Ekaterina Mamontova

Morgane Daurat

Jérôme Long
email: jerome.long@umontpellier.fr

Anastasia Godefroy

Fabrice Salles

Yannick Guari

Magali Gary-Bobo
email: magali.gary-bobo@inserm.fr

Joulia Larionova

Fashioning Prussian blue nanoparticles by adsorption of luminophores: synthesis, properties and in vitro imaging

We report the post-synthetic functionalization of Prussian blue (PB) nanoparticles by two different luminophores (2-aminoanthracene and rhodamine B). We show that the photoluminescence properties of the fluorophores are modified by a confinement effect upon adsorption and demonstrate that such multifunctional nanosized systems could be used for in vitro imaging.

Introduction

Prussian blue (PB) and their analogues are widely recognized as the first coordination network materials in which transition metal ions are assembled through cyano-bridges, generating a 3D cubic structure. PB is a mixed-valence system exhibiting the general formula A1-xFe III [Fe II (CN)6]1x/4x/4, in which A is an alkaline ion and  denotes the hexacyanoferrate vacancies. The synthesis of PB at the nanoscale has recently attracted a great deal of interest due to several advantages arising from their molecule-based nature with respect to classical inorganic nanoparticles. 1 More specifically, an upturn has recently been taken to utilize PB nanoparticles for biomedical applications thanks to their adjustable physical and chemical properties, their porosity and their chemical robustness in water over a large pH range. [START_REF] Long | Prussian blue type nanoparticles for biomedical applications[END_REF] Moreover, they benefit from an excellent biocompatibility since the so-called "insoluble" form of the bulk PB has been approved by the Food and Drug Administration (FDA) as an antidote for radioactive Cs + and nonradioactive Tl + poisoning. 3 Consequently, the efficiency of nanosized PB has been demonstrated for a myriad of biomedical applications including Magnetic Resonance Imaging (MRI), 4 scintigraphy, [START_REF] Perrier | 201Tl+-labelled Prussian blue nanoparticles as contrast agents for SPECT scintigraphy[END_REF] photoacoustic imaging, 6 or therapy through different kinds of treatment 7 together with drug delivery 7a, 8 and photothermal therapy. 9

On the other hand, fluorescence imaging is an affordable technique that benefits from a high temporal resolution and high sensitivity. 10 Numerous nanosized systems, such as gold and lanthanide-based upconverting nanoparticles or semiconductors quantum dots have been employed for optical imaging. 11 This approach has recently been extended to nanosized Metal-Organic Framework. 12 In this sense, optical imaging would be therefore highly needed to fully exploit the potentials of nanosized PB with the aim to easily monitor the nanoparticles uptake into the cells, understand their biodistribution and the efficiency of the targeted treatment. However, efforts to implement luminescence in these intrinsically multifunctional PB nanosystems remain almost unexplored except the two examples of Mn 2+ -doped PB nanoparticles with an avidin functionalized surface and core@shell polypyrrole@PB nanoparticles. 4e, 13 Combining the aforementioned properties of nanosized PB with luminescence would therefore allow the design of simple theranostic agents. 14 Hence, new luminescent PB nanoparticles with different emission ranges are therefore required.

In this sense, various synthetic approaches could be used to post-functionalize PB nanoparticles.

Firstly, surface modification of the PB nanoparticles could be achieved using bulky ligands able to interact with the metal ions located at the surface. 15 On the other hand, we have also recently reported an original strategy to render magnetic nickel hexacyanochromate nanoparticles luminescent by a post-synthetic functionalization within the internal porosity of the cyano-bridged framework using the 2-aminoanthracene (AA) fluorophore to yield a bifunctional magnetoluminescent system. [START_REF] Mamontova | Making Prussian blue analogues nanoparticles luminescent: effect of the luminophore confinement over the properties[END_REF] Taking advantage of these simple methodologies, we have extended this to PB nanosystems by their functionalization with two different fluorophores. We report here the understanding of PB nanoparticles' functionalization by combining experimental and theoretical methods as well as the in-depth investigation of their photophysical properties and cellular uptake. The post-functionalization was performed by mixing the pristine Na + /Fe 3+ /[Fe II (CN)6] 4 NPs 1 (30 mg, 0.10 mmol) with the 2-aminoanthracene (AA) (96 mg, 0.50 mmol) in ethanol for 24 h under stirring. Then, the solution was centrifuged at 37,500 × g (20,000 rpm) during 15 min. The supernatant was removed and the NPs were washed with ethanol several times. The NPs were dispersed in the minimum of ethanol to perform a 2-days dialysis using SnakeSkin Dialysis Tubing. Finally, the solid was recovered by centrifugation at 37,500 × g (20,000 rpm) during 15

Experimental Section

Materials and Methods

Luminophore's release

In order to estimate the possibility of luminophores' leaching, the NPs 1@AA or 1@RhB (10 mg)

were dispersed in 3 mL of ultrapure water to perform a 24-hours dialysis using SnakeSkin Dialysis tubing in 10 mL of water. The NPs were removed by centrifugation and the receiving medium was studied by UV-Vis spectroscopy.

Cell culture

Human breast adenocarcinoma MDA-MB-231-GFP and MDA-MB-231 cell lines were used.

MDA-MB-231 cell line stably expresses GFP inducing the visualization of green nuclei in contrast to MDA-MB-231. Cells were cultured in Dulbecco's Modified Eagle's Medium (DMEM) supplemented with 10% fetal bovine serum and antibiotic (0.05 mg.mL 1 gentamicin). These cells grown in humidified atmosphere at 37°C and under 5% CO2.

Cytotoxicity experiments

MDA-MB-231 cells were seeded in 96-well plates. After 24 h of cell growth, cells were treated with increasing concentrations (0, 50, 100, 150 and 200 μg.mL -1 ) of nanoparticles (1, 1@AA and 1@RhB). Three days after treatment, cells were stained with nuclei colorant (Hoechst) and visualized with microscope. The cells were counted using ImageJ program.

Infrared Spectroscopy

Infrared spectra were recorded as KBr disks on a PerkinElmer Spectrum two spectrophotometer.

Ultraviolet-visible spectroscopy

UV-Vis spectra were collected on a JASCO V-650 spectrometer in water or ethanol (C = 500 µg.mL 1 ).

X-ray Powder Diffraction

X-ray powder diffraction patterns were recorded in the 2 interval 5-60° at room temperature with the PANalytical X'Pert Powder diffractometer analytical diffractometer mounted in a Debye-Scherrer configuration and equipped with Cu radiation (λ = 1.5418 Å).

Thermogravimetric Analysis

Thermogravimetric analyses were obtained with a thermal analyser STA 409 Luxx® (Netzsch) in the range 25 -650 °C at heating speed of 5 °C.min 1 .

Zeta potential measurements

Zeta potential measurements were performed with a Zetasizer Nano-series Malvern instrument.

Aqueous suspension were used (C = 1 mg.mL 1 ). Three individual measurements were accumulated and averaged until the final zeta potential result was achieved.

Electron microscopy

Transmission Electron Microscopy (TEM) observations were carried out at 100 kV (JEOL 1200 EXII). Samples for TEM measurements were deposited from solutions on copper grids.

Nanoparticles' size distribution histograms were determined using enlarged TEM micrographs taken at magnification of 100 K on a statistical sample of ca. 300 nanoparticles.

TEM imaging

MDA-MB-231 cells were seeded on glass coverslips for 24 h. After controlling their adherence and growing, cells were exposed (or not for Control tests) to 100 µg. mL 1 of 1@AA and 1@RhB for 20 h. The medium was removed and cells were washed twice with DPBS. Cells were fixed by incubation with 2.5% (v/v) glutaraldehyde in DPBS buffer and stored at 4°C. Then, cells were extensively washed with DPBS. The staining of samples was obtained upon incubation with 1% osmium tetroxide. Samples were dehydrated by ascending grades of EtOH; for impregnation, the samples were firstly treated with a mix EtOH/EPON resin (1:1, v/v) for 1 h, and twice in EPON for 2 h. The polymerization was performed by embedding cells in EPON resin for 12 h at 60°C, plunged in liquid nitrogen at -195°C to detach the coverslip, and placed for two days at 60°C for completing polymerization. The ultrathin sections (70 nm) were obtained using an ultramicrotome (Leica Ultracut) and disposed on the copper grids. The grids were incubated in uranyl acetate for 2 min, rinsed in water, and then incubated in lead citrate for 2 min, and finally rinsed with water.

Scanning Electronic Microscopy

Scanning Electronic Microscopy (EDS) analyses were performed on a FEI Quanta FEG 200 instrument. The powders were deposited on an adhesive carbon film and analyzed under vacuum.

The quantification of the heavy elements was carried out with the INCA software, with a dwell time of 3 µs.

Photoluminescence measurements

The emission and excitation spectra were recorded at 295 K using an Edinburgh FLS-920 spectrofluorimeter in water or ethanol suspension (C = 500 µg.mL 1 ) or for powdered samples.

The excitation source was a 450 W Xe arc lamp. The emission spectra were corrected for detection and optical spectral response of the spectrofluorometer.

Photostability

Ethanolic suspensions (500 µg.mL 1 ) were irradiated in a Edinburgh FLS-920 spectrofluorimeter (maximum slits opening) during 16 hours at 365 and 520 nm for 1@AA and 1@RhB respectively.

Cellular uptake

The cellular uptake experiment was performed using confocal fluorescence microscopy on living cells. MDA-MB-231-GFP cells were plated onto bottom glass dishes (World Precision Instrument, Stevenage, UK) in 1 mL culture medium for 24 h. Cancerous cells were treated or not for 20 h with 1@AA or 1@RhB at a concentration of 100 µg.mL 1 , and AA or RhB at the same grafted concentration on 1. Before the visualization, cells were washed two times with culture medium.

Confocal fluorescence microscopy observations were carried out at Carl Zeiss Microscope. The measurements were performed on living cells under a 488 nm wavelength excitation for nucleus, 458 nm for AA and 561 nm for RhB. All images were performed with a high magnification (63x/1.4 OIL DIC Plan-Apo).

Conventionally, GFP signal was represented in green, here GFP is in blue in order to show the AA fluorescence in green. In addition, MDA-MB-31-GFP cells were used for confocal imaging to have nuclei labeling other than DAPI, in order to avoid the excitation spectrum overlap between DAPI and AA.

Internalization quantification by FACS

MDA-MB-231 cells were treated or not with 100 µg.mL 1 of 1@RhB for 1, 3, 6 and 24 h. After treatment, control and treated cells were washed once in cold Phosphate Buffer Saline (PBS), harvested and centrifuged (1300 rpm, 5 min). Cell pellet were resuspended in PBS enriched with CaCl2, MgCl2 and stained by propidium iodide (1 μg.mL 1 ) (Sigma-Aldrich Chimie, Lyon, France), a cell death indicator. Flow cytometric determination of living cells and micelles with FITC positive cells was done by FACS CytoFlex Flow Cytometer (Beckman Coulter, France) with a minimum of 10.000 living cells collected.

Computational section

Monte Carlo simulations were performed at 300 K to determine the amount adsorbed at saturation for both, RhB and AA in the PB structure 1, as well as the adsorption energy between the first adsorbed molecule and the PB framework. During these simulations, 3D periodic boundary conditions were applied, while the AA and RhB molecules can undergo different motions including translation, rotation, deletion and insertion randomly considered during the equilibration steps. In order to predict the amount adsorbed in a porosity, a standard methodology based on the equilibrium of chemical potentials between the adsorbed phase inside the pores and the pseudo gaseous phase at a given temperature was considered. For that purpose, 510 6 steps for equilibration and 510 6 steps of production were considered for calculations using Universal Force Field (UFF) combined with partial charges for both the molecules and the framework. The force fields were implemented in the home-made Monte Carlo code. Following the strategy proposed in Rojas et al., [START_REF] Rojas | Toward Understanding Drug Incorporation and Delivery from Biocompatible Metal-Organic Frameworks in View of Cutaneous Administration[END_REF] ESP charges were extracted from DMol3 calculations for RhB and AA molecules, while qEq partial charges (corresponding to the partial charges obtained from the electronegativity equalization method) were considered for the solid framework and formal charges for the compensating cations. Concerning the Lennard Jones interactions, the Lorentz-Berthelot rules were applied with a cut-off radius fixed at 12.5 Å. Ewald summation for the electrostatic part was handled to increase the convergence for the energy calculations.

The structures considered for the solid have already been presented in the literature and reproduced in Scheme 1.

From these Monte Carlo calculations, it is therefore possible to determine the ability of the internal porosity of the solid to accommodate both AA and RhB molecules. Indeed different positions of the molecules are tested during the simulations with a large number of equilibration steps.

In addition, classical calculations performed with GULP have been used to optimize the geometry of the solid considering a chemical interaction with the AA in order to compare the physical interaction (similar to adsorption of the molecule) and the chemical interaction (similar to chemisorption of the molecule on the unsaturated metal center). For these calculations, the same parameters used before have been considered.

The molecular simulations have focused on the microscopic pores and not on the mesopores, which were present in the structure. From Monte Carlo simulations, the adsorption enthalpy at low coverage (1 molecule per cell unit) is estimated at 30 kcal.mol 1 , which is weaker than the chemical interaction between AA and 1 (evaluated at 120 kcal.mol 1 ) calculated using similar force fields. This value is obtained from the difference between the energy of the {PB + AA} structure (where the AA is interacting with the Fe 3+ cation by NH2 group) and the sum of the energy of the empty PB structure and the energy of the AA molecule. These results point out that AA should interact with 1 by strong chemical coordination interactions.

In complement, the pore size distribution has been calculated using the same parametrization for the framework (UFF) and the methodology developed by Gelb and Gubbins, considering a probe sphere including in the porosity and for which the diameter is growing up to contact of the solid surface. [START_REF] Gelb | Pore Size Distributions in Porous Glasses: A Computer Simulation Study[END_REF] The pores size distribution shows two different types: the tetrahedral sites of the fcc structure (with pore diameter close to 4 Å) and larger pores (with pore radius estimated at 7.5 Å from Figure S9 if we consider a spherical ball to probe the porosity, but the pores are elongated and dimensions are close to 7.5 Å x 7.5 Å x 20 Å)) formed by cyanometallates vacancies. Due to the shape of these pores (cubic and elongated for mono-lacuna and bi-lacuna containing PB models respectively), they are suitable to accommodate AA of 10 Å x 5 Å x 1 Å, but are unable to accommodate RhB of 16 x 12 x 7 Å. Indeed mono-lacunas generated by single cyanometallate vacancies have a diameter that can be estimated at 7.5 Å x 7.5 Å x 11 Å, while bi-lacuna generated by double cyanometallate vacancies have a diameter estimated at 7.5 Å x 7.5 Å x 20 Å. Keep in mind that these two kinds of pores cannot be distinguished by the probe sphere approach since the lowest dimension imposes the diameter of the sphere.

Results and discussion

Synthesis and characterizations. Implementing luminescence in our PB nanosized systems was performed by a two-steps procedure involving the synthesis of surfactant-free PB nanoparticles, followed by their post-functionalization with two targeted luminophores, 2-aminoanthracene (AA)

and Rhodamine B (RhB), which differ from their size and emission features (Scheme 1). They (1@AA) and Na0.23Fe III [Fe II (CN)6]0.81@(RhB)0.02•3.7H2O (1@RhB). The loading capacity, defined as the luminophore's weight divided by the total weight of the nanoparticles, is of 4.8 and 2.9 % for 1@AA and 1@RhB, respectively. Besides, TGA analyses indicate that, in both cases, the luminophore's decomposition occurs at higher (1@AA) or similar temperatures (1@RhB) compared to PB. This suggests the integration of AA within the porosity of the PB network, while

RhB is localized at the surface. In comparison with the pristine nanoparticles, 1@AA and 1@RhB exhibit similar structural features, such as a stretching cyanide ν(Fe II -CN-Fe III ) vibration located at around 2080 cm -1 in the FT-IR spectra (Figure S4-S5), and a Powder X-Ray Diffraction Patterns

showing the typical fcc structure with a lattice parameter a = 10.0 Å (Figure S6, Table S1). The post-functionalization with the luminophores is confirmed by the typical vibrations of either AA and RhB in the 600-1700 cm 1 region (Figure S5). Scherrer analysis does not reveal a modification in the crystallite sizes with respect to the pristine nanoparticles 1 (i.e. 55 nm, Table S1), which is in good accordance with the Transmission Electronic Microscopy (TEM) analyses showing cubic nanoparticles with a size of 72 ± 5 nm and 73 ± 5 nm for 1@AA and 1@RhB, respectively (Figure 1, Figure S7-S8). The negative values of the zeta potentials for 1 and 1@AA are rather similar indicating a comparable surface state (Table S2). In contrast, a significant increase of the zeta potential is observed for 1@RhB, while no chloride ion (that may originate from RhB) could be detected by the EDS analysis. All these results suggest that AA may be adsorbed within the S9) highlights two distinct porosity in the coordination network, namely the tetrahedral sites of the fcc structure and the larger pores of pore diameter estimated by molecular simulations at 7.5 Å but, due to the cubic shape where larger size can be calculated by considering the cube diagonale 7.5 Å  7.5 Å  11 Å for mono-lacuna and 7.5 Å x 7.5 Å x 20 Å for bi-lacuna models. These larger pores are generated by cyanometallate vacancies and suitable to accommodate only AA (10 Å  5 Å  1 Å). In contrast, the size of RhB (16 Å  12 Å  7 Å) is clearly too large to enter into these cavities, suggesting rather an interaction at the nanoparticles' surface by electrostatic or covalent bonds. Additionally, the AA adsorbed amount in an empty structure is estimated by Monte Carlo simulations at 0.13 AA molecules per unit cell (as defined below), which is slightly higher than the experimental value of 0.08. This loading capacity of 7.6 wt% is to be compared with the experimental one of 4.8 wt%. This discrepancy could be rationalized by the presence of solvent molecules, which are able to chemically interact with unsaturated metal centers (CUS) and enter in competition with AA. To go further, the distribution of AA in the pores reveals that it is adsorbed within the largest pores generated by cyanometallate vacancies (Figure 2). Complementary calculations have also been performed to quantify the interaction energy when the AA adsorption is mainly governed by physisorption (since Universal Force Field is unable to predict the formation of a covalent interaction). Firstly, the enthalpy corresponding to a physisorption at low coverage (1 molecule per cell) is estimated at 30 kcal.mol 1 , while the chemisorption involving a coordination bond between AA and Fe 3+ gives a greater value of 120 kcal.mol 1 . This large energy difference suggests a favourable interaction by coordinative bond which is confirmed by the short distance of 1.96 Å between AA and Fe 3+ . Yet, it should be mentioned that additional interactions between NH2 from AA and Fe 3+ of 2.7-2.9 Å and between NH2 and cyano-groups could be also observed (Figure 2). Both experimental and theoretical approaches point out therefore a different mechanism of adsorption depending on the nature of the investigated luminophores: AA could be chemisorbed within the internal porosity of the PB framework due to its relatively small size whereas RhB interacts most likely at the nanoparticles's surface.

Optical properties.

The optical properties of the resulting functionalized PB nanoparticles were investigated in suspension and solid-state. Firstly, the electronic absorption spectra of 1@AA and 1@RhB show the typical broad band in the NIR associated with the intervalence Fe 2+ /Fe 3+ charge transfer of PB (Figure 3). Moreover, the typical absorption bands related to the fluorophores could also be discerned at around 400 nm and 550 nm for AA and RhB, respectively. Figure 3. Left: Room temperature absorption spectra for 1, AA and 1@AA in ethanol (500 µg.mL -1 ). Right: Room temperature absorption spectra for 1, RhB and 1@RhB in water (500 µg.mL -1 ).

As regards the photoluminescence features, Figure 1 shows that while a suspension of 1 is optically inactive, 1@AA and 1@RhB exhibit a blue-green and orange emission, respectively, when excited by a UV-lamp. For both functionalized systems, the photoluminescence spectra acquired in suspension exhibit a blue-shift (e.g. 10 nm) with respect to the isolated luminophores (Figure 4, Figure S10). Such phenomenon has previously been observed in related functionalized PBA nanoparticles and could be ascribed to a modification of the environment of the luminophores upon adsorption. [START_REF] Mamontova | Making Prussian blue analogues nanoparticles luminescent: effect of the luminophore confinement over the properties[END_REF] Thus, when excited in the UV, suspension of 1@AA exhibits a broad emission band located at 500 nm, while the excitation of 1@RhB at 520 nm shows an emission band centred at 575 nm (Figure 4, Figures S11-S12). In solid-state, 1@AA displays a comparable green emission 1 RhB 1@RhB Absorbance Wavelength / nm feature at the exception of a weak emission component located in the 400-430 nm region (Figure S13) which has previously been identified as being dependent on the chromophore's concentration upon adsorption in PBA. [START_REF] Mamontova | Making Prussian blue analogues nanoparticles luminescent: effect of the luminophore confinement over the properties[END_REF] In contrast, the solid-state emission spectrum for 1@RhB (Figure S14) is comparable to that obtained in aqueous suspension, while only a red-shifted emission is observed for the RhB due to the well-known concentration effect (fluorophore-fluorophore interactions). [START_REF] Itoh | Fluorescence quenching processes of Rhodamine B on oxide semiconductors and light-harvesting action of its dimers[END_REF] Aqueous suspensions of 1, 1@AA and 1@RhB are found to be stable for several hours (Figure S15).

Figure 4.

Room temperature normalized emission spectra for 1@AA (excited at 360 nm) and 1@RhB (excited at 520 nm) re-dispersed in ethanol (500 g.mL -1 ).

Moreover, the photostability of the functionalized nanoparticles was investigated upon long-time exposure (16 H) to their respective excitation wavelengths, 365 and 520 nm for 1@AA and 1@RhB respectively (Figure S16). While an almost two-fold decrease (i.e. 48 %) in the emission intensity could be observed for 1@AA, 1@RhB only shows a slight reduction of less than 10 %, pointing out a remarkable photostability. Such results could be favorably compared to the free luminophores with observed decrease of 60 and 22 % for AA and RbB respectively (Figure S17).  exc = 520 nm 1@AA 1@RhB

Normalized intensity

Wavenlength / nm

 exc = 360 nm
This suggests that the luminophores are in some extent protected by their adsorption with PB nanoparticles. For both 1@AA and 1@RhB, no release of the luminophores could be detected after their dialysis in water during 24 h (Figure S18) confirming the strong interaction between the fluorophores and nanoparticles. These results suggest that the luminescence properties of PB nanoparticles could be used to follow in vitro their cell internalization.

Cellular uptake and in vitro imaging. Firstly, the nanoparticles safety was evaluated by studying the cytotoxicity in human breast adenocarcinoma cells (MDA-MB-231). The cells were treated with different concentrations (50, 100, 150 and 200 µg.mL -1 ) during three days. The results indicate no significant differences with the different nanoparticles at the studied doses compared to control, allowing to consider that these nanoparticles do not exhibit toxicity at these concentrations (Figure S19). Secondly, the MDA-MB-231 cells were exposed for 20 h to 1@AA and 1@RhB with a concentration of 100 µg.mL -1 . Fluorescent imaging of the MDA-MB-231-GFP living cells showed that both nanoparticles were efficiently internalized by cancerous cells and after 20 h incubation are localized in cell cytoplasm (Figure 5). Comparison between 1@AA and 1@RhB internalization with the independent luminophores indicates that AA cannot be internalized by the cells in contrast to RhB (Figure 5, Figure S20); this later being indeed well known to cross the membranes of cells. 40 Moreover, control experiments were performed with 1 using similar conditions (Figure S21). No fluorescence could be detected demonstrating the efficiency of the post-functionalization. To ultimately confirm the cancer cell internalization of the functionalized 1@AA and 1@RhB, TEM imaging was performed after 20 h of incubation. The results point out a clear localization of the functionalized PB into the MDA-MB-231 cells (Figure 1, Figure S22), in accordance with the results from fluorescence imaging. Cancerous cells were incubated or not for 20 h with 1@AA or 1@RhB at a concentration of 100 µg.mL-1. Blue emission shows the cell nucleus of MDA-MB-231-GFP cells, green and red emissions characterize the presence of 1@AA and 1@RhB, respectively.

Finally, quantification of the 1@RhB internalization by cancerous cells by flow cytometry experiments was performed (Figure S23). 1@RhB was incubated in the culture cells at a concentration of 100 µg.mL 1 during different times (1 h, 3 h, 6 h and 24 h). After 3 h of incubation, a high internalization level (55% stained cells) was reached and after 24 h, 79% of cells were stained. All together, these biological experiments demonstrate the potential of such PB nanoparticles to highly penetrate the cancer cells.

Nuclei Compounds Merged

Control 1@AA 1@RhB

Conclusions

In conclusion, we have demonstrated that PB nanoparticles could be easily post-functionalized by various luminophores to render them luminescent. Depending on the luminophore's nature, different adsorption mechanisms could be observed as confirmed by molecular modelling. With respect to the previously reported luminescent nanosized PB, the facile and low-cost character of the reported methods constitute an asset. Such approach could be easily extended to various luminophores to tune the emission range. Additionally, we unambiguously show that these simple functionalized nanosized probes could be efficiently used to monitor the cell internalization of the nanoparticles by fluorescence imaging. This opens exciting perspectives for using these PB functionalized nanoparticles for multimodal imaging.

ASSOCIATED CONTENT

Supporting Information.

Additional TEM images, TGA analyses, Infra-Red Spectra, PXRD patterns, nanoparticles size distribution, pore size distribution, additional luminescence spectra, cell viability, one-photon fluorescence imaging, values of the zeta potential and crystallographic parameters.

  min and dried under vacuum to obtain a blue solid. IR (KBr): (O-H) = 3630 cm 1 (coordinated water), (O-H) = 3390 cm 1 (crystallized water/primary alcohol groups), (C≡N) = 2078 cm 1 (Fe III -C≡N-Fe II ), (N-H) = 1635 cm -1 (N-H amine),  O-H1609 cm 1 (crystallized water), (C-C) = 1485-1410 cm 1 (aromatic), (C-H) = 1346-600 cm 1 (aromatic), (Fe II -CN) = 600 cm -1 , (Fe II -CN) = 501 cm -1 .

  Scheme 1. a) Typical structure of a lacunary PB. Color code: purple, Fe II and Fe III ;, blue, N; grey,

Figure 1 .

 1 Figure 1. TEM images of 1@AA (a) and 1@AA in MDA-MB-231 cancer cells (b); Photographs

Figure 2 .

 2 Figure 2. Top: Snapshot illustrating the density of presence of the AA molecules (green) and of

Figure 5 .

 5 Figure 5. Human breast adenocarcinoma MDA-MB-231-GFP cells uptake of nanoparticles.

synthetic functionalization of Na + /Fe 3+ /[Fe II (CN)6] 4 nanoparticles with 2- aminoanthracene (1@AA).

	All chemical reagents were purchased and used without further purification: Iron (III)
	chloride hexahydrate (Sigma Aldrich, 97%), Sodium hexacyanoferrate (II) decahydrate
	(Alfa Aesar, 99%), 2-aminoanthracene (Alfa Aesar, 94%), Rhodamine B (Acros Organics,
	98+%), ultra-pure water, ethanol 96% vol (TechniSolv), SnakeSkin Dialysis Tubing
	(3,5000 MWCO, 22 mm × 35 feet dry diameter, 34 mm dry flat width, ThermoScientific).
	Synthesis of Na+/Fe 3+ /[Fe II (CN)6] 4 PB nanoparticles (1).

The PB nanoparticles (NPs) were obtained following general procedures.

9c, 17

At 25°C, aqueous solutions of FeCl36H2O (10.00 mM, 10 mL) and Na4[Fe(CN)6]•10H2O (11.25 mM, 10 mL) were added simultaneously to 100 mL of pure water at 2 mL.h 1 rate, using a syringe pump. After addition, the mixture was stirred one hour before being centrifuged at 37,500 × g (20,000 rpm) during 15 min. The supernatant was removed and the NPs were washed successively with water and ethanol and dried under vacuum to obtain a dark blue powder. IR (KBr): (O-H) = 3630 cm 1 (coordinated water), (O-H) = 3400 cm 1 (crystallized water), (C≡N) = 2082 cm 1 (Fe III -C≡N-Fe II ), O-H1606cm -1 (crystallized water), (Fe II -CN) = 602 cm 1 , (Fe II -CN) = 501 cm 1 . EDS: 14.04/85.96 (Na/Fe). Formula found: Na0.30Fe III [Fe II (CN)6]0.82•3.7H2O.

Elemental analysis calcd (%): C,

19.49; H, 2.44; N, 22.72

; found (%): C,

19.22; H, 3.36; N, 22.15

.

Post-

Post-synthetic functionalization of Na + /Fe 3+ /[Fe II (CN)6] 4 nanoparticles with Rhodamine B (1@RhB).

	IR (KBr): (O-H) = 3630 cm 1 (coordinated water), (O-H) = 3400 cm 1 (crystallized
	water/primary alcohol groups), (C≡N) = 2082 cm 1 (Fe III -C≡N-Fe II ), (C=O) = 1691 cm 1
	(COOH acide), (C=N) = 1635 cm 1 , O-H1606cm -1 (crystallized water), (C-C) = 1485-
	1410 cm 1 (aromatic), (C-H) = 1346-600 cm 1 (aromatic), (Fe II -CN) = 603 cm 1 , (Fe II -CN) =
	501 cm 1 .
	EDS: 11.59/88.06 (Na/Fe).
	Elemental analysis calcd (%): C, 18.29; H, 2.53; N, 20.45. Found (%): C, 18.57; H, 2.62; N, 20.95.
	Estimated formula for 1@RhB: Na0.23Fe III [Fe II (CN)6]0.81@(RhB)0.02•3.7H2O.
	2.56; N, 20.78.
	Estimated formula for 1@AA: Na0.30Fe III [Fe II (CN)6]0.83@(AA)0.08•3.6H2O.
	The post-functionalization was performed by mixing the pristine Na + /Fe 3+ /[Fe II (CN)6] 4 NPs 1 (30
	mg, 0.1 mmol) with the rhodamine B (RhB) (240 mg, 0.50 mmol) in ultrapure water for 24 h under
	stirring. Then, the solution was centrifuged at 37,500 × g (20,000 rpm) during 15 min. The
	supernatant was removed and the NPs were washed with water several times. The NPs were
	dispersed in the minimum of water to perform a 2-days dialysis using SnakeSkin Dialysis Tubing.
	Finally, the solid was recovered by centrifugation at 37,500 × g (20,000 rpm) during 15 min,
	washed with water and ethanol and dried under vacuum to give a blue solid.

EDS: 15.12/84.88 (Na/Fe). Elemental analysis calcd (%): C, 21.60; H, 2.49; N, 21.05. Found (%): C, 21.33; H,

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	The following
	files are available free of charge.
	AUTHOR INFORMATION
	Corresponding Author
	*E-mail: jerome.long@umontpellier.fr

*E-mail: magali.gary-bobo@inserm.fr

Oliviero, E.; Photodiagnosis Photodyn. Ther. 2018, 22, 65-69; (d)

Zou, L. W., H.; He, B.;, Zeng, L.; Tan, T.; Cao, H.; He, X.; Zhang, Z.; Guo, S.; Li, Y. , Current Approaches of Photothermal Therapy in Treating Cancer Metastasis with Nanotherapeutics. Theranostics 2016, 6 (6), 762-772; (e) Ma, Y.; Chen, H.; Hao, B.; Zhou, J.; He, G.; Miao, Z.; Xu, Y.; Gao, L.; Zhou, W.; Zha, Z., A chloroquine-loaded Prussian blue platform with controllable autophagy inhibition for enhanced photothermal therapy. J.. Mater. Chem. B 2018, 6 (37), 5854-5859; (f) Liu, B.; Wang, W.; Fan, J.; Long, Y.; Xiao, F.; Daniyal, M.; Tong, C.; Xie, Q.; Jian, Y.; Li, B.; Ma, X.; 2011, 47 (24), 6831-6833. (14) Qin, Z.; Li, Y.; Gu, N., Progress in Applications of Prussian Blue Nanoparticles in Biomedicine. Advanced Healthcare Materials 2018, 7 (20), 1800347.

Author Contributions

The manuscript was written through contributions of all authors. All authors have given approval to the final version of the manuscript. ‡These authors contributed equally.

ACKNOWLEDGMENT

The authors thank the University of Montpellier, CNRS and PAC of ICGM.


Julien Maillard
email: julien.maillard@ens.uvsq.fr

Isabelle Schmitz-Afonso

Thomas Gautier

Carlos Afonso

Nathalie Carrasco

Suggested plausible structures for Titan's haze analogs using tandem mass spectrometry

Keywords: Titan, atmospheric chemistry, haze, Structural analyses, Icy world, Tandem mass spectrometry Graphical

Understanding the formation and composition of the organic photochemical haze produced in Titan's upper atmosphere is an important milestone in prebiotic chemistry, for the early Earth climate and for the origin of life. However, retrieving knowledge about this organic fog is limited because of non-sample return possibilities. Chemical analyses from previous laboratory experiments allowed to obtain molecular information from analogues of this haze that were produced on Earth. In the current work, we push forward our comprehension of the structure composing this complex sample using tandem mass spectrometry. This type of analysis allows to recover structural information thanks to the observation of systematic fragmentation patterns. Strong differences are found between soluble and insoluble fractions of the aerosols and putative structures are proposed. Both highly aromatic structures as well as archipelago-like structures were observed in the insoluble fraction. The soluble fraction contains chemical structures with weaker bonds, composed of small aromatics cores linked together by short chains.

Introduction

Due to highly charged particles coming from Saturn's magnetosphere and UV photons from the sun, nitrogen and methane present in the upper atmosphere of Titan initiate an intense atmospheric chemistry chain [START_REF] Mckay | The thermal structure of Titan's atmosphere[END_REF][START_REF] Sagan | Polycyclic aromatic hydrocarbons in the atmospheres of Titan and Jupiter[END_REF][START_REF] Samuelson | Thermal infrared properties of Titan's stratospheric aerosol[END_REF]. The very effective neutral and ion chemistry occurring induces the formation of large species than can be above 10 000 Da [START_REF] Vuitton | Negative ion chemistry in Titan's upper atmosphere[END_REF][START_REF] Waite | The process of tholin formation in Titan's upper atmosphere[END_REF][START_REF] Westlake | The role of ionmolecule reactions in the growth of heavy ions in Titan's ionosphere[END_REF] that end with the production of a complex organic haze lower in the atmosphere [START_REF] Gupta | ORGANIC-SYNTHESIS IN THE ATMOSPHERE OF TITAN[END_REF][START_REF] Sagan | Production and condensation of organic gases in the atmosphere of Titan[END_REF]. This thick haze hides the surface of the largest satellite of Saturn [START_REF] Israel | Complex organic matter in Titan's atmospheric aerosols from in situ pyrolysis and analysis[END_REF]. The Voyager and Cassini-Huygens space missions brought to light the importance of the carbon and nitrogen chemistry in the formation of this haze [START_REF] Vuitton | The nitrogen chemistry of Titan's upper atmosphere revealed[END_REF][START_REF] Vuitton | Simulating the density of organic species in the atmosphere of Titan with a coupled ion-neutral photochemical model[END_REF][START_REF] Vuitton | Ion chemistry and N-containing molecules in Titan's upper atmosphere[END_REF].

However, due to the technical limitations of the onboard instruments, fully understanding the growing path of the haze particles is still out of range and many questions remain on their chemical composition. To address this issue, analogues of Titan's haze are produced on Earth [START_REF] Coll | Experimantal laboratory simulation of Titan's atmosphere: aerosols and gas phase[END_REF][START_REF] Mckay | The thermal structure of Titan's atmosphere[END_REF][START_REF] Poch | Production yields of organics of astrobiological interest from H2O-NH3 hydrolysis of Titan's tholins[END_REF][START_REF] Sagan | Production and condensation of organic gases in the atmosphere of Titan[END_REF][START_REF] Sciamma-O'brien | Titan's atmosphere: An optimal gas mixture for aerosol production?[END_REF][START_REF] Szopa | PAMPRE: A dusty plasma experiment for Titan's tholins production and study[END_REF][START_REF] Trainer | Organic haze on Titan and the early Earth[END_REF]. These samples, called tholins, are analyzed in the laboratory with different analytical techniques. Several analyses, including infrared spectroscopy [START_REF] Cable | Identification of primary amines in Titan tholins using microchip nonaqueous capillary electrophoresis[END_REF][START_REF] Gautier | Mid-and far-infrared absorption spectroscopy of Titan's aerosols analogues[END_REF][START_REF] Imanaka | Laboratory experiments of Titan tholin formed in cold plasma at various pressures: implications for nitrogen-containing polycyclic aromatic compounds in Titan haze[END_REF], capillary electrophoresis [START_REF] Cable | Identification of primary amines in Titan tholins using microchip nonaqueous capillary electrophoresis[END_REF] and nuclear magnetic resonance [START_REF] Derenne | New insights into the structure and chemistry of Titan's tholins via 13C and 15N solid state nuclear magnetic resonance spectroscopy[END_REF]He andSmith, 2014b, 2015) revealed the presence of molecular families such as nitriles, amines and hydrocarbons. Few structures, regarding the complexity of the sample, such as triazine, pyrazole and pyrazine were identified thanks to liquid and gas chromatography coupled to ultrahigh resolution mass spectrometry [START_REF] Gautier | Nitrogen incorporation in Titan's tholins inferred by high resolution orbitrap mass spectrometry and gas chromatography-mass spectrometry[END_REF][START_REF] Gautier | Development of HPLC-Orbitrap method for identification of N-bearing molecules in complex organic material relevant to planetary environments[END_REF]. Mass spectrometry analyses revealed repetitive wave shapes in the mass spectra [START_REF] Somogyi | Organic environments on Saturn's moon, Titan: simulating chemical reactions and analyzing products by FT-ICR and ion-trap mass spectrometry[END_REF][START_REF] Somogyi | Chemical ionization in the atmosphere? A model study on negatively charged "exotic" ions generated from Titan's tholins by ultrahigh resolution MS and MS/MS[END_REF]. A polymeric structure was suggested by several research teams [START_REF] Matthews | Hydrogen cyanide polymers, comets and the origin of life[END_REF][START_REF] Vuitton | Very high resolution mass spectrometry of HCN polymers and tholins[END_REF] . But even if a repetition unit of (CH2)n-(HCN)m was found to statistically explain the structure of tholins, the material is assumed to be not a pure polymer but rather a random one [START_REF] Bonnet | Compositional and structural investigation of HCN polymer through high resolution mass spectrometry[END_REF][START_REF] Vuitton | Very high resolution mass spectrometry of HCN polymers and tholins[END_REF].

Recently, ultrahigh resolution mass spectrometry analyses through Fourier transform ion cyclotron resonance mass spectrometers have enabled an in-depth analysis of the chemical complexity of these tholins by generating global cartographies of the molecular diversity found in these samples [START_REF] Maillard | Comparison of soluble and insoluble organic matter in analogues of Titan's aerosols[END_REF][START_REF] Somogyi | Chemical ionization in the atmosphere? A model study on negatively charged "exotic" ions generated from Titan's tholins by ultrahigh resolution MS and MS/MS[END_REF][START_REF] Somogyi | The Role of Ultrahigh Resolution Fourier Transform Mass Spectrometry (FT-MS) in Astrobiology-Related Research: Analysis of Meteorites and Tholins[END_REF]. FTICR-MS is a state-of-the-art instrument, generally used to decipher the chemical composition of ultra-complex mixtures such as petroleum [START_REF] Hughey | Resolution of 11 000 Compositionally Distinct Components in a Single Electrospray Ionization Fourier Transform Ion Cyclotron Resonance Mass Spectrum of Crude Oil[END_REF][START_REF] Le Maître | Structural analysis of heavy oil fractions after hydrodenitrogenation by high-resolution tandem mass spectrometry and ion mobility spectrometry. Faraday Discussions[END_REF][START_REF] Marshall | Petroleomics: chemistry of the underworld[END_REF]Smith et al., 2018). This instrument allows an unprecedented resolving power and dynamic range [START_REF] Nikolaev | Initial experimental characterization of a new ultra-high resolution FTICR cell with dynamic harmonization[END_REF], making it ideal for the analysis of tholins samples which are an extremely complex material [START_REF] Maillard | Comparison of soluble and insoluble organic matter in analogues of Titan's aerosols[END_REF]. These analyses provide access to all the molecular formulae present in the sample.

However, the isomeric and structural diversity remained unknown. In order to obtain an overview of the global structure present in tholins sample, ion mobility coupled with mass spectrometry was performed [START_REF] Maillard | Structural elucidation of soluble organic matter: Application to Titan's haze[END_REF][START_REF] Ruger | Structural Study of Analogues of Titan's Haze by Trapped Ion Mobility Coupled with a Fourier Transform Ion Cyclotron Mass Spectrometer[END_REF]. These studies allowed for a narrowing of the possibilities of structures by excluding several families of compounds.

Hypothetical structures obtained by theoretical calculations were also proposed.

Several tandem mass spectrometry analyses were previously performed in order to push the chemical characterisation [START_REF] Somogyi | Organic environments on Saturn's moon, Titan: simulating chemical reactions and analyzing products by FT-ICR and ion-trap mass spectrometry[END_REF][START_REF] Somogyi | Chemical ionization in the atmosphere? A model study on negatively charged "exotic" ions generated from Titan's tholins by ultrahigh resolution MS and MS/MS[END_REF] These studies have been performed on small species detected in the tholins sample (maximum m/z 190). They found systematic losses such as HCN and CH2N2 as well as evidence of aromatic structures. However, higher species were not studied. In this work, in order to get further evidence about the growth pathways and the structure of these tholins, tandem mass spectrometry (MS/MS) was performed at higher masses (m/z > 400).

Tholins samples studied here are from the PAMPRE experiment. They are mainly insoluble in methanol [START_REF] Maillard | Comparison of soluble and insoluble organic matter in analogues of Titan's aerosols[END_REF]. Using the combination of electrospray (ESI) and laser desorption ionization (LDI) coupled to a 12 T FTICR, results concerning the fragmentation of both fractions are reported in this work.

Methods

Analogues of Titan's aerosols production

Titan's aerosol analogues were produced with the PAMPRE experiment following the same procedure detailed in previous publications [START_REF] Gautier | Nitrile gas chemistry in Titan's atmosphere[END_REF][START_REF] Szopa | PAMPRE: A dusty plasma experiment for Titan's tholins production and study[END_REF]. The reactor is composed of a stainless-steel cylindrical reactor in which a RF-CCP discharge is established thanks to an RF 13.56 MHz frequency generator. A N2:CH4 95:5% gas mixture is injected in the chamber as a continuous flow through polarized electrodes and is then extracted by a primary vacuum pump to ensure that gases are homogeneously distributed. The plasma discharge is maintained at a pressure of 0.9 ± 0.1 mbar and at room temperature. A brown powder is recovered after few hours. Tholins are then stored in an inert atmosphere and protected from light. Limited oxidation occurs during the harvest, thus the oxygenated species are sorted out and are not studied during the mass spectrometry data treatment. It should be noted that the pressure and temperature are lower in Titan's ionosphere (respectively ~10 -5 -10 -8 mbar and 200 K) than in our experiment, but the ionization rate is the same (several CH4 molecules ionized over millions). As ion-molecule reaction rates are relatively insensitive to the temperature, the lower temperature in Titan's ionosphere (200K instead of 293K in the laboratory) is not an important issue in our case. The higher pressure ensures a faster kinetics in the experiment, and is low enough to limit termolecular reactions. The similar ionization rate enables a realistic contribution of ions into the whole ion-neutral coupled chemical network.

Sample preparation

To recover the soluble part of the sample, 4 mg of tholins are dissolved in 1 mL of methanol in a vial. The vial is vigorously stirred for 3 minutes to solubilize the maximum of species.

The brown mixture is then filtered using a 0.2 µm polytetrafluoroethylene (PTFE) membrane filter on a filter holder. The filtered solution is transferred into a vial and then analyzed after half dilution with a 50/50 water/methanol mixture. This sample preparation was made few minutes before the analysis in order to prevent the hydrolysis of the sample. The insoluble part is recovered for analysis.

Tandem mass spectrometry analysis

All analyses are performed on a hybrid quadrupole -FTICR Solarix XR from Bruker equipped with a 12 Tesla superconducting magnet and a dynamically harmonized ICR cell.

The mass spectrometer is also equipped with an electrospray ionization source and a laser desorption ionization source (laser NdYAg×3 355 nm). The dynamically harmonized cell of the instrument was optimized using a design of experiments in order to obtain best spectra performance [START_REF] Maillard | Optimization of ion trajectories in a dynamically harmonized Fourier-Transform Ion Cyclotron Resonance cell using a Design of Experiments strategy[END_REF]. The mass spectrometer is externally calibrated with a solution of sodium trifluoroacetate. Afterwards, mass spectra are internally calibrated with confidently assigned signals. This calibration procedure yields to a mass accuracy below 300 ppb in the considered mass range. The following ion transfer parameters are used for both electrospray (ESI) and laser desorption ionization (LDI) analyses in positive ion mode: m/z 78 to 1200, capillary exit 150 V, deflector plate 200 V, funnel1 150 V, skimmer1 15 V, funnel RF amplitude 60 Vpp, octopole frequency 5 MHz, octopole RF amplitude 350 Vpp, lower cut-off of the quadrupole at m/z 93, time-of-flight 0.6 ms, frequency TOF 6 MHz, TOF RF amplitude 200 Vpp, side kick offset -1 V, front and back trapping plate 1.75 V. The soluble fraction was analyzed with electrospray ionization. Insoluble fraction is deposited using a solvent-free method, following a previously published procedure [START_REF] Barrere | Solvent-based and solvent-free characterization of low solubility and low molecular weight polyamides by mass spectrometry: a complementary approach[END_REF] before being analyzed by laser desorption ionization. Both ionization methods were compared between themselves in a previous work [START_REF] Maillard | Comparison of soluble and insoluble organic matter in analogues of Titan's aerosols[END_REF]. The same molecules were detected for the soluble fraction by the two ionization sources. This suggests that in this study, it is valid to compare both the soluble and insoluble fractions even though the ionization mechanism is different.

Tandem mass spectrometry experiments are carried out using collision-induced dissociation (CID) in order to produce fragments [START_REF] Chacon-Patino | Advances in Asphaltene Petroleomics. Part 1: Asphaltenes Are Composed of Abundant Island and Archipelago Structural Motifs[END_REF], 2018a, b;[START_REF] Rüger | Thermal Analysis Coupled to Ultrahigh Resolution Mass Spectrometry with Collision Induced Dissociation for Complex Petroleum Samples: Heavy Oil Composition and Asphaltene Precipitation Effects[END_REF]. This technique is based on the acceleration of mass selected ions species into a collision cell filled with a neutral gas such as argon or nitrogen, generating laboratory collisional energies. The extent of fragmentation generated depends on the nature of the target gas, the pressure and acceleration voltage applied [START_REF] Shukla | Tandem mass spectrometry: dissociation of ions by collisional activation[END_REF]. A scheme of the instrument is given in figure S1 in order to have a better comprehension of experiments carried out here. To sum up, the first selecting quadrupole allowed for isolation of a defined m/z range which is then fragmented in the collision cell. Resulting fragments are detected in the ICR cell. Tandem mass spectrometry is performed using collision energy values ranging from 0 eV to 60 eV. This variation of acceleration voltages allows to induce more fragmentation and so, the apparition of new fragments. This process allows for a better understanding of the fragmentation pathway. For the nominal mass fragmentation, a window of one Dalton was set around the selected mass in order to isolate as few peaks as possible to simplify the comprehension of the analysis. Experiments were recorded in triplicates and only species recovered in the three triplicates were considered as real. Data treatment was carried out using DataAnalysis 5.0, OriginPro and Matlab. In addition, the collision energy in the laboratory frame of reference (Elab in eV) is defined by Elab=z×ΔV with z as the charge of the ion. Because the detected ions are singly-charged, the collision energy applied to the precursor ions is equal to the used acceleration voltage

Results

In order the obtain the maximum information about tholins, several types of tandem experiments were conducted. Experiments without mass selection were evaluated for the insoluble and soluble species. In this experiment, the selecting quadrupole is not set as a filter but only as an ion guide, meaning that all ions generated are fragmented in the collision cell.

However, regarding the lack of information given by these experiments due to the extremely high complexity of this sample, results of this broadband CID are only reported in supporting information by the Figure S2 andS3. Nominal mass fragmentation was carried out on three nominal masses for the insoluble and soluble fraction. As the conclusions between each mass are the same, only the fragmentation of the nominal mass 504 will be discussed in the rest of this study. The two others masses (m/z 304 and m/z 404) are reported in supporting information by figure S4 -S5.

Figure 1 displays representative tandem mass spectra obtained from mass selected ions (m/z 504 nominal mass) for the insoluble and soluble fraction. The inset above each spectrum shows the same isolated species without acceleration voltages. Many species are co-isolated by the selecting quadrupole due to its low mass resolution (< 3000). Therefore, it is impossible to study particular/individual species, and all following results are reported using global views and molecular maps.

The first observation that is recovered from Figure 1 is the strong difference in the fragment ion distribution between both fractions. In order to have a better understanding of what is happening, energy resolved mass spectra are given in Figure 2 for each fraction showing the evolution of precursor ions relative intensity depending on the collision energy. As observed, when the fragmentation starts for the insoluble fraction around 20 eV, soluble precursors are already fragmented at 66%. Even if the ionisation source is different between the soluble and the insoluble fraction, this highlights the fact that species present in the insoluble fraction are harder to break than in the soluble fraction. In our previous work, we have shown that the difference at the molecular level between each fraction is related to the number of unsaturations [START_REF] Maillard | Comparison of soluble and insoluble organic matter in analogues of Titan's aerosols[END_REF].

The insoluble fraction species present in average a higher number of unsaturations compared to the soluble fraction. With the insoluble fraction, at 40 eV collision energy, major fragment peaks are close to the precursor peak at m/z 504 indicating that they are produced through small neutral molecules losses. In contrast, major peaks in the soluble fraction are located under m/z 200 indicating that mainly relatively high mass neutral losses are produced. The insoluble fraction presents a relatively low H/C ratio which is consistent to more aromatic species that are more stable and are therefore harder to fragment. In addition, the loss between major peaks is different in both fractions. Several HCN are lost in the insoluble part whereas several NH3 are lost in the soluble one. In order to go further in the description of both fractions, each one will be discussed separately in the rest of the manuscript.

$

Fragmentation of the species detected in the insoluble fraction

In this section, the fragmentation of species presents in the methanol insoluble fraction is studied. Figure 3 displays the results obtained for one nominal mass (m/z 504) which was isolated monoisotopically and fragmented at different voltages. The Double Bond Equivalent (DBE) vs carbon number map is commonly used to visualize the diversity of compounds present in a complex sample or to observe particular fragmentation patterns [START_REF] Chacon-Patino | Advances in Asphaltene Petroleomics. Part 1: Asphaltenes Are Composed of Abundant Island and Archipelago Structural Motifs[END_REF], 2018a, b;[START_REF] Le Maître | Structural analysis of heavy oil fractions after hydrodenitrogenation by high-resolution tandem mass spectrometry and ion mobility spectrometry. Faraday Discussions[END_REF]. This metric is defined by equation 1 and represents the amount of unsaturation present in a molecule. In other words, the greater the DBE value, the more unsaturated the molecule.

(Equation 1) $

In addition, to highlight the degree of aromaticity of the fragment ions, polyaromatic hydrocarbon (planar limit) family has been added and is displayed by the red line [START_REF] Cho | Planar limit-assisted structural interpretation of saturates/ aromatics/resins/asphaltenes fractionated crude oil compounds observed by Fourier transform ion cyclotron resonance mass spectrometry[END_REF]. This family of compound is generally added to a DBE graph because it allows to compare the analysed sample with one of the most aromatic family containing only carbons and hydrogens atoms. This line is generated using benzene as starting point. The DBE and the number of carbons of successive linear additions of benzene to the initial point are then calculated. The resulting increase in DBE and number of carbons between two points is 3 and 4 respectively.

DBE = C atoms - H atoms 2 + N atoms 2 + 1
In Figure 3, each ion intensity is represented by the size of the circle and the amount of nitrogen contained in each species by a color code. Figure 3a plots the detected species present at m/z 504 with no collision energy. As mentioned before, many different ions at nominal masses m/z 504 are co-isolated due to the low resolution delivered by the quadrupole.

Two main ions series appear at higher and lower DBE values. These series seem to have a similar number of carbons but a strong difference on the aromaticity degree. The more aromatic one will be called series 1 and the other series 2. To illustrate this observation, putative structural formulas of compounds representative of the two major series were added in the Figure . These putative structures are based on hypotheses made in previous works [START_REF] Gautier | Influence of trace aromatics on the chemical growth mechanisms of Titan aerosol analogues[END_REF][START_REF] Maillard | Structural elucidation of soluble organic matter: Application to Titan's haze[END_REF][START_REF] Ruger | Structural Study of Analogues of Titan's Haze by Trapped Ion Mobility Coupled with a Fourier Transform Ion Cyclotron Mass Spectrometer[END_REF]. Fragments for the two series are observed.

!

Less fragments are produced in the series 1 in agreement with the fact that this series corresponds to more aromatic compounds and so, much more resistant to fragmentation. Due to the low resolution of the quadrupole used for mass selection, attributing fragments to one precursor is difficult regarding the number of isolated isobaric precursor ions. However, it can be observed that at 20 eV, the amount of DBE remains nearly constant for each series and mainly the amount of carbon is slightly decreasing (DBE value around 25 for the series 1 and 20 for the series 2). This is consistent with a dealkylation process, meaning that peripheral alkyl branches are broken first.

Results at 30 eV are reported in Figure 3c. For the series 2, fragment ions presenting low DBE values compared to the precursor ions are observed. The N/C ratio which was at 0.330 for precursor species is measured for the fragments at a value of 0.325 which is roughly the same. In the case of tholins -unlike petroleum molecules -nitrogen atoms are likely being removed as neutral losses that would not counted as part of these DBE vs. C# diagrams (vide infra) [START_REF] Le Maître | Structural analysis of heavy oil fractions after hydrodenitrogenation by high-resolution tandem mass spectrometry and ion mobility spectrometry. Faraday Discussions[END_REF]. Indeed, the amount of nitrogen is responsible of a part of the DBE number as showed by the equation 1. However, exchanging a nitrogen atom for a carbon one will not alter the DBE value but will shift the amount of C. This is why we added the nitrogen amount as a color code. The Figure 3c color code reveals a decrease of the amount of nitrogen with the apparition of fragments at lower DBE. This observation highlights the loss of nitrogen atoms during the fragmentation. In addition, the Figure S8 presents the corresponding van Krevelen diagrams showing the H/C vs N/C ratio in order to highlight the losses of nitrogen as well as the losses of DBE (through the increase of H/C ratio). For series 1, it can be observed that fragments remain around the precursor's region even at 40 eV and 50 eV. There is also no observation of nitrogen loss. At 60 eV, DBE, carbons and nitrogen losses starts to be observable. This means that the fragmentation of this series can be explained by three steps: In the first one, at low energy, peripheral bonds are broken, revealing in Figure 3 a dealkylation pattern with some losses of DBE and nitrogen. Then, the fragmentation remains stable with an increasing energy until a certain level (between 50 eV and 60 eV). Finally, as observed in the Figure 2f, at high energies, large losses of DBE, carbons and nitrogen are observed, meaning that stronger bonds, such as aromatics rings are broken. This fragmentation process goes in favour of the island model for the series 1.

For series 2, the fragmentation process is slightly different from the other one and can be explained as follows. The first step of the fragmentation, at low energy, involves the rupture of weak bonds. As mentioned, during this step, large amount of DBE, carbons and nitrogen are lost. Then, no new fragments are observed during the increase of the collision energy.

Finally, such as for the series 1, between 50 eV and 60 eV, the fragmentation occurs again with losses of DBE, carbon and nitrogen. Large neutral losses during the first step go into favour of an archipelago-like model. Links between cores are broken and then at higher energy, ring opening occurs.

In conclusion, Figure 4 presents two putative structures with the fragmentation scheme in agreement with all our observations for series 1 and series 2.

$

Fragmentation of the species detected in the soluble fraction

In this part, as in the section 3.1., the ions at m/z 504 were isolated and fragmented at different acceleration voltages in order to retrieve additional information about structures composing the soluble fraction. Results are illustrated by the Figure 5. In opposition with the insoluble fraction, only one series of compound is detected (Figure 5a); this series will be called series 3. As reported in previous work, the molecules of this fraction contain in average more hydrogen atoms than the insoluble fraction and present therefore a lower amount of unsaturations [START_REF] Maillard | Comparison of soluble and insoluble organic matter in analogues of Titan's aerosols[END_REF]. A hypothetical formula of one detected compound is described in Figure 5a.

At 5 eV (Figure 5b), many fragments are already observed. Detected species are located lower and more on the left on the map, meaning that large losses of DBE and #C are observed. Such fragmentation at this low energy means that weak bonds are broken.

Furthermore, the large losses of DBE reveal that the broken part of the precursor species contain DBE and aromatic rings because of the nonlinear intensities of the detected fragments (the most intense are highlighted by a red star). showing the H/C vs N/C ratio in order to highlight the losses of nitrogen as well as the losses of DBE (through the increase of H/C ratio).

!

As for the insoluble species, this fragmentation can be explained in two distinct steps. First, at low energy, weak bonds between aromatic moieties are broken, showing large losses of DBE, carbon and nitrogen. Then, at higher energies, these moieties are dealkylated and denitrogenated. This fragmentation pattern is characteristic of an archipelago.

Regarding binding energies, the lower possible energy corresponds to the energy of a N -N binding, at 1. Finally, both models, islands and archipelagos, seem to be present in the sample, with islands more located in the insoluble fraction and archipelagos located in both fractions.

Conclusions and discussions

In this study, tandem mass spectrometry was performed on analogues of Titan's haze. Due to the complexity of the analyzed sample, cartographies were used to facilitate the comprehension of the fragmentation patterns. Both insoluble and soluble fractions were studied. Fragmentation patterns were compared to island and archipelago models proposed in the literature. Island fragmentation was observed for the most aromatic family of the insoluble fraction. The second family was attributed to archipelagos because of the large losses of DBE observed at low collisional energies. Soluble structures were also attributed to archipelagos.

On main difference between these two molecules is the higher aromaticity of the Figure 6b (DBE 19.5) vs the Figure 6c (13.5). The more aromatic molecules are, the more they tend to form aggregates and precipitate [START_REF] Spiecker | Aggregation and solubility behavior of asphaltenes and their subfractions[END_REF]. As a summary, Figure 6 shows three hypotheses of structures present in the tholins sample.

This work confirms structural hypotheses proposed in previous works performed by ion mobility concerning the presence of bulky structures in the soluble molecules [START_REF] Maillard | Structural elucidation of soluble organic matter: Application to Titan's haze[END_REF][START_REF] Ruger | Structural Study of Analogues of Titan's Haze by Trapped Ion Mobility Coupled with a Fourier Transform Ion Cyclotron Mass Spectrometer[END_REF]. Such part of these structure were identified in several NMR studies [START_REF] Derenne | New insights into the structure and chemistry of Titan's tholins via 13C and 15N solid state nuclear magnetic resonance spectroscopy[END_REF]He and Smith, 2014a, b) and liquid-chromatography-mass spectrometry analyses [START_REF] Gautier | Development of HPLC-Orbitrap method for identification of N-bearing molecules in complex organic material relevant to planetary environments[END_REF]. It has also allowed to go further into the chemical characterisation of the tholins by studying the major part, the insoluble fraction. Retrieving such diversity of families for the compounds contained in Titan's tholins is an important information about Titan's haze. Firstly, such Nitrogenated-PAH structures are consistent with in situ measurements performed by the Cassini Huygens mission (Dinelli et al., 2013;López-Puertas et al., 2013). Then, regarding the recovered structures, soluble species are more fragile and much more reactive so they will react with their environment as a contrary to the insoluble ones which will deposit on the surface of the satellite.

Finally, in a previous work, tholins sample were analysed comparatively with FTICR and the LAB-Cosmorbitrap, an Orbitrap that is intended to be boarded in a space probe [START_REF] Selliez | High resolution mass spectrometry for future space missions: comparative analysis of complex organic matter with LAb-CosmOrbitrap and LDI-FTICR[END_REF]. Large differences were observed between each analysis: higher masses were detected by FTICR compared to LAB-Cosmorbitrap, but the ions detected in the 100-200 u mass range were found the same. Fragmentation was very likely present with the cosmorbitrap analysis, induced by the difference of power delivered by the ionization laser. It is interesting to see that the fragmentation of such a complex mixture leads to new fragmented molecules consistent with the smaller non-fragmented molecules contained in the sample. And thus, studying these species is representative of larger masses. The use of a laser with tunable

Figure 1 :Figure 2 :

 12 Figure 1: Fragmentation of the nominal mass 504 Da at 40 eV. (Top) Insoluble fraction, (Bottom) Soluble fraction.

Figure 3 :

 3 Figure 3: Nominal mass fragmentation of the m/z 504 insoluble displayed by DBE vs #C maps at a) 0 eV, b) 20 eV, c) 30 eV, d) 40e V, e) 50 eV and f) 60 eV collision energy, including some putative representative structures. The ions intensity is represented by the size of the circle and the amount of nitrogen contained in each species by the color code.

Figure 3d ,

 3d Figure 3d, 3e and 3f present the results obtained at 40 eV, 50 eV and 60 eV. The first

Figure 4 :

 4 Figure 4: Hypothetical structures of one detected species at m/z 504 with (a) Archipelago type, (b) island type

Figure 5 :

 5 Figure 5 : Nominal mass fragmentation of the m/z 504 soluble fraction displayed by DBE vs #C maps at (a) 0 eV, (b) 5 eV, (c) 10 eV, (d) 30 eV, (e) 40 eV, (f) 60 eV collision energy, including some putative representative structures. The ions intensity is represented by the size of the circle and the amount of nitrogen contained in each species by the color code.At other acceleration voltages, we observe no more losses of DBE. However, a dealkylation

  7 eV. The next one is the C -N binding with an energy of 3.192 eV. Imine bonds (C = N) have an energy of 7.856 eV: these kinds of bonds are not broken at this acceleration voltage. Thus, preferential encountered links between cores should be C -N bonds or N -N bonds in the soluble fraction. Several structures of the most intense detected species are given in the Figure 5f.

$Figure 6 :

 6 Figure 6 : Hypotheses of structures for (a) Insoluble fraction series 1, (b) Insoluble fraction series 2 and (c) Soluble fraction series 3

Figure S2 :Figure S3 :$Figure S4 :Figure S5 :Figure S6 :Figure S7 :Figure S8 :Figure S9 :

 S2S3S4S5S6S7S8S9 Figures S2d, S2e and S2f present the fragmentation of the insoluble fraction at 40 eV, 60 eV

Acknowledgments N.C. thanks the European Research Council for funding via the ERC PrimChem project (grant agreement No. 636829.). This work was supported, at COBRA Laboratory, by the European Regional Development Fund (ERDF) N° HN0001343, the European Union's Horizon 2020 Research Infrastructures program (Grant Agreement 731077), the Région Normandie, and the Laboratoire d'Excellence (LabEx) SynOrg (ANR-11-LABX-0029). Access to a CNRS FTICR research infrastructure (FR3624) is gratefully acknowledged.

Supporting Information

-15 and #C 7 -18. At 20 eV, the square is now located between DBE 5-11, and #C 6 -14.

The area given by the square also becomes smaller as the energy increases. A loss of DBE and carbon is observed meaning that functional groups containing double bond are broken for voltages lower than 20 eV.


Le Toan Duong
email: ltduong@laas.fr

Louise Travé-Massuyès
email: louise@laas.fr

Audine Subias
email: subias@laas.fr

Nathalie Barbosa
email: nathalie.barbosa.roa@continental-corporation.com

Assessing product quality from the production process health status

In the manufacturing industry, it is of prime importance to be able to retrieve the health status of production lines and to know how products navigate across operations. Products that are suspected to be faulty are deviated from the nominal path and inspected more closely. The fact that some products deviate from the nominal path and others fail at some check operations might indicate a risk for quality. This paper proposes a method to obtain the process model automatically, following the principles of the process mining field. The found model not only shows the product transitions from one operation to another, but also includes the time constraints bounding the "normal" time for each transition. From this model, products can be clustered according to the path they follow along the process. The clustering results for the product batch and the product itself are ultimately used to compute a quality index. The whole method is applied to a SMT PCB production line from Vitesco Technologies.

Introduction

In the Printed circuit boards (PCB) industry today, parts are manufactured continuously 7 days a week, 24 hours a day. In this paper, we present a quality index for produced parts based on data collected from the assembly processes. To do this, we applied different approaches of process mining to discover and represent the process model as well as to check the conformance of the product production path. Along the path, products are submitted to a series of operations, some of which are control processes that can verify a product as good or faulty .

The field of process mining is concerned with extracting useful information about process execution, by analysing event logs [Song et al., 2008]. Process discovery and conformance checking are the most important tasks in the process mining field. The former consists of finding a process model by analysing a set of sequences or traces extracted This project is supported by ANITI, the "Artificial and Natural Intelligence Toulouse Institute", through the French "Investing for the Future -PIA3" program under the Grant agreement n o ANR-19-PI3A-0004. from event log, while the later refers to the analysis of relation between the behaviour of a process described in a process model and event logs that have been recorded during the execution of the process [START_REF] Carmona | [END_REF] . Many process discovery algorithms were proposed, among them the α algorithm [START_REF] Song | Towards comprehensive support for organizational mining[END_REF], Heuristic Miner [Weijters andRibeiro, 2011][Weijters et al., 2006], and Inductive Miner [START_REF] Leemans | [END_REF]. The algorithm that we propose in this paper used Directly-Follows Graphs (DFGs) for process representation and frequencybased filtering to tackle complexity problem. Conformance checking is useful in several tasks, like performance analysis [Adriansyah and Buijs, 2012], detecting high-level deviations [START_REF] Adriansyah | [END_REF], and alignment-based precision measures [START_REF] Adriansyah | [END_REF]. Our conformance checking approach is original in the sense that it uses a decision tree with various criteria to compare real behaviour with the process model taking into account temporal constraints.

Figure 1, taken from [START_REF][END_REF], summarizes the process mining framework in a manufacturing process environment. In the data preparation step (step 1), we extracted raw data in form of text messages from MES databases and converted them to event log. Then, the event logs were preprocessed by checking missing values, filtering out redundant data and duplicated values (step 2). In the Process Mining and Analysis step (step 3), for our use case, we focused on process perspective that consists of discovering process model, conformance checking and other performance analysis. Finally, results and evaluation are presented in step 4. These tasks are presented in more detail all along the paper.

In our work, process discovery and conformance checking are ultimately used to sort products in each batch into different classes that can be associated to an individual penalty index based on the normal or faulty path. These indexes were decided by process experts and used to compute the quality score of product batches. The data that we analyzed comes from different machines and assembly lines producing a single family of products in a real PCB assembly process.

The paper is organized as follows. Section 2 describes our use case, the SMT PCB Assembly process. Section 3 presents some basic mathematical concepts, the process model generation method as well as related time constraints. Section 4 explains how product populations are clustered and the penalty indexes that are used to assess product quality. Section 5 presents the results that we have obtained on our use case. Finally, Section 6 concludes the work and Figure 1: Process mining Framework in a manufacturing environment [START_REF][END_REF] provides some perspective ideas.

Use case: a PCB Assembly process

The PCB assembly process is divided into two main phases (see Figure 2) :

Front End Assembly (FE): electronic components are placed and soldered onto the PCB using Surface Mount Technology (SMT). A schematic view of a SMT line is presented in Figure 2. The first operation is achieved by the Solder Paste Printing (SPP) that prints a solder paste layer on the surface of the PCB. This is followed by the Solder Paste Inspection (SPI) machine. Next, electrical components are mounted onto the surface of the PCB and pass through the reflow oven in order to be permanently attached to the PCB. The front end phase ends with an Automated Optical Inspection (AOI) that checks components position and solders quality. This whole process can be repeated for double-sided boards, in which case we denote FE1 and FE2 the two phases.

Back End Assembly (BE): connectors that communicate with peripherals are fixed to the PCB and the set is assembled in a housing for protection and thermal dissipation. Next, functional tests that verify the product operation are finally performed. Back end lines are suited to the specific needs of each product, and in many cases, employ a combination of human operators and specialized robots [Shaw, 2012]. In this section, we introduce several primary concepts used in process mining, then the notion of timed process model and how to construct it from event logs. The aim of a timed process model is, on the one hand, to represent the real process and, on the other hand, to represent time constraints that will be used for product's quality characterization.

Let us formally define the concept of event which relies on the concept of event type expressing the semantic label associated with the event. The set of all event types is denoted by E, E * is the set of all finite sequences over E.

Definition 1 (Event). An event is defined as a pair (e i , t i ), where e i ∈ E is an event type that identifies the event and t i is the event date. Time representation relies on the time point algebra and time is considered as a linearly ordered discrete set of instants whose resolution is sufficient to capture the process dynamics. Events generally come in streams forming sequences.

Definition 2 (Sequence). A sequence S ∈ E * × N is an ordered set of events denoted S = (e i , t i ) i∈N l = {(e i , t i } / e i ∈ E, i = 1, . . . , l, t i < t i+1 , i = 1, . . . , l -1}, where l is the dimension of the sequence S.

Given a set of process activities, events are useful to represent whether the beginning or the end of each activity. Event types provide an identifier for the activity and the date place them in an ordered sequence.

A process instance i.e. a product manufacturing is characterized by the sequence of activities that are executed, hence by a sequence of events qualified as trace. An event log gathers several traces.

Definition 3 (Trace).

A trace is a sequence of events σ ∈ E * × N executed for some process instance. The trace support E σ is given by the ordered set of event types present in the trace.

Definition 4 (EventLog). An event log L is a multi-set over E * , i.e., a trace can appear multiple times in an event log.

Definition 5. (Directly Follows Graph) A Directly Follows Graph, denoted as DF G is a pair (G, L) such that:

• G is a directed graph G = (V, E) with vertex set V
and edge set E.

• L is a set of labels, where l i,j is associated with the edge between vertex v i and vertex v j .

Definition 6. (Process model) A process model, denoted as P M is a DF G, where V is instantiated with the set of event types related to the process and the edges of E represent the precedence relation according to the traces of the process.

V includes two specific nodes, a source node V start and a well node V end . The edge labels in L can represent different information, such as transition counts, durations, etc.

In our real use case, V is the set of event types occurring in the traces of production process, E is the set of all possible transitions representing product state changes, and L contains information about the frequency of these state changes stored in the event log.

Figure 3a shows the DF G of our production process obtained from all event logs data collected in 2019. The graph was constructed with the Process Mining library developed in Python PM4Py [Berti et al., 2019]. In this graph, the event types associated to nodes are numeric identifiers. The edges refer to product state transitions, for example between operations. Additionally, the label on top of each edge, l i,j represents the number of products that have taken the corresponding transition. For example, there are 10258 product transitions between the node marked with the event type Operation 1 (the Laser Marking operation) and the node marked Operation 2 that corresponds to the Solder Paste Inspection Side 1. The constructed P M is a representation of all the transitions that have been undertaken by the products in 2019.

In the first instance, we used a simple algorithm to find the nominal process model. Before describing this, let us notice that a trace as defined in definition 3 corresponds to a path between V start and V end in P M . The P M shown in Figure 3a includes various such paths. In our algorithm, the nominal process model is constructed based on the path that most products follow. We call this path the nominal path P * . Products of the same family follow the same path if nothing wrong happens along the production chain. The nominal path P * is computed by the following formula:

P * = argmax P∈P M f req(P) (1)
where f req(P) returns the number of traces, i.e. process instances, that follow the path P.

In Figure 3, the nominal path can be retrieved by following purple color operation identifiers.

Time constraint extraction

Time constraints are quite indicative of process problems and this is why we are interested in retrieving what would be a normal time between operations. To do so, we opted for an interval time label that represents the most frequent time support among products.

Let us consider an event log L that gathers a set of traces T representing different instances of the same process P M . Considering two adjacent edges v i and v j corresponding to two event types e i and e j belonging to the support of all the traces in the subset T k ⊆ T , we assume that in any trace σ ∈ T k , t j > t i , then the label l ij is determined as follows:

l ij = [t - i,j , t + i,j ] (2)
where:

t - i,j = min ei,ej ∈Eσ,σ∈T k (t j -t i ), t + i,j = max ei,ej ∈Eσ,σ∈T k (t j -t i ).

The time intervals defined in equation ( 2) represent the min and max bounds on the elapsed time between two consecutive product state changes in the process.

Timed process model

In the previous paragraph, we presented the time constraint extraction method. The process model labelled with these time intervals is called the Timed process model. Its definition is given as follows: Definition 7. (Timed process model) A timed process model (t-PM) is a process model P M for which edges are labelled according to the time constraints given by equation (2).

A process instance, or a trace σ = (e 1 , t 1 ), (e 2 , t 2 ), ..., (e n , t n ) is said to satisfy the timed process model if:

1. The sequence of event types e 1 , e 2 , ..., e n can be replayed in the graph G of t-PM.

2. All event pairs (e i , t i ) and (e j , t j ) satisfy the time con-

straint [t - i,j , t + i,j ], i.e. t j -t i ∈ [t - i,j , t + i,j ].

Characterizing production quality

The timed process model obtained in 3.2 provides a real model of the process but it is also a support to better characterize the production quality i.e. the quality of final products. The idea is to sort the product populations (i.e the set of traces related to the product) and to associate each population with an individual penalty index depending on the faulty operations that are encountered along the path.

For this, we developed a classification algorithm to cluster products into different populations or classes based on their production path (i.e. process instance) .

Sorting populations by conformance checking

Populations are defined by constructing a decision tree for the classification [START_REF] Myles | [END_REF]. The primary advantage of using a decision tree is that it is easy to follow and understand. Decision trees have four main parts: a root node, internal nodes, leaf nodes and branches. The root node is the starting point of the tree, and both root and internal nodes contain a test or criteria on an attribute. Each branch represents the answer to the test, and each leaf node represents a class label. Let Y = {y 1 , y 2 , ..., y m } be a set of m class labels for all process instances. The aim of the partition issued from the decision tree is to gather homogeneous process instances together. Classes are then associated with individual penalty indexes that characterize their property.

For a first study, classes and individual penalty indexes are defined by process experts. The partition of products into these classes is done by checking the conformance of the corresponding trace with the timed process model. Several criteria are used as the consistency with the time constraints, the order of operations, the presence or absence of some important operations. A description of these criteria or classification rules are presented by a decision tree given in Figure 4. The light blue boxes represent internal nodes that contain split rules. The gray boxes are leaf nodes with corresponding class labels. Each class is associated with a score or individual penalty index (red number).

Penalty index of products

As mentioned previously, each product is associated with a so-called individual penalty index, a real number in [0, 10] depending on which class it belongs to. In our context, this index is not sufficient as we assume that the quality of a product depends mainly on its production path and on its production batch. A batch is defined as a sub-set of products that are produced continuously and consecutively. Products within a batch are assembled under almost the same conditions and configuration. Then we can extract batches from the event logs by analysing the regularity of products in the production line, i.e. by identifying inactive period. An inactive period is a pre-defined time interval in which no product passes due to configuration changes in production.

We formally define batches as follows. Let B = {b j , j = 1, ..., |B|} be a set of |B| batches, where b j = {(x j i , y j i ), i = 1, ..., |b j |}, x j i is the i-th product in the batch j, y j i ∈ Y is the associated class label and |b j | the batch size, i.e. the number of products that the batch contains. We denote p(x j i ) ∈ [0, 10], the individual penalty index of product x j i . For example, a product x j i has penalty p(x j i ) = 0 if it belongs to class nominal (see Figure 4). This means that this product follows the nominal path without fail operations and respects the temporal constraints between operations.

The penalty index of a product in a batch, denoted p(x j i ), is defined as a combination of its individual penalty index p(x j i ) and the penalty index of its batch p(b j ).

• Penalty index of the batch b j :

p(b j ) = 1 |b j | |bj | i=1 p(x j i ) ∈ [0, 10] (3)
• Penalty index of i-th product in batch b j :

p(x j i ) = λ× p(x j i )+(1-λ)×p(b j ) λ ∈ [0, 1] (4
) Generally products go through several phases from separate components to assembly and packaging. Due to the heterogeneity in configuration of different phases, the partition of products into batches in each phase is also different. Hence, we compute the penalty index of product after each phase and the penalty index for final product at the end of process is defined as the weighted average of them. Let X = {x i , i = 1, ..., n} the set of all products. In each phase, a product x i belongs to a batch b j and the associated penalty index is computed by formula (4). Let I i the set of indexes j such that b j contains x i over phases:

I i = {j ∈ N|x i ∈ b j }.
The final penalty index of product x i noted p f (x i ) is defined as:

• Final penalty index of product x i :

p f (x i ) = j∈Ii α j × p(x j i ) , j∈Ii α j = 1 (5)
Next section presents the application of our proposal.

5 Quality evaluation of the PCB production line

The proposed approach has been illustrated using data set from a Vitesco Technologies (VT) plant, an automotive company specialized in drivetrain technology for all types of vehicles. At VT plants, all products are tracked by a unique ID in form of a data matrix marked on the PCB. This matrix is read each time the PCB goes through an operation. Furthermore, the manufacturing processes are tracked, traced and controlled during the production by the Manufacturing Execution Systems (MES).

Data description

Raw data used in this use case are generated in real-time or near-real-time by the MES. In fact, machines generate data in form of messages that contain information about production process. Figure 5 presents an example of decoded messages from Vitesco Technologies cloud storage service. Each message is generated from every single operation through which a product (PCB) is performed. Hence, messages contain features related to machine, operation and product. We introduce below a list of common features (Figure 5):

• Type of message (red): There are mainly two types of message, informative message and control message. While informative messages notify that the PCB has entered or exited some operations, control messages give us detailed information about the quality of product. Hence, these messages have a feature of sanction that could be Pass (P) or Fail (F). An example of control messages is the one generated from the AOI machine.

• Machine host name/Machine_ID (pink): The identification of the machine or computer that performed an operation in the PCB.

• Optional description (violet): comments about the performed operation.

• Family (green): Product family that is being produced.

• Serial Number/Board_ID (orange): The identification number of the product.

• Operation Code/Operation_ID (blue): The identification number of the operation being performed.

• Sanction (brown): (For control message) The sanction given by the operation (F/P), the "F" means the operation has failed meanwhile the "P" letter means the operation has been performed successfully. • Timestamp (purple): The date and time an operation was performed. The aim of our study is to analyse the path of products and their transition time through event logs. Hence, we considered only features that allow us to transform messages into event logs. These are Type of message, Serial Number, Operation Code, Sanction and Timestamp. The experimentation was carried out on data related to a specific product family collected during 2019. A pre-processing stage was needed in order to removing duplicated messages, filtering bad format messages, gathering and sorting messages related to the same product and transform data into event logs.

The final data set corresponds to over 10000 single electronic boards. We analyzed that over 98, 5% (9902 products) of them are good products i.e. successfully produced and delivered to clients.

During production process, events tagged with a fail notification indicate that the corresponding operation has not been performed as supposed. These fail sanctions can come from a real defect or an equipment measurement error. Diagnosis need to be performed on these products in order to reveal the real defect cause. Figure 6 represents the distribution per product of the number of fail events appearing during production process for good and bad products. There are more fail events generated in bad products than in good products, which allows us to take into account this in our product quality characterizing approach. Moreover, the data analysis pointed out that most products were manufactured within 2 days, as expected, but some of them were in production process longer than that.

Quality evaluation of electronic boards

The characterization of the product quality was performed using the penalty index score from a timed process model (see Sections 3.2 and 4).

In this use case, for the time constraint extraction (see Section 3.1) instead of using the min and max value to define the bounds of time constraints, we used a statistical value (5 th and 95 th percentile) to exclude extreme and outlier values. The process model for this product family is quite simple (see Figure 3b). Operations are performed successively, one after the other. There is no deviation and there are not operations that perform at the same time (problem of concurrency). The next step is to classify products by comparing their production path with the timed process model. Split rules based on the conformance of product's path with the timed process model and classes are presented in the decision tree in Figure 4. With a process expert, we also associated to each class an individual penalty index between 0 an 10 which characterizes the conformance level. Note that we excluded two classes half begin and half end because paths in these classes were incomplete. Once products are classified, we computed penalty index for batches and for final products based on equations (3) and (5). As a reminder, a batch is a sub-set of products that are produced continuously and consecutively. Batches are extracted from event log and the result shows that the number of products in batch is varied. Figure 7 presents the relation between penalty index and batch size in the three phases F E1, F E2 and BE of production process. Most of batches have a small penalty index, between 0 and 1 over three phases. This result shows that the majority of batches respect the timed process model. It also shows that there are no perfect batch which has a penalty index of 0. This means that there is no batch in which all products pass through the production line without any failure or deviation and within the accepted time interval. Additionally, Figure 7 shows that batches with a high penalty index (orange points) have small size. Without an in-depth investigation, this correlation is explainable. Indeed, the fact that these batches are small could be due to process interruption or changes in configuration after a sequence of products with bad behaviour. Hence, the penalty index for them is pretty high.

Figure 8 shows the distribution of penalty indexes for products in three phases of production process. As expected, most products have a penalty index in [0, 1] which indicates a good compliance with process model. Moreover, we can see that penalty indexes of products in F E1 phase have less variation than in F E2 and BE phase. These results open the door for further examination and improvement actions. Finally, we present below the final penalty index for products as defined in equation ( 5) (see Figure 9). As expected from previous results most of products have small penalty index. In association with process experts detailed analysis should be performed on products with high penalty index. The results obtained give us relevant information about product behaviours during assembly process. These results were sent to production process experts for further analysis and process optimisation.

Conclusion and Future work

In this paper, we presented a log-based framework for health status characterization of a production process based on a quality index. Process mining techniques are used to build a timed process model from event logs and to check the conformance of product's paths. An experiment was conducted on a large dataset from the automotive manufacturing company Vitesco Technologies.

The experiment demonstrated that the proposed framework works well. The results showed to be effective for domain experts to have a deeper knowledge of real production process. The proposed indicator evaluates the behaviour of products during their production time. This indicator provides an overview of products circulation and reveals abnormal ones. For future work, we need to fine-tune the model parameters under the supervision of process experts. The products behaviour information found does not seem to be enough to assess their quality. We expect, for the next study, to have information on product quality that we could integrate into our approach to build a learning model for classification and optimizing parameters. The proposed frame-work is generic and could be applied for various problems in process monitoring and optimisation.

Figure 2 :

 2 Figure 2: A schematic view of PCB assembly process

Figure 3 :

 3 Figure 3: Process model (a) Directly-Follows Graph. (b) Timed process model.

Figure 4 :

 4 Figure 4: Decision Tree for product classification.

Figure 5 :

 5 Figure 5: Snapshot of messages file from MES. The data was encoded for preserving confidentiality.

Figure 6 :

 6 Figure 6: Distribution of Fail events per product. Left: Bad products, Right: Good products.

Figure 7 :

 7 Figure 7: Batch penalty indexes and batch sizes for each production phase

Figure 8 :

 8 Figure 8: Product penalty indexes for each production phase

Figure 9 :

 9 Figure 9: Final penalty indexes for all products.