that is maximally distant from them (known as the maximal margin hyper-plane). For cases in which no linear separation is possible, they can work in combination with the technique of "kernels", that automatically realizes a non-linear mapping to a feature space. The hyper-plane found by the SVM in the feature space corresponds to a non-linear decision boundary in the input space. To extend SVMs to multi-class scenario, a typical conventional way is to decompose a multi-class problem into a series of two-class problems. One can distinguish between two implementations:

• One Against All "OAA" approach • One Against One "OAO" approach

The "OAO" and the "OAA" are two popular strategies for multiclass SVM. "OAO" builds one SVM for each pair of classes while "OAA" consists of building one SVM per class, trained to distinguish the samples in a single class from the samples in all remaining classes. In this work, a Gaussian kernel SVM is considered. SVM and Kernel Methods (SVM-KM) Matlab toolbox [START_REF] Canu | SVM and Kernel Methods Matlab Toolbox[END_REF] is used to perform multiclass classification.

PCA Principal component analysis (PCA) [START_REF] Jolliffe | Principal Component Analysis[END_REF] is a popular tool for linear dimensionality reduction and feature extraction. Intuitively, PCA can supply the user with a lower-dimensional picture of data when viewed from its most informative viewpoint. Several extensions of the standard PCA have been proposed such as the Kernel PCA which is the nonlinear form of PCA and which better exploits the complicated spatial structure of high-dimensional features. In this work we opted for the standard PCA since our features vector is not very high-dimensional.

Input features scenarios

As mentioned previously, the two main issues addressed in this paper consist in finding out if the use of nonlinear model-based features increases classification performance and whether reducing features vector dimension using PCA allows robustifying the suggested classification/quantification approach. Several input features scenarios are, thus, considered according to which features to select to feed and train the SVM algorithm:

f shift = f d -f ref f ref (10)
• f d is the frequency of the first mode of the structure in the damaged case • f ref is the frequency of the first mode of the structure in the reference case • These frequencies can here be easily extracted from the estimated nonlinear model as the kernel h1(t)

Ratio of the nonlinear to the linear energy

NLL = f 2 f 1 |S NL (f )| 2 df f 2 f 1 |S L (f )| 2 df (11)
• S NL (f) is the nonlinear part of the system output in the frequency domain • S L (f) is the linear part of the system output in the frequency domain • [f1, f2] is the frequency interval in which signals of interest are analyzed Ratio of the even to the odd nonlinear energies EO =

f 2 f 1 |S NL e (f )| 2 df f 2 f 1 |S NL o (f )| 2 df (12)
• S NL e (f ) corresponds to even harmonics contribution to the nonlinear part of the system output in the frequency domain • S NL o (f ) corresponds to odd harmonics contribution to the nonlinear part of the system output in the frequency domain

. Simulation data

The actual application case behind the proposed methodology is for an industrial end user to be able to assess when its structure is "damaged enough" so that maintenance is mandatory in order to guarantee safety. Having an exact knowledge of damage severity is not what is sought here and the objective is more to be able to classify the damaged state among the categories "low", "mid" or "severe" for example, than to provide an exact damage severity value. This is relevant in the context of predictive maintenance and not in the context of estimation of the remaining useful life of the component under study. The discrete damage severity categories have been chosen on a relatively fine grid in order to get a correct coverage of all the damaged states of the structure without having too many damage categories. Thus, for each damage type, simulations are done with damage severities varying from 0 to 0.9 with steps of 0.15. Each damage severity is considered at damage location NLpos equal to 0.5. Setting NLpos to a given value v, v ∈ [0, 1], stands for a damage position at v × 100% of the beam length from the clamped side. The position of the excitation force is set to 1, standing for 100% of the beam length from the clamped side. The position of the observation node is set at the excitation location. After running simulations, white Gaussian noise with SNR (Eq. ( 13)) varying from 40 dB to 100 dB by steps of 5 dB is added to the output signals.

SNR dB = 10log 10 P signal P noise [START_REF] Mesnil | Instantaneous and local wavenumber estimations for damage quantification in composites[END_REF] where P signal and P noise are output signal and noise power respectively.

In order to enrich our features database, 20 repetitions were considered for each SNR value. With such parameters, a total of 7280 noisy output signals are obtained; 1820 noisy outputs for each class of damage type. The output signals obtained at this step are used to compute damage sensitive features. Thus, one obtains 1820 instances for each class of damage type. Various classifiers are afterwards trained according to SVM input features scenarios (see Section 3.5). For training, only damage sensitive features computed

Classification results

Fig. 7(a) and (b) illustrate, respectively, the recognition rate of various "OAO" and "OAA" multiclass classifiers on test data (in%) versus SNR (in dB) while addressing damage type classification issue. It can be seen that classifiers trained on NMBF or both SBF and NMBF have the same performances for all values of SNR. Furthermore, for high signal to noise ratios (namely SNR greater than 70 dB), those classifiers have the best performance in terms of classification rate on test data. The performance of classifiers trained on the first three principal components, which retain 95.4% of data variance, is slightly less effective than the performance of NMBF trained classifiers. However, such classifiers are more robust to noise. The same applies to the classifiers trained on the first two principal components which retain 83.3% of data variance. Such classifiers perform less well than three principle components trained classifiers. However, they are more robust to noise compared to NMBF trained classifiers. Finally, SBF trained classifiers rank last among all classifiers but do not show any sensitivity to output noise. Table 3 provides coefficients for the first three principal components in original damage-sensitive features space. For the first principal component, a nearly-equal contribution of CC, NRE, MA, ENV and f shift is noted. For the second and third Princi- pal components, one can note a dominant contribution of NLL and EO compared to all other damage sensitive features. Thus, one can draw from this that NLL and EO bring forward information with regard to damage type and, consequently, are very discriminant features.

Damage severity quantification 4.2.1. Simulation data

For damage quantification issue, one sets a specific damage type among "saturation", "dead zone" or "Coulomb friction" nonlinearities. The case of "bilinear" nonlinearity has already been dealt with in a previous IFAC paper [START_REF] Ghrib | Vermot des Roches, Automatic damage quantification using signal based and nonlinear model based damage sensitive features[END_REF]. Once the damage type fixed and with the simulation parameters detailed above, a total of 1820 noisy output signals are obtained: 260 noisy outputs for each class of damage severity. The output signals are afterwards used to compute damage sensitive features. Thus, one obtains 260 instances for each class of damage severity. Similarly, various classifiers are trained according to SVM input features scenarios (See Section 3.5). Fig. 8 plots output signals amplitude as a function of damage severity in the case of "dead zone" nonlinearity. It can be seen that an increase in damage severity results in greater distortions of output signals. This also applies for the other types of nonlinearities.

Classification results

Fig. 9(a) and (b) illustrate, respectively, the recognition rate of various "OAO" and "OAA" multiclass classifiers on test data (in%) versus SNR (in dB) while addressing damage severity quantification issue for dead zone nonlinearity. Classifiers trained on the first two or three principal components are the best classifiers in terms of test data recognition rate. Furthermore, they show good robustness to output noise. NMBF trained classifiers have good performance for high values of SNR but they are very sensitive to output noise. Classifiers trained on SBF show poorer performance than all other classifiers but are robust to noise. All these remarks also apply to the cases of " Coulomb friction" and "saturation" nonlinearities (Figs. 10 and11).

Results for the composite aeronautic plate

Experimental signals

Twenty experimental repetitions are considered. At each repetition one goes around all PZT elements and only one PZT operates as an actuator while others operate as sensors. Acquisition frequency was set to 1 MHz. An instance of measurements corresponding to a given repetition is illustrated in Fig. 12.

From output signals to input features

Once signals are acquired, we proceeded to information condensation, that is, to damage-sensitive features computing. Each class of damage state (0: healthy, 1: one laser impact, 2: two contiguous laser impacts and 3: three contiguous laser impacts) has 400 instances. Each instance is characterized by a total of seven features. Stratified sampling is considered; 70% of data is used for training the SVM model and 30% of data is used for testing the model. In the following figures, we represent training data in SBF space, NMBF space and in principal components spaces. The first three principal components retain 94% of data variance while the first two principal components retain 78% of data variance. Figs. 13 and 14 reveal a structure of four classes. Each corresponds to a state of damage severity. However, class separability is much better in NMBF space and in principal components spaces than in SBF space.

Classification performance

Ten SVM models are established according to which approach is adopted ('OAO' or 'OAA') and to which features are chosen as inputs (SBF, NMBF, SBF & NMBF, 3 PCs or 2 PCs). Models performance on test data is then assessed. As illustrated in Fig. 15, independently of which approach is used, SVM models trained on NMBF or on principal components perform better than those trained on SBF. This is clearly due to class overlapping introduced by SBF and which induces the classifiers into error.

Discussion

Limits of supervised damage classification and quantification algorithms

One important point to mention here is that the damage classification and quantification methods proposed here both belong to the class of "supervised" learning algorithms. This means that, in opposition to "unsupervised" learning algorithms, data in the damaged cases are needed for the learning phase in both the damage classification and quantification stages.

The first point to discuss here is whether simulated data, which are much more easier to access than experimental data, may be sufficient to train the damage classification and quantification algorithm. Indeed, it is true that simulation models may not represent exactly real-life data and thus that training on real data may be mandatory to design robust damage classification and quantification algorithms. However, accessing to experimental data in a damaged case is not always possible, and merely impossible in most of the cases. In particular, designing a full experimental plan for various severities and all damage cases will be very costly and time consuming. However, in order to relax this assumption, it can be assumed that the effect of the damage type (related to the damage classification task) may be a qualitative change of the nonlinear dynamical behavior of the structure and thus may be correctly qualitatively described by the simulation model, and as a consequence may be sufficient for learning. Obviously, as no experimental data are accessible here, this point cannot be justified here and remains an idea to be validated. Furthermore, the quantification process is quantitative by nature and it is thus expected that even sophisticated simulation models may hardly scale exactly as the experimental specimens. As a consequence, experimental data for various damage level seems to be more mandatory as a basis for damage quantification algorithm learning. This is the process that has been followed here, but still damage quantification algorithm learning through simulated data remains a point that can be investigated.

More optimal alternatives to principal component analysis

In the proposed approach, Principal Component Analysis (PCA) has been used as a dimension reduction technique in order to investigate whether the information contained within all the damage indexes can be condensed to a lower dimensional space without loosing its classification abilities. However, as reported in [START_REF] Barker | Partial least squares for discrimination[END_REF], the aim of PCA is to find directions that explained the maximum of variance in the input data. One should here recall that in addition to input data, output data are also available as the classification problem is set up as a supervised classification problem here (see previous Section). There is thus a lot to learn with respect the dimension reduction directions that may be the more efficient for discrimination, as the goal is here indeed discrimination. In order to achieve this goal, some methods have been proposed such as Partial Least Squares (PLS) and Canonical Correlation Analysis (CCA) as well as their nonlinear kernel extensions [START_REF] Rosipal | Kernel PLS-SVC for linear and nonlinear classification[END_REF][START_REF] Rosipal | Overview and recent advances in partial least squares[END_REF][START_REF] Arenas-Garcia | Kernel multivariate analysis framework for supervised subspace learning: a tutorial on linear and kernel multivariate methods[END_REF]. Using one of these approaches in the present context is thus more appropriate than relying on standard PCA. However, when looking at the results obtained using PCA, which are extremely good, the gain in classification accuracy expected from PLS and CCA for the present example should be relatively minor. Nevertheless, using these methods for damage classification and quantification purposes remains a very relevant perspective. The multiclass SVM component, kernel selection and variable selection could also be further developed, as for example the multiple cores (MKL) and PLS discrimination or SVMs with integrated variable selection as in adaptive scaling.

Balanced efficiencies between SBF and NLMBF with decreasing SNR

The simulation results highlight the fact that when both SBF and NMBF are used for classification, the performance is identical to using NMBF alone with high SNR values. This suggests that the information coming from NMBF is dominant for these cases of SNR. However, the PCA augmented approach show significant improvement for low SNR situations, which suggest that SBF plays a dominant role for low SNR. It is hypothesized here that as NLBF rely on a model of the structure under study, then they allow to access to some "knowledge" about the structure under study, and thus that they are able to provide quite reliable information with respect to its current damage state. When dealing with SBF, the structure is not really modeled but the procedure is just an attempt to check out whether there is some strange behavior of one arbitrarily chosen attribute of the signal. As a consequence, it is to be believed that by nature SBF features carry less interesting information than NMBF features, which explains why NMBF features perform better as high SNR values. However, in order to extract reliable NMBF features, a reliable model is needed. Unfortunately, the estimated model quality degrades relatively quickly as SNR decreases [START_REF] Rebillat | Comparison of least squares and exponential sine sweep methods for parallel Hammerstein models estimation[END_REF]. Thus, when SNR is decreasing, the NMBF features become less and less reliable in comparison with SBF. This, in our opinion, explains the observed trends.

Conclusions and perspectives

In this paper, damage type classification and severity quantification issues are dealt with as classification problems whereby each class corresponds to a given damage type or a certain damage extent. SVM learning algorithm is used to perform multi-class classification task. Two main issues are addressed and consist in finding out if the use of nonlinear model-based features increases classification performance and whether reducing features vector dimension using PCA allows robustifying the suggested classification/quantification approach. A study of the sensitivity of classification performance to the noise contained in output signals is also conducted. From the outcome of our investigation it is possible to conclude that:

• For high values of SNR, NMBF bring forward more information on damage severity and type. Thus by introducing such features within the inputs of the SVM classifiers, classification performances are significantly improved. This applies to both "OAO" and "OAA" approaches. • For high values of SNR and by performing PCA on both SBF and NMBF, classification performances are improved. Thus, PCA allows getting higher recognition rates on test data while reducing the dimension of features vector. Furthermore principal components trained classifiers show a higher robustness to noise compared to NMBF classifiers. • For low values of SNR, it is necessary to take a step back with regard to the use of NMBF trained classifiers because it has been shown that their performance may be degraded. • Classifiers trained on SBF show poorer performance than all other classifiers but are robust to noise.

As perspectives, the multiclass SVM component, kernel selection and variable selection could be further developed, as for example the multiple cores (MKL) and PLS discrimination or SVMs with integrated variable selection as in adaptive scaling.

Fig. 1 .

 1 Fig. 1. Illustration of the simulated model. (For interpretation of the references to color in text near the figure citation, the reader is referred to the web version of this article.)

Fig. 2 .

 2 Fig. 2. Stiffness non-linearities illustrated: (a) bilinear, (b) saturation, (c) dead zone, (d) Coulomb friction.

Fig. 3 .

 3 Fig. 3. Left: Experimental set-up of symmetrical laser shock configuration. Right: Cross-sectional observation showing a delamination generated using LSWT.

Fig. 4 .

 4 Fig. 4. The four coupons (healthy, one impact, two impacts and three impacts). Left: photography other coupons. Right: C-Scan analysis of the coupon showing up the delamination generated using LSWT (from left to right: one impact, two impacts, three impacts and healthy).

Fig. 5 .

 5 Fig. 5. Workflow suggested for damage classification/quantification.

2 •Fig. 6 .

 26 Fig. 6. Parallel Hammerstein models.

• Scenario 1 :

 1 Only SBF are used to train the SVM algorithm • Scenario 2: Only NMBF are used to train the SVM algorithm • Scenario 3: Both SBF and NMBF are used to train the SVM algorithm • Scenario 4: PCA is performed on both SBF and NMBF and only 2 principal components are used to train the SVM algorithm • Scenario 5: PCA is performed on both SBF and NMBF and only 3 principal components are used to train the SVM algorithm

4. Results for the simulated beam model 4 . 1 .

 41 Damage type classification 4.1.1

Fig. 7 .

 7 Fig. 7. Comparison of classification performance for various features scenarios -Damage type classification: (a) "OAO" approach, (b) "OAA" approach.

Fig. 8 .

 8 Fig. 8. Output signal amplitude for increasing damage severity -case of dead zone nonlinearity.

Fig. 9 .

 9 Fig. 9. Comparison of classification performance for various features scenarios -Damage severity quantification for dead zone nonlinearity: (a) "OAO" approach, (b) "OAA" approach.

Fig. 10 .

 10 Fig. 10. Comparison of classification performance for various features scenarios -Damage severity quantification for Coulomb friction nonlinearity: (a) "OAO" approach, (b) "OAA" approach.

Fig. 11 .

 11 Fig. 11. Comparison of classification performance for various features scenarios -Damage severity quantification for saturation nonlinearity: (a) "OAO" approach, (b) "OAA" approach.

Fig. 12 .

 12 Fig. 12. Left: Excitation ESS signal, Right: Structure response signal.

Fig. 13 .

 13 Fig. 13. Data representation -Left: in SBF space, Right in NMBF space.

Fig. 14 .

 14 Fig. 14. Data representation in principal components spaces.

Fig. 15 .

 15 Fig. 15. Performance assessment of the considered SVM models on test data.

Table 1

 1 Signal based features. • cov(s ref (t), s d (t)) is the covariance of s ref (t) and s d (t) • s ref (t) and s d (t) are the standard deviations of s ref (t) and s d (t) respectively

	Name	Damage-sensitive feature	Details
	Cross-correlation feature	CC = 1 -	cov(s ref (t), s d (t))	(1)
			s ref (t) s d (t)
	Normalized residual energy	NRE =	T 2 t=T 1	(s ref (t) -s d (t)) T 2 t=T 1 s ref (t)	2

2 (2) • [T1, T2] is the time interval in which signals of interest are analyzed Maximum amplitude MA = maxt (|s ref (t)s d (t)|) maxt |s ref (t)|

Table 2

 2 Nonlinear model based features.

	Name	Damage-sensitive feature	Details
	Frequency shift

Table 3

 3 Coefficients for principal components in original damage-sensitive features space.

		PC1	PC2	PC3
	CC	0.4497	-0.0885	0.0267
	NRE	0.4247	-0.1023	0.1891
	MA	0.4248	-0.1520	-0.1344
	ENV	0.4515	0.0182	0.1022
	f shift	0.4422	-0.0564	0.0249
	NLL	0.0912	0.8262	0.5170
	EO	-0.1708	-0.5220	0.8167

using output signals with a maximum SNR (SNR max = 100 dB) are used. We assume that for training we consider the most favourable case where noise is very low. For real applications this may correspond to learning via models or in well-controlled environments. For test we consider less favourable situations where output noise is not negelected. Thus features computed using output signals with SNR lower than SNR max are used to test the classifiers.


Ph.D Catherine Ladaviere
email: catherine.ladaviere@univ-lyon1.fr

Ph.D Soline Peers

Pierre Alcouffe

Ph.D.; Alexandra Montembault
email: alexandra.clayer-montembault@univ-lyon1.fr

C Ladavière

Lyon

Embedment of Liposomes into Chitosan Physical Hydrogel for the Delayed Release of Antibiotics or Anaesthetics, and its First ESEM Characterization

Keywords: Chitosan, physical hydrogels, liposomes, drug delivery systems, rifampicin, lidocaine Chitosan, physical hydrogels, liposomes, drug delivery systems, carboxyfluorescein, rifampicin, lidocaine

This work describes the characterization of an original liposomes/hydrogel assembly, and its application as a delayed-release system of antibiotics and anaesthetics. This system corresponds to drugloaded liposomes entrapped within a chitosan (CS) physical hydrogel. To this end, a suspension of pre-formed 1,2-dipalmitoyl-sn-glycero-3phosphocoline liposomes loaded with an antibiotic (rifampicin, RIF), an anaesthetic (lidocaine, LID), or a model fluorescent molecule (carboxyfluorescein, CF), was added to a CS solution. The CS gelation was subsequently carried out without any trace of chemical cross-linking agent or organic solvent in the final system. Liposomes within the resulting gelled CS matrix were characterized for the first time by environmental scanning electron microscopy. The release of drugs from the assembly was investigated by fluorescence or UV spectroscopy. The cumulative release profiles of RIF and LID (and also CF for comparison) were found to be lower from the "drug-in-liposomes-in-hydrogel" (DLH) assembly in comparison to "drug-in-hydrogel" (DH) system.

Introduction

The property of prolonged release of therapeutic agents from liposomes or biocompatible gels, and particularly chitosan (CS) hydrogels, has been widely investigated during the last decade. Liposomes were the first drug delivery systems demonstrating the transition from concept to clinical application [START_REF] Allen | Liposomal drug delivery systems: From concept to clinical applications[END_REF]- [START_REF] Torchilin | Recent advances with liposomes as pharmaceutical carriers[END_REF], and several liposomal formulations are now commercially available [START_REF] Chezy | Doxil® -The first FDA-approved nano-drug: Lessons learned[END_REF]. Concerning the CS hydrogels, thanks to their biocompatibility [START_REF] Ueno | Topical formulations and wound healing applications of chitosan[END_REF], biodegradability [START_REF] Hirano | Bio-compatibility of chitosan by oral and intravenous administrations[END_REF] and bioactive properties (e.g., mucoadhesivity [START_REF] Bhattarai | Chitosan-based hydrogels for controlled, localized drug delivery[END_REF] or bacteriostaticity [START_REF] Guo | Novel derivatives of chitosan and their antifungal activities in vitro[END_REF]), they are frequently considered for biomedical applications [START_REF] Dash | Chitosan-A versatile semi-synthetic polymer in biomedical applications[END_REF]. For instance, CS hydrogels have been used as effective biomaterials for the regeneration of different tissues as skin [START_REF] Ribeiro | Development of a new chitosan hydrogel for wound dressing[END_REF], [START_REF] Boucard | The use of physical hydrogels of chitosan for skin regeneration following third-degree burns[END_REF], cartilage [START_REF] Suh | Application of chitosan-based polysaccharide biomaterials in cartilage tissue engineering: a review[END_REF], [START_REF] Montembault | A material decoy of biological media based on chitosan physical hydrogels: application to cartilage tissue engineering[END_REF], bone [START_REF] Sun | The osteogenic differentiation of dog bone marrow mesenchymal stem cells in a thermo-sensitive injectable chitosan/collagen/β-glycerophosphate hydrogel: in vitro and in vivo[END_REF], muscle [START_REF] Maryam | Chitosan-gelatin sheets as scaffolds for muscle tissue engineering[END_REF], or neural tissue [START_REF] Chedly | Physical chitosan microhydrogels as scaffolds for spinal cord injury restoration and axon regeneration[END_REF]. They also showed a considerable interest for the delayed release of hydrophilic and hydrophobic drugs, growth factors, and proteins [START_REF] Bhattarai | Chitosan-based hydrogels for controlled, localized drug delivery[END_REF], [START_REF] Azevedo | Chitosan hydrogels for drug delivery and tissue engineering applications[END_REF]- [START_REF] Ahsan | Chitosan as biomaterial in drug delivery and tissue engineering[END_REF].

Despite the great advantages in using liposomes in biomedical area, their main drawbacks can be their fast elimination from the blood, the capture by the reticuloendothelial system, and their low encapsulation efficiency [START_REF] Torchilin | Recent advances with liposomes as pharmaceutical carriers[END_REF], [START_REF] Sharma | An Updated Review on: Liposomes as drug delivery system[END_REF]. As regards the hydrogels, their limitation is the initial fast, burst-like, and uncontrolled release of drugs. To overcome these disadvantages of both drug delivery systems, liposomes, expected to act as drug reservoirs, have been added into CS hydrogels. Indeed, this strategy could be interesting to maintain liposomes at the delivery site, and to delay the release of entrapped drugs from hydrogels. This has already been observed for the release of bromotymol blue [START_REF] Rao | Effect of crosslinking agent on the release of an aqueous marker from liposomes sequestered in collagen and chitosan gels[END_REF], calcein [START_REF] Ciobanu | Modulated release from liposomes entrapped in chitosan/gelatin hydrogels[END_REF], ofloxacine [START_REF] Hosny | Preparation and Evaluation of Thermosensitive Liposomal Hydrogel for Enhanced Transcorneal Permeation of Ofloxacin[END_REF], doxorubicine [START_REF] Adolfo | Hyperthermia-Induced Drug Delivery from Thermosensitive Liposomes Encapsulated in an Injectable Hydrogel for Local Chemotherapy[END_REF], cytarabine [START_REF] Mulik | Chitosan-Based Thermosensitive Hydrogel Containing Liposomes for Sustained Delivery of Cytarabine[END_REF], or hormones [START_REF] Alinaghi | Hydrogel-embeded vesicles, as a novel approach for prolonged release and delivery of liposome, in vitro and in vivo[END_REF] incorporated in chitosan drug-inliposome-in-hydrogel (DLH) assemblies. Nevertheless, it is worthy of note that the CS hydrogels described in these studies were elaborated thanks to the addition of a cross-linking agent in the gel formulation. However, such compounds can be cytotoxic, which significantly diminishes the biomedical interest of the whole assembly [START_REF] Chen | A novel pH-sensitive hydrogel composed of N,O-carboxymethyl chitosan and alginate cross-linked by genipin for protein drug delivery[END_REF]. Moreover, most of these studies deal with antibiotics or anticancer agents, but to the best of our knowledge, none refers to the incorporation of anaesthetics into CS DLH assemblies.

The work described herein follows a previous investigation already achieved in our team on the elaboration of these DLH assemblies. This preceding study was about the elaboration of DLH assemblies and their interesting property of delayed-release of a model water-soluble molecule, carboxyfluorescein (CF) [START_REF] Billard | Liposomeloaded chitosan physical hydrogel: Toward a promising delayed-release biosystem[END_REF]. In this present work, one of the goals is to demonstrate that this property can be transferred to molecules of biomedical interest, such as antibiotics (RIF) and anaesthetics (LID), both relatively water soluble. CF is also examined as reference, in order to compare the release of these three water-soluble molecules with different chemical nature and molar masses. Another crucial aim of this work is to prove by environmental scanning electron microscopy (ESEM) the presence of liposomes into the CS hydrogel using observation conditions as close as possible to a native state. To the best of our knowledge, this liposome embedment in a polymer matrix has never been observed before by this technique.

Experimental part

Materials

DPPC, chloroform, acetic acid, sodium carbonate, sodium bicarbonate, ammonium hydroxide 28%, CF (> 95% purity), RIF (> 95% purity), LID (lidocaine hydrochloride monohydrate, pharmaceutical secondary standard) were purchased from Sigma Aldrich. CS was purchased from Mahtani Chitosan Pvt. Ltd.

CS purification and characterization

Purification and characterization methods of CS were previously described by Montembault et al. [START_REF] Montembault | Physico-chemical studies of the gelation of chitosan in a hydroalcoholic medium[END_REF]. Briefly, CS was dissolved in an aqueous acetic acid solution: acid was added to achieve the stoichiometric protonation of the polymer -NH 2 functions. The CS solution was then filtered through Millipore membranes with successive pore sizes of 3.0, 1.2, 0.8, and finally 0.45 µm. The polymer was then fully precipitated by addition of dilute ammonia (0.3 w/v). The resulting precipitate was rinsed several times with deionized water to reach a supernatant with a neutral pH.

The weight-average molar mass of CS (Mw = 670 000 ± 34 000 g.mol -1 ) and dispersity (Ð = 1.6 ± 0.2) were determined by size exclusion chromatography (SEC) coupled with a differential refractometer (RI, Optilab T-rEX from Wyatt Technology), and a multi-angle laser-light scattering detector (MALLS, HELEOS II from Wyatt Technology) equipped with a laser operating at 664 nm. SEC was performed by means of TSKgel G2500PW and G6000PW columns. A degassed and filtered (0.1 µm) 0.15 M ammonium acetate/0.20 M acetic acid buffer (pH = 4.5) was used as eluent at a flow rate of 0.5 mL.min -1 . The refractive index increment (dn/dc) used for the CS analysis was 0.194 mL.g -1 [START_REF] Schatz | Typical Physicochemical Behaviors of Chitosan in Aqueous Solution[END_REF]. The degree of acetylation, DA, calculated from the 1 H NMR spectrum was close to 9.0 ± 0.5 % [START_REF] Montembault | Physico-chemical studies of the gelation of chitosan in a hydroalcoholic medium[END_REF].

Liposome elaboration and characterization

Multilamellar vesicles (MLV) and small unilamellar vesicles (SUV) were obtained by the thin film hydration method [START_REF] Bangham | Diffusion of univalent ions across the lamellae of swollen phospholipids[END_REF]. DPPC lipids were dissolved in chloroform which was then evaporated by rotary evaporation under reduced pressure until the formation of an homogeneous and thin lipid film. The latter was then hydrated by adding distilled sterile water, CF, RIF, or LID solution (see concentrations below), and stirring in a water bath at 70°C to obtain a MLV suspension. To achieve the SUV formation, the MLV suspension with a lipid concentration of 0.01 mol.L -1 was then extruded 21 times through polycarbonate membrane filters with a pore size of 100 nm (Avanti Polar Lipids). The free dyes/drugs were purposely not removed from the liposomal suspension with the aim of comparing the same amounts of dyes/drugs in the both systems, DH and DLH assemblies. The optimized initial concentrations for analysis conditions of CF, RIF, and LID, were 0.1, 3.0, and 30 mmol.L -1 (excepted for the release profile comparison carried out at a same concentration experiment of 3.0 mmol.L -1 ), respectively, in a carbonate buffer (pH = 9.1, 0.1 mol.L -1 ).

The mean hydrodynamic diameter (120 ± 8 nm, N = 66, with N the number of measurements) and mean size distribution (polydispersity index, PDI = 0.11 ± 0.05, N = 66) of liposome suspensions were determined in water at 25°C by dynamic light scattering (DLS) at an angle of 173°, using a Zetasizer Nano ZS (Malvern Instruments) after a 10 times dilution of samples.

CS physical hydrogels elaboration and liposome incorporation into CS physical hydrogels

CS physical hydrogels were prepared from aqueous CS solutions, without any organic solvent, cross-linking agent or additive, as described by Montembault et al. [START_REF] Montembault | Rheometric Study of the Gelation of Chitosan in Aqueous Solution without Cross-Linking Agent[END_REF]. The purified CS was firstly dissolved in an aqueous acetic acidic solution in order to achieve CS solutions concentrated at 2% (w/w). The amount of acid added corresponded to the amount necessary to stoichiometrically protonate -NH 2 sites. After complete CS dissolution, 800 µL of liposome (incorporating CF, RIF, or LID) suspension, or 800 µL of CF, RIF, or LID solution (at 0.1, 3.0 or 30 mmol.L -1 in a carbonate buffer pH 9.1, respectively) were added in 4.8 g of CS solution, and the whole was stirred (magnetic stirrer, 250 rpm) for 5 min at room temperature.

The solution was then transferred in a Petri dish (diameter 35 mm) and let to stand for degassing for 3 hours. The Petri dish was put in contact with gaseous ammonia to achieve gelation (practically, the dish was displayed over 100 mL of an aqueous solution of ammonia at a concentration of 2 mol.L -1 in a glass reactor). The solution was let for 15h in the reactor.

After taking it off from the reactor, the formed hydrogel was repeatedly washed with deionized water to eliminate the excess of ammonia and ammonium salts. The washing protocol used is described as follows: i) the gel was successively immersed into 3 mL of deionised water during 15 min for the first five washings, ii) then, during 30 min for the next 4 washings, iii) and 45 min for the following 3 washings.

Dye/drug release measurements from DH and DLH assemblies

Dye/drug release is studied during the washing protocol of DH and DLH assemblies.

The gel was immersed into 3 mL of deionised water. This "washing" water was regularly removed and replaced by a fresh one. Each collected washing water was diluted in a phosphate buffer (pH = 9.1, 0.1 mol.L-1), and analyzed either by fluorescence spectroscopy (for CF measurements) or by UV spectroscopy (for RIF and LID measurements) to estimate the amount of dye/drug released during this considering washing step. The time interval between two washing steps was 15 min for the first 5 ones, 30 min for the next 4 ones, and 45 min for the 3 final ones.

Fluorescent analyses for CF release measurements. Fluorescent analyses were carried out as previously described by Billard et al. [START_REF] Billard | Liposomeloaded chitosan physical hydrogel: Toward a promising delayed-release biosystem[END_REF] with a multi-mode Synergy Mx microplate reader (BioTek Instruments) at 25°C (excitation wavelength at 490 and emission wavelength at 518 nm).The fluorescence intensity of CF solutions prepared in carbonate buffer (pH = 9.1, 0.1 mol.L -1 ) was measured as a function of the CF concentration, in the range from 1.10 -10 to 1.10 -6 mol.L -1 . The calibration curve (8 points) was obtained with a correlation coefficient (R²) above 0.9950.

UV analyses for RIF and LID release measurements. UV analyses were carried out with also a multi-mode Synergy Mx microplate reader (BioTek Instruments) at 25°C (at a wavelength of 334 and 262 nm for RIF and LID, respectively). The absorbance values of RIF and LID solutions prepared in a carbonate buffer (pH = 9.1, 0.1 mol.L -1 ) were measured as a function of the RIF or LID concentration, in the range from 1.10 -6 to 1.10 -4 mol.L -1 or 1.10 -5 to 1.5.10 -2 mol.L -1 , respectively. The calibration curves (7 points) were obtained with a correlation coefficient (R²) above 0.9950.

Rheological measurements of DH and DLH assemblies

Dynamical rheological studies were performed at 25°C with an ARES rheometer (TA Instruments) equipped with a plate-plate tool of 25 mm diameter. The normal force sensor of the rheometer was used to measure the first contact between the hydrogel slab and the upper plate. The applied strain was chosen at 1% from the linear viscoelastic region. The elastic and viscous moduli of gels were measured from a constant strain frequency sweep over frequency ranges of 100-0.05 rad.s -1 .

Electron microscopy of DH and DLH assemblies

Transmission electron microscopy (TEM). TEM images of liposomal suspensions were acquired on a CM 120 Philips electron microscope at an accelerating voltage of 120 keV (at CTμ, Lyon 1 University, France). A droplet of the vesicle suspension (0.001 mol.L -1 ) was deposed during 60 s on a 200 mesh carbon-coated grid (CF200-Cu, EMS, Delta Microscopies) for CF-and RIF-loaded liposomes, and on a 200 mesh Formvar® coated grid (FF200-Cu, EMS, Delta Microscopies) for LID-loaded liposomes. Then, a droplet of sodium silicotungstate (STS, 1% w/w) was added in the liposomal droplet and left for 60 s.

Afterwards, the excess liquid was blotted off. The sample was then air-dried for at least 15 min before observation by TEM. The size and size distribution of liposomes from TEM images were analysed by ImageJ software 1.47v (NIH, Bethesda, MD).

Environmental scanning electron microscopy (ESEM).

Liposomes and CS hydrogel pieces about 2 mm² were analysed using a FEI Quanta 250 environmental scanning electron microscope. Images were performed at 20 keV using a gaseous secondary electron detector.

Samples were put to a Peltier stage maintained at 5°C with a partial water vapour pressure in the microscope chamber, and observed in a real time through a gradual reduction of water vapour pressure in the microscope chamber. At the beginning of observations, the chamber pressure was 5.5 Torr (at 5°C, relative humidity rate = 80%) to keep the hydrogels and the liposomes at a hydrated state, and was then slowly decreased to reveal the surface of hydrogels and liposomes to the dehydration state of the sample around 3 Torr (at 5°C, relative humidity rate = 40%). The purge of the microscope chamber was achieved at 5°C with custom pressure between 8 and 5.5 Torr to keep the sample in a hydrated state.

Results and discussion

Description of molecules chosen to be incorporated in liposomes and DH assemblies

For this study, three water-soluble molecules were selected to be incorporated in liposomes, and directly in DH assemblies: RIF, LID, and CF (Table 1). RIF is one of the first choice amphiphilic anti-tuberculosis drugs. It is active against Mycobacterium tuberculosis, Mycobacterium leprae and Mycobacterium bovis [START_REF]VIDAL -Rifampicine[END_REF], [START_REF]Rifampin FDA Label -Capsule, injection (powder, lyophilized, for solution)[END_REF] by forming a stable complex with DNA-dependent RNA-polymerase of bacteria, inhibiting the activity of the enzyme. RIF is thus able to kill intracellular microorganisms by entering into phagocytic cells. However, because of its poor bioavailability, efficient treatment requires a high dose of drug injected over a period a several months. This causes various serious side effects including rheumatoid, allergic rashes or hepatotoxicity [START_REF] Girling | The hepatic toxicity of antituberculosis regimens containing isoniazid, rifampicin and pyrazinamide[END_REF]. For these reasons, the incorporation of RIF inside liposomes is extensively studied with the goal of improving mucoadhesivity, retention time and slow release, and reducing side effects [START_REF] Changsan | Physicochemical characterization and stability of rifampicin liposome dry powder formulations for inhalation[END_REF]- [START_REF] Gürsoy | Co-encapsulation of isoniazid and rifampicin in liposomes and characterization of liposomes by derivative spectroscopy[END_REF]. Note that its molar mass is higher than CF one (823 versus 376 g.mol -1 ). LID (289 g.mol -1 ) is a local anaesthetic agent known for its rapid action on mucosal tissues or skin for example, with a low systemic toxicity [START_REF] Wang | Preparation and evaluation of lidocaine hydrochloride-loaded TAT-conjugated polymeric liposomes for transdermal delivery[END_REF]. LID can also be used as an antiarrhythmic agent [START_REF] Harrison | The antiarrhytmic properties of lidocaine and procaine amide: clinical and physiologic studies of their cardiovascular effects in man[END_REF]. The mechanism of action of LID is based on inhibiting nervous influx thanks to the fixation of the molecule on specific receptors of the sodium channel of nerves [START_REF]VIDAL -Lidocaïne[END_REF]. One of the major challenges associated to LID is the skin penetration and transdermal delivery. The insertion of LID into hydrogels [START_REF] Liu | Release characteristics of lidocaine from local implant of polyanionic and polycationic hydrogels[END_REF], or in liposomes [START_REF] Hou | Comparison of absorption of aqueous lidocaine and liposome lidocaine following topical application on rabbit vessels[END_REF], [START_REF] González-Rodríguez | Application of statistical experimental design to study the formulation variables influencing the coating process of lidocaine liposomes[END_REF] was consequently studied. Concerning the last molecule chosen as reference, CF (376 g.mol -1 ) is a fluorescent dye which is anionic due to its carboxylic acid group. Thanks to its water-solubility, CF is well-known as a marker of the inner cavity of liposomes [START_REF] Kendall | A fluorescence assay to monitor vesicle fusion and lysis[END_REF]- [START_REF] Nishijo | Interactions of Cyclodextrins with Dipalmitoyl, Distearoyl, and Dimyristoyl Phosphatidyl Choline Liposomes. A Study by Leakage of Carboxyfluorescein in Inner Aqueous Phase of Unilamellar Liposomes[END_REF] to examine for example the liposome/cell interactions [START_REF] Weinstein | Liposome-cell interaction: transfer and intracellular release of a trapped fluorescent marker[END_REF], [START_REF] Ralston | Carboxyfluorescein as a probe for liposome-cell interactions effect of impurities, and purification of the dye[END_REF] or skin penetration of drugs [START_REF] Verma | Liposomes increase skin penetration of entrapped and non-entrapped hydrophilic substances into human skin: a skin penetration and confocal laser scanning microscopy study[END_REF].

Elaboration of DH and DLH assemblies

After the elaboration of liposomes, the next step of DLH preparation is the mixing of dye/drug-loaded liposome suspensions with an acidic CS solution (during 5 min under stirring at room temperature). Note that for the reference DH preparation (i.e., drugs directly incorporated into hydrogels, without liposome), CF, RIF or LID solutions were added instead of dye/drug-loaded liposome suspensions. The mixture was let to stand for degassing during 3 hours. Finally, the gelation of resulting mixtures was achieved according to the method described earlier [START_REF] Montembault | Physico-chemical studies of the gelation of chitosan in a hydroalcoholic medium[END_REF]. This method requires no cross-linking agent or additive. The mechanism of this gelation route consists in the modification of the balance between hydrophilic and hydrophobic interactions in the system. The ammonia gas dissolved into the chitosan solution, and neutralized amine functions. The consequence was a decrease of the apparent charge density of chitosan chains, thus favouring both hydrophobic effects and hydrogen bonds. After the formation of the hydrogel, DLH or DH assemblies were then washed several times to eliminate the formed ammonium acetate, as well as the excess of ammonia.

(B) (A) (C)

Microscopy characterization of DH and DLH assemblies

With the aim of confirming the presence of liposomes in the hydrogels, a well-suitable microscopic technique was used. Indeed, ESEM allows the observation of specimens in their hydrated state thanks to partial vacuum inside the specimen chamber without any further sample preparation [START_REF] Perrie | Environmental Scanning Electron Microscope Imaging of Vesicle Systems[END_REF]. ESEM has been already used for the visualisation of liposomes [START_REF] Ruozi | AFM, ESEM, TEM, and CLSM in liposomal characterization: a comparative study[END_REF]- [START_REF] Bibi | Microscopy imaging of liposomes: From coverslips to environmental SEM[END_REF], but DLH assemblies have never been observed by ESEM to the best of our knowledge.

For these observations, the incorporation of SUV-type liposomes was replaced by MLV-type ones inside hydrogels because MLV were much easier than SUV to be distinguished within the CS hydrogels. This could be explained by a better stability of MLV under electron beam (a SUV suspension was invisible in the same conditions). The SUV observation requires a higher magnification than MLV (mean diameter of MLV measured by DLS was found to be between 1.5 and 2.9 µm) which implies higher electron dose rate per area destroying them (higher curvature radius). Liposomal suspensions (loaded with CF, RIF, LID, see images in SI), hydrogels with and without dye/drug-loaded MLV were observed by ESEM (Figure 2), thanks to a gradually pressure decrease in the chamber.

Images were obtained at 5°C with pressure around 3.5 Torr (relative humidity rate = 50%). Similar morphologies of liposomes were obtained with all the 3 formulations (given in SI). Sphere diameters were measured by Image J software and were about 3.9 ± 1. Size measurements of spheres observed inside hydrogels and calculated with Image J (about 6.1 ± 3.7 µm (N = 17), 5.9 ± 2.1 µm (N = 29) and 7.2 ± 2.7 µm (N = 32) for CF, RIF and LID, respectively) were found to be slightly higher than liposomal suspensions ones (3.9 ± 1.3 µm (N = 203), 3.5 ± 1.3 µm (N = 200) and 5.7 ± 2.1 µm (N = 51) for CF, RIF, and LID liposomal suspensions). This difference could be explained by an easier disruption of smaller liposomes (with a higher curvature) with the decrease of chamber pressure. Indeed, this phenomenon was detected for all liposomes studied herein (example given in Figure 3).

Rheological characterization of DH and DLH assemblies

The DLH assemblies (and DH ones for comparison) were characterized by rheology measurements to determine if the presence of liposomes in the polymer solution hindered the gelation process, and interfered on the rheological behaviour of assemblies. To this end, the elastic and viscous moduli (G' and G'') were measured over a frequency-range of 100-0.05 rad.s -1 (see example in SI). The average values obtained at low frequencies are presented in Figure 5 (based on the average of the 3 last measured points of the curves on the equilibrium plateau at the lowest frequencies, and then mean of minimum 3 sets of measurements).

These values named here as G' eq and G'' eq for the DH assemblies are similar to those previously obtained by Montembault et al. [START_REF] Montembault | Rheometric Study of the Gelation of Chitosan in Aqueous Solution without Cross-Linking Agent[END_REF]. As in previous studies [START_REF] Mulik | Chitosan-Based Thermosensitive Hydrogel Containing Liposomes for Sustained Delivery of Cytarabine[END_REF], [START_REF] Billard | Liposomeloaded chitosan physical hydrogel: Toward a promising delayed-release biosystem[END_REF], [START_REF] Mourtas | Liposomal drugs dispersed in hydrogels: Effect of liposome, drug and gel properties on drug release kinetics[END_REF], [START_REF] Ruel-Gariépy | Thermosensitive chitosanbased hydrogel containing liposomes for the delivery of hydrophilic molecules[END_REF] about the incorporation of liposomes in CS hydrogels (with the addition of a cross-linking agent in the gel formulation), the G' eq value of all DLH assemblies was found to be at least 10 times higher than G'' eq . All these assemblies displayed a gel-like behaviour. This proves that liposomes did not prevent the gelation of the CS solutions. Moreover, note that G' eq and G'' eq The calculation of tan δ values (= G'' eq / G' eq ) assesses the viscoelastic nature of materials. According to Table 2, tan δ values were found to be very close to 0.1 for all the assemblies (DH and DLH). These values were similar to the ones obtained by Crompton et al.

[59], and in accordance with the definition of a gel [START_REF] Almdal | Towards a phenomenological definition of the term 'gel[END_REF]. Thus, the ratio of the viscous contribution to the elastic contribution was nearly the same for all samples. This also means that these assemblies are relatively homogeneous in terms of viscoelasticity.

Dye and drug release from DH assemblies

As a first step, dye/drug release from DH assemblies was investigated. To this end, the dye and drug molecules were directly incorporated in CS physical hydrogels, and their release from them during the washing process was examined (Figure 6). For the sake of comparison between release profiles of dye and drugs from DH, a same dye/drug amount was inserted into CS hydrogels (i.e., 2.4.10 -6 mol per hydrogel). The shape of cumulated release profiles of three water-soluble dye and drugs shows the same trend (Figure 6). Nevertheless, the release of LID molecules from DH is faster than CF molecules, which is faster than RIF molecules. The higher diffusion of CF and LID molecules through the DH assemblies could be explained by their low molar masses (376 g.mol -1 for CF, and 289 g.mol -1 for LID), in comparison with RIF with a higher molar mass (823.mol -1 ). This has been already observed in a previous study of Ruel-Gariepy et al. [START_REF] Ruel-Gariépy | Characterization of thermosensitive chitosan gels for the sustained delivery of drugs[END_REF],

which showed that the higher the molar mass, the slower the drug release with four molecules of different molar masses (275 g.mol -1 for chlorpheniramine, 320 g.mol -1 for methylene blue, 623 g.mol -1 for calcein, and from 12,000 to 148,000 g.mol -1 for albumin). However, considering this molar mass aspect, the release of CF molecules is surprisingly slow in comparison of LID ones. This behaviour could be also assigned to electrostatic aspects. chains (pKa ~ 6.5), whereas CF are anionic molecules due to their carboxylic acid group (pKa ~ 6.5). The negative charges of CF could establish attractive electrostatic interactions with some cationic charges of CS, resulting in a slower release of CF (in comparison with anionic LID). This has also been observed by Ruel-Gariepy et al. [START_REF] Ruel-Gariépy | Characterization of thermosensitive chitosan gels for the sustained delivery of drugs[END_REF] who mentioned that at high concentration, the higher the anionic charge of the molecule, the slower the release for drugs loaded in CS chemical hydrogel cross-linked with glycerophosphate.

Dye and drug release from DLH assemblies, in comparison with DH

After the elaboration and characterization of DLH assemblies, the role of dye/drug reservoirs of liposomes entrapped in CS hydrogels was examined. Consequently, dye and drugs were loaded in the aqueous cavity of DPPC liposomes before to be entrapped into the polymer network of hydrogels (DLH). Simultaneously, same dye and drugs were directly inserted into CS hydrogels as "reference" hydrogels (DH). After CS gelation, the RIF, LID, or CF release were studied during the washing step of hydrogels.

Before that, the stability of dye and both drugs as a function of the temperature increase (performed during the liposome elaboration) and pH variations (acidic medium and ammonia atmosphere implemented during the gelation process) was checked. All these conditions did no lead to a shift of RIF, LID absorption peaks, and CF fluorescence emission peak (data not shown).

Firstly, the CF release was monitored in the washing waters by fluorescence measurements (λ excitation = 490 nm and λ emission = 518 nm). Concerning RIF and LID, they were assayed by UV spectroscopy at 334 nm for RIF) and 262 nm for LID. For this purpose, samples of each washing water were collected and diluted into a phosphate buffer solution Firstly, the model CF release analysis (Figure 7) was performed in order to compare with the previous results obtained in the team with only SUV-type liposomes [START_REF] Billard | Liposomeloaded chitosan physical hydrogel: Toward a promising delayed-release biosystem[END_REF]. Figure 7 shows the average cumulated percentage of CF released from DLH in each washing water, with an average final cumulative release of ca 57 ± 4 % for CF-loaded SUV (N=4), 72 ± 5 % for CF-loaded MLV (N=4), and 74 ± 3 % for CF in CS physical hydrogel (N=6). As observed by Billard et al. [START_REF] Billard | Liposomeloaded chitosan physical hydrogel: Toward a promising delayed-release biosystem[END_REF] in the previous study, the release of the dye is delayed when CF is incorporated in SUV before being entrapped in the CS hydrogel after the same number of washings. Thanks to the liposomes embedded into the polymer network, acting as a drug "reservoir", and also creating a second diffusion barrier to drug diffusion, the release of CF through the DLH incorporating SUV is slower than DH. After the release analysis of an antibiotic, the release of an anaesthetic, LID, was studied. Figure 9 displays the average cumulated percentage of LID released from DLH in each washing water, with an average final cumulative release of ca 80 ± 4 % % for LIDloaded SUV (N=2), 85 ± 11 % for LID-loaded MLV (N=1), and 88 ± 1 % for LID in CS physical hydrogel (N=5). SUV embedded in the CS matrix act as "reservoirs" and delay the release of the LID incorporated in DLH assemblies.

The results also showed that the release profiles of DLH with molecules of interestloaded MLV were quite similar to molecules of interest-loaded-SUV, even if the trend was less pronounced. Ciobanu et al. [START_REF] Ciobanu | Modulated release from liposomes entrapped in chitosan/gelatin hydrogels[END_REF] observed that the release was more delayed for MLV incorporated into hydrogels (versus SUV). The authors explained this trend to the better drug encapsulation efficiency of MLV in comparison to SUV. On the contrary, Hurler et al.

Conclusions

In this work, biocompatible "liposomes in hydrogels" assemblies were successfully elaborated without any cross-linking agent or additive. Liposomes were able to incorporate a model water-soluble dye (carboxyfluorescein), as well as different water-soluble active agents such as rifampicin or lidocaine. They were efficiently then embedded into chitosan physical hydrogels without implying the modification of their rheological properties, regardless of the dye or drug loaded. The presence of liposomes inside the polymer matrix of hydrogel was confirmed for the first time by ESEM thanks to a gradually decrease of the water vapour pressure in the microscope chamber. Measurements of dye or drug release from hydrogels during the washing step showed that the loading of dye/drug in DPPC liposomes delays their escape through the system. Consequently, cumulative final release of CF, RIF and LID respectively. This is an evidence of the delayed-release property of these designed assemblies thanks to liposomes that act as drug reservoirs. The influence of the lipid/CS mass ratio, CS acetylation degree, as well as lipid composition still need be explored for a better understanding of the release mechanism of different active agents through these systems.

Furthermore, the simultaneous release of a cocktail of drugs could also be studied by incorporating two or more drugs with different biomedical properties in the same system. The in vivo release of drugs loaded in these "liposomes in hydrogels" assemblies is also planned to be studied. Indeed, these systems could be promising candidates in biomedical applications for example as wound dressings.

Captions of Tables and Figures

Tables and Figures

Table 1 :Figure 1 .

 11 Figure 1. TEM images (with STS contrast agent at 1% w/w) and size characterization by TEM (using Image J, mean Dn) and DLS (mean Dz, PDI ) of DPPC liposomal suspensions (SUV) incorporating CF (A), RIF (B) and LID (C). N is the measurement number. The scale bars in the insets are 125 nm.

Figure 2 .

 2 Figure 2. ESEM images at the left-hand column: DH with (A) CF, (B) RIF, (C) LID (without liposome), at the right-hand: DLH with (A) CF, (B) RIF, (C) LID MLV-loaded, at 3.5 Torr and 5°C after 12 washings ([CS] = 1.7% w/w, DA = 9%).

Figure 3 .

 3 Figure 3. ESEM images of LID MLV-loaded CS hydrogels (DLH) at t =0 (A) and t = after 2 minutes (B) at 3.5 Torr, and 5°C, showing that the smallest liposomes with highest curvature radius disrupt the first in the ESEM chamber (a few examples are given in dotted circles as

Figure 4 .

 4 Figure 4. Examples of ESEM images at 3.5 Torr, and 5°C, showing the embedment of LID MLV-type liposomes in the CS matrix of the hydrogel (DLH).

Figure 5 .

 5 Figure 5. Variation of G' eq and G'' eq moduli as a function of free dye/drug-loaded physical CS hydrogel (DH, filled bars) and dye/drug-loaded liposomes embedded into CS hydrogels (DLH, dotted bars) ([CS] = 1.7% w/w, DA = 9%, lipid/CS mass ratio = 0.06 w/w). (A) Reference, no dye/drug, (B) CF, (C) RIF, (D) LID. Insert corresponds to an image of a DH assembly placed between the two plates of rheometer. N≥3 for each measurement, with N the number of measurements carried out on different hydrogel batches.

Figure 6 .

 6 Figure 6. Dye/drug release profiles at 25°C from DH assemblies containing initially 2.4.10 -6 mol per hydrogel of RIF (N=21, orange diamonds), LID (N=3, blue triangles) and CF (N= 1, green circles).

  Indeed, LID are cationic molecules (tertiary amine group, pKa ~ 7.8) like the CS cationic

(

  pH = 9.1, 0.1 M) before being assayed. The release of dye/drugs is then estimated thanks to a calibration curve, established for each molecule. Experimental data were compared between dye/drug encapsulated in MLV (dotted symbols) or SUV (full symbols) embedded in CS hydrogels, and dye/drug directly loaded into polymer network (open symbols). Note that for analytical reasons, different dye/drug concentrations were used depending on the drug entrapped. In contrast, for both DH and DLH assemblies, the initial total dye/drug amount for each molecule was strictly equal. Average cumulated percentages of CF, RIF and LID released in each washing water are given as a function of the number of washings.

Figure 7 .

 7 Figure 7. CF release profile at 25°C from CS hydrogels containing CF ([CF] i = 10 -4 M, n CF = 8.10 -8 mol) without liposome (empty circles, N=6) or CF loaded MLV (dotted circles, N=4) or SUV (filled circles, N=4). [CS] = 1.7%, DA = 9% (w/w). Inset corresponds to a photography of a CF-DLH.

Figure 8 .

 8 Figure 8. RIF release profile at 25°C from CS hydrogels containing RIF ([RIF] i = 3.10 -3 M) without liposome (empty diamonds, N=21), RIF loaded MLV (dotted diamonds, N=4) or SUV (filled diamonds, N=21). [CS] = 1.7%, DA = 9% (w/w). Inset corresponds to a photography of a RIF-DLH.

  release for MLV incorporated into hydrogels, although any significant difference on the release profiles of mupirocine from liposomes with various diameters was detected.

Figure 9 .

 9 Figure 9. LID release profile at 25°C from CS hydrogels containing LID ([LID] i = 3.10 -2 M) without liposome (N=5), LID loaded MLV (N=1) or LID loaded SUV (N=2). [CS] = 1.7%, DA = 9% (w/w). Inset corresponds to a photography of a LID-DLH.

  of 16.9%, 11.6% and 7.5% between DLH and DH assemblies,

Figure 1 .

 1 Figure 1. TEM images (with STS contrast agent at 1% w/w) and size characterization by TEM (using Image J, mean Dn) and DLS (mean Dz, PDI ) of DPPC liposomal suspensions (SUV) incorporating CF (A), RIF (B) and LID (C). N is the measurement number. The scale bares in the insets are 125 nm.

Figure 2 .

 2 Figure 2. ESEM images at the left-hand column: DH with (A) CF, (B) RIF, (C) LID (without liposome), at the right-hand: DLH with (A) CF, (B) RIF, (C) LID MLV-loaded, at 3.5 Torr and 5°C after 12 washings ([CS] = 1.7% w/w, DA = 9%).

Figure 3 .

 3 Figure 3. ESEM images of LID MLV-loaded CS hydrogels (DLH) at t =0 (A) and t = after 2 minutes (B) at 3.5 Torr, and 5°C, showing that the smallest liposomes with highest curvatures disrupt the first in the ESEM chamber (a few examples are given in dotted circles as guide eyes) ([CS] = 1.7% w/w, DA = 9%).

Figure 4 .

 4 Figure 4. Examples of ESEM images at 3.5 Torr, and 5°C, showing the embedment of LID MLV-type liposomes in the CS matrix of the hydrogel (DLH).

Figure 5 .

 5 Figure 5. Variation of G' eq and G'' eq moduli as a function of free dye/drug-loaded physical CS hydrogel (DH, filled bars) and dye/drug-loaded liposomes embedded into CS hydrogels (DLH, dotted bars) ([CS] = 1.7% w/w, DA = 9%, lipid/CS mass ratio = 0.06 w/w). (A) Reference, no dye/drug, (B) CF, (C) RIF, (D) LID. Insert corresponds to an image of a DH assembly placed between the two plates of rheometer. N≥3 for each measurement, with N the number of measurements carried out on different hydrogel batches.

Figure 6 .

 6 Figure 6. Dye/drug release profiles from DH assemblies containing initially 2.4.10 -6 mol per hydrogel of RIF (N=21, orange diamonds), LID (N=3, blue triangles) and CF (N= 1, green circles).

Figure 7 .

 7 Figure 7. CF release profile from CS hydrogels containing CF ([CF] i = 10 -4 M) without liposome (empty circles, N=6) or CF loaded MLV (dotted circles, N=4) or SUV (filled circles, N=4). [CS] = 1.7%, DA = 9% (w/w). Inset corresponds to a photography of a CF-DLH.

Figure 8 .

 8 Figure 8. RIF release profile from CS hydrogels containing RIF ([RIF] i = 3.10 -3 M) without liposome (empty diamonds, N=21), RIF loaded MLV (dotted diamonds, N=4) or SUV (filled diamonds, N=21). [CS] = 1.7%, DA = 9% (w/w). Inset corresponds to a photography of a RIF-DLH.

Figure 9 .

 9 Figure 9. LID release profile from CS hydrogels containing LID ([LID] i = 3.10 -2 M) without liposome (N=5), LID loaded MLV (N=1) or SUV (N=2). [CS] = 1.7%, DA = 9% (w/w). Inset corresponds to a photography of a LID-DLH.

Figure 1 .Figure 2 .

 12 Figure 1. TEM images (with STS contrast agent at 1% w/w) and size characterization by

Figure 3 .Figure 4 .

 34 Figure 3. ESEM images of LID MLV-loaded CS hydrogels (DLH) at t =0 (A) and t = after 2 minutes (B) at 3.5 Torr, and 5°C, showing that the smallest liposomes with highest curvatures disrupt the first in the ESEM chamber (a few examples are given in dotted circles as guide eyes) ([CS] = 1.7% w/w, DA = 9%).

Figure 5 .Figure 6 .Figure 7 .Figure 8 .RIFFigure 9 .

 56789 Figure 5. Variation of G' eq and G'' eq moduli as a function of free dye/drug-loaded physical CS hydrogel (DH, filled bars) and dye/drug-loaded liposomes embedded into CS hydrogels (DLH, dotted bars) ([CS] = 1.7% w/w, DA = 9%, lipid/CS mass ratio = 0.06 w/w). (A) Reference, no dye/drug, (B) CF, (C) RIF, (D) LID. Insert corresponds to an image of a DH assembly placed between the two plates of rheometer. N≥3 for each measurement, with N the number of measurements carried out on different hydrogels batches.

Figure 1 .Figure 2 .

 12 Figure 1. TEM images (with STS contrast agent at 1% w/w) and size characterization by TEM (using Image J, mean Dn) and DLS (mean Dz, PDI ) of DPPC liposomal suspensions (SUV) incorporating CF (A), RIF (B) and LID (C). N is the measurement number. The scale bare in the insets are 125 nm.

Figure 3 .

 3 Figure 3. ESEM images of LID MLV-loaded CS hydrogels (DLH) at t =0 (A) and t = after 2 minutes (B) at 3.5 Torr, and 5°C, showing that the smallest liposomes with highest curvatures disrupt the first in the ESEM chamber (a few examples are given in dotted circles as guide eyes) ([CS] = 1.7% w/w, DA = 9%).

Figure 4 .

 4 Figure 4. Examples of ESEM images at 3.5 Torr, and 5°C, showing the embedment of LID MLV-type liposomes in the CS matrix of the hydrogel (DLH).

Figure 5 .Figure 6 .Figure 7 .Figure 8 .RIFFigure 9 .

 56789 Figure 5. Variation of G' eq and G'' eq moduli as a function of free dye/drug-loaded physical CS hydrogel (DH, filled bars) and dye/drug-loaded liposomes embedded into CS hydrogels (DLH, dotted bars) ([CS] = 1.7% w/w, DA = 9%, lipid/CS mass ratio = 0.06 w/w). (A) Reference, no dye/drug, (B) CF, (C) RIF, (D) LID. Insert corresponds to an image of a DH assembly placed between the two plates of rheometer. N≥3 for each measurement, with N the number of measurements carried out on different hydrogels batches.

Table 2 .

 2 Tan

	tan δ

δ values of DH (free dye/drug loaded physical CS hydrogel) and DLH (dye/drug loaded liposomes embedded into CS hydrogels) assemblies. N≥2 for each measurement, with N the number of measurements carried out on different hydrogels batches ([CS] = 1.7% w/w).

Table 1 .

 1 Structural formula and molar masses of molecules incorporated into liposomes and CS hydrogels in this work.

Table 2 .

 2 Tan δ values of DH (free dye/drug loaded physical CS hydrogel) and DLH (dye/drug loaded liposomes embedded into CS hydrogels) assemblies. N≥2 for each measurement, with N the number of measurements carried out on different hydrogels batches ([CS] = 1.7% w/w).

Table 1 :

 1 Structural formula and molar masses of molecules incorporated into liposomes and DH assemblies.

		CF	RIF	LID
	Structural
	formula
	Molar mass (g.mol -1 )	376,3	822,9	288,8

Table 2 .

 2 Tan δ values of DH (free dye/drug loaded physical CS hydrogel) and DLH (dye/drug loaded liposomes embedded into CS hydrogels) assemblies. N≥2 for each measurement, with N the number of measurements carried out on different hydrogels batches ([CS] = 1.7% w/w).

Table 1 .

 1 Tan δ values of DH (free dye/drug loaded physical CS hydrogel) and DLH (dye/drug loaded liposomes embedded into CS hydrogels) assemblies. N≥2 for each measurement, with N the number of measurements carried out on different hydrogels batches ([CS] = 1.7% w/w).

	Table(s)

Table 1 .

 1 Tan δ values of DH (free dye/drug loaded physical CS hydrogel) and DLH (dye/drug loaded liposomes embedded into CS hydrogels) assemblies. N≥2 for each measurement, with N the number of measurements carried out on different hydrogels batches ([CS] = 1.7% w/w).

Acknowledgments

This work was financially supported by a grant from the French Ministry of Higher Education and Research. The authors gratefully acknowledge the "CTµ" (Centre Technologique des Microstructures de l'Université Lyon1) and "liquid chromatography" (Institut de Chimie de Lyon) platforms for the access to TEM, ESEM, and MALLS/RI/SEC, respectively.

Graphical abstract -TOC

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Supplementary data

Click here to download Supplementary data: SI_Embedment of liposomes into CS phys HG_S Peers.docx