Thomas Provent

Audrey Mauvy

Rémi Manczak #

Sofiane Saada

Claire Dalmay

Barbara Bessette

Fabrice Lalloué
email: 7fabrice.lalloue@unilim.fr

Arnaud Pothier
email: 8arnaud.pothier@xlim.fr2audrey.mauvy

A High Frequency Dielectrophoresis Cytometer for Continuous Flow Biological Cells Refinement

Keywords: UHF-Dielectrophoresis, microfluidics, biological cells, label-free sorting, crossover frequency

This paper presents a microfluidic radiofrequency device operating at a few hundred MHz, which is able to sort biological cells. It uses a non-invasive and label-free technic based on intracellular dielectric specificities of biological cells. The sorting principle relies on a dynamic dielectrophoresis (DEP) deviation resulting from the interaction between a high frequency electric signal and the cell cytoplasm content. Driven in a microfluidic channel by a continuous flow, cells are individually deflected from their primary trajectories after having entered a non-uniform electric field generated by a microelectrode system. Designed with different slopes, these electrodes allow a selective guiding of cells to different outlets depending on the dielectrophoresis deviation efficiency. To allow a successful cell sorting, the intensity of deviation forces acting on cells is modulated according to the particle speed, the dielectrophoresis signal frequency and the electrode slope angles related to the Clausius-Mossotti factor of each cell. As proof of concept, experiments with cells from glioblastoma line were carried out, using different DEP signal frequencies to highlight system ability to sort cells from heterogeneous basal population into less disparate sub-populations.

K eywords-

I. INTRODUCTION

Past years, dielectrophoresis (DEP) technics have proved their great efficiency in sorting biological cells [START_REF] Pethig | Dielectrophoresis: Theory, Methodology and Biological Applications[END_REF], based on their ability to measure the dielectric properties of each cells. Characterizing and sorting cells is a key point in cellular biology research as well as in the biomedical field to achieve therapeutic assessment.

A great advantage of the DEP principle is that it is a contactless and label-free method. When cells, set in suspension, are subjected to a non-uniform electric field (E field), a dielectrophoresis force is induced, resulting in cell's motion. This motion can be an attractive, achieved via a positive DEP force (pDEP), or repulsive though a negative DEP force (nDEP). Applications of such forces to sort cells based on their size or their dielectric property differences were hence largely demonstrated in the literature [START_REF] Salimi | Dielectric model for Chinese hamster ovary cells obtained by dielectrophoresis cytometry[END_REF]- [START_REF] Dash | Dielectrophoretic separation of micron and submicron particles: A review[END_REF]. However, most of the previous works implies DEP electromanipulation in the range of 10 kHz to 10 MHz for which the difference of membrane dielectric property between cells is exploited. In this paper, we use Ultra-High Frequencies (UHF), i.e. several hundreds of MHz, as DEP signal frequency. High frequency signals are able to bypass the cell membrane and penetrate inside the cells to probe the intracellular dielectric properties of their cytoplasm [START_REF] Salimi | Dielectric model for Chinese hamster ovary cells obtained by dielectrophoresis cytometry[END_REF]. In the present study, a continuous flow cell cytometer has been developed for exploiting such DEP signal sorting capabilities (Fig. 1). Using a flow-focusing principle, this microfluidic device aims to focus flowing cells suspended in a low conductive osmotic medium to a sorting area where a specifically designed electrode system has been implemented and is biased with a UHF-DEP signal. Polarized by the generated E field, cells can be deflected to different outlets related to the intensity of the resulting DEP force (FDEP) applied to them. This FDEP is modulated as a function of the particle initial speed, the DEP signal frequency and the electrode slope angles are related to the investigated cell intracellular dielectric properties.

Along the section II of this paper, the DEP phenomenon and the cytometer sorting principle are presented. We will see how cell trajectories can be adjusted as a function of the intensity of the FDEP they are submitted. The section III presents sorting experiments where heterogeneous population of glioblastoma cells was successfully refined in four subpopulations, highlighting the importance of setting properly the DEP frequency choice during the sorting process. As previously mentioned, cells exposed to a non-uniform electric field become polarized and are subject to dielectrophoresis forces which can induce a course change during their travel in the cytometer main microchannel. This phenomenon is exploited here and we will see that it can be modulated as function as the dispersion of dielectric properties appearing in a cell population.

II. PRINCIPLE OF UHF-DEP SORTING AND DESIGN OF MICROELECTRODE

A. How Exploiting Cell Dielectric Specificities

Actually, the intensity of this FDEP force can be estimated using equation (1) [START_REF] Dash | Dielectrophoretic separation of micron and submicron particles: A review[END_REF]. Where Ɛ is the relative permittivity of the cell suspension medium, fCM and r are respectively, the Clausius-Mossotti factor and the radius of the cell, and ∇|Erms| is the gradient of the electric field, which relies both on the applied DEP signal potential and the electrode geometry.

= 2 Ɛ [ ] | |². (1)
The Clausius-Mossotti factor (CMF) relies on the difference of complex dielectric permittivity between the particle (Ɛ * ) and the medium in which it is immerged (Ɛ * ) as shows (2):

= Ɛ * Ɛ * Ɛ * Ɛ * . ( 2
)
This factor modulates the DEP force intensity and imposes its repulsive (nDEP) or attractive (pDEP) behaviour. It can have different values as function of the dielectric properties of investigated cells. It depends also on the selected frequency of the DEP signal applied on the microelectrode system. A frequency that can be easily adjusted in order to look for appropriate sorting conditions to achieve the targeted cell separation.

Working with UHF frequencies is actually relevant since such signals are able to probe the internal dielectric properties of the cell cytoplasm whereas conventionally used kHz frequency range allows electromanipulation mostly based on the difference of cell size and membrane dielectric properties. With hundreds of MHz, the sorting selectivity is hence expected to be more efficient and may reflect faithfully the biological heterogeneity occurring in a cell population.

Usually, derived cells form patients present a large heterogeneity reflecting the natural cell diversity occurring in lining tissues or organs. Such disparities are sometime difficult to handle, especially to interpret properly experiment's results. Such heterogeneity related issue also occurs for commonly used patient derived cell lines as the one (U87-MG) which was used in this study. Actually, it was previously shown [START_REF] Manczak | Discrimination of Glioblastoma Cancer Stem Cells by measuring their UHF-Dielectrophoresis Crossover Frequency[END_REF]- [START_REF] Manczak | HF-Dielectrophoresis Crossover Frequency as a New Marker for Discrimination of Glioblastoma Undifferentiated Cells[END_REF] that glioblastoma cultures result in a wide diversity of cell crossover frequencies (CF). This diversity is illustrated for U87-MG line by the Fig. 2 box plot. Each dot on the box plot figure represents a measured CF for an individual cell. The measured range of CF is indeed a consequence of the cell heterogeneity occurring for such cell line. One may notice that half of the U87-MG cell population presents CF ranging between 90MHz and 160MHz (red and blue arrows of the box plot quartiles) with a median value of 110MHz (green arrow). The two others quarters of the population show CF that spread from 30MHz to 90MHz and from 160MHz to 215MHz. Such dispersive characteristic trend is consistent and has been reported for other cell types [6]- [START_REF] Casciati | Human Medulloblastoma Cell Lines: Investigating on Cancer Stem Cell-Like Phenotype[END_REF].

One may understand than the mentioned crossover frequency in fact matches with the conditions for which the cell passes from a positive to a negative DEP behaviour. This specific frequency is hence characteristic of the cell own dielectric properties since it implies that its CMF is inevitably null at the crossover. The heterogeneity of the investigated cell population also translates in a dispersion of CMF values at frequency higher than the range of CF. The Fig. 2 plot this expected diversity in CMF value varying with the frequency.

Hence, as function as the chosen frequency to be set for the cytometer, some CMF sub-ranges can match with several refined cell sub-fractions of the whole population. Subpopulations of separated cells will show much more homogeneous dielectric properties.

The proposed cytometer system aims to sort cells based on their CMF values, using as discrimination factor the diversity of intensity of DEP force acting on each cell during the sorting process. Consequently, it can be used to separate and refine the whole cell population into four sub-populations expecting to regroup cells with similar dielectric specificities. As in the fictitious example given in the Fig. 2, considering a 300MHz sorting frequency, it can be envisioned to potentially split at equal share (~25%) the glioblastoma cell suspension into four distinct sub-populations. To collect such refined subpopulations, appropriated electrode system geometry should be used, in order to selectively drive separated cell groups to a different device outlet. We will introduce now how it was designed. The implemented microfluidic system consists of two inlets (on the right in Fig. 1): one is used to inject the cell suspension to be sorted whereas the wider one has been implemented to inject an osmotic buffer solution alone. Tuning the flow rate difference between these two inlets allows mastering the cell suspension initial trajectory and focussing them along the microfluidic channel edge in order to focus cells above the upper part of the microelectrode. At the other device end, four outlet channels have been implemented to collect sorted cells. In between, the sorting zone consists of a 780µm large microfluidic channel in which a three sections gold electrode system is patterned at the bottom of the channel. Each section has a different angle, respectively 35°, 45° and 60°, related to the initial trajectory of the flowing cells. The cell population to be refined is injected in the device from the top inlet. The cell flow is guided in a narrow strip of the microchannel until the 35° slope electrode zone. Once reaching the vicinity of the electrode, the cells enter under the E field influence. Only the cells that are subject to low FDEP intensity will continue their way to end up in the outlet n°1 (blue trajectory in Fig. 3); other cells will be repelled and deflected laterally. Due to the continuous flow taking place in the microchannel, their new trajectory follows the electrode until reaching the second electrode slope part. Because of the angle change, i.e. 45° vs 35°, efficiency of DEP deflection suddenly decreases. Consequently, part of the cells previously deflected, now can cross the repellent E field area and end up their way in outlet n°2 (green trajectory in Fig. 3), others will be deflected again. The same phenomenon occurs with the following change of angle (45° to 60°) where, cells with insufficient DEP repulsion will pass through the E field and continue to the outlet n°3 (yellow trajectory in Fig. 3) whereas those subject to the strongest FDEP influence will be driven alongside the electrode to the outlet n°4 (orange trajectory in Fig. 3).

Hence, thanks to this successive angle change approach, a proper cell separation on the base of their difference of CMF can be achieved. For the proposed electrode system design, the selection of implemented electrode slopes has been done based on multiphysics fluidic and electrostatic simulations and should be suitable for a wide range of sorting requirements.

III. EXPERIENCE SET UP AND RESULTS

A. Material Preparation

In order to demonstrate the capabilities of the developed sorting system, experiments on U87-MG glioblastoma cell line culture have been led. This choice was mainly motivated by the substantial biological heterogeneity presented in such cell population [START_REF] Oraiopoulou | Integrating in vitro experiments with in silico approaches for Glioblastoma invasion[END_REF]. Associated to this high biological heterogeneity, various dielectric properties [START_REF] Manczak | HF-Dielectrophoresis Crossover Frequency as a New Marker for Discrimination of Glioblastoma Undifferentiated Cells[END_REF] can be expressed, making this cell population a good candidate to demonstrate the refinement capabilities of the developed system. The human glioblastoma cell line U87-MG was purchased from the American Type Culture Collection (ATCC, Manassas, VA, USA). They were grown during 5 days in DMEM medium, supplemented by 10% SVF, 1% PEN / STREP antibiotic, 1% MEM Non-essential amino acids (Gibco) and 1% Sodium Bicarbonate (Gibco) at 37°C in a humidified 5% CO2-95% air incubator before been seeded in appropriate flasks at 5×105 viable cells/ml . Cells are detached with 3mL of trypsin during 5min at 37°C. Then, after two successive centrifuge washes, cells were suspended in an ion free osmotic medium osmotic sucrose based medium (pH: 7.4, conductivity: 38mS/m) conventionally used for DEP experiments. To maintain cells alive during experiment, the medium osmolality has been set at 304mOsm by adjusting the sucrose concentration in pH buffered deionized water. Finally, the cell concentration in the suspension to be injected in the cytometer has been adjusted with an appropriate dilution factor to limit cell-clustering phenomenon inside the cytometer and decrease risk of errors during cell counting sequences.

Used cytometer prototype was fabricated on high resistivity silicon wafer, using a 2μm thick gold layer to define the microelectrode geometry. The microfluidic channel has been patterned using 50µm thick negative epoxy based dry film: DF-1050 from EMS, laminated on the top of micro-electrodes and defined by standard UV photolithography process. A removable PDMS cover is stacked and mechanically maintained on the top of DF film, hence forming at the end a 35-45μm high microchannel network modulated as function the pressure applied on the PDMS piece.

B. Experimental Setup

For the present experiments, the cell suspension was injected at constant flow rate into the micro-channels and the cytometer microelectrodes was biased with a constant magnitude DEP signal. Only the signal frequency was tuned. Cell trajectories of such DEP signal have been monitored and recorded using an image capture system (CV-X vision sensor from Keyence). It allows an automatic tracking and a real time counting of particles during experiments. It has been configured to count the number of cells going out the cytometer from each outlet during a defined acquisition time.

B. Cytometer Sorting Principle -Microelectrode Design

flow focussing ensures that cells are well directed at the beginning of the 35° angle section of the microelectrode (Fig. 3). Then, a high frequency DEP signal is applied to the electrode system, for which most of the cell population is expected to be strongly repelled by the E field. The magnitude of the signal is chosen to ensure that a large proportion of the cell population is fully deflected and goes out though the outlet n°4. The effective cell separation is recorded and the number of cells coming out though each outlet counted.

Next, same type of experiments is done decreasing the DEP signal frequency gradually. As presented in Fig. 2 for an increasing number of cells, decreasing frequency should imply a decrease of their CMF. Hence, it also leads to a decrease of the resulting intensity of the applied deviation force on each cell passing though the cytometer. Therefore, as function of the frequency set, the whole cell population can be split in different way between the four outlets and spread in fractions with a related cell proportion that can be adjusted.

IV. RESULTS

As illustration of the cytometer sorting capability, the results of three cell counting performed at respectively 700, 350 and 250MHz are summarized in Fig. 4. As expected, the sorting frequency affects the proportion of cells counted at each outlet. When a high frequency is set, cells are submitted to a strong repulsion from the E field, as one see in Fig. 4, where 83% of U87 cells are directed to the outlets n°3 and n°4 at 700 MHz (with 55% exclusively in outlet n°4). Only few cells (less than 7%) are escaping though the outlet 1. To the contrary, at 250MHz, the number of cells going out from outlet 1 strongly increases reaching 40%, which testifies that for a representative part of the population the CMF decreases significantly. In the meantime, the number of cells driven to outlet 4 drops to 15%, which is consistent. The relative fractions of cells that coming out though outlet 2 or 3 are more complex to predict, since they really rely on the effective distribution of dielectric properties present in the investigated cell population. Objectively, there is no biologic or statistically supported reason that such population present a linear continuum of CMF values instead of a Gaussian or even a more randomized distribution.

The cell spread observed for 300MHz sorting conditions is quite interesting, since it illustrates that some configuration can be found for which an almost balanced split of the population between the four outlets is achievable. Above the three mentioned examples, others performed experiments have demonstrated us the capability of this cytometer to split a heterogeneous population in four cell subpopulations with a stronger dielectric properties similarities. Hence, appropriately adjusting the working frequency might also allow separating and isolating some particular cells with some specific features: for example cells with stronger aggressiveness potential. Future work will aim to increase the sample collection efficiency requires to perform dedicated biological analyses for each subpopulation collected at the outlets; in order to see if some correlations between specific CMF ranges and biological specificities can be established. V. CONCLUSION This article presents a continuous flow UHF DEP lab-onchip cytometer concept that exploits the intracellular dielectric specificities of biological cells to refine them into subpopulations with more homogenous dielectric properties. Indeed, the proposed design exploits a modulation of the repulsive DEP force intensity acting on cells during their flow through the system. It results in a discriminating trajectory change as function as difference between their cytoplasm dielectric properties. Hence, the presented work illustrates the radio frequency capabilities to perform in a full label-free way a proper population refinement and purification starting from a heterogeneous native cell culture. Led experiments especially highlight the importance of the chosen RF signal frequency to perform such cell sorting, which requires to be properly selected in order to achieve the targeted cell refinement. To initiate the experiments, the fluidics conditions are set up to ensure a cell speed flow of 1.5mm/s in the sorting area. The

Fig. 1 .

 1 Fig. 1. Photograph of the main DEP cytometer sorting area. The implemented microelectrode system includes 3 sections, each presenting a different slope to separate and guide the cells to 4 different outlets.

Fig. 2 .

 2 Fig. 2. Typical expected range of CMF for a heterogeneous cell population as function as the working frequency. Box plot representation of the measured crossover frequencies dispersion for glioblastoma U87-MG cells demonstrate the heterogeneity of dielectric properties for such cell culture. Setting properly the sorting frequency may allow to achieve an efficient whole population refinement in sub-groups of cells with more homogenous properties.

Fig. 3 .

 3 Fig. 3. Stack of images extracted from a 240 seconds sequence of cell sorting for which the applied DEP frequency has been tuned from 250MHz to 500MHz: Illustration of the microelectrodes influence on cell trajectories and resulting cell spread at the cytometer outlets.

Fig. 4 .

 4 Fig. 4. Distribution of collected cells at each sorting system outlets as function the selected sorting DEP signal frequency.

ACKNOWLEDGMENT

This project has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement No 737164.


Majedaldein Almahasneh

Adeline Paiement

Xianghua Xie

Jean Aboudarham

Active region detection in multi-spectral solar images

Keywords: Joint Analysis, Solar Images, Active Regions, Multi-spectral Images

) and a state-of-the-art deep learning method (Faster RCNN) and show enhanced performances in detecting ARs jointly from multiple bands.

INTRODUCTION

Active regions (ARs) detection is essential in studying solar behaviours and space weather. The solar atmosphere is monitored on multiple wavelengths, as seen in Fig. 1. However, unlike traditional multi-spectral scenarios such as Earth imaging, e.g. [START_REF] Wagner | Multispectral pedestrian detection using deep fusion convolutional neural networks[END_REF][START_REF] Ishii | Detection by classification of buildings in multispectral satellite imagery[END_REF], where multiple imaging bands reveal different aspects (e.g. composition) of a same scene, different bands image the solar atmosphere at different temperatures, which correspond to different altitudes [START_REF] Revathy | Fractal-based fuzzy technique for detection of active regions from solar images[END_REF]. Therefore, imaging the sun using different wavelengths shows different 2D cuts of the 3D objects that span the solar atmosphere. This makes handling the multi-spectral nature of the data not straightforward. Moreover, the variety of shapes and brightness, and fuzzy boundaries, of ARs also introduce a high complexity in precisely localising them.

Very few solutions were presented to the AR detection problem. Most of these methods exploited single image bands only. [START_REF] Benkhalil | Active region detection and verification with the solar feature catalogue[END_REF] proposed a method for single-band images from Paris-Meudon Spectroheliograph (PM/SH) and SOHO/EIT. In [START_REF] Revathy | Fractal-based fuzzy technique for detection of active regions from solar images[END_REF], ARs were segmented from a single band at a time, which the authors justify by the fact that they each provide information from a different solar altitude, and they showed how the area of ARs differs between the different bands. While we also aim at getting specialised results for each image band, we argue that inter-dependencies exist between bands, which can be exploited for increased robustness. The SPOCA method [START_REF] Verbeeck | The SPoCA-suite: Software for extraction, characterization, and tracking of active regions and coronal holes on EUV images[END_REF], used in the Heliophysics Feature Catalogue (HFC)1 , segments ARs and coronal holes from SOHO/EIT 171 Å and 195 Å combined images. These two bands image overlapping (but different) regions of the solar atmosphere. SPOCA considers that they should yield identical detections. This approximation may result in a bad analysis of at least one of these bands. We provide separate but related results for these bands. We also exploit more bands for richer information on the solar atmosphere, with separate results for each band.

SPOCA's segmentation is based on clustering, with Fuzzy and Possibilistic C-means followed by morphological operations. The method of [START_REF] Benkhalil | Active region detection and verification with the solar feature catalogue[END_REF] uses local thresholding and morphological operations followed by region growing. The method was evaluated against manual detections (synoptic maps) produced at PM and National Oceanic and Atmospheric Administration (NOAA), and detected similar numbers of ARs as PM, and ∼ 50% more than NOAA. In [START_REF] Revathy | Fractal-based fuzzy technique for detection of active regions from solar images[END_REF], ARs were segmented by computing the pixel-wise fractal dimension (a measure of non-linear growth that reflects the degree of irregularity over multiple scales) in a convolutional fashion, and feeding the resulting feature map to a Fuzzy C-means algorithm. Overall, these methods, mainly based on clustering and morphological operations, are very pre-and postprocessing dependant. This makes them difficult to adapt to new image domains. We address this limitation using deep learning (DL).

Deep learning methods generally aim at analysing 2D images or dense 3D volumes, while the sparse 3D nature of the solar imaging data requires designing a specialised DL framework. Furthermore, multispectral images are commonly treated in a similar manner to RGB images, by stacking different bands into composite multi-channel images, extracting a common feature map, and producing a single detection result for the composite image, e.g. [START_REF] Mohajerani | Cloud-Net: An endto-end cloud detection algorithm for Landsat 8 imagery[END_REF][START_REF] Ishii | Detection by classification of buildings in multispectral satellite imagery[END_REF][START_REF] Guo | Deep learning-based image segmentation on multimodal medical imaging[END_REF]. This multi-channel strategy is ill-suited to the solar imaging scenario, since different images show different scenes and should have their own detection results.

In [START_REF] Wagner | Multispectral pedestrian detection using deep fusion convolutional neural networks[END_REF], a feature-fusion approach was proposed where HOG features extracted separately from the RGB+thermal images were concatenated before performing the final analysis by a fully connected layer. This strategy obtained better results than the previously mentioned image-level fusion. Authors discussed that the network may optimise the learned features for each band. Moreover, they reckon that small misalignments may be overcome as spatial information gets less relevant in late network stages. This may be an advantage in our case of images showing different parts of a scene.

However, when comparing image-level and feature-level fusion, [START_REF] Guo | Deep learning-based image segmentation on multimodal medical imaging[END_REF] found on the contrary that image fusion worked best when segmenting soft tissue sarcomas in multi-modal medical images. These different results suggest that there is no universal best fusion strategy, and it needs to be adapted to each case. In our detection scenario, we investigate the best stages where to apply fusion.

Another feature-fusion strategy was used in [START_REF] Jarolim | Multi-channel coronal hole detection with a CNN[END_REF] to segment coronal holes from 7 SDO bands and a magnetogram. The method relies on training a CNN to segment coronal holes from a single band, followed by fine-tuning the learned CNN over the other bands consecutively. The feature maps of each specialised CNN are used in combination as input to a final segmentation CNN, resulting in a unique final prediction. The production of a unique localisation result for all multi-spectral images is a common limitation to all cited works, which we address in this study with a multi-task network. We introduce MultiSpectral-MultiTask-CNN (MSMT-CNN), a multi-tasking DNN framework, as a robust solution for solar AR detection that takes into consideration the multi-spectral aspect of the data and the 3-dimensional spatial dependencies between image bands. This multi-spectral and multi-tasking concept may be applied to any CNN backbone.

The 3D nature of our multi-spectral imaging scenario, which differs from previous multi-spectral applications, requires a new benchmark. We introduce two annotated datasets comprised of solar images from both ground and space, and which cover evenly all phases of solar activity, which follows an 11-year cycle. To the best of our knowledge, no detection ground-truth was previously available for such data. A labeling tool was hence designed to cope with its temporal and multi-spectral nature and will be also released.

METHODOLOGY

While some existing works were developed for analysing multi-spectral images, to our best knowledge, the problem of detecting objects over sparse 3D multi-spectral imagery, in which different bands show different scenes, was not yet addressed. Our framework exploits jointly several time-matched image bands in parallel, to predict separate, although related, detection results for each image. This framework is general and may be used with any DNN backbone, we demonstrate it using Faster RCNN [START_REF] Ren | Faster R-CNN: Towards real-time object detection with region proposal networks[END_REF].

The intuition behind our framework manifests in 3 key principles:

1. Extracting features from different image bands individually using parallel feature extraction branches. This allows the network to learn independent features from each band, according to their specific modality.

2. Aggregating the learned features from the different branches using some appropriate fusion operator. This assists the network to jointly analyse the extracted features from different bands and thus learn their interdependencies. In this work, we test fusion by addition and concatenation, at different feature levels (i.e. early and late fusion).

3. Generating a set of results per image band, based on a multi-task loss, allowing the detection of different sections or layers of 3D objects.

Points 1 and 3 are motivated by the nature of the multi-spectral data, where different bands image different locations in a 3D scene, each providing a unique information. Our multi-tasking framework aims at getting specialised results for each image band, in contrast to most existing works where focus is on producing a unique prediction to all image bands. This is crucial since the localisation information may differ from one band to another in solar (sparse) multi-spectral images. Yet, all bands are correlated, which motivates point 2. Our framework exploits the inter-dependencies between the different bands by its joint analysis strategy, increasing the robustness of its performance in individual bands. Furthermore, our framework emulates how experts manually detect ARs (see also Section 3.1), where a suspected region's correlation with other bands is evaluated prior to its final classification. This demonstrates the usefulness and importance of accounting for (spatially and temporally) neighbouring slices in robustly detecting ARs.

The MSMT-CNN framework is very modular and flexible. It may accommodate any number of available multi-spectral images. Additionally, since different scenarios may require different fusion strategies (as suggested by existing works), the modularity of our framework allows it to be easily adapted to different types and levels of feature fusion (e.g. addition and concatenation, early and late). This modular design also allows our framework to adopt different backbone architectures (e.g. Faster RCNN in our experiments). Indeed, its 3 key principles are applicable to any backbone, as they are not architecture dependent.

Pre-processing

The input of our system are time-matched observations, possibly acquired by different instruments or at different orientations of the same instrument. As such they need to be spatially aligned. We harmonise the radius and center location of the solar disk, either using SOHO image preparation routines, or through Otsu thresholding of the solar disc of PM/SH images followed by minimum enclosing circle fitting and reprojection into a unified center and radius. Orientation is normalised by SOHO and PM routines to a vertical north-south solar axis. Although this process does not correct a possible small time difference and resulting east-west rotation of the Sun between two acquisitions, it ensures a sufficient alignment for our purpose of AR detections from spatially (and temporally) correlated solar disks.

The SOHO/EIT images are prepared by EIT routines. We eliminate any prominences or solar erup-tions by masking out all areas outside the solar disk. The contrast of SOHO/MDI Magnetograms is enhanced by intensity rescaling. Contrast enhancement was not used on SOHO/EIT and PM/SH images, as it was found to have minimal effect on our detection results.

Both datasets are augmented using north-south flipping, east-west flipping, and a combination of the two. Augmentation with arbitrary rotations of the images is a popular way of augmenting astronomy datasets. However, such rotations are ruled out from our study because ARs tend to appear predominantly alongside the solar equator.

Finally, a single-channel solar image was repeated along the depth axis resulting in a 3-channel image in order to match the pre-trained CNN's input depth.

MSMT-CNN

Our detection DNN is presented in Fig. 2. A CNN (ResNet50 or VGG16 in our experiments) is first used as a feature extraction network. Parallel branches (subnetworks) produce a feature map per image band, following the late feature fusion strategy. This allows the subnetworks' filters to be optimised for their input bands individually. The feature maps are then concatenated across the bands.

The combined feature map is jointly analysed by one parallel module per image band that performs Faster RCNN's region proposal network (RPN). The RPN stage uses three aspect ratios ([1:1], [1:2], [2:1]) and four sizes of anchor (32, 64, 128, and 256 pixel width), found empirically to match well the typical size and shape of ARs. One specialised RPN per image band is trained.

At training, for each band, the correspondent region proposals along with the combined feature map are used by a Faster RCNN's detector module to perform the final prediction for the band. However, at testing time, the band-specialised detector modules use the region proposals from all bands. This combination of region proposals helps finding potential AR locations in bands where they are more difficult to identify.

It is good to note that during training, the RPN's proposals for a band are filtered (i.e. labeled as positive or negative) with respect to their overlap with the band's ground-truth. Hence, combining them in the training time would mean implicitly inheriting the ground-truth of a band to another, in contradiction with the band-specific ground-truth used for training the detector module. This may hinder the learning of both the RPN and detector modules. Therefore, region proposals are only combined at testing time to ensure a better learning of the final detection modules.

Using the combined feature map for both RPN prediction and classification helps the network learn the relationship between the image bands and hence provide more consistent region proposals and final predictions. We demonstrate in Section 3 that this is particularly helpful in cases where an AR is difficult to detect in a single band.

The network is trained in the same way as the original Faster RCNN, using all input bands and branches according to a combined loss function:

L = ∑ b 1 N cls ∑ i L cls (p b i , p * b i ) + λ 1 N reg ∑ i p * b i L reg (t b i ,t * b i ) (1)
where b and i refer to the image band and the index of the bounding box being processed, respectively. The terms L cls and L reg are the boundingbox classification loss and the bounding-box regression loss defined in [START_REF] Ren | Faster R-CNN: Towards real-time object detection with region proposal networks[END_REF]. N cls and N reg represent the size of the mini batch being processed and the number of anchors, respectively. λ balances the classification and the regression losses (we set λ to 10 as suggested in [START_REF] Ren | Faster R-CNN: Towards real-time object detection with region proposal networks[END_REF]). p and p are the predicted anchor's class probability and its actual label, respectively. Lastly, t and t represent the predicted bounding box coordinates and the groundtruth coordinates, respectively. It is worth noting that our proposed framework is not limited to using Faster RCNN's loss and may be trained with using other task-suitable loss functions.

During training, the weights of each stage (i.e. feature extraction, region proposal, and detection) are stored independently whenever the related Faster RCNN loss decreases. At testing time, the best performing set of weights is retrieved per stage. We refer to this practice as 'Multi-Objective Optimisation' (MOO). The improved performance that we observe in Section 3 may be explained by each stage having a different objective to optimise, which may be reached at different times.

In this paper, we experiment with a 2, 3, and 4band pipeline. However, the approach may generalise straightforwardly to n bands and new imaging modalities. Similarly, our framework may exploit any DNN architecture, and may be updated with new state-ofthe-art DL architectures easily.

EXPERIMENTS

Our framework was implemented using Tensorflow and run on an NVIDIA GeForce GTX 1080 Ti. We evaluate our detection stage using precision, recall, and F1-score. A detection is considered a true positive if its intersection with a ground-truth box is greater or equal to 50% of either the predicted or ground-truth area. We empirically found that this provides a good trade-off of precision over recall. Non maximum suppression (NMS) is used to discard any redundant detections.

All tested CNN were initialised with pre-trained ImageNet weights. Indeed, we demonstrated in [START_REF] Crabbe | Skeleton-free body pose estimation from depth images for movement analysis[END_REF] that CNNs pre-trained on RGB images may fine-tune and adapt well to other modalities such as depth images, provided that the image's gain and contrast are suitably enhanced to match those of the pre-training RGB images.

Data

We consider data from SOHO (space-based) and PM observatory (ground-based). We use the bands 171 Å and 195 Å (transition region), 284 Å (corona), and 304 Å (chromosphere and base of transition region) from SOHO/EIT, 3934 Å (chromosphere) from PM/SH, and line-of-sight magnetograms (photosphere) from SOHO/MDI, as illustrated in Fig. 1. To account for the regular solar cycle, we select images evenly from each of 3 periods of varying solar activity level, with years 2002-03, 2004-05, and 2008-10 for high, medium, and low activity respectively.

We publish two new datasets with detection annotations (i.e bounding boxes): the Lower Atmosphere Dataset (LAD) and Upper Atmosphere Dataset (UAD). All annotations were validated by a solar physics expert.

Localising ARs in multi-spectral images can be challenging due to inconsistent numbers of polarity centers, shapes, sizes, and activity levels. To mitigate this issue, when manually annotating, we exploit neighboring image bands, magnetograms, as well as temporal information where we examine the evolution of a suspected AR to validate its detection and localisation. We designed a new multi-spectral labeling tool2 which displays, side by side, images from an auxiliary modality and from a sequence of 3 previous and 3 subsequent time steps. ARs have a high spatial coherence in 3934 Å and magnetogram images due to the physical proximity of the two imaged regions. Hence, when annotating 3934 Å images, time-matched magnetograms were used as an extra support. Furthermore, the 3934 Å bounding boxes could be considered to be good approximations of magnetograms' annotations. Table 1 presents an overview of annotated images for both datasets. We split the datasets into training and testing as indicated in the table.

To compare against SPOCA, we consider a subset of the UAD testing set for which SPOCA detection results are available in HFC: the SPOCA subset. It consists of 26 testing images (181, 168, 213, and 166 bounding boxes in the 304 Å, 171 Å, 195 Å and 284 Å images respectively).

Independent detection on single image bands

We first compare detection results of Faster RCNN over individual image bands analysed independently (Table 2). This aims to evaluate different feature extraction DNNs, and will further serve as baseline to assess our proposed framework. The ResNet50 architecture consistently produces better results than the VGG one with higher F1 scores in all experiments. Based on these results, we adopt the ResNet50 architecture as backbone for our framework in the next experiments. When comparing the detection results per image band, we notice that 304 Å images are repeatedly amongst the most difficult to analyse in UAD, having the lowest F1-scores in all tests. On the other hand, 171 Å has the best results of UAD bands, followed by 284 Å and 195 Å. This may be explained by ARs having a denser or less ambiguous appearance in 171 Å, 195 Å, and 284 Å image bands than in 304 Å since they are higher in the corona. A similar observation can be made in the LAD dataset when comparing the magnetogram results to 3934 Å, where magnetograms observe a lower altitude than 3934 Å.

We also notice a strong contrast between a same detector's precision and recall on the different UAD bands. This further demonstrates that these bands are not equal in how easily they may be analysed, even though they were acquired at the same time with same size and resolution.

Detections are visually verified to be poorer for small ARs and for spread and faint ones with more ambiguous boundaries. Visual inspection also confirms the different performances in various bands being caused by differing visual complexities of ARs. These observations suggest that detecting ARs using information provided by a single band may be an under-constrained problem.

Joint detection on multiple image bands

We now present the results of our framework when detecting ARs over the LAD/UAD bands jointly. Joint detection results are summarised in Table 3. We first experimented with different types of featurefusion (image-level and feature-level, by addition and concatenation) over the LAD dataset.

Overall, the three approaches show an enhanced performance in contrast to single band based detection. However, we find that late fusion with concatenation performs best. Hence, we pick this approach for all following experiments.

We also evaluate the benefit of our MOO strategy using our 2-band architecture over the UAD. This approach generally improves the F1-scores in most bands comparing to the non-MOO architectures. This behaviour may indicate that the two feature extraction stages were indeed more effectively optimised for their different tasks at different epochs. We retain this MOO approach for all other experiments.

On the UAD dataset, with various combinations of 2 bands, we notice a general improvement over single band detections. In addition, the performance varies in correspondence to the bands being used. Combining bands that are difficult to analyse (304 Å or 195 Å that have lowest F1-scores in the single band analyses) with easier bands (171 Å and 284 Å) unsurprisingly enhances their respective performance. More interestingly, combining the difficult 304 Å and 195 Å bands together also improve on their individual per-formance. Similarly, when combining bands that are easier to analyse (171 Å and 284 Å), in contrast to using combinations of difficult and easy bands in the analysis, performances are also improved over their individual analyses. Following these settings, our 2band based approach was able to record higher or similar F1-scores in contrast to the best performing single-band detector. This supports our hypothesis that joint detection may provide an increased robustness through learning the inter-dependencies between the image bands. Moreover, the most dramatic improvement in F1scores across both LAD and UAD datasets is for the analysis. This is in line with the current understanding of AR having strong magnetic signatures.

Generally, in the UAD dataset, we find that using a combination of 2 bands produces the best results in comparison to using 3 or 4 bands. This may be caused by the fact that optimising the network for multiple tasks (2, 3, or 4 detection tasks) simultaneously increases the complexity of the problem. While the network successfully learned to produce better detections in the case of 2 bands, it was difficult to find a generalised yet optimal model for 3 or 4 bands at the same time.

Furthermore, since bands imaging consecutive layers of solar atmosphere are expected to be highly correlated, we test our framework by combining directly neighbouring bands together, such that a prediction for a band is performed using the band's own feature map combined with the feature map(s) of its available (1 or 2) direct neighbour(s). This approach gets the highest recall score on the UAD band 284 Å of all tests, where it is combined with the 195 Å band. However, it does not improve the performance on the other bands comparing to the single-band and 2-band based experiments.

We compare against state-of-the-art SPOCA (Verbeeck et al., 2013) on the SPOCA subset, and against the first stage of [START_REF] Jarolim | Multi-channel coronal hole detection with a CNN[END_REF] (sequentially fine-tuned networks) by adapting approach to Faster RCNN and testing it on UAD. SPOCA detections were obtained from 171 Å and 195 Å images only, combined as two channels of an RGB image, and SPOCA produces a single detection for both bands. We compare this detection against the ground truth of each of the bands individually. To prove the robustness and versatility of our detector, we also experiment with a combination of chromosphere, transition region, and corona bands on the SPOCA subset in addition to the whole UAD.

On the SPOCA subset, over the bands 171 Å and 195 Å for which it is designed, SPOCA gets the poorest performance of all multi-band and single-band experiments. It is worth noting that this method relies on manually tuned parameters according to the developers' own definition and interpretation of AR boundaries, which may differ from the ones we used when annotating the dataset. While supervised DL-based methods could integrate this definition during training, SPOCA could not perform such adaptation. This may have had a negative impact on its scores. Furthermore, visual inspection shows a poor performance for SPOCA on low solar activity images. This may be due to the use of clustering in SPOCA, since in low activity periods the number of AR pixels (if any) is significantly smaller than solar background pixels, which makes it hard to identify clusters.

Moreover, the fine-tuned networks of [START_REF] Jarolim | Multi-channel coronal hole detection with a CNN[END_REF] suffer from a high rate of false positives, and show a close performance to single band detection using Faster RCNN with an identical precision, recall and F1-score over the band 304 Å and a slight decrease over the other 3 bands. This may be due to the fact that its transfer learning does not incorporate the inter-dependencies directly when analysing the different bands.

CONCLUSION

We presented MSMT-CNN, a multi-branch and multitasking framework to tackle the 3D solar AR detection problem from multi-spectral images that observe different cuts of the 3D solar atmosphere. MSMT-CNN analyses multiple image bands jointly to produce consistent detection across them. It is a flexible framework that may use any CNN backbone, and may be be straightforwardly generalised to any number and modalities of images. MSMT-CNN showed competitive results against baseline and state-of-theart detection methods.

Figure 1 :

 1 Figure 1: Ground-truth (red) and MSMT-CNN's (green) detection of ARs in randomly selected images from (left to right) SOHO/MDI Magnetogram and PM/SH 3934 Å, SOHO/EIT 304 Å, 171 Å, 195 Å, and 284 Å. Contrast has been increased for convenience of visualisation.

Figure 2 :

 2 Figure 2: MSMT for detection using the Faster-RCNN backbone. 'Plus' sign denotes concatenation of the feature maps, or of the lists of region proposals (at testing time).

Table 1 :

 1 Technical summary of the two annotated datasets. # numbers are in train / test format.

	Dataset	Modality	# images	# boxes
		SOHO/EIT 284 Å	283 / 40 2205 / 287
	UAD	SOHO/EIT 171 Å SOHO/EIT 195 Å	283 / 40 1919 / 262 283 / 40 2341 / 330
		SOHO/EIT 304 Å	283 / 40 2016 / 263
	LAD	PM/SH 3934 Å SOHO/MDI magn. 213 / 53 1380 / 406 213 / 53 1380 / 406
	Auxiliary imaging modalities may have different
	observation frequencies and times, therefore we work
	with time-matched images, i.e. the time-closest im-
	age, if any, in a 12-hour window during which ARs
	may not undergo any significant change.

Table 2 :

 2 Detection performance of the single image band detectors. For each band, the highest scores are highlighted in bold.

	Detector	Dataset	Band	Precision Recall	F1
		LAD	3934 Å	0.93	0.82	0.87
		LAD	Magn.	0.89	0.78	0.83
		UAD	304 Å	0.73	0.83	0.78
	Faster RCNN (ResNet50)	UAD UAD UAD SPOCA 304 Å 171 Å 195 Å 284 Å	0.84 0.81 0.86 0.72	0.89 0.75 0.82 0.82	0.86 0.78 0.84 0.77
		SPOCA 171 Å	0.87	0.87	0.87
		SPOCA 195 Å	0.82	0.73	0.77
		SPOCA 284 Å	0.86	0.82	0.84
		UAD	304 Å	0.67	0.78	0.72
		UAD	171 Å	0.84	0.81	0.82
	Faster RCNN (VGG16)	UAD UAD SPOCA 304 Å 195 Å 284 Å SPOCA 171 Å	0.79 0.83 0.68 0.85	0.73 0.81 0.80 0.80	0.76 0.82 0.74 0.82
		SPOCA 195 Å	0.78	0.72	0.75
		SPOCA 284 Å	0.84	0.82	0.83

Table 3 :

 3 AR detection performance of the MSMT-CNN detectors. For each band, the highest scores are highlighted in bold.

	Detector	Dataset	Band	Prec. Recall	F1
	MSMT (ResNet50 -MOO)	LAD	3934 Å 0.97 Magn. 0.96	0.82 0.85	0.89 0.90
			171 Å	0.92	0.77	0.84
			284 Å	0.90	0.81	0.85
			171 Å	0.82	0.85	0.83
	MSMT (ResNet50)	UAD	195 Å 195 Å	0.86 0.88	0.72 0.67	0.78 0.77
			284 Å	0.84	0.78	0.81
			304 Å	0.82	0.79	0.80
			195 Å	0.87	0.75	0.80
		UAD	171 Å 284 Å	0.90 0.93	0.83 0.80	0.87 0.86
		SPOCA	171 Å 284 Å	0.89 0.92	0.83 0.80	0.86 0.86
		UAD	171 Å 195 Å	0.86 0.89	0.77 0.75	0.82 0.81
		SPOCA	171 Å 195 Å	0.83 0.86	0.77 0.73	0.80 0.79
		UAD	195 Å 284 Å	0.88 0.84	0.68 0.78	0.77 0.81
	MSMT (ResNet50 -MOO)	SPOCA	195 Å 284 Å	0.87 0.81	0.67 0.78	0.75 0.80
		UAD	304 Å 195 Å	0.82 0.88	0.78 0.78	0.80 0.83
		SPOCA	304 Å 195 Å	0.79 0.85	0.78 0.77	0.79 0.81
			304 Å	0.78	0.74	0.76
		UAD	171 Å	0.76	0.76	0.76
			284 Å	0.79	0.78	0.78
			304 Å	0.93	0.69	0.79
		UAD	171 Å 195 Å	0.94 0.91	0.66 0.72	0.78 0.80
			284 Å	0.93	0.66	0.77
	MSMT (ResNet50		304 Å	0.72	0.76	0.74
	-MOO) Combining	UAD	171 Å 195 Å	0.74 0.81	0.79 0.73	0.76 0.77
	neighbour bands		284 Å	0.68	0.84	0.75
	SPOCA	SPOCA	171 Å 195 Å	0.54 0.58	0.93 0.82	0.68 0.68
	(Jarolim et al., 2019) using Faster RCNN (ResNet50)	UAD	304 Å 171 Å 195 Å 284 Å	0.73 0.80 0.83 0.86	0.83 0.90 0.72 0.80	0.78 0.84 0.77 0.83

http://voparis-helio.obspm.fr/hfc-gui/

Our labeling tool will be released on the project's website.


Lynn Carr

Sofiane Saada

Barbara Bessette

Cristiano Palego

Arnaud Pothier

Delia Arnaud-Cormos

Philippe Leveque

Fabrice Lalloué

BioEM2020, Joint Meeting of the BioElectroMagnetics Society and the European BioElectromagnetics

The SUMCASTEC project aims to isolate and neutralise glioblastoma cancer stem-like cells (CSC) using electromagnetic stimulation. With these objectives in mind, normal populations and CSC enriched populations of U-87 MG cells were exposed to nanosecond pulsed electric fields and mRNA and protein expression differences were analysed.

Presented Work

Glioblastoma multiforme (GBM) is a common and aggressive tumour of the glia affecting the central nervous system. It is highly invasive and resistant to all current treatments, with the median survival time for patients being 14.6 months. Current standard treatment includes surgical resection, chemotherapy and radiotherapy [START_REF] Stupp | Radiotherapy plus Concomitant and Adjuvant Temozolomide for Glioblastoma[END_REF][START_REF] Wilson | Glioblastoma multiforme : State of the art and future therapeutics[END_REF] . Cancer stem-like cells (CSC) have been identified in GBM tumour masses [START_REF] Singh | Identification of human brain tumour initiating cells[END_REF] and their multipotentiality, ability to self-renew and resistance to chemotherapy make them a key target for novel GBM treatments. SUMCASTEC 4 is part of the European Union's Horizon 2020 research and innovation program. The project aims to isolate and neutralise glioblastoma (CSC) using electromagnetic stimulation.

As part of SUMCASTEC this study focuses on the effects of nanosecond pulsed electric fields (nsPEF) on the glioblastoma cell lines U-87 MG. A cuvette exposure system was used to apply nsPEF to cells that had been cultured under normal conditions and to those that have been cultured under conditions that result in an enrichment in CSC. Transcriptional analysis and protein expression studies were carried out on these cells.

Acknowledgement

This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 737164 SUMCASTEC.


Anne Desponds
email: anne.desponds@ens-lyon.fr

Akos Banyasz

Gilles Montagnac

Chantal Andraud

Patrice Baldeck

Stephane Parola
email: stephane.parola@ens-lyon.fr

Microfabrication by two-photon lithography, and characterization, of SiO2/TiO2 based hybrid and ceramic microstructures

Keywords: Microfabrication, Ceramics, Sol-gel, Hybrids, Two-photon polymerization

High resolution fabrication using two-photon lithography is extensively studied for a large range of materials, from polymer to inorganics. Hybrid materials including a sol-gel step have been developed since two decades to increase mechanical or optical properties in particular on silicon based materials. Among the metal oxide, few studies have been dedicated to titanium and, because of the high reactivity of titanium precursors, obtaining a resin with a high part of titanium is challenging. Indeed, resins for two-photon lithography have to be stable for the processing time and titanium precursors are more difficult to operate due to their higher reactivity and often require drastic working conditions in order to control the chemical processes. Here, we propose a method, working at ambient conditions, to print submicronic structures of organicinorganic hybrids with a large proportion of titanium and ceramics using high resolution twophoton process. The material obtained and its evolution during the pyrolysis at 600°C and 1000°C are characterized. We show that TiO2/SiO2 based micro-ceramics can be obtained after the pyrolysis of the microstructures. The respective roles of the two chemical reactions involved in this lithography process, sol-gel condensation and radical photopolymerization, are highlighted.

-Sol-gel approaches towards high resolution 3D printed ceramics is an extremely promising method.

Introduction

The patterning of sub-micrometric structures with high three-dimensional (3D) spatial resolution is in continual development to meet and improve the requirements of applications in various domains (automotive, aerospace, defense, healthcare, consumer, industrial automation, robotics, telecommunications). Most of the processes used to obtain the microdevices are composed of several steps including chemical and/or physical treatments [START_REF] Zaouk | Introduction to Microfabrication Techniques[END_REF], and the utilization of molds or masks, especially with inorganic materials [START_REF] Park | Fabrication of 3D Nanostructured Titania Materials by Prism Holographic Lithography and the Sol-Gel Reaction[END_REF][START_REF] Lim | Direct imprinting of high resolution TiO2 nanostructures[END_REF]. The multiphotonic lithography technic, also called "direct writing lithography", may replace these mixed processes which are heavy and complicated, through a fabrication by a 3D printing process involving reactions of radical photopolymerization. The two-photon lithography, based on a two-photon absorption process, has attracted a high level of interest due to its high spatial resolution, making possible the production of microdevices and microstructured surface. This technique was explored in the 90's and the 2000's [START_REF] Maruo | Three-dimensional microfabrication with twophoton-absorbed photopolymerization[END_REF][START_REF] Wang | Three-dimensional microfabrication by two-photon-initiated polymerization with a low-cost microlaser[END_REF][START_REF] Sun | Two-Photon Photopolymerization and 3D Lithographic Microfabrication[END_REF][START_REF] Farsari | Two-photon fabrication[END_REF]. Many materials are compatible with the two-photon lithography such as photopolymers [START_REF] Ostendorf | Two-photon polymerization: A new approach to micromachining[END_REF], inorganic-organic material composed of silica precursors built up in organic network (ORMOCERS) [START_REF] Serbin | Femtosecond laser-induced two-photon polymerization of inorganic-organic hybrid materials for applications in photonics[END_REF], and its derivatives including titanium [START_REF] Sakellari | Two-photon polymerization of titaniumcontaining sol-gel composites for three-dimensional structure fabrication[END_REF], zirconium [START_REF] Ovsianikov | Two-Photon Polymerization of Hybrid Sol-Gel Materials for Photonics Applications[END_REF][START_REF] Ovsianikov | Ultra-Low Shrinkage Hybrid Photosensitive Material for Two-Photon Polymerization Microfabrication[END_REF], or metal hybrids [START_REF] Vyatskikh | Additive manufacturing of 3D nano-architected metals[END_REF]. Full inorganic condensation sol-gel process has also been developed but remains hitherto limited to silica based materials [START_REF] Kustra | Elaboration of micro and mesostructured sol-gel materials using polysilsesquioxane molecular precursors[END_REF][START_REF] Kustra | Two-photon controlled sol-gel condensation for the microfabrication of silica based microstructures. The role of photoacids and photobases[END_REF]. To obtain full inorganic materials, the fabrication in a polymeric matrix including inorganics followed by a debinding of the organic matter is a route that has been explored for SiCN microstructures [START_REF] Lim | Net Shape Manufacturing of Three-Dimensional SiCN Ceramic Microstructures Using an Isotropic Shrinkage Method by Introducing Shrinkage Guiders[END_REF], SiO2/ZrO2 [START_REF] Gailevičius | Additive-manufacturing of 3D glass-ceramics down to nanoscale resolution[END_REF] or SiOC [START_REF] Eckel | Additive manufacturing of polymer-derived ceramics[END_REF].

The incorporation of TiO2 in such micromaterials has been widely studied as this metal oxide presents excellent optical properties (high refractive index) while keeping electronic properties [START_REF] Enyashin | Structure, stability and electronic properties of TiO2 nanostructures[END_REF][START_REF] Tang | Electrical and optical properties of TiO2 anatase thin films[END_REF]. Similar to the ORMOCER, inorganic-organic resins including Titania precursors have been studied for photonics [START_REF] Sakellari | Two-photon polymerization of titaniumcontaining sol-gel composites for three-dimensional structure fabrication[END_REF][START_REF] Declerck | High Refractive Index Inorganic-Organic Hybrid Materials for Photonic Applications[END_REF] pressure sensors [START_REF] Yu | Direct Laser Writing of Crystallized TiO2 and TiO2/Carbon Microstructures with Tunable Conductive Properties[END_REF] and biological [START_REF] Psycharakis | Tailor-made threedimensional hybrid scaffolds for cell cultures[END_REF][START_REF] Balčiūnas | 3D printing hybrid organometallic polymer-based biomaterials via laser two-photon polymerization[END_REF] applications.

However, contrary to the silica precursors, the titanium is much more sensitive to the environment because of (i) its stronger electrophile character and (ii) its coordination number which is superior to its valence, making nucleophilic addition reactions possible when coordination of the metal is not complete [START_REF] Rozes | Titania-Based Hybrid Materials: From Molecular Precursors to the Controlled Design of Hierarchical Hybrid Materials[END_REF]. Consequently, this metal center is very sensitive to reaction with ambient H2O. To handle the titanium precursor and permit two-photon lithography resin, these compounds are stabilized by methacrylic acid to form less reactive clusters. These reactions between alkoxides and ligands have been particularly studied [START_REF] Schubert | Chemical modification of titanium alkoxides for sol-gel processing[END_REF]. It has been highlighted that the clusters formation are due to partial hydrolysis, thus making possible the incorporation of such compounds as building blocks for hybrid inorganic-organic matrix [START_REF] Schubert | Organofunctional Metal Oxide Clusters as Building Blocks for Inorganic-Organic Hybrid Materials[END_REF].

However, even under cluster form, the system remains very reactive. The addition of a silicon precursor makes it possible to reach a manageable composition but resins including a molar ratio Ti/Si superior to 1 are not achievable in these conditions, and the microstructures presented eventually a chipped surface aspect [START_REF] Sakellari | Two-photon polymerization of titaniumcontaining sol-gel composites for three-dimensional structure fabrication[END_REF]. More recently, it has however being demonstrated that, under an argon environment without oxygen, the proportion of titanium precursor could be increased [START_REF] Yu | Direct Laser Writing of Crystallized TiO2 and TiO2/Carbon Microstructures with Tunable Conductive Properties[END_REF].

We propose here a simple route, in easily controllable conditions, to obtain ceramic microstructures with high titanium dioxide content from sol-gel based resins. The principle is to create mixed composite oligomers during the patterning of the microstructures, either by prehydrolysis and aging condensation of the alkoxide precursor, followed by two-photon polymerization, or by simultaneous two-photon controlled inorganic condensation and twophoton organic photopolymerization. We have noted that the type of titanium precursor has an influence on the stability of the resin. In particular, the replacement of the traditionally used titanium isopropoxide by the titanium butoxide allows us to increase the molar ratio Ti/Si up to 2.3 in the resin in our experimental conditions. Infrared spectroscopy allows characterization of the two chemical reactions involved during the transition from resin to micromaterial. In a one hand, the sol-gel condensation occurs, creating pre-form network and depending on the aging time, this reaction is more or less advanced. In another hand, the radical photopolymerization permits to connect the pre-form sol-gel network, and thus to form the micromaterial.

The fabricated microstructures of composite inorganic-organic hybrid can then be calcined to remove the organic and thus lead to inorganic microstructures with increased optical and mechanical properties. Such process towards Titania based ceramics remains barely studied. Here is reported the two-photon fabrication of TiO2 based micro-ceramics from such hybrids. In an amber vial, the silicon precursor solution is prepared: a solution of MAPTMS at 80 wt% in acetone is prepared, and a solution of diluted HCl (0.1M) is added to hydrolyze the silane. In order to evaluate the influence of the hydrolysis conditions, four molar ratios of H2O/MAPTMS (0.25, 0.5, 1 and 3) are considered (Table 1). The reaction medium is stirred for one hour.

Experimental

Simultaneously, in another amber vial, the titanium precursor solution is prepared: a solution of Ti(OiPr)4 (TiP) at 70 wt% in acetone is prepared and the MAA is added slowly under stirring, with a molar ratio equal to 1 (Table 1). The reaction is stirred for one hour.

Finally, the resin is prepared in a third amber vial. The silicon solution is added to the photoinitiator, Michler's ketone, (0.15 mmol). The titanium precursor solution is then dropped under stirring. The molar proportion of the TiP solution added to the MAPTMS solution is tuned between samples according to the following molar ratios Ti/Si: 0.4, 0.7, 1, 1.5, 2.3 (Table 1). Finally, the resin is prepared in a third amber vial. The silicon solution is added to the photoinitiator, Michler's ketone, (0.15 mmol). The titanium solution is dropped under stirring.

The molar proportion of the TiB solution added to the MAPTMS solution is tuned, in the different samples, from 0 to 1. The exact quantities used for the resins are specified in Table 2.

Two-photon lithography

The microfabrication setup (Microlight 3D) is based on an inverted microscope. The irradiation source is a Q-switched, frequency doubled Nd-YAG laser (532 nm, 12 kHz, 560 ps) focused by a microscope objective (Plan-Apochromat 40x/0.95, Zeiss) into the sample that is placed on a quartz cover slip (24 mm x 24 mm x 0.17 mm). During the microfabrication the focused laser spot is scanned through the trajectory defining the 3D microstructure to be printed, which is realized by varying the sample position by a piezo stage with respect to the focal spot. The microfabrication parameters such as 3D trajectory, laser power, exposure time are controlled by a dedicated software. For the microfabrication of structures with hybrid resins, the irradiation power is varied from 10 to 100 µW (measured at the entrance of the objective) and exposure time from 1 to 10 ms.

The sample is placed on a piezoelectric plate (PI Mars) also controlled via the dedicated software and allowing displacements of 100 µ m in the 3 directions. The microstructure is divided into lines via modeling software, to obtain the layout of the fabrication. The laser and the piezo stage then follow the instructions of the software to create the microstructure.

To prepare the sample, a quartz microscope slide (24mmx24mmx0.17mm) is treated with an ultrasonic bath of a 5 wt% KOH solution in water for 10 minutes, then an ultrasonic bath of acetone for 5 minutes. Finally, it is dried under isopropanol vapor. A drop of 5 µL of resin is deposited on a quartz microscope slide substrate and is left for aging before performing the fabrication. Three aging times of 1h, 1h30 and 2h have been considered. Microfabrication is carried out on the surface of the quartz slide. Once the fabrication is complete, the substrate is removed from the instrumentation to be washed in an acetone bath for 5 minutes, in order to remove the excess of unreacted resin and reveal the microstructures fabricated by two-photon lithography.

Pyrolysis process

The microfabricated structures are calcined in an alumina crucible directly on their quartz substrates using a muffle furnace (Thermolyne FB1300, Thermo Scientific) without particular airflow regulation. For the structures calcined at 600°C, a temperature ramp of 5°C/min is applied up to 600°C, and this temperature is maintained for 30 minutes before allowing the oven to cool down to room temperature without any particular control. For the structures calcined at 1000°C, a temperature ramp of 5°C/min is applied up to 1000°C, and this temperature is maintained for 60 minutes before allowing the oven to cool down to room temperature without any particular control.

FTIR spectroscopy

For each resin, a drop of 5 µL is deposited onto a glass slide, in order to form a film. An aging time of 1h is fulfilled and the measure is conducted using a FTIR spectrometer equipped with an ATR device from PerkinElmer (Spectrum65). The background due to the glass slide is recorded and directly subtracted from the obtained signal. Then the films are exposed to UV light (255nm; 20µW/cm 2 ) for 15 minutes, in order to polymerize the material, and a second measure is performed. The FTIR spectra were performed from 550 to 4000 cm -1 and are presented as obtained without any post treatment. 10

SEM observations

The microstructures are sputtered with a 5 nm layer of platinum using a high vacuum coater (EM ACE600, Leica) and observed with SEM (Supra55VP, Zeiss) at a beam of 10 kV, through a secondary electron detector.

XRD experiments

The resins are exposed to UV light (255nm; 20µ W/cm 2 ) for 2 hours, until a solid is formed. The materials are crushed in fine powders in a mortar. They are then calcined as described in the pyrolysis process and the diffractograms are recorded after the heating treatment at 600°C and 1000°C. The diffractograms are performed from 10° to 80° (2theta) but they are presented here from 10° to 65° where the structural information is located. The diffractograms were post-treated to adjust the background and to smoothed the lines.

MicroRaman spectroscopy

The microRaman instrument allows analyzing directly the microstructures, with a focalization of the laser through a microscope objective, on a precise location of the micro-object, without further preparation. The micro Raman analyses were conducted with a Horiba LabRamn HR evolution spectrometer. In backscattering geometry, the excitation laser (532 nm) was focused and the Raman diffusion was collected through the same objective (x100/0.9 Olympus). The materials based on the MAPTMS/TiB/MAA resins are characterized.

Microgrids of 50 µm are fabricated by two-photon lithography for different molar ratios Ti/Si with TiB and calcined according to the pyrolysis process. Microfabrication of the resin O (No Si)

is not achievable therefore a drop of the resin is deposited onto a quartz substrate and calcined according to the pyrolysis process. The probe size of the device is lower than 1 µm that is adapted to a precise localization on the microstructure, so Raman spectra are obtained at this precise position. The green point on Fig. 2 allows to figure out the position of laser on the grid. 3 Results and discussion

Influence of the titanium precursors on the microfabrication process

The first resin MAPTMS/TiP/MAA, used as a comparative reference, is similar to those realized in a previous study [START_REF] Sakellari | Two-photon polymerization of titaniumcontaining sol-gel composites for three-dimensional structure fabrication[END_REF]. The maximum of molar ratio Ti/Si is 1 and at this concentration, the microfabrication is limited and the microstructures surface is often rough. The conditions of the preparation of the resins are varied and microgrids are realized using two-photon lithography to observe the difference in resolution on the linewidth of the structures (Fig. 3 (a)). The common definition for the resolution of microstructures fabricated by two-photon lithography is the minimum distance between the centers of two lines without overlapping between them [START_REF] Baldacchini | Three-Dimensional Microfabrication Using Two-Photon Polymerization: Fundamentals, Technology, and Applications[END_REF].

Here, as a simplified approach, a simple measure of the linewidth is performed.

With the resin MAPTMS/TiP/MAA, as in the previous study, no fabrication above a molar ratio Ti/Si of 1 is observed and at this concentration, the structures are stiff and present cracks. In the second resin, the titanium precursor, i.e. the titanium isopropoxide, is replaced by a more sterically hindrance precursor, the titanium butoxide. The modified alkoxide is more stable and allows fabrication up to a molar ratio Ti/Si of 2.3. Moreover, the microgrids obtained with TiB give more resolved structures that break less easily (Fig. 3 (b)). resin I (No Ti) is around 2.5 mW whereas for the resin N (molar ratio Ti/Si = 2.3) the threshold is only around 50 µW, which is 50 times more sensitive. Moreover, observations show that the titanium butoxide allows a better stability of the resin than titanium isopropoxide, consequently leading to a higher polymerization threshold (Table 3). The photopolymerizable resin is obtained following basically a three-steps procedure, (i) the hydrolysis of the MAPTMS, (ii) mixing of the precursors and (iii) the sol-gel aging. Then it is possible to perform the two-photon polymerization and have the final micro-object. These key parameters of the process are varied in order to evaluate the incidence on the microfabrication resolution.

First the hydrolysis ratio of MAPTMS with in acidic medium (HCl) is investigated (molar ratio H2O/MAPTMS = 0.25, 0.5, 1 and 3 -corresponding to the resins A, B, C and D) to evaluate the influence on the overall process. After an aging time fixed to 1h, microgrids are patterned with a laser power of 70µW (exposition time 5ms) and the width of the printed lines is determined from the SEM images (Fig. 4 (a)). The resolutions obtained for a molar ratio H2O/MAPTMS of 0.25, 0.5 and 1 are compared. The best resolutions are obtained with a ratio of 0.5 and the results are surprisingly not linear. For a ratio of 3, which is the condition of total hydrolysis (resin D), the material is formed but we observe that it swells almost immediately. There is an optimal molar H2O/MAPTMS ratio to balance the hydrolysis/condensation and the polymerization reactions.

Before starting the process of the microfabrication, an aging time is respected. During this time, the solvent starts to evaporate and the sol-gel condensation occurs in order to pre-form the hybrid network. The influence of this parameter is investigated and appears to have a major impact in the control of the linewidth of fabricated objects. Microgrids are fabricated from the resin B with a laser power of 70µW (exposition time 5 ms) after three different aging times of 1h, 1h30 and 2h. The widths of the lines are estimated from SEM images (Fig. 4 (b)). The SEM images show that when the aging time increases, inducing higher condensation ratio, the linewidth is increasing too and the resolution is lost.

Addition of the photobase towards simultaneous two-photon sol-gel condensation and polymerization

The mechanism for the lithography of the hybrid resins implies two chemical reactions in two steps: sol-gel condensation followed by radical photopolymerization. The aging step to provoke the sol-gel condensation is essential but requires time. A recent study by our group has highlighted the possibility to photo-induce the sol-gel condensation by activating a photobase present in a sol-gel resin [START_REF] Kustra | Elaboration of micro and mesostructured sol-gel materials using polysilsesquioxane molecular precursors[END_REF][START_REF] Kustra | Two-photon controlled sol-gel condensation for the microfabrication of silica based microstructures. The role of photoacids and photobases[END_REF]. This photobase is added to typical formulation of a hydrid resin (resin P) in order to simultaneously perform the two polymerization steps required for the lithography of the hybrid resins. No improvement of resolution is observed during the microfabrication process as well as the power required for obtaining microstructure is a little higher: 0.07 V are required for the resin P (in presence of the photobase) instead of 0.05 V necessary for the resin J (without the photobase). Finally, at this stage in our experimental conditions, the combination of photobase and photinitiator do not allow improvement the process but the system deserves to be further explored.

Characterization of MAPTMS/TiB/MAA resins by FTIR spectroscopy

Films of MAPTMS/TiB/MAA resins containing the different molar ratios Ti/Si with TiB are characterized by FTIR spectroscopy in order to investigate the different reactions involved during the microfabrication process. For the sol-gel condensation reaction, the elongation band of OH groups at 3500 cm -1 is an indication of the quantity of silanol groups available to condense and form photoreactive clusters. For the radical photopolymerization, the representative elongation band of C=C bond at 1640 cm -1 is known to decrease during the process of polymerization. Fig. 6 shows the FTIR spectra of these films before and after a 1 photon excitation through an exposure to UV light (255 nm), for different molar ratios Ti/Si. The spectral observation zone is reduced to reveal the elongation of C=C at 1640 cm -1 . This corresponds to the acrylate functions present on the MAA and the MAPTMS, supposed to decrease under UV exposure while the radical photopolymerization of the acrylate being achieved. In such UV exposure conditions, for the resin I (no Ti), no radical photopolymerization occurs. At the same time the sol-gel condensation is not achieved. In contrast, for the for the resins J and N, containing mixtures of Si and Ti precursors, the radical photopolymerization is efficient, as indicated by the decrease of the C=C band. At the same time the condensation of silanol groups is ongoing. For the resin O (no Si), the radical photopolymerization is not as efficient and at the same time, the condensation is nearly total. The advancement of the sol-gel condensation, leading to the formation of photoreactive clusters, is influencing the efficiency of the radical photopolymerization.

During the microfabrication, the reaction becomes more sensitive when the titanium precursor content increases. The FTIR results show that the increase of Ti leads to a higher condensation ratio and eventually to a complete condensation. The results also show that the radical photopolymerization is favored in presence of Ti but inhibited in case of complete condensation.

Thus, the organic reaction strongly depends on the inorganic reaction. The chemical process implemented during the microfabrication implies thus a double polymerization either sequential or simultaneous. The sol-gel condensation forms a network of photoreactive clusters (inorganic condensation), which is consolidated by the radical photopolymerization (organic polymerization). The presence and quantity of the titanium precursor has a significant role in the execution of these reactions and their respective kinetics. It increases the part devoted to the inorganic condensation reaction in competition with the organic photopolymerization, and as a consequence the microfabrication becomes more difficult to control. Regarding the simultaneous two-photon sol-gel condensation and polymerization process adding the photobase, the radical polymerization is still efficient in presence of the photobase, but no notable influence is observed regarding the sol-gel condensation (Fig. 7). The XRD results (Fig. 9 and Fig. 10) show that when the ratio of Ti increases, the formation of titanium dioxide phases is favored for both pyrolysis temperatures. At 600°C, the diffractograms show that the samples are only partially crystalline, whereas at 1000°C the crystallinity is better for all compositions. At 600°C, for the sample O (no Si), a mixture of anatase and rutile phases is obtained, whereas at 1000°C, the pure rutile is observed. That is in adequacy with several reports [START_REF] Lee | Preparation of photocatalytic TiO2-SiO2 thin film by sol-gel coating[END_REF][START_REF] Lin | Facile Synthesis and Characterization of N-Doped TiO2 Photocatalyst and Its Visible-Light Activity for Photo-Oxidation of Ethylene[END_REF]. The microRaman spectroscopy is a useful tool to characterize objects at the microscale.

MicroRaman analyzes are performed directly on the microfabricated grids on materials prepared at different molar ratio Ti/Si. Two pyrolysis temperature are considered, 600°C and 1000°C.

Before the pyrolysis, the system is partially organic whereas after the calcination, the presence of a mixture of TiO2 and SiO2 is observed in line with XRD experiments and previous reports [START_REF] Bahtat | Waveguide Raman spectroscopy of TiO2:SiO2 thin films[END_REF][START_REF] Best | A raman study of TiO2-SiO2 glasses prepared by sol-gel processes[END_REF]. It demonstrates that the ceramics obtained by the process of microfabrication followed by pyrolysis show the same crystallographic phases as those obtained from bulk samples. So, the process of fabrication has no influence on the obtained crystalline phases.

After the pyrolysis at 600°C (Fig. 11), a mixture of anatase and rutile crystalline TiO2 is observed. The typical band of titanium dioxide anatase at 141, 197, 395, 517 and 638 cm -1 , titanium dioxide rutile at 145, 445 and 610 cm -1 , and fused silica at 490 cm -1 are observed [START_REF] Ohsaka | Raman spectrum of anatase, TiO2[END_REF][START_REF] Montagnac | SSHADE/REAP: Raman Experiments for Astrobiology and Planetology[END_REF]. The full indexation of the Raman bands is provided in Table 4. The typical pattern of fused silica, with a large band at 490 cm -1 , is observable for the sample I (no Ti). For low ratios of Intensity (arb. units)

2θ (deg) 100% 70% 50% 30% 0% R R R R A A R R A,R A,R R A R R R R R SiO 2 R A R R R I J L N O O (no Si) N (Ti/Si =2.3) L (Ti/Si =1) J (Ti/Si =0.4) I (no Ti)
titanium, the bands of the anatase are still present but less intense, broader and eventually shifted.

For example, the band at 141 cm -1 is shifted to 150 cm -1 with a molar ratio of 1.5 (sample M) in presence of Si in the material. For the mixed SiO2/TiO2 material, the bands are very broad, indicating the materials have probably an amorphous part. The origin of the band at 550 cm -1 is not clear. This can be tentatively attributed to either the possible formation of titanium carbide during the pyrolysis, or artifacts due to surface effects [START_REF] Thirumalai | Thin Film Processes: Artifacts on Surface Phenomena and Technological Facets[END_REF].

After the pyrolysis at 1000°C (Fig. 12), more crystallized phases are obtained. The same typical band of anatase, rutile and fused quartz are observed and indexed in Table 4. Regarding the sample O (no Si), the phase transition from a rutile and anatase mixture at 600°C to the pure rutile phase at 1000°C is noted, being consistent to XRD experiments. The materials containing mixtures of Si and Ti present systematically a mixture between rutile and anatase phases, with the trend to form more rutile when the titanium amount increases. The presence of silicon limits the formation of rutile, for the benefit of the anatase phase. Note that the Raman band due to SiO2 is covered by the presence of the titanium dioxide phases.

The combined XRD and microRaman experiments bring important information on the material formed during the pyrolysis. The temperature of the pyrolysis has an influence on the ceramic phase obtained. At 600°C, the anatase Titania is formed whereas at 1000°C, the rutile phase appears. The signal due to SiO2 is reduced when the molar ratio Ti/Si increases, confirming so a higher content of titanium. By combining XRD and microRaman experiments, we prove the capacity of the initial inorganicorganic material to transform to a ceramic material composed of SiO2/TiO2 through the pyrolysis and thus to obtain ceramic materials from the two-photon lithography.

Conclusion

In this paper, the possibility of obtaining microceramics by two-photon fabrication and pyrolysis is demonstrated. The deep investigation in the process parameters highlights the importance of controlling the hydrolysis/condensation parameters of the sol-gel reaction versus the polymerization for the control of the microfabrication of hybrid resins and the resolution of the printed materials. Traditionally, photoinduced free-radical polymerization of acrylate monomers is the driving phenomena of the two-photon fabrication processes. In this case there is a double polymerization process. On the one hand, the inorganic part obtained by the sol-gel reaction, during which the alkoxide precursors hydrolyze and condense. On the other hand, the organic polymerization takes part, which binds the oligomers formed by sol-gel reactions and thus finalize the microfabrication process through the double cross-linking. The impact of the inorganic reaction on the resolution and the organic polymerization threshold is crucial. The aging step is thus to be considered before the microfabrication. In particular, this step strongly impacts the polymerization threshold, opening the possibilities to improve the final resolution compared to pure organic polymeric systems. The influence of the choice of titanium precursor is also highlighted, indeed the use of titanium butoxide instead of titanium isopropoxide allows increasing the molar ratio Ti/Si from 1 to 2.3 and so to increase the titanium amount in the final composite microstructures. By performing the pyrolysis of the two-photon fabricated microstructures, it is possible to obtain TiO2 based micro-ceramics, identifiable by XRD and microRaman analyses, and thus to obtain microstructures with optical, electronic and mechanical properties that are potentially much more interesting.

-

  Figure abstract:

Fig. 1 2 N-methylnifedipine ethyl ester derivative 6 2. 2

 1262 Fig. 1 Reactants used in the different hybrid resins

Fig. 2

 2 Fig. 2 Example of microgrid obtained with resin J observed with the x100 objective of the microRaman set-up. The green dot is the analyzed location

Fig. 3 F

 3 Fig. 3 (a) Example of microgrids obtained from resins at different molar ratios Ti/Si with TiP and TiB (resins C, F, J, L, N) (b) Comparison between microlines obtained from resins with TiP and TiB, printed at 70µW, after an aging time of 1h (resins F, L) (c) Microlines obtained at 70µW from resins at different molar ratios Ti/Si with TiP, after an aging time of 1h (resins C, F)

Fig. 4

 4 Fig. 4 Microlines printed at 70µW, obtained from resins in function of different experimental preparation conditions (a) Variation of molar ratio H2O/MAPTMS, after an aging time of 1h (resins A, B and C) (b) Variation of aging time (resin B)

Fig. 5

 5 Fig.5shows the evolution of the elongation band of the OH groups around 3500 cm -1 as a function of the molar ratio Ti/Si after an aging time of 1 hour. It appears that this band is reducing with the increase of Ti amount. The condensation ratio is nearly total for the resin O (No Si) while numerous silanol groups are remaining for the resin I (No Ti), indicating that the Ti is more sensitive to the condensation as expected, and favoring also the condensation of the silanol groups.

Fig. 5

 5 Fig.5IR spectra of films for different molar ratios Ti/Si (resins I, J, L, N, O), focusing on the OH elongation band at 3500 cm-1

Fig. 6

 6 Fig.6IR spectra on films with different molar ratios Ti/Si (resins I, J, N, O) before and after UV exposure, focusing on the C=C elongation band at 1640 cm-1

Fig. 7 1 3. 5

 715 Fig.7(a) IR spectra on films of the photobase resin (resin P) and the photoinitiator resin (resin J) as reference, focusing on the OH elongation band at 3500 cm -1 (b) IR spectra on films of the photobase resin (resin P) before and after UV exposure, focusing on the C=C elongation band at 1640 cm-1

Fig. 8

 8 Fig.[START_REF] Ostendorf | Two-photon polymerization: A new approach to micromachining[END_REF] Linewidths of microgrids obtained from resins at different molar ratios Ti/Si before and after pyrolysis

Fig. 9 XFig. 10 X

 910 Fig.9X-ray diffractograms of materials obtained from resins at different molar ratios Ti/Si (resins I, J, L, N, O) after pyrolysis at 600°C

Fig. 11 Fig. 12

 1112 Fig. 11Raman spectra of materials obtained from resins at different molar ratios Ti/Si (resins I, J, K, L, M, N, O) after pyrolysis at 600°C

Table 1

 1 Composition of photopolymerizable resins MAPTMS/TiP/MAA

	Resin	Molar ratio		Composition (mmol)
		Molar ratio Ti/Si	Molar ratio H2O/MAPTMS	MAPTMS	Acetone (MAPTMS)	HCl 0.1M	TiP	Acetone (TiP)	MAA	Michler's ketone
	A	0.4	0.25	8.13	8.69	2.03 3.48 7.31	3.48	0.15
	B	0.4	0.5	8.13	8.69	4.07 3.48 7.31	3.48	0.15
	C	0.4	1	8.13	8.69	8.13 3.48 7.31	3.48	0.15
	D	0.4	3	8.13	8.69	24.40 3.48 7.31	3.48	0.15
	E	0.7	1	6.89	7.36	6.89 4.57 9.59	4.57	0.15
	F	1	1	5.64	6.03	5.64 5.66 11.88 5.66	0.15
	G	1.5	1	4.43	4.73	4.43 6.72 14.09 6.72	0.15
	H	2.3	1	3.30	3.53	3.30 7.71 16.16 7.71	0.15

Table 2

 2 Composition of photopolymerizable resins MAPTMS/TiB/MAA Combined photopolymerizable and photobase resin This resin (resin P) is adapted from the resin J in Table 2, by simply adding the photobase Nmethylnifedipine ethyl ester derivative with the photoinitiator in the same experimental conditions. In an amber vial, 8.70 mmol of MAPTMS are diluted with 9.30 mmol of acetone and 8.70 mmol of diluted HCl (0.1M) are added to hydrolyze the silane. The reaction is stirred for one hour. Simultaneously, in another amber vial, 3.73 mmol of Ti(OBu)4 (TiB) are dissolved in 9.37 mmol of acetone. 3.73 mmol of MAA are added slowly under stirring. The reaction is stirred for one hour. Finally, the resin is prepared in a third amber vial. The silicon solution is added to the

	Resin Molar ratio			Composition (mmol)
		Molar ratio Ti/Si	MAPTMS	Acetone (MAPTMS)	HCl 0.1M	TiB	Acetone (TiB)	MAA	Michler's ketone
	I	No Ti	13.81	14.76	13.81	0	0	0	0.15
	J	0.4	8.70	9.30	8.70	3.73	9.37	3.73	0.15
	K	0.7	7.21	7.70	7.21	4.82	12.10	4.82	0.15
	L	1	5.84	6.24	5.84	5.82	14.61	5.82	0.15
	M	1.5	4.47	4.78	4.47	6.82	17.12	6.82	0.15
	N	2.3	3.30	3.53	3.30	7.67	19.26	7.67	0.15
	O	No Si	0	0	0	10.08	25.31	10.08	0.15

photoinitiator, Michler's ketone, (0.15 mmol) and the photobase N-methylnifedipine ethyl ester derivative (0.41 mmol). The titanium solution is dropped under stirring. To ensure full solubilization of the photobase, 86 mmol of acetone are then added.

Table 3

 3 Observations of the stability of the resins (storage at 4°C, sheltered from light) and

			polymerization thresholds
	Resin	Molar ratio Ti/Si	Stability	Polymerization threshold
	I	No Ti	More than 3 months	~2.5 mW
	C	0.4 (TiP)	2 months	~50 µW
	E	0.7 (TiP)	1 month	ND
	F	1 (TiP)	2 weeks	~30 µW
	G	1.5 (TiP)	1 week	No fabrication
	H	2.3 (TiP)	1 week	No fabrication
	J	0.4 (TiB)	More than 3 months	~70 µW
	K	0.7 (TiB)	More than 3 months	ND
	L	1 (TiB)	2 months	~60 µW
	M	1.5 (TiB)	2 weeks	ND
	N	2.3 (TiB)	1 week	~50 µW
	O	No Si (TiB)	2 days	No fabrication
	3.2 Influence of experimental conditions on the microfabrication with MAPTMS/TiP/MAA

Table 4

 4 Indexation of the Raman bands observed of materials obtained from resins at different molar ratios Ti/Si (resins I, J, K, L, M, N, O) after pyrolysis at 600°C (top) and 1000°C (down)

	600°C	I	J	K	L	M	N	O
	Molar ratio	No Ti	0.4	0.7	1	1.5	2.3	No Si
	Mode E g anatase		200 180 160 150 141 141
	Mode B 1g rutile							141
	Mode E g anatase				195			195
	Mode B 1g anatase				396 396 396 396
	Mode E g rutile					445 445 445
	Fused quartz	490 490 490
	Mode A 1g +B 1g anatase					516 516 516
	Mode A 1g rutile		616 616 616 616 610 608
	Mode E g anatase		640 640 640 640 640 640
	1000°C	I	J	K	L	M	N	O
	Molar ratio	No Ti	0.4	0.7	1	1.5	2.3	No Si
	Mode E g anatase		141 141 141 141 141
	Mode B 1g rutile							143
	Mode E g anatase		195 195 195 195 195
	Mode B 1g anatase		396 396 396 396 396
	Mode E g rutile					445 445 440
	Fused quartz	490
	Mode A 1g +B 1g anatase		516 516 516 516 516
	Mode A 1g rutile					611 611 608
	Mode E g anatase		640 640 640 640 640

Acknowledgement

The authors are grateful to the European Union's Horizon 2020 research and innovation program PHENOmenon under grant agreement nº 780278 for funding and to the Raman facility in Lyon (France) supported by the Institut National des Sciences de l'Univers (INSU) and the "Programme National de Planétologie". This is a contribution of the LABEX Lyon Institute of Origins (ANR-10-LABX-0066), within the program "Investissements d'Avenir" (ANR-11-IDEX-0007) at Université de Lyon. The region Auvergne-Rhône-Alpes is acknowledged for the partial funding through the IRICE project 3DFAB.